{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/pxrag/ohhi/blob/bf735a7190d458217bfc2aff103c6366f8f8fae0/Ohhi.CPP", "id": "cpp-0-3abe0e7465c618b82ce542da7aaa3240", "text": "#include<process.h>\n#include<graphics.h>\n#include<fstream.h>\n#include<stdlib.h>\n#include<conio.h>\n#include<stdio.h>\n#include<dos.h>\n\nunion REGS in,out;\n\nint callmouse()\n{\n\tin.x.ax=1;\n\tint86(51,&in,&out);\n\treturn 1;\n}\n\nvoid mouseposi(int &xpos,int &ypos,int &click)\n{\n\tin.x.ax=3;\n\tint86(51,&in,&out);\n\tclick=out.x.bx;\n\txpos=out.x.cx;\n\typos=out.x.dx;\n}\n\nint mousehide()\n{\n\tin.x.ax=2;\n\tint86(51,&in,&out);\n\treturn 1;\n}\n\nvoid setposi(int &xpos, int&ypos)\n{\n\tin.x.ax=4;\n\tin.x.cx=xpos;\n\tin.x.dx=ypos;\n\tint86(51,&in,&out);\n}\n\nclass BOARD\n{\n\tint A[6][6];\n    public:\n    BOARD();\n    int board_fill(int);\n    int check_row_cons(int);\n    int check_column_cons(int);\n    int equal_blocks_row(int);\n    int check_same_row(int);\n    int check_same_col(int);\n    int check1D(int *,int *,int);\n    void copy(int,int[],int);\n    friend void change(int,int);\n    void displayboard();\n}B;\n\nBOARD::BOARD()\n{\n\tint B[6][6];\n\tfstream f(\"solution.txt\",ios::in|ios::binary);\n\tf.seekp(random(5)*sizeof(B));\n\trandomize();\n\tf.read((char*)&B,sizeof(B));\n\tint i,j,c=0;\n\tfor(i=0;i<6;i++)\n\tfor(j=0;j<6;j++)\n\t\tA[i][j]=0;\n\tfor(i=random(6),j=random(6);c<=20;i=random(6),j=random(6),c++)\n\t\tA[i][j]=B[i][j];   //randomly placing the tiles\n\tf.close();\n}\nint BOARD::check_same_col(int size)\n{\n\tint i,j,p[6],q[6];\n    for(i=0;i<size;i++)\n    {\n\tfor(j=i+1;j<size;j++)\n\t{\n\t\tcopy(i,p,size);\n\t\tcopy(j,q,size);\n\t    if(check1D(p,q,size))\n\t    return 0;\n\t}\n    }\n    return 1;\n}\nvoid BOARD::copy(int k,int p[],int size)\n{\n    for(int i=0;i<size;i++)\n\tp[i]=A[i][k];\n}\nint BOARD::check1D(int a[],int b[],int n)\n{\n\n\tint i,c=0;\n    for(i=0;i<n;i++)\n    {\n\tif(a[i]==b[i]||a[i]==b[i]+2||a[i]==b[i]-2)\n\tc++;\n\telse\n\treturn 0;\n    }\n    return 1;\n}\n\n\n\n\nint BOARD::board_fill(int size)\n{\n\tint i,j;\n\tfor(i=0;i<size;i++)\n    {\n\tfor(j=0;j<size;j++)\n\t{\n\t\tif(A[i][j]==0)  //condition for empty tile\n\t    return 0;\n\t}\n    }\n    return 1;\n}\n\nint BOARD::check_row_cons(int n)\n{\n\n\tint c=0,i,j;\n    for(i=0;i<n;i++)\n    {\n\tc=0;\n\tfor(j=0;j<n-1;j++)\n\t{\n\t//condition for same consecutive elements\n\tif(A[i][j]==A[i][j+1]||A[i][j]==A[i][j+1]+2||A[i][j]==A[i][j+1]-2)\n\t    c++;\n\t    else\n\t    c=0;\n\t}\n\tprintf(\"%d\\n\",c);\n\tif(c>1)\n\treturn 0;\n    }\n    return 1;\n}\n\nint BOARD::check_column_cons(int size)\n{\n\tint i,j,c=0;\n\tfor(j=0;j<size;j++)\n\t{\n\tc=0;\n\tfor(i=0;i<size-1;i++)\n\t{\n\t\t//condition for same consecutive elements\n\t    if(A[i][j]==A[i+1][j]||A[i][j]==A[i+1][j]+2||A[i][j]==A[i+1][j]-2)\n\t    c++;\n\t    else\n\t    c=0;\n\t }\n     if(c>1)\n     return 0;\n   }\n   return 1;\n}\n\nint BOARD::equal_blocks_row(int size)\n{\n  int i,j,r=0,b=0;\n  for(i=0;i<size;i++)\n  {\n\tfor(j=0;j<size;j++)\n\tif(A[i][j]==1||A[i][j]==3)\n\t\tr++; //counting red\n\telse\n\t\tb++; //counting blue\n  }\n\tif(r!=b)\n    return 0;\n    else\n    return 1;\n}\nint BOARD::check_same_row(int size)\n{\n\tint i,j;\n    for(i=0;i<size;i++)\n    {\n\tfor(j=i+1;j<size;j++)\n\t{\n\t\tif(check1D(A[i],A[j],size))\n\t    return 0;\n\t}\n    }\n    return 1;\n}\n\nvoid BOARD::displayboard()\n{\n\tint i,j,p,q;\n\tfor(j=30,p=0; j<=400; j=j+65,p++)\n\tfor(i=100,q=0; i<=450; i=i+65,q++)\n\t{\tif(A[p][q]==0)\n\t\t{\n\t\tsetfillstyle(SOLID_FILL,WHITE);\n\t\t}\n\t\telse if(B.A[p][q]==3)\n\t\t{\n\t\tsetfillstyle(SOLID_FILL,RED);\n\t\t}\n\t\telse if(B.A[p][q]==4)\n\t\t{\n\t\tsetfillstyle(SOLID_FILL,BLUE);\n\t\t}\n\t\t\tbar(i,j,i+55,j+55);\n\t\t  }\n\t\tsettextstyle(3,HORIZ_DIR,3);\n\t\tsetcolor(RED);\n\t\touttextxy(590,1,\"X\");\n}\n\nvoid change(int x,int y)\n{\n\tint p,q,j,i;\n\tfor(j=30,p=0; j<=400; j=j+65,p++)\n\tfor(i=100,q=0; i<=450; i=i+65,q++)\n\tif(x>=i && x<=i+55 && y>=j && y<=j+55)\n\t{\n\t\tif(B.A[p][q]==0)\n\t\t{\n\t\tsetfillstyle(SOLID_FILL,RED);\n\t\tB.A[p][q]=1;\n\t\tbar(i,j,i+55,j+55);\n\t\t}\n\t\telse if(B.A[p][q]==1)\n\t\t{\n\t\tsetfillstyle(SOLID_FILL,BLUE);\n\t\tB.A[p][q]=2;\n\t\tbar(i,j,i+55,j+55);\n\t\t}\n\t\telse if(B.A[p][q]==2)\n\t\t{\n\t\tsetfillstyle(SOLID_FILL,WHITE);\n\t\tB.A[p][q]=0;\n\t\tbar(i,j,i+55,j+55);\n\t\t}\n\t\telse\n\t\t{\n\t\t\tsetcolor(WHITE);\n\t\t\tsettextstyle(3,HORIZ_DIR,4);\n\t\t\touttextxy(getmaxx()-450,getmaxy()-50,\"BLOCKED TILE\");\n\t\t\tdelay(500);\n\t\t\tsetcolor(BLACK);\n\t\t\touttextxy(getmaxx()-450,getmaxy()-50,\"BLOCKED TILE\");\n\n\t\t}\n\t}\n\tif(x>=590 && x<=620 && y>=1 && y<=20)\n\texit(0);\n}\nvoid welcome()\n{\n\tsettextstyle(3,HORIZ_DIR,4);\n\tfor(int i=1;i<=12;i++)\n\t{\n\t\tsetbkcolor(i);\n\t\touttextxy(getmaxx()/2-50,getmaxy()/2-50,\"oh hi!\");\n\t\tdelay(500);\n\t}\n}\nvoid rules()\n{\n\tsetbkcolor(BLUE);\n\tsettextstyle(1,HORIZ_DIR,5);\n\touttextxy(165,1,\"INSTRUCTIONS\");\n\tsettextstyle(3,HORIZ_DIR,3);\n\touttextxy(100,100,\"Three adjacent tiles of the same color\");\n\touttextxy(125,125,\"in a row or column are not allowed.\");\n\touttextxy(70,170,\"Rows and columns have an equal number of\");\n\touttextxy(250,195,\"each color.\");\n\touttextxy(125,235,\"No two rows and no two columns\");\n\touttextxy(230,255,\"are the same.\");\n\touttextxy(150,335,\"Click on a tile to turn it red\");\n\touttextxy(135,355,\"Click twice to turn the tile blue.\");\n\tsetfillstyle(SOLID_FILL,BLUE);\n\tbar(400,420,250,460);\n\tsetcolor(WHITE);\n\tsettextstyle(3,HORIZ_DIR,5);\n\touttextxy(270,415,\"PLAY!\");\n\n}\nvoid message()\n{\n\n\tsettextstyle(3,HORIZ_DIR,2);\n\tsetcolor(WHITE);\n\tif(!B.check_row_cons(6))\n\t\touttextxy(getmaxx()-600,getmaxy()-50,\"3 tiles of same color aren't allowed next to each other\");\n\telse if(!B.check_column_cons(6))\n\t\touttextxy(getmaxx()-600,getmaxy()-50,\"3 tiles of same color aren't allowed next to each other\");\n\telse if(!B.equal_blocks_row(6))\n\t\touttextxy(getmaxx()-600,getmaxy()-50,\"Unequal number of tiles of each color\");\n\telse if(!B.check_same_row(6))\n\t\touttextxy(getmaxx()-600,getmaxy()-50,\"No two rows are the same\");\n\telse if(!B.check_same_col(6))\n\t\touttextxy(getmaxx()-600,getmaxy()-50,\"No two columns are the same\");\n\telse\n\t{\n\t\tfstream f(\"comments.txt\",ios::in|ios::binary);\n\t\tf.seekp((random(5))*20);\n\t\tchar a[20];\n\t\tf.read((char*)&a,sizeof(a));\n\t\tsettextstyle(3,HORIZ_DIR,4);\n\t\touttextxy(getmaxx()-550,getmaxy()-50,a);\n\t\tdelay(2000);\n\t\tf.close();\n\t\texit(0);\n\t}\n\tdelay(100);\n}\nvoid main()\n{\n\tint gd=DETECT,gm;\n\tint x,y,cl;\n\tchar ch;\n\tinitgraph(&gd,&gm,\"c:\\\\turboc3\\\\bgi\");\n\tcleardevice();\n\t//welcome();\n\tcleardevice();\n\tsetposi(0,0);\n\tcallmouse();\n\tint c=1;\n\tdo{\n\trules();\n\tmouseposi(x,y,cl);\n\tif(cl==1)\n\t{\n\t\tif(x>=250 && x<=400 && y>=420 && y<=460)\n\t\t{ mousehide();      c--; }\n\t}\n\t}while(c);\n\n\tcleardevice();\n\tsetbkcolor(BLACK);\n\tcallmouse();\n\tsetposi(0,0);\n\trectangle(70,25,500,420);\n\tB.displayboard();\n\tdo\n\t{\n\t\tcallmouse();\n\t\tsetcolor(BLACK);\n\t\tmouseposi(x,y,cl);\n\t\tgotoxy(2,3);\n\t\t\tprintf(\"%d \",cl);\n\t\tif(cl==1)\n\t\t{\n\t\t\tmousehide();\n\t\t\tchange(x,y);\n\t\t\tdelay(250);\n\t\t\tsetfillstyle(SOLID_FILL,BLACK);\n\t\t\tbar(getmaxx()-600,getmaxy()-50,getmaxx(),getmaxy());\n\t\t}\n\t\tsettextstyle(3,HORIZ_DIR,4);\n\n\t\tif(B.board_fill(6))\n\t\t{\n\t\t\tsetcolor(WHITE);\n\t\t\tmessage();\n\t\t}\n\t       //\tmouseposi(x,y,cl);\n\n\n\t}while(cl!=2);\n\tgetch();\n\n\tclosegraph();\n\tgetch();\n\n}\n\n", "text_preview": "#include<process.h> #include<graphics.h> #include<fstream.h> #include<stdlib.h> #include<conio.h> #include<stdio.h> #include<dos.h> union REGS in,out; int callmouse() { in.x.ax=1; int86(51,&in,&out); return 1; } void mouseposi(int &xpos,int", "input_sha256": "25e4fa9263da46f9cecf0171c9b8c70cd5239a16870fa54f27b88899e35ce9ba", "text_sha256": "3abe0e7465c618b82ce542da7aaa32404824fdca9332846363a7a205d1e1faf4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 1, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.027571006792013603, "noise_severity": 0.0649255228916317, "missing_content_severity": 0.02367354868478364, "factual_density": 3.114096278214313, "pretraining_utility": 0.038715658795575196, "math_density": 0.9172579911166675, "code_density": 3.8784974455940326, "reasoning_density": 0.9556467862665992, "education_level": 2.0358740640671074, "creative_writing_quality": -0.7306804744719914}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0023802100493389242, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9904592722130136, "secondary": 0.9715116849957532, "tertiary": 0.9722538730072455, "structured_records": 0.0057703376434246325, "procedural_steps": 0.06165486722358024, "worked_solution": 0.011959317633533108, "contextual_dialogue": 9.327792534254072e-06, "content_quality": 0.42842644849823397, "noise_severity": 0.9458953975903069, "missing_content_severity": 0.9802720427626803, "factual_density": 0.26174689851192734, "pretraining_utility": 0.5119752288239201, "math_density": 0.23561834073611043, "code_density": 0.8987478713283606, "reasoning_density": 0.20362767811116733, "education_level": 0.7741836947231058, "creative_writing_quality": 0.9955560682010084, "primary_topic": 0.9264708944037583, "secondary_topic": 0.6596365777739124}, "probability_margin": {"primary": 0.9843233847637661, "secondary": 0.9613803856171367, "tertiary": 0.9606906351471634, "education_level": 0.7136960561414437, "creative_writing_quality": 0.9948967204132536, "primary_topic": 0.8654707775241415, "secondary_topic": 0.4316440913400539}, "raw_probabilities": {"primary": {"Academic Writing": 5.095028610792794e-05, "Content Listing": 3.174545967930365e-05, "Creative Writing": 0.0001904091956824049, "Customer Support Page": 1.55218896860982e-05, "Q&A Forum": 1.9811794646198897e-05, "Discussion Thread": 1.4989459623391503e-05, "Comment Section": 2.539488970782596e-05, "Missing Content": 6.239708534814535e-05, "Knowledge Article": 8.614625148684761e-05, "Legal Notices": 4.969188781198832e-05, "News Article": 9.733126928121423e-06, "Organizational Announcement": 1.624842018339007e-05, "Nonfiction Writing": 2.370249085447325e-05, "Organizational About Page": 2.0289656034475152e-05, "Personal About Page": 4.23629172711682e-05, "Personal Blog": 1.625440262503587e-05, "Product Page": 1.4158473045331582e-05, "Spam / Ads": 2.509287940578755e-05, "Structured Data": 0.00036632081242239504, "Technical Writing": 0.9908568025374713, "Transcript / Interview": 3.1204657506457715e-05, "Tutorial / How-To Guide": 0.0014840536007382737, "User Reviews": 1.3300052028524348e-05, "Other / Unknown": 0.006533417773705202}, "secondary": {"Academic Writing": 0.00024083201071255876, "Content Listing": 0.00013660102369845187, "Creative Writing": 0.0011058100889259962, "Customer Support Page": 0.0002312902430333134, "Q&A Forum": 7.171278827786318e-05, "Discussion Thread": 5.416217092513021e-05, "Comment Section": 0.0001280121804523954, "Missing Content": 0.0003540186980202626, "Knowledge Article": 0.0006153204775655627, "Legal Notices": 0.0001796258826813516, "News Article": 8.988924693518937e-05, "Organizational Announcement": 8.917224557010628e-05, "Nonfiction Writing": 0.0001597065842277952, "Organizational About Page": 0.00011352271924705879, "Personal About Page": 0.00014573269749983725, "Personal Blog": 9.009225848156553e-05, "Product Page": 5.8571452819779955e-05, "Spam / Ads": 0.00015537683956838049, "Structured Data": 0.0021888986081159576, "Technical Writing": 0.009589178723595878, "Transcript / Interview": 0.0001294776754352393, "Tutorial / How-To Guide": 0.01131831250379361, "User Reviews": 5.598475948634798e-05, "NONE": 0.9726986981209302}, "tertiary": {"Academic Writing": 0.00023886179811309004, "Content Listing": 0.0001102624503294445, "Creative Writing": 0.0018521509299610065, "Customer Support Page": 0.00028504233166552675, "Q&A Forum": 5.38664135598916e-05, "Discussion Thread": 4.955199433890272e-05, "Comment Section": 0.0001461468894085831, "Missing Content": 0.0004302309745070722, "Knowledge Article": 0.0006193943536281783, "Legal Notices": 0.0002715099754669479, "News Article": 6.691529290225675e-05, "Organizational Announcement": 8.231165116189054e-05, "Nonfiction Writing": 0.00017262322782802408, "Organizational About Page": 0.0001138718605739541, "Personal About Page": 0.00012578348357469006, "Personal Blog": 8.200146151406347e-05, "Product Page": 6.146559357668765e-05, "Spam / Ads": 0.0001668251803908745, "Structured Data": 0.0015595930858727147, "Technical Writing": 0.007208968674256955, "Transcript / Interview": 0.00011676579805246738, "Tutorial / How-To Guide": 0.012719326484780248, "User Reviews": 5.6568462592827547e-05, "NONE": 0.9734099616319436}, "structured_records": {"false": 0.9942296623565754, "true": 0.0057703376434246325}, "procedural_steps": {"false": 0.9383451327764197, "true": 0.06165486722358024}, "worked_solution": {"false": 0.9880406823664669, "true": 0.011959317633533108}, "contextual_dialogue": {"false": 0.9999906722074657, "true": 9.327792534254072e-06}, "content_quality": {"-2": 0.060439224502386674, "-1": 0.23585118529229307, "0": 0.4205043319950536, "1": 0.23725188891548063, "2": 0.04595336929478609}, "noise_severity": {"0": 0.9487677542442808, "1": 0.04121369049711985, "2": 0.007439718910294166, "3": 0.001482950819297542, "4": 0.001095885529007725}, "missing_content_severity": {"0": 0.9862198054597213, "1": 0.006799859212069707, "2": 0.004459656434998205, "3": 0.002128338970125617, "4": 0.00039233992308516783}, "factual_density": {"0": 0.024479775037705067, "1": 0.03776604809231845, "2": 0.15590527277409505, "3": 0.3628759318097214, "4": 0.41897297228616}, "pretraining_utility": {"-2": 0.024430623962153536, "-1": 0.22459578538355324, "0": 0.4846113130491969, "1": 0.2205518631067568, "2": 0.04581041449833934}, "math_density": {"0": 0.5860415334437705, "1": 0.16856906559332963, "2": 0.0880546141525488, "3": 0.05675945002316414, "4": 0.10057533678718697}, "code_density": {"0": 0.00029087017937709, "1": 0.0005535141157274593, "2": 0.013056968058282416, "3": 0.0925645952247117, "4": 0.8935340524219013}, "reasoning_density": {"0": 0.6085539634969533, "1": 0.09001691539558969, "2": 0.1263052644636419, "3": 0.08747608463153472, "4": 0.08764777201228037}, "education_level": {"none": 0.00190638051156978, "0": 0.0021291188109023172, "1": 0.07670063620547724, "2": 0.8118197456025882, "3": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Xperiaxtr/sweeper-des/blob/0e246f3a483c98d685215dc8cd87da561b186b0c/navigation/planning/include/double_circle_method.h", "id": "cpp-0-4fa336ce1ec8d293388d910e74439866", "text": "#include <iostream>\n#include <opencv2/opencv.hpp>\n#include <vector>\n\n#include \"../../../common/log.h\"\n#include \"road_edge_detect.h\"\n#include \"sweeper_msgs/SensorFaultInformation.h\"\n#include \"tracker/object.h\"\n\n#define PI 3.1415926\n#define RAD_TO_DEGREE (180.0 / PI)\n#define DEGREE_TO_RAD (PI / 180.0)\n\nnamespace sweeper {\nnamespace navigation {\n\nstruct LinePara {\n  float k;\n  float b;\n};\n\ntypedef struct Point {\n  int index;\n  int location;\n  int flag = -1;\n  float slop_angle;\n  float curvity;\n  cv::Point point;\n  double r;\n} PathPoint;\n\ntypedef std::vector<std::shared_ptr<Object>> Objects;\ntypedef std::vector<std::vector<cv::Point>> ContoursVector;\n\ntypedef struct PlanParam {\n  int mode;\n  int curb_sweep_mode;\n  int road_edge_thickness;\n  int current_type;\n  int obstacle_expand_width;\n  bool avoid_obstacle_flag;\n  bool only_trace_flag;\n  bool trace_good_flag;\n  bool road_edge_flag;\n  double vehicle_speed;\n  double straight_distance_thre;\n  Objects obj;\n  ContoursVector road_edge_boundary;\n  std::vector<cv::Point> reference_line;\n} PlanParam;\n\nclass DoubleCircleMethod {\n public:\n  DoubleCircleMethod(ros::NodeHandle &private_nh, double car_width,\n                     double e_stop_x, double e_stop_y,\n                     RoadEdgeDetect *road_edge_detect);\n  ~DoubleCircleMethod();\n  void GetPlanningTrajectory(const PlanParam &plan_param, bool *estop,\n                             cv::Mat *result_img,\n                             std::vector<cv::Point> *output_line,\n                             sweeper_msgs::SensorFaultInformation *state_code);\n  void SubsectionExpand(const int obj_expand_x, const int obj_expand_y,\n                        const int patch_expand_width,\n                        const std::vector<cv::Point> &contour,\n                        cv::Mat *expand_img);\n  void SplitContour(const std::vector<cv::Point> &contours,\n                    std::vector<cv::Point> *left_line,\n                    std::vector<cv::Point> *right_line,\n                    std::vector<cv::Point> *up_line,\n                    std::vector<cv::Point> *down_line, bool hull_flag = false);\n  void Reset();\n\n private:\n  void GetOptimalPath(const cv::Mat &obstacle_img, const cv::Mat &boundary_img,\n                      const bool have_obstacle_flag,\n                      std::vector<cv::Point> *output_line, bool *right_flag);\n  void GetPathPoint(const std::vector<cv::Point> &contours,\n                    std::vector<cv::Point> *left_line,\n                    std::vector<cv::Point> *right_line);\n  bool OptimizePathKinematic(const cv::Mat &reference_img,\n                             const cv::Mat &obstacle_img,\n                             std::vector<cv::Point> *patch_line,\n                             std::vector<cv::Point> *final_path,\n                             int interval_num = 20);\n  void CalculateRadiusAndLocation(PathPoint *path_point);\n  double CalculateCurvity(const cv::Point &last, const cv::Point &current,\n                          const cv::Point &next);\n  bool DoubleCircleMethodPlan(const PathPoint &current_point,\n                              const cv::Mat &obstacle_img,\n                              std::vector<cv::Point> *patch_points);\n  bool IsIntersectBoundary(const std::vector<cv::Point> &line,\n                           const cv::Mat boundary_img);\n  void ExpandRotatedRect(const int x, const int y,\n                         const std::vector<cv::Point> &contour, cv::Mat *img);\n  void CorrectPointSlop(const int interval_num, const int path_index,\n                        const std::vector<PathPoint> &path_points,\n                        PathPoint *point);\n  void SparseLine(const int sparse_factor, const std::vector<cv::Point> &line,\n                  std::vector<cv::Point> *sparse_line);\n\n  bool avoid_obstacle_flag_;\n  bool double_circle_flag_;\n  bool last_obstacle_flag_;\n  bool dis_mutation_flag_;\n  bool last_have_obstacle_flag_;\n  bool pause_flag_;\n  bool start_in_obj_flag_;\n  bool end_in_obj_flag_;\n  bool only_trace_flag_;\n\n  int mode_;\n  int default_x_;\n\n  int avoid_obstacle_direction_;\n  int curb_sweep_mode_;\n  int obstacle_count_;\n  int no_obstacle_count_;\n  int current_type_;\n  int last_obj_speed_x_;\n  int height_, width_;\n\n  double speed_thre_;\n  double front_dis_;\n  double broadcast_thre_;\n  double min_turn_radius_;\n  double resolution_;\n  double sweep_width_;\n  double car_width_;\n  double turn_obstacle_radius_;\n  double barrier_min_height_;\n  double last_time_;\n  double last_stop_time_;\n  double obstacle_thickness_;\n  double last_obstacle_dis_x_;\n  double last_obstacle_dis_y_;\n  double straight_distance_;\n  double delta_dis_min_trace_;\n  double straight_distance_thre_;\n  double accumulation_time_;\n  double current_stop_time_;\n  double e_stop_x_, e_stop_y_;\n  double erode_x_, erode_y_;\n  double obstacle_x_, obstacle_y_;\n  double obj_expand_x_, obj_expand_y_;\n  double intersect_x_, intersect_y_;\n  double trace_velocity_x_thre_, trace_velocity_y_thre_;\n  double curb_velocity_x_thre_, curb_velocity_y_thre_;\n  double curb_obstacle_min_, curb_obstacle_max_;\n  double trace_obstacle_min_, trace_obstacle_max_;\n\n  int last_line_times_;\n  std::vector<cv::Point> last_line_;\n  std::vector<cv::Point> last_trace_line_;\n  std::vector<cv::Point> reference_line_;\n  std::unordered_map<int, int> obstacle_v_x_;\n  std::unordered_map<int, int> continuous_v_x_;\n  std::unordered_map<int, int> continuous_v_x_1_;\n  std::unordered_map<int, int> continuous_v_y_;\n  std::unordered_map<int, int> continuous_v_y_1_;\n\n  cv::Point center_;\n  cv::Mat *result_img_;\n  ContoursVector road_edge_boundary_;\n  RoadEdgeDetect *road_edge_detect_;\n  sweeper_msgs::SensorFaultInformation *state_code_;\n};\n\n}  // namespace navigation\n}  // namespace sweeper", "text_preview": "#include <iostream> #include <opencv2/opencv.hpp> #include <vector> #include \"../../../common/log.h\" #include \"road_edge_detect.h\" #include \"sweeper_msgs/SensorFaultInformation.h\" #include \"tracker/object.h\" #define PI 3.1415926 #define RAD", "input_sha256": "b5e01a4360d2e746c572da65e4ed478679ae18be3defc09f3ae86c3a99357fa1", "text_sha256": "4fa336ce1ec8d293388d910e74439866baccdc6831af16cf4527fd1878164333", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 4, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/h4kyu/Advent-of-Code/blob/0fe50e663adcff60114b8f5885f790fcc9f900cf/2024/3/main.cpp", "id": "cpp-0-896b01818bed084d38b8fce0e5009945", "text": "#include <iostream>\n#include <fstream>\n\n/* find all instances of 'mul('\n * for each instance, check if valid:\n *  check if ')' exists within 8 characters inclusive\n *      yes -->\n *      check for comma: if exists, split starting from position after '(' on the left and before ')' on the right\n *          --> check if only numbers exist in those regions, if yes, cast as ints and multiply, add to sum\n*/\n\nint part_1(std::string& input) {\n    int mulSum{0};\n    const std::string delimiter{\"mul(\"};\n\n    // size_t searchStart{0};\n    size_t searchStart{input.find(delimiter)};\n\n    while (searchStart != std::string::npos) {\n\n        // check if ')' exists within 8 characters\n        std::string toSearch{input.substr(searchStart + delimiter.size(), 8)}; // maximum of 8 characters between '(' and ')' inclusive\n        size_t pos{toSearch.find(')')}; // zero based index\n\n        if (pos != std::string::npos) {\n            // search for comma until ')' pos\n            toSearch = toSearch.substr(0, pos); // substr of chars btw '(' and ')'\n\n            if (toSearch.find(',') != std::string::npos) {\n                // check if only numbers exist between comma position and '(' and ')'\n                std::string leftOfComma{toSearch.substr(0, toSearch.find(','))};\n                std::string rightOfComma{toSearch.substr(toSearch.find(',') + 1)};\n\n                if (!leftOfComma.empty() && std::all_of(leftOfComma.begin(), leftOfComma.end(), ::isdigit)\n                    && !rightOfComma.empty() && std::all_of(rightOfComma.begin(), rightOfComma.end(), ::isdigit)) {\n                    mulSum += std::stoi(leftOfComma)*std::stoi(rightOfComma);\n                }\n            }\n        }\n\n        // update search area\n        // searchStart = searchEnd + 4;\n        searchStart = input.find(delimiter, searchStart + delimiter.size());\n\n    }\n    return mulSum;\n}\n\n/* default bool enabled as true. same procedure as part_1, except add an extra step to check state of enabled.\n * store last position of mul( and search for 'don't()' and 'do()' in between.\n * compare indices and change enabled accordingly.\n*/\n\n\nint part_2(std::string& input) {\n    bool enabled{true};\n    int mulSum{0};\n    const std::string delimiter{\"mul(\"};\n\n    size_t prevStart{0};\n    size_t searchStart{input.find(delimiter)};\n\n    while (searchStart != std::string::npos) {\n\n        std::string toSearchEnabled{input.substr(prevStart, searchStart - prevStart)};\n        size_t posDo{toSearchEnabled.find(\"do()\")};\n        size_t posDont{toSearchEnabled.find(\"don't()\")};\n\n        if (posDo != std::string::npos && posDont != std::string::npos && posDo > posDont) { // do() exists and is after don't()\n            enabled = true;\n        } else if (posDo != std::string::npos && posDont == std::string::npos) {\n            enabled = true;\n        } else if (posDont != std::string::npos && posDo != std::string::npos && posDont > posDo) {\n            enabled = false;\n            prevStart = searchStart;\n            searchStart = input.find(delimiter, searchStart + delimiter.size());\n            continue;\n        } else if (posDont != std::string::npos && posDo == std::string::npos) {\n            enabled = false;\n            prevStart = searchStart;\n            searchStart = input.find(delimiter, searchStart + delimiter.size());\n            continue;\n        }\n\n        // check if ')' exists within 8 characters\n        std::string toSearch{input.substr(searchStart + delimiter.size(), 8)}; // maximum of 8 characters between '(' and ')' inclusive\n        size_t pos{toSearch.find(')')}; // zero based index\n\n        if (pos != std::string::npos) {\n            // search for comma until ')' pos\n            toSearch = toSearch.substr(0, pos); // substr of chars btw '(' and ')'\n\n            if (toSearch.find(',') != std::string::npos) {\n                // check if only numbers exist between comma position and '(' and ')'\n                std::string leftOfComma{toSearch.substr(0, toSearch.find(','))};\n                std::string rightOfComma{toSearch.substr(toSearch.find(',') + 1)};\n\n                if (!leftOfComma.empty() && std::all_of(leftOfComma.begin(), leftOfComma.end(), ::isdigit)\n                    && !rightOfComma.empty() && std::all_of(rightOfComma.begin(), rightOfComma.end(), ::isdigit)) {\n                    if (enabled) {\n                        mulSum += std::stoi(leftOfComma)*std::stoi(rightOfComma);\n                    }\n                }\n            }\n        }\n\n        // update search area\n        // searchStart = searchEnd + 4;\n        searchStart = input.find(delimiter, searchStart + delimiter.size());\n\n    }\n    return mulSum;\n}\n\n\nint main() {\n    // read file into string input\n    std::ifstream file(\"input.txt\");\n    std::string input{std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>()};\n\n    // std::ifstream file(\"test.txt\");\n    // std::string input{std::istreambuf_iterator<char>(file), std::istreambuf_iterator<char>()};\n\n    std::cout << part_1(input) << std::endl;\n    std::cout << part_2(input) << std::endl;\n    return 0;\n}\n", "text_preview": "#include <iostream> #include <fstream> /* find all instances of 'mul(' * for each instance, check if valid: * check if ')' exists within 8 characters inclusive * yes --> * check for comma: if exists, split starting from position after '(' o", "input_sha256": "16ce17a9b3f2260a521581d7b48421833170dae48c441947df369b533ffe455a", "text_sha256": "896b01818bed084d38b8fce0e500994587584d4d78236f6e9de11026678c0443", "run_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/vincentvillani/RFIMStar/blob/af6ee13fe9379e888945ecfd99c28ec5c66b6b6d/Source/WorkerThread.cpp", "id": "cpp-0-6ecb4dfda6f3668ab38334dd8633a027", "text": "/*\n * WorkerThread.cpp\n *\n *  Created on: 9 May 2016\n *      Author: vincentvillani\n */\n\n\n#include \"../Header/WorkerThread.h\"\n\n#include <math.h>\n#include <string.h>\n#include <algorithm>\n\n#include \"../Header/RFIMHelperFunctions.h\"\n\n#ifdef BUILD_WITH_MKL\n#include <mkl.h>\n#include <mkl_trans.h>\n#endif\n\n\n//Private worker functions\n//'pack' and 'unpack' are written by E<NAME>arr\n//https://github.com/ewanbarr/sigpyproc/blob/master/c_src/SigPyProc.c\n\n\n/*Function to unpack 1,2 and 4 bit data\ndata is unpacked into an empty buffer\nNote: Only unpacks big endian bit ordering*/\nvoid unpack(unsigned char* inbuffer, float* outbuffer, int nbits, int nbytes)\n{\n  int ii,jj;\n\n  switch(nbits)\n  {\n\n  case 1:\n\n\tfor(ii = 0; ii < nbytes; ++ii)\n\t{\n\t  for(jj = 0; jj < 8; jj++)\n\t  {\n\t\t  outbuffer[(ii*8) + jj] = (inbuffer[ii] >> jj) & 1;\n\t  }\n\t}\n\n\tbreak;\n\n  case 2:\n\n    for(ii=0; ii < nbytes; ii++)\n    {\n\t\toutbuffer[(ii*4) + 3] = inbuffer[ii] & LO2BITS;\n\t\toutbuffer[(ii*4) + 2] = (inbuffer[ii] & LOMED2BITS) >> 2;\n\t\toutbuffer[(ii*4) + 1] = (inbuffer[ii] & UPMED2BITS) >> 4;\n\t\toutbuffer[(ii*4) + 0 ] = (inbuffer[ii] & HI2BITS) >> 6;\n    }\n\n    break;\n\n  case 4:\n\n\tfor(ii=0;ii<nbytes;ii++)\n\t{\n\t\toutbuffer[(ii*2) + 1] = inbuffer[ii] & LO4BITS;\n\t\toutbuffer[(ii*2) + 0] = (inbuffer[ii] & HI4BITS) >> 4;\n\t}\n\n\tbreak;\n  }\n}\n\n\n\n/*Function to pack bit data into an empty buffer*/\nvoid pack(unsigned char* buffer, unsigned char* outbuffer, int nbits, int nbytes)\n{\n\tint ii,pos;\n\t//int times = pow(nbits,2);\n\tint bitfact = 8/nbits;\n\tunsigned char val;\n\n\tswitch(nbits)\n\t{\n\n\t\tcase 1:\n\t\tfor(ii=0;ii<nbytes/bitfact; ++ii)\n\t\t{\n\n\t\t\tpos = ii*8;\n\n\t\t\tval = (buffer[pos+7]<<7) |\n\t\t\t\t(buffer[pos+6]<<6) |\n\t\t\t\t(buffer[pos+5]<<5) |\n\t\t\t\t(buffer[pos+4]<<4) |\n\t\t\t\t(buffer[pos+3]<<3) |\n\t\t\t\t(buffer[pos+2]<<2) |\n\t\t\t\t(buffer[pos+1]<<1) |\n\t\t\t\tbuffer[pos+0];\n\n\t\t\toutbuffer[ii] = val;\n\n\t\t}\n\n\t\tbreak;\n\n\t\tcase 2:\n\t\tfor(ii=0;ii<nbytes/bitfact; ++ii) //for(ii=0;ii<nbytes/bitfact; ++ii)\n\t\t{\n\t\t\t//printf(\"Running\\n\");\n\t\t\tpos = ii*4;\n\n\t\t\tval = (buffer[pos]<<6) |\n\t\t\t(buffer[pos+1]<<4) |\n\t\t\t(buffer[pos+2]<<2) |\n\t\t\tbuffer[pos+3];\n\n\t\t\toutbuffer[ii] = val;\n\t\t}\n\n\t\tbreak;\n\n\t\tcase 4:\n\n\t\tfor(ii = 0; ii < nbytes / bitfact; ++ii)\n\t\t{\n\t\t\tpos = ii*2;\n\t\t\tval = (buffer[pos]<<4) | buffer[pos+1];\n\t\t\toutbuffer[ii] = val;\n\t\t}\n\n\t\tbreak;\n\n\t\t}\n}\n\n\n\n\nvoid RFIM(RFIMMemoryBlock* rfimMemoryBlock, RFIMConfiguration* RFIMConfiguration)\n{\n\tCalculateCovarianceMatrix(rfimMemoryBlock);\n\n\tEigenvalueSolver(rfimMemoryBlock);\n\n\tEigenReductionAndFiltering(rfimMemoryBlock, RFIMConfiguration);\n}\n\n\n\n\nvoid WorkerThreadMain(uint32_t workerThreadID, WorkerThreadData* threadData, MasterMailbox* masterMailbox,\n\t\tRFIMConfiguration* configuration)\n{\n\n\tbool shouldShutdown = false;\n\n\t//Allocate space for the RFIM algorithm\n\t//h_valuesPerSample = number of beams (or filterbank files)\n\t//h_numberOfSamples = window size\n\t//batchSize = number of channels\n\tRFIMMemoryBlock* rfimMemoryBlock = new RFIMMemoryBlock(configuration->beamNum, configuration->windowSize, configuration->dimensionsToReduce, configuration->channelNum,\n\t\t\tconfiguration->generatingMask);\n\n\n\t//Is there any work to do?\n\t//TODO: Figure out when to exit\n\twhile(shouldShutdown == false)\n\t{\n\t\t//Lock the mutex\n\t\tstd::unique_lock<std::mutex> lockGuard(threadData->workQueueMutex);\n\n\n\t\t//There is currently no work to do\n\t\tif(threadData->workQueue.size() == 0)\n\t\t{\n\n\t\t\t//std::cout << \"Worker thread going to sleep...\" << std::endl;\n\n\t\t\t//Go to sleep\n\t\t\tthreadData->workQueueConditionVariable.wait(lockGuard, [&] { return threadData->workQueue.size(); } );\n\n\t\t}\n\n\t\t//std::cout << \"Worker thread starting work...\" << std::endl;\n\n\t\t//Get the current piece of work that needs to be done\n\t\t//At this point the mutex should be locked\n\t\tRawDataBlock* rawData = threadData->workQueue.front();\n\n\t\t//Remove it from the work queue and unlock the mutex\n\t\tthreadData->workQueue.pop();\n\t\tlockGuard.unlock();\n\n\t\t//Check if there is any actual work to do?\n\t\t//Calculate the total number of samples that the block contains (this can change if it is near the end of the file)\n\t\t//Calculate if there is any work for this worker thread to do\n\t\tuint64_t totalSamplesInRawDataBlock = (rawData->usedDataLength * 8) /\n\t\t\t\t(configuration->channelNum * configuration->beamNum * configuration->numBitsPerSample);\n\t\tuint64_t threadSampleOffset = workerThreadID * configuration->windowSize;\n\t\tuint64_t sampleDifference = totalSamplesInRawDataBlock - threadSampleOffset;\n\n\t\t//If it is greater than zero, then there is work to be done\n\t\t//Calculate how much should be done\n\t\tif(sampleDifference > 0)\n\t\t{\n\t\t\t//Make sure the samples are bounded by the max window size\n\t\t\trfimMemoryBlock->h_numberOfSamples = std::min((uint64_t)configuration->windowSize, sampleDifference);\n\n\t\t\t//printf(\"threadID: %llu\\n\", workerThreadID);\n\t\t\t//printf(\"numberOfSamples: %llu\\n\", rfimMemoryBlock->h_numberOfSamples);\n\t\t\t//printf(\"usedDataLength: %llu\\n\\n\", rawData->usedDataLength);\n\t\t}\n\t\telse //There is no work to do! Let the writer thread know your done\n\t\t{\n\t\t\t//printf(\"threadID: %llu\\n\", workerThreadID);\n\t\t\t//printf(\"numberOfSamples: %llu\\n\", 0);\n\t\t\t//printf(\"usedDataLength: %llu\\n\\n\", rawData->usedDataLength);\n\n\t\t\t//Is this the last block we should process?\n\t\t\tshouldShutdown = rawData->isLastBlock;\n\n\t\t\t//Pass to the writer thread\n\t\t\tmasterMailbox->workerWriterMailbox->Worker_NotifyFinishedWithBlock(rawData);\n\n\t\t\tbreak; //Skip everything else in this loop and shutdown\n\t\t}\n\n\n\t\t//Unpack the data for use\n\t\tWorkerThreadUnpackData(workerThreadID, rawData, rfimMemoryBlock, configuration);\n\n\n\n\t\t//Multiplex the data\n\t\tWorkerThreadMultiplexData(rfimMemoryBlock, configuration);\n\n\n\t\t//Normalise the data's underlying distribution\n\t\t//WorkerThreadNormaliseDistribution(rfimMemoryBlock);\n\n\n\n\t\t//Run RFIM\n\t\tRFIM(rfimMemoryBlock, configuration);\n\n\n\n\t\t//De-Multiplex the data\n\t\tWorkerThreadDeMultiplexData(rfimMemoryBlock, configuration);\n\n\t\t//Pack the data\n\t\tWorkerThreadPackData(workerThreadID, rawData, rfimMemoryBlock, configuration);\n\n\t\t//Is this the last block we should process?\n\t\tshouldShutdown = rawData->isLastBlock;\n\n\n\t\t//Pass to the writer thread\n\t\tmasterMailbox->workerWriterMailbox->Worker_NotifyFinishedWithBlock(rawData);\n\n\t}\n\n\n\t//std::cout << \"Worker thread finishing...\" << std::endl;\n\n\tdelete rfimMemoryBlock;\n\n}\n\n\n\nvoid WorkerThreadUnpackData(uint32_t workerThreadID, RawDataBlock* rawDataBlock, RFIMMemoryBlock* rfimMemoryBlock,\n\t\tRFIMConfiguration* configuration)\n{\n\n\n\n\t//Calculate the filterbank and thread offsets\n\t//uint64_t oneFilterbankByteSize = (rawDataBlock->usedDataLength / configuration->beamNum);\n\tuint64_t oneFilterbankByteSize = (rawDataBlock->totalDataLength / configuration->beamNum);\n\tuint64_t threadFilterbankByteSize = (oneFilterbankByteSize / configuration->numberOfWorkerThreads);\n\tuint64_t threadStartingOffset = (oneFilterbankByteSize / configuration->numberOfWorkerThreads) * workerThreadID;\n\tuint64_t outputOffset = (configuration->windowSize * rfimMemoryBlock->h_batchSize);\n\t//uint64_t outputOffset = (rfimMemoryBlock->h_numberOfSamples * rfimMemoryBlock->h_batchSize);\n\n\t//std::cout << \"Number of Samples: \" << rfimMemoryBlock->h_numberOfSamples << std::endl;\n\t//std::cout << \"Values per Sample: \" << rfimMemoryBlock->h_valuesPerSample << std::endl;\n\t//std::cout << \"Batches: \" << rfimMemoryBlock->h_batchSize << std::endl;\n\t//std::cout << \"Output offset: \" << outputOffset << std::endl;\n\n\t//std::cout << \"Raw data used data length: \" << rawDataBlock->usedDataLength << std::endl;\n\t//std::cout << \"oneFilterbankByteSize byte size: \" << oneFilterbankByteSize << std::endl;\n\t//std::cout << \"threadFilterbankByteSize offset: \" << threadFilterbankByteSize << std::endl;\n\t//std::cout << \"threadStaringOffset: \" << threadStartingOffset << std::endl;\n\n\n\t//unsigned char* testArray = new unsigned char[oneFilterbankByteSize * configuration->beamNum\n\t//\t\t\t\t\t\t\t\t\t\t\t * ( 8 / configuration->numBitsPerSample)];\n\n\n\n\t//For each filterbank beam, unpack the needed data for this worker thread\n\tfor(uint32_t i = 0; i < configuration->beamNum; ++i)\n\t{\n\t\t//Unpack data from each filterbank file that we are going to use\n\t\tunpack(rawDataBlock->packedRawData + (i * oneFilterbankByteSize) + threadStartingOffset,\n\t\t\t\trfimMemoryBlock->h_outputSignal + (outputOffset * i),\n\t\t\t\tconfiguration->numBitsPerSample, threadFilterbankByteSize);\n\t}\n\n}\n\n\n\n\nvoid WorkerThreadMultiplexData(RFIMMemoryBlock* rfimMemoryBlock, RFIMConfiguration* configuration)\n{\n\n\t//Offsets for the input signal\n\tuint64_t inputFreqChannelOffset = configuration->beamNum * configuration->windowSize;\n\tuint64_t inputTimeSampleOffset = configuration->beamNum;\n\n\t//Offsets for the output signal\n    uint64_t outputBeamOffset = configuration->channelNum * configuration->windowSize;\n\n\n\t//For each frequency channel\n\tfor(uint64_t f = 0; f < configuration->channelNum; ++f)\n\t{\n\t\t//The current frequency channel offset\n\t\tuint64_t currentInputFreqChannelOffset = f * inputFreqChannelOffset;\n\n\t\t//For each time sample\n\t\tfor(uint64_t t = 0; t < configuration->windowSize; ++t)\n\t\t{\n\t\t\tuint64_t currentInputTimeSampleOffset = t * inputTimeSampleOffset;\n\n\t\t\t//For each beam\n\t\t\tfor(uint64_t b = 0; b < configuration->beamNum; ++b)\n\t\t\t{\n\t\t\t\t//printf(\"inputSignalIndex: %llu\\n\", currentInputFreqChannelOffset + currentInputTimeSampleOffset + b);\n\t\t\t\t//printf(\"outputSignalIndex: %llu\\n\\n\", (b * outputBeamOffset) + currentInputTimeSampleOffset + f);\n\n\t\t\t\trfimMemoryBlock->h_inputSignal[currentInputFreqChannelOffset + currentInputTimeSampleOffset + b] =\n\t\t\t\t\trfimMemoryBlock->h_outputSignal[(b * (outputBeamOffset)) +\n\t\t\t\t\t\t\t\t\t\t\t\t\tt * configuration->channelNum + f];\n\n\t\t\t}\n\t\t}\n\n\t}\n\n\n\t/*\n\t#ifdef BUILD_WITH_MKL\n\tmkl_somatcopy('c', 't',\n\t\t\tconfiguration->windowSize * rfimMemoryBlock->h_batchSize, rfimMemoryBlock->h_valuesPerSample,\n\t\t\t1.0f, rfimMemoryBlock->h_outputSignal, configuration->windowSize * rfimMemoryBlock->h_batchSize,\n\t\t\trfimMemoryBlock->h_inputSignal, rfimMemoryBlock->h_valuesPerSample);\n\t#endif\n\t*/\n\n\n\n\n\n}\n\n\n\n\nvoid WorkerThreadNormaliseDistribution(RFIMMemoryBlock* rfimMemoryBlock)\n{\n\tuint64_t multiplexedSignalLength = rfimMemoryBlock->h_valuesPerSample * rfimMemoryBlock->h_numberOfSamples;\n\n\t//For each freq channel\n\tfor(uint64_t i = 0; i < rfimMemoryBlock->h_batchSize; ++i)\n\t{\n\t\t//Calculate the mean and std dev\n\t\tfloat* currentDataSet = rfimMemoryBlock->h_inputSignal + (i * rfimMemoryBlock->h_inputSignalBatchOffset);\n\t\tfloat mean = CalculateMean(currentDataSet, multiplexedSignalLength);\n\t\tfloat stdDev = CalculateStandardDeviation(currentDataSet, multiplexedSignalLength, mean);\n\n\t\t//For each sample in a channel\n\t\t//Compute normalise its stats\n\t\tfor(uint64_t j = 0; j < multiplexedSignalLength; ++j)\n\t\t{\n\t\t\tcurrentDataSet[j] = (currentDataSet[j] - mean) / stdDev;\n\t\t}\n\t}\n\n}\n\n\n\n\nvoid WorkerThreadDeMultiplexData(RFIMMemoryBlock* rfimMemoryBlock, RFIMConfiguration* configuration)\n{\n\n\t//Offsets for the input signal\n\tuint64_t inputFreqChannelOffset = configuration->beamNum * configuration->windowSize;\n\tuint64_t inputTimeSampleOffset = configuration->beamNum;\n\n\t//Offsets for the output signal\n    uint64_t outputBeamOffset = configuration->channelNum * configuration->windowSize;\n\n\n\t//For each frequency channel\n\tfor(uint64_t f = 0; f < configuration->channelNum; ++f)\n\t{\n\t\t//The current frequency channel offset\n\t\tuint64_t currentInputFreqChannelOffset = f * inputFreqChannelOffset;\n\n\t\t//For each time sample\n\t\tfor(uint64_t t = 0; t < configuration->windowSize; ++t)\n\t\t{\n\t\t\tuint64_t currentInputTimeSampleOffset = t * inputTimeSampleOffset;\n\n\t\t\t//For each beam\n\t\t\tfor(uint64_t b = 0; b < configuration->beamNum; ++b)\n\t\t\t{\n\t\t\t\t//printf(\"inputSignalIndex: %llu\\n\", currentInputFreqChannelOffset + currentInputTimeSampleOffset + b);\n\t\t\t\t//printf(\"outputSignalIndex: %llu\\n\\n\", (b * outputBeamOffset) + currentInputTimeSampleOffset + f);\n\n\t\t\t\trfimMemoryBlock->h_outputSignal[(b * (outputBeamOffset)) + t * configuration->channelNum + f] =\n\t\t\t\t\t\trfimMemoryBlock->h_inputSignal[currentInputFreqChannelOffset + currentInputTimeSampleOffset + b];\n\n\n\t\t\t}\n\t\t}\n\n\t}\n\n\n\n\n\n\n\n\t/*\n\t#ifdef BUILD_WITH_MKL\n\tmkl_somatcopy('c', 't',\n\t\t\trfimMemoryBlock->h_valuesPerSample, configuration->windowSize * rfimMemoryBlock->h_batchSize,\n\t\t\t1.0f, rfimMemoryBlock->h_inputSignal, rfimMemoryBlock->h_valuesPerSample,\n\t\t\trfimMemoryBlock->h_outputSignal, configuration->windowSize * rfimMemoryBlock->h_batchSize);\n\t#endif\n\t*/\n\n}\n\n\n\nvoid WorkerThreadPackData(uint32_t workerThreadID, RawDataBlock* rawDataBlock, RFIMMemoryBlock* rfimMemoryBlock,\n\t\tRFIMConfiguration* configuration)\n{\n\n\t//use the input signal data as a buffer to convert the outputSignal floats into unsigned chars\n\tunsigned char* outputCharData = (unsigned char*)rfimMemoryBlock->h_inputSignal;\n\n\t//Interpret the output data as chars\n\t//TODO: add this back? rfimMemoryBlock->h_numberOfSamples instead of configuration->windowSize\n\tuint64_t totalSignalLength = rfimMemoryBlock->h_valuesPerSample * configuration->windowSize * rfimMemoryBlock->h_batchSize;\n\tfloat maxValue = powf(2, configuration->numBitsPerSample) - 1.0f;\n\n\n\tfor(uint64_t i = 0; i < totalSignalLength; ++i)\n\t{\n\t\t//MAKE SURE IT'S ROUNDED UP OR DOWN APPROPRIATELY\n\t\t//MAKE SURE IT'S WITHIN THE RANGE OF THE NBITS\n\t\t//BOUND IT IF IT'S NOT?\n\t\t//(std::min(std::max(value, 0.0f), maxValue) + 0.5f);\n\t\toutputCharData[i] =  (std::min( std::max( (rfimMemoryBlock->h_outputSignal[i] + 0.5f), 0.0f ), maxValue));\n\t}\n\n\n\t/*\n\t * \tuint64_t oneFilterbankByteSize = (rawDataBlock->totalDataLength / configuration->beamNum);\n\t\tuint64_t threadFilterbankByteSize = (oneFilterbankByteSize / configuration->numberOfWorkerThreads);\n\t\tuint64_t threadStartingOffset = (oneFilterbankByteSize / configuration->numberOfWorkerThreads) * workerThreadID;\n\t\tuint64_t outputOffset = (configuration->windowSize * rfimMemoryBlock->h_batchSize);\n\t *\n\t */\n\n\t//rfimMemoryBlock->h_numberOfSamples instead of configuration->windowSize\n\tuint64_t outputCharDataOffset = configuration->windowSize * rfimMemoryBlock->h_batchSize;\n\n\t//rawDataBlock->usedDataLength instead of rawDataBlock->totalDataLength\n\tuint64_t oneFilterbankByteSize = (rawDataBlock->totalDataLength / configuration->beamNum);\n\t//uint64_t threadFilterbankByteSize = (oneFilterbankByteSize / configuration->numberOfWorkerThreads);\n\tuint64_t threadStartingOffset = (oneFilterbankByteSize / configuration->numberOfWorkerThreads) * workerThreadID;\n\n\t/*\n\tprintf(\"workerThreadID: %llu\\n\", workerThreadID);\n\tprintf(\"TotalSignalLength: %llu\\n\", totalSignalLength);\n\tprintf(\"outputCharDataOffset: %llu\\n\", outputCharDataOffset);\n\tprintf(\"oneFilterbankByteSize: %llu\\n\", oneFilterbankByteSize);\n\tprintf(\"threadFilterbankByteSize: %llu\\n\", threadFilterbankByteSize);\n\tprintf(\"threadStartingOffset: %llu\\n\", threadStartingOffset);\n\tprintf(\"configuration->beamNum: %lu\\n\", configuration->beamNum);\n\tprintf(\"configuration->numBitsPerSample: %lu\\n\\n\", configuration->numBitsPerSample);\n\t*/\n\n\n\t//For each filterbank beam, pack the processed data\n\tfor(uint32_t i = 0; i < configuration->beamNum; ++i)\n\t{\n\n\t\t//pack data back into the filterbank format required\n\t\tpack(outputCharData + (i * outputCharDataOffset),\n\t\t\t\trawDataBlock->packedRawData + (i * oneFilterbankByteSize) + threadStartingOffset,\n\t\t\t\tconfiguration->numBitsPerSample, outputCharDataOffset);\n\n\t}\n\n\n\t//If we are generating a mask, pack the values into the appropriate place in the raw data block\n\tif(rfimMemoryBlock->h_generatingMask)\n\t{\n\n\t\t//Pack the h_maskValues into 1 bit data\n\t\tpack(rfimMemoryBlock->h_maskValues,\n\t\t\t\trawDataBlock->packedMaskData + (rfimMemoryBlock->h_maskValuesLength / 8) * workerThreadID,\n\t\t\t\t1, rfimMemoryBlock->h_maskValuesLength);\n\n\t\t//Set all the data to zero, so the next work this thread does has a clean set of mask values\n\t\tmemset(rfimMemoryBlock->h_maskValues, 0, sizeof(unsigned char) * rfimMemoryBlock->h_maskValuesLength);\n\t}\n\n}\n\n\n\n", "text_preview": "/* * WorkerThread.cpp * * Created on: 9 May 2016 * Author: vincentvillani */ #include \"../Header/WorkerThread.h\" #include <math.h> #include <string.h> #include <algorithm> #include 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/KarinaKorsgaard/Fazer-Interactive-Wall-Installation/blob/4910f21abc73f8afb52f5d36991bdcea92e7c085/src/swarmParticle.cpp", "id": "cpp-0-f46faf98b7f9e8e0c324ce8818130a91", "text": "#include \"swarmParticle.h\"\n#include \"defines.h\"\n//------------------------------------------------------------------\nswarmParticle::swarmParticle(){\n\tattractPoints = NULL;\n}\n\n\n\n//------------------------------------------------------------------\nvoid swarmParticle::setAttractPoints( vector <ofPoint> * attract ){\n\tattractPoints = attract;\n}\n\n//------------------------------------------------------------------\nvoid swarmParticle::reset(){\n\t//the unique val allows us to set properties slightly differently for each particle\n\tuniqueVal = ofRandom(-10000, 10000);\n\t\n\tpos.x =  ofRandom(RES_W);\n\tpos.y = ofRandom(RES_H);\n\t\n\tvel.x = ofRandom(-1.9, 1.9);\n\tvel.y = ofRandom(-1.9, 1.9);\n\t\n\tfrc   = ofPoint(0,0,0);\n    \n    //float r = ofRandom(1);\n    //if(r>0.5)\n    twoModes=1;\n    //else twoModes = -1;\n\n\tscale = ofRandom(0.5, 3.0);\n    \n\tdrag  = ofRandom(0.75, 0.998);\n    closestPt=ofPoint(500,500);\n    closest = -1;\n    closestDist = 9999999;\n   // radius = int(ofRandom(2,10));\n//    if( mode == PARTICLE_MODE_NOISE ){\n//\t\tdrag  = ofRandom(0.97, 0.99);\n//\t\tvel.y = fabs(vel.y) * 3.0; //make the particles all be going down\n//\t}else{\n//\t\t\n//\t}\n}\n\n//------------------------------------------------------------------\nvoid swarmParticle::update(){\n\n\t\t\n\t\tif( attractPoints ){\n\n            if(frameMod>0){\n                //1 - find closest attractPoint\n                closestPt;\n                closest = -1;\n                closestDist = 9999999;\n                \n                for(unsigned int i = 0; i < attractPoints->size(); i++){\n                    \n                    float lenSq = ( attractPoints->at(i)-pos ).lengthSquared();\n                    \n                    if(  lenSq < closestDist ){\n                        closestDist = lenSq;\n                        closest = i;\n                    }\n                }\n            }\n\t\t\t\n\t\t\t//2 - if we have a closest point - lets calcuate the force towards it\n\t\t\tif( closest != -1 ){\n\t\t\t\tclosestPt = attractPoints->at(closest);\n                \n\n\t\t\t\tfloat dist = 1/b2InvSqrt(closestDist);\n                //float dist = sqrt(closestDist);\n                \n\t\t\t\t//in this case we don't normalize as we want to have the force proportional to distance \n\t\t\t\tfrc = closestPt - pos;\n                frc *= 1;\n\t\t\t\tvel *= drag;\n\t\t\t\t \n\t\t\t\t//lets also limit our attraction to a certain distance and don't apply if 'f' key is pressed\n\t\t\t\tif( dist < 300 && dist > 20  ){\n                    \n\t\t\t\t\tvel += frc * 0.003;\n\t\t\t\t}else{\n\t\t\t\t\t//if the particles are not close to us, lets add a little bit of random movement using noise. this is where uniqueVal comes in handy. \t\t\t\n\t\t\t\t\tfrc.x = ofSignedNoise(uniqueVal, pos.y * 0.02, ofGetElapsedTimef()*0.2);\n\t\t\t\t\tfrc.y = ofSignedNoise(uniqueVal, pos.x * 0.02, ofGetElapsedTimef()*0.2);\n\t\t\t\t\tvel += frc * 0.4;\n\t\t\t\t}\n\t\t\t\t\n\t\t\t}\n\t\t\n\t\t}\n   // }\n\t\t\n\t//}\n\t\n\t\n\t//2 - UPDATE OUR POSITION\n\t\n\tpos += vel; \n\t\n\t\n\t//3 - (optional) LIMIT THE PARTICLES TO STAY ON SCREEN \n\t//we could also pass in bounds to check - or alternatively do this at the ofApp level\n\tif( pos.x > RES_W ){\n\t\tpos.x = RES_W;\n\t\tvel.x *= -1.0;\n\t}else if( pos.x < 0 ){\n\t\tpos.x = 0;\n\t\tvel.x *= -1.0;\n\t}\n\tif( pos.y > RES_H ){\n\t\tpos.y = RES_H;\n\t\tvel.y *= -1.0;\n\t}\n\telse if( pos.y < 0 ){\n\t\tpos.y = 0;\n\t\tvel.y *= -1.0;\n\t}\t\n\t\t\n}\n\n//------------------------------------------------------------------\nvoid swarmParticle::draw(){\n\n//\tif( mode == PARTICLE_MODE_ATTRACT ){\n//\t\tofSetColor(255, 63, 180);\n//\t}\n//\telse if( mode == PARTICLE_MODE_REPEL ){\n//\t\tofSetColor(208, 255, 63);\n//\t}\n//\telse if( mode == PARTICLE_MODE_NOISE ){\n//\t\tofSetColor(99, 63, 255);\n//\t}\n//\telse if( mode == PARTICLE_MODE_NEAREST_POINTS ){\n//\t\t\n//\t}\n    if(twoModes == 1)ofSetColor(103, 160, 237, 200);\n    else ofSetColor(237, 160, 103, 200);\n\tofDrawCircle(pos.x, pos.y, scale * 4.0);\n    \n    \n}\n\n", "text_preview": "#include \"swarmParticle.h\" #include \"defines.h\" //------------------------------------------------------------------ swarmParticle::swarmParticle(){ attractPoints = NULL; } //-----------------------------------------------------------------", "input_sha256": "ce1a5f531730c8e5697b09eb72330a346ddb5347f3130a76d626e91c1bf74e68", "text_sha256": "f46faf98b7f9e8e0c324ce8818130a91f41e5d0159fa37b3fc3aedec53574b7d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 6, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/NCAR/lrose-core/blob/6acab89ee4651b39eff98280e48336fe244e920d/codebase/apps/radar/src/Lucid/old/contours.cc", "id": "cpp-0-a26ee4d3f0729c97117d9616e821ab03", "text": "// *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=* \n// ** Copyright UCAR (c) 1990 - 2016                                         \n// ** University Corporation for Atmospheric Research (UCAR)                 \n// ** National Center for Atmospheric Research (NCAR)                        \n// ** Boulder, Colorado, USA                                                 \n// ** BSD licence applies - redistribution and use in source and binary      \n// ** forms, with or without modification, are permitted provided that       \n// ** the following conditions are met:                                      \n// ** 1) If the software is modified to produce derivative works,            \n// ** such modified software should be clearly marked, so as not             \n// ** to confuse it with the version available from UCAR.                    \n// ** 2) Redistributions of source code must retain the above copyright      \n// ** notice, this list of conditions and the following disclaimer.          \n// ** 3) Redistributions in binary form must reproduce the above copyright   \n// ** notice, this list of conditions and the following disclaimer in the    \n// ** documentation and/or other materials provided with the distribution.   \n// ** 4) Neither the name of UCAR nor the names of its contributors,         \n// ** if any, may be used to endorse or promote products derived from        \n// ** this software without specific prior written permission.               \n// ** DISCLAIMER: THIS SOFTWARE IS PROVIDED \"AS IS\" AND WITHOUT ANY EXPRESS  \n// ** OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED      \n// ** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.    \n// *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=* \n/**********************************************************************\n * CONTOURS.CC\n *\n * Contour rendering\n */\n\n#include \"cidd.h\"\n#include <Mdv/MdvxContour.hh>\n#include <vector>\n\n#define IDEAL_NUM_VERT_CONT_LEVELS 10.0\n#define MAX_N_LABELS_X 15\n#define MAX_N_LABELS_Y 45\n\n#ifdef NOTYET\nstatic void draw_lines_for_level(const GframeObj &frame,\n                                 int contourIndex,\n                                 const MdvxContourLevel &level,\n                                 contour_info_t *crec );\n\nstatic void draw_labels_for_level(const GframeObj &frame,\n\t\t\t\t  int contourIndex,\n\t\t\t\t  const MdvxContourLevel &level,\n\t\t\t\t  contour_info_t *crec,\n\t\t\t\t  QFont *font,\n\t\t\t\t  char *label_loc,\n\t\t\t\t  int max_n_labels_x,\n\t\t\t\t  int max_n_labels_y);\n  \nstatic void draw_labels_for_line(const GframeObj *gframe,\n                                 int contourIndex,\n                                 int n_points,\n                                 GPoint *gpoints,\n                                 double level,\n                                 QBrush brush,\n                                 QFont *font,\n                                 char *label_loc,\n                                 int max_n_labels_x,\n                                 int max_n_labels_y);\n\nstatic void fill_triangle(const GframeObj *frame, QBrush xgc, \n\t\t\t  const MdvxContourTriangle &triangle, int mode);\n#endif\n\n/////////////////////////////////////////////////////////////////////////\n\nvoid RenderLineContours(QPaintDevice *pdev, contour_info_t *crec,\n\t\t\tbool is_vert /* = false */)\n\n{\n\n  // get the data field\n\n  MetRecord *mr = gd.mrec[crec->field];\n  MdvxField field;\n  if (is_vert) {\n    if(mr->v_mdvx->getField(0) == NULL) return;\n    field = *mr->v_mdvx->getField(0);\n  } else {\n    if(mr->h_mdvx->getField(0) == NULL) return;\n    field = *mr->h_mdvx->getField(0);\n  }\n  const Mdvx::field_header_t &fhdr = field.getFieldHeader();\n  fl32 *data = (fl32 *) field.getVol();\n  int npts = fhdr.nx * fhdr.ny;\n  fl32 missing = fhdr.missing_data_value;\n  fl32 bad = fhdr.bad_data_value;\n\n  // map bad to missing\n\n  if (bad != missing) {\n    fl32 *fptr = data;\n    for (int ii = 0; ii < npts; ii++, fptr++) {\n      if (*fptr == bad) {\n\t*fptr = missing;\n      }\n    }\n  }\n\n  // map missing to min value\n\n  if (gd.layers.map_missing_to_min_value) {\n    fl32 min_val = fhdr.min_value;\n    fl32 *fptr = data;\n    for (int ii = 0; ii < npts; ii++, fptr++) {\n      if (*fptr == missing) {\n\t*fptr = min_val;\n      }\n    }\n  }\n\n  MdvxContour cont;\n\n  if (crec->interval != 0) {\n\n    // set the contours vals from min, max and interval\n    \n    double interval = crec->interval;\n    if (is_vert) {\n      double dyn_range = (fhdr.max_value - fhdr.min_value);\n      if (dyn_range > 0) {\n\tinterval =\n\t  compute_cont_interval(dyn_range / IDEAL_NUM_VERT_CONT_LEVELS);\n      }\n    }\n    \n    cont.setVals(crec->min, crec->max, interval);\n\n  } else {\n\n    // set the contour values from the color scale\n\n    bool increasing = true;\n\n    Valcolormap_t *vcm;\n    if (is_vert) {\n      vcm = &mr->h_vcm;\n    } else {\n      vcm = &mr->v_vcm;\n    }\n\n    if (vcm->nentries > 1) {\n      if (vcm->vc[vcm->nentries - 1]->min < vcm->vc[0]->min) {\n\tincreasing = false;\n      }\n    }\n    \n    if (increasing) {\n      for (int ilevel = 0; ilevel < vcm->nentries; ilevel++) {\n\tcont.addVal(vcm->vc[ilevel]->min);\n      }\n      cont.addVal(vcm->vc[vcm->nentries - 1]->max);\n    } else {\n      for (int ilevel = vcm->nentries - 1; ilevel >= 0; ilevel--) {\n\tcont.addVal(vcm->vc[ilevel]->min);\n      }\n      cont.addVal(vcm->vc[0]->max);\n    }\n\n  }\n    \n  if (crec->labels_on) {\n    if (cont.computeLines(field)) {\n      cerr << \"ERROR - RenderLineContours\" << endl;\n      return;\n    }\n  } else {\n    if (cont.computeSegments(field)) {\n      cerr << \"ERROR - RenderLineContours\" << endl;\n      return;\n    }\n  }\n\n#ifdef NOTYET\n\n  // Pick a font size based on the detail thresholds and domain\n\n  // int font_index = gd.uparams->getLong(\"contour_font_num\", 6);\n  int font_index = 0;\n  if(font_index <0) font_index = 0;\n  if(font_index >= _params.fonts_n) font_index = _params.fonts_n -1;\n  QFont *font = gd.fontst[font_index];\n  \n  // set up the domain for rendering\n\n  win_param_t *win;\n  double min_x, max_x;\n  double min_y, max_y;\n  if (is_vert) {\n    win = &gd.v_win;\n    min_x = 0;\n    max_x = gd.h_win.route.total_length;\n    min_y = win->min_ht;\n    max_y = win->max_ht;\n  } else {\n    win = &gd.h_win;\n    min_x = win->cmin_x;\n    max_x = win->cmax_x;\n    min_y = win->cmin_y;\n    max_y = win->cmax_y;\n  }\n  \n  double x_dproj_per_pixel = (max_x - min_x) / (double)win->img_dim.width;\n  double y_dproj_per_pixel = (max_y - min_y) / (double)win->img_dim.height;\n\n  double left_km = (double)win->margin.left * x_dproj_per_pixel;\n  double right_km = (double)win->margin.right * x_dproj_per_pixel;\n  double top_km = (double)win->margin.top * y_dproj_per_pixel;\n  double bot_km = (double)win->margin.bot * y_dproj_per_pixel;  \n  \n  GframeObj *gframe = RenderContext::gCreateFrame(min_x - left_km,\n                                                  min_y - bot_km,\n                                                  max_x + right_km,\n                                                  max_y + top_km);\n\n  GSetXRef(gframe, gd.dpy, xid,\n\t   win->can_dim.width, win->can_dim.height,\n\t   font);\n\n  // create array for keeping track of label positions\n  int max_n_labels_x = MAX_N_LABELS_X;\n  int max_n_labels_y = MAX_N_LABELS_Y;\n  vector<char> label_loc;\n  label_loc.resize(max_n_labels_y * max_n_labels_x);\n  memset(label_loc.data(), 0, max_n_labels_y * max_n_labels_x * sizeof(char));\n\n  // render lines for each level\n\n  const vector<MdvxContourLevel> levels = cont.getLevels();\n  for (size_t ii = 0; ii < levels.size(); ii++) {\n    draw_lines_for_level(*gframe, ii, levels[ii], crec);\n  } // ii\n\n  // render labels for each level\n\n  for (size_t ii = 0; ii < levels.size(); ii++) {\n    draw_labels_for_level(*gframe, ii, levels[ii], crec,\n\t\t\t  font, label_loc.data(), max_n_labels_x, max_n_labels_y);\n  } // ii\n  \n  // Add the list of times to the time plot\n\n  if(!is_vert && mr->time_list.num_entries > 0) {\n    if(gd.time_plot) gd.time_plot->add_grid_tlist(mr->button_name,\n\t\t\t\t mr->time_list.tim,\n\t\t\t\t mr->time_list.num_entries,\n\t\t\t\t mr->h_date.unix_time);\n  }\n\n  delete gframe;\n\n#endif\n\n}\n\n#ifdef NOTYET\n  \n//////////////////////////////\n// draw given contour level\n\nstatic void draw_lines_for_level(const GframeObj &frame,\n\t\t\t\t int contourIndex,\n\t\t\t\t const MdvxContourLevel &level,\n\t\t\t\t contour_info_t *crec)\n\n{\n\n  // set gc\n  \n  QBrush brush;\n  double val = level.val;\n\n  // check for special value\n\n  bool val_is_special = false;\n  if (fabs(val - gd.layers.special_contour_value) < 1.0e-6) {\n      val_is_special = true;\n  }\n\n  if (crec->interval != 0) { \n      gc =  crec->color->gc;\n  } else {\n      gc = Val2GC(crec->vcm,val);\n  }\n\n  if(gc == NULL) return; // No color for this level\n\n  if(val_is_special) {\n    XSetLineAttributes(gd.dpy, gc, gd.layers.contour_line_width*2,\n\t\t       LineSolid, CapButt, JoinMiter);\n  } else {\n    XSetLineAttributes(gd.dpy, gc, gd.layers.contour_line_width,\n\t\t       LineSolid, CapButt, JoinMiter);\n  }\n\n  if (level.lines.size() > 0) {\n    \n    // contiguous lines\n    \n    for (size_t il = 0; il < level.lines.size(); il++) {\n      \n      const MdvxContourLine &line = level.lines[il];\n      \n      if (line.pts.size() > 0) {\n\t\n\t// load up gpoint array\n\n        vector<GPoint> gpoints;\n        gpoints.resize(line.pts.size());\n\t\n\tfor (size_t ii = 0; ii < line.pts.size(); ii++) {\n\t  gpoints[ii].x = line.pts[ii].x;\n\t  gpoints[ii].y = line.pts[ii].y;\n\t} // i\n\t\n\t// draw the lines\n\t\n\tGDrawLines(XDEV, &frame, gc, NULL,\n\t\t   gpoints.data(), line.pts.size(), CoordModeOrigin);\n\t\n      } // if (line.pts.size() > 0)\n      \n    } // il\n\n  } else {\n\n    // line segments\n\n    for (size_t is = 0; is < level.segments.size(); is++) {\n      \n      const MdvxContourSegment &seg = level.segments[is];\n      \n      GDrawLine(XDEV, &frame, gc, NULL,\n\t\tseg.start.x,\n\t\tseg.start.y,\n\t\tseg.end.x,\n\t\tseg.end.y);\n\n    } // is\n\n  } // if (level.lines.size() > 0)\n\n}\n\n#endif\n\n///////////////////////////////////////\n// draw labels for given contour level\n\n#ifdef NOTYET\n  \nstatic void draw_labels_for_level(const GframeObj &frame,\n\t\t\t\t  int contourIndex,\n\t\t\t\t  const MdvxContourLevel &level,\n\t\t\t\t  contour_info_t *crec,\n\t\t\t\t  QFont *font,\n\t\t\t\t  char *label_loc,\n\t\t\t\t  int max_n_labels_x,\n\t\t\t\t  int max_n_labels_y)\n  \n{\n\n  if (level.lines.size() == 0) {\n    return;\n  }\n    \n  // set gc\n  \n  QBrush brush;\n  double val = level.val;\n\n  // check for special value\n\n  // bool val_is_special = false;\n  // if (fabs(val - gd.layers.special_contour_value) < 1.0e-6) {\n  //     val_is_special = true;\n  // }\n\n  if (crec->interval != 0) { \n      gc =  crec->color->gc;\n  } else {\n      gc = Val2GC(crec->vcm,val);\n  }\n\n  if(gc == NULL) return; // No color for this level\n\n  XSetFont(gd.dpy, gc, font->fid);\n  \n  // contiguous lines\n  \n  for (size_t il = 0; il < level.lines.size(); il++) {\n    \n    const MdvxContourLine &line = level.lines[il];\n    \n    if (line.pts.size() > 0) {\n      \n      // load up gpoint array\n      \n      vector<GPoint> gpoints;\n      gpoints.resize(line.pts.size());\n      \n      for (size_t ii = 0; ii < line.pts.size(); ii++) {\n\tgpoints[ii].x = line.pts[ii].x;\n\tgpoints[ii].y = line.pts[ii].y;\n      } // i\n      \n      // put in the labels\n      \n      draw_labels_for_line(&frame, contourIndex, (int) line.pts.size(),\n\t\t\t   gpoints.data(), level.val, gc, font,\n\t\t\t   label_loc, max_n_labels_x, max_n_labels_y);\n      \n    } // if (line.pts.size() > 0)\n    \n  } // il\n\n}\n\n#endif\n  \n#ifdef NOTYET\n  \n/*********************************\n * draw labels for given line\n */\n\nstatic void draw_labels_for_line(const GframeObj *gframe,\n\t\t\t\t int contourIndex,\n\t\t\t\t int n_points,\n\t\t\t\t GPoint *gpoints,\n\t\t\t\t double level,\n\t\t\t\t QBrush brush,\n\t\t\t\t QFont *font,\n\t\t\t\t char *label_loc,\n\t\t\t\t int max_n_labels_x,\n\t\t\t\t int max_n_labels_y)\n\n{\n\n  // int n_ideal_labels = _params.n_ideal_contour_labels;\n  int n_ideal_labels = 6;\n  double mean_dim = ((gframe->w_xmax - gframe->w_xmin) +\n\t\t     (gframe->w_ymax - gframe->w_ymin)) * 0.5;\n  double label_spacing = mean_dim / n_ideal_labels;\n  \n  double dist_to_next =\n    (label_spacing * (0.25 + (contourIndex % 10) * 0.1));\n  \n  char label_str[64];\n  snprintf(label_str, 64, \"%g\", level);\n\n  bool plotted = false;\n  for (int ii = 0; ii < n_points - 1; ii++) {\n\n    double x1 = gpoints[ii].x;\n    double x2 = gpoints[ii+1].x;\n    double y1 = gpoints[ii].y;\n    double y2 = gpoints[ii+1].y;\n\n    double dx = x2 - x1;\n    double dy = y2 - y1;\n\n    double len = sqrt(dx * dx + dy * dy);\n    \n    if (len > dist_to_next) {\n      \n      double dist_since_last = label_spacing - dist_to_next;\n      int nThisSeg = (int) ((len + dist_since_last) / label_spacing);\n      double d_left = dist_to_next - len;\n\n      for (int ii = 1; ii <= nThisSeg; ii++) {\n\n\tdouble dist = ii * label_spacing - dist_since_last;\n\td_left = len - dist;\n\tdouble fraction = dist / len;\n\tdouble xincr = dx * fraction;\n\tdouble yincr = dy * fraction;\n\tdouble labelx = x1 + xincr;\n\tdouble labely = y1 + yincr;\n\t\n\tint ipos_x = (int)\n\t  (((labelx - gframe->w_xmin) / (gframe->w_xmax - gframe->w_xmin)) *\n\t   max_n_labels_x);\n\tif (ipos_x < 0) {\n\t  ipos_x = 0;\n\t}\n\tif (ipos_x > max_n_labels_x - 1) {\n\t  ipos_x = max_n_labels_x - 1;\n\t}\n\t\n\tint ipos_y = (int)\n\t  (((labely - gframe->w_ymin) / (gframe->w_ymax - gframe->w_ymin)) *\n\t   max_n_labels_y);\n\tif (ipos_y < 0) {\n\t  ipos_y = 0;\n\t}\n\tif (ipos_y > max_n_labels_y - 1) {\n\t  ipos_y = max_n_labels_y - 1;\n\t}\n\tint ipos = ipos_y * max_n_labels_x + ipos_x;\n\t\n\tif (label_loc[ipos] == 0) {\n\t  GDrawImageString(XDEV, gframe, gc, font, NULL,\n\t\t\t   XJ_CENTER, XJ_CENTER,\n\t\t\t   labelx, labely,\n\t\t\t   label_str);\n\t  label_loc[ipos] = 1;\n\t}\n\t\n\tplotted = true;\n\n      } // ii\n      \n      dist_to_next = label_spacing - d_left;\n      \n    } else {\n      \n      dist_to_next -= len;\n\n    }\n\n  } // ii\n\n  // if not plotted, label the mid point\n\n  if (!plotted) {\n    int ii = n_points / 2;\n    GDrawImageString(XDEV, gframe, gc, font, NULL,\n\t\t     XJ_CENTER, XJ_CENTER,\n\t\t     gpoints[ii].x, gpoints[ii].y,\n\t\t     label_str);\n  }\n\n}\n\n#endif\n\n//////////////////////////\n// render filled polygons\n\nvoid RenderFilledPolygons(QPaintDevice *pdev,\n\t\t\t  MetRecord *mr, \n\t\t\t  bool is_vert /* = false */)\n\n{\n\n#ifdef NOTYET\n  \n  // get the data field\n\n  MdvxField field;\n  if (is_vert) {\n    if(mr->v_mdvx->getField(0) == NULL) return;\n    field = *mr->v_mdvx->getField(0);\n  } else {\n    if(mr->h_mdvx->getField(0) == NULL) return;\n    field = *mr->h_mdvx->getField(0);\n  }\n  const Mdvx::field_header_t &fhdr = field.getFieldHeader();\n  fl32 *data = (fl32 *) field.getVol();\n  int npts = fhdr.nx * fhdr.ny;\n  fl32 missing = fhdr.missing_data_value;\n  fl32 bad = fhdr.bad_data_value;\n\n  // map bad to missing\n\n  if (bad != missing) {\n    fl32 *fptr = data;\n    for (int ii = 0; ii < npts; ii++, fptr++) {\n      if (*fptr == bad) {\n\t*fptr = missing;\n      }\n    }\n  }\n\n  // map missing to min value\n\n  if (gd.layers.map_missing_to_min_value) {\n    fl32 min_val = fhdr.min_value;\n    fl32 *fptr = data;\n    for (int ii = 0; ii < npts; ii++, fptr++) {\n      if (*fptr == missing) {\n\t*fptr = min_val;\n      }\n    }\n  }\n\n  // compute the contour polygons\n\n  Valcolormap_t *vcm;\n  if (is_vert) {\n    vcm = &mr->h_vcm;\n  } else {\n    vcm = &mr->v_vcm;\n  }\n  \n  MdvxContour cont;\n\n  // check for contour ordering\n\n  bool increasing = true;\n  if (vcm->nentries > 1) {\n    if (vcm->vc[vcm->nentries - 1]->min < vcm->vc[0]->min) {\n      increasing = false;\n    }\n  }\n\n  // set up contour levels\n\n  if (increasing) {\n    for (int ilevel = 0; ilevel < vcm->nentries; ilevel++) {\n      cont.addVal(vcm->vc[ilevel]->min);\n    }\n    cont.addVal(vcm->vc[vcm->nentries - 1]->max);\n  } else {\n    for (int ilevel = vcm->nentries - 1; ilevel >= 0; ilevel--) {\n      cont.addVal(vcm->vc[ilevel]->min);\n    }\n    cont.addVal(vcm->vc[0]->max);\n  }\n  \n  if (cont.computeTriangles(field)) {\n    cerr << \"ERROR - RenderFilledPolygons\" << endl;\n    return;\n  }\n  \n  // set up the domain for rendering\n\n  win_param_t *win;\n  double min_x, max_x;\n  double min_y, max_y;\n  if (is_vert) {\n    win = &gd.v_win;\n    min_x = 0;\n    max_x = gd.h_win.route.total_length;\n    min_y = win->min_ht;\n    max_y = win->max_ht;\n  } else {\n    win = &gd.h_win;\n    min_x = win->cmin_x;\n    max_x = win->cmax_x;\n    min_y = win->cmin_y;\n    max_y = win->cmax_y;\n  }\n  \n  double x_dproj_per_pixel = (max_x - min_x) / (double)win->img_dim.width;\n  double y_dproj_per_pixel = (max_y - min_y) / (double)win->img_dim.height;\n\n  double left_km = (double)win->margin.left * x_dproj_per_pixel;\n  double right_km = (double)win->margin.right * x_dproj_per_pixel;\n  double top_km = (double)win->margin.top * y_dproj_per_pixel;\n  double bot_km = (double)win->margin.bot * y_dproj_per_pixel;  \n  \n  GframeObj *gframe = GCreateFrame(min_x - left_km,\n\t\t\t\t  min_y - bot_km,\n\t\t\t\t  max_x + right_km,\n\t\t\t\t  max_y + top_km);\n  \n  GSetXRef(gframe, gd.dpy, xid,\n\t   win->can_dim.width, win->can_dim.height, NULL);\n\n  //  Fill in background color\n  if(!is_vert && gd.layers.missing_data_color->gc != NULL) {\n      double    x_dproj,y_dproj;\n      double    x_dproj2,y_dproj2;\n\n      // Map grid point 0,0 to the display projection\n      grid_to_disp_proj(mr,0,0,&x_dproj,&y_dproj);\n\n      // Map grid point nx,ny,0 to the display projection\n      grid_to_disp_proj(mr,mr->h_fhdr.nx-1,mr->h_fhdr.ny-1,&x_dproj2,&y_dproj2);\n      \n      GFillRectangle(XDEV, gframe, gd.layers.missing_data_color->gc, NULL,\n\t\t     x_dproj,y_dproj,(x_dproj2 - x_dproj), (y_dproj2 - y_dproj));\n  }\n\n  if(!is_vert && gd.layers.bad_data_color->gc != NULL) {\n      double    x_dproj,y_dproj;\n      double    x_dproj2,y_dproj2;\n\n      // Map grid point 0,0 to the display projection\n      grid_to_disp_proj(mr,0,0,&x_dproj,&y_dproj);\n\n      // Map grid point nx,ny,0 to the display projection\n      grid_to_disp_proj(mr,mr->h_fhdr.nx-1,mr->h_fhdr.ny-1,&x_dproj2,&y_dproj2);\n      \n      GFillRectangle(XDEV, gframe, gd.layers.bad_data_color->gc, NULL,\n\t\t     x_dproj,y_dproj,(x_dproj2 - x_dproj), (y_dproj2 - y_dproj));\n  }\n\n  // render for each level\n\n  const vector<MdvxContourLevel> levels = cont.getLevels();\n\n  for (size_t ilevel = 0; ilevel < levels.size(); ilevel++) {\n\n    const MdvxContourLevel level = levels[ilevel];\n\n    QBrush brush;\n    if ((int) ilevel < vcm->nentries) {\n      if (level.extra) {\n\tgc = gd.legends.background_color->gc;\n      } else {\n\tif (increasing) {\n\t  gc = vcm->vc[ilevel]->gc;\n\t} else {\n\t  gc = vcm->vc[vcm->nentries-ilevel-1]->gc;\n\t}\n      }\n    } else {\n      gc = gd.legends.background_color->gc;\n    }\n\n    for (size_t ii = 0; ii < level.triangles.size(); ii++) {\n      fill_triangle(gframe, gc, level.triangles[ii], CoordModeOrigin);\n    } // ii\n    \n  } // ilevel\n\n  // clean up\n\n  GFreeFrame(gframe);\n\n#endif\n\n}\n\n#ifdef NOTYET\n  \n//////////////////////////////////////////////////////////////////////////////\n// GFillTriangle()\n\nstatic void fill_triangle(const GframeObj *frame, QBrush xgc, \n\t\t\t  const MdvxContourTriangle &triangle,\n\t\t\t  int mode)\n  \n{\n\n  // clip\n  \n  double xmin = LARGE_DOUBLE;\n  double ymin = LARGE_DOUBLE;\n  double xmax = -LARGE_DOUBLE;\n  double ymax = -LARGE_DOUBLE;\n\n  const MdvxContourPt *vtex = triangle.vertex;\n\n  for (int i = 0; i < 3; i++, vtex++) {\n    xmin = MIN(xmin, vtex->x);\n    ymin = MIN(ymin, vtex->y);\n    xmax = MAX(xmax, vtex->x);\n    ymax = MAX(ymax, vtex->y);\n  }\n\n  if (xmax < frame->w_xmin || ymax < frame->w_ymin ||\n      xmin > frame->w_xmax || ymin > frame->w_ymax) {\n    return;\n  }\n\n  // create array of XPoints, and load them up\n  \n  XPoint 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/UniversalRobots/Universal_Robots_ROS_controllers_cartesian/blob/6dbfea943c0ea8135e392f45ed1bbed5f5cf6b4c/cartesian_trajectory_interpolation/include/cartesian_trajectory_interpolation/cartesian_trajectory_segment.h", "id": "cpp-0-2ae1b26b2df7b56d271553d870f80c5e", "text": "// -- BEGIN LICENSE BLOCK ----------------------------------------------\n// Copyright 2021 FZI Forschungszentrum Informatik\n// Created on behalf of Universal Robots A/S\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n//     http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n// -- END LICENSE BLOCK ------------------------------------------------\n\n//-----------------------------------------------------------------------------\n/*!\\file    cartesian_trajectory_segment.h\n *\n * \\author  <NAME> <<EMAIL>>\n * \\date    2021/01/14\n *\n */\n//-----------------------------------------------------------------------------\n\n#pragma once\n\n#include <trajectory_interface/quintic_spline_segment.h>\n#include <trajectory_interface/pos_vel_acc_state.h>\n#include <cartesian_trajectory_interpolation/cartesian_state.h>\n\nnamespace ros_controllers_cartesian\n{\n/**\n * @brief 7-dimensional quintic spline segment\n *\n * This two-point segment is uniquely defined by its start and end\n * ros_controllers_cartesian::SplineState.\n */\nusing QuinticSplineSegment = trajectory_interface::QuinticSplineSegment<double>;\n\n/**\n * @brief Cartesian segment between two trajectory waypoints.\n *\n * Various of these segments form a Cartesian trajectory.\n *\n * They use the \\ref QuinticSplineSegment implementation from the\n * <a\n * href=\"https://github.com/ros-controls/ros_controllers/blob/noetic-devel/joint_trajectory_controller/include/trajectory_interface/quintic_spline_segment.h\">joint_trajectory_controller</a>\n * package.  For each of the seven Cartesian\n * dimensions, they implement linear, cubic or quintic spline interpolation,\n * depending on the waypoints specification by parameterizing unique\n * polynomials between the two waypoints.\n *\n * This wrapper takes care of converting Cartesian pose, velocity and\n * acceleration setpoints into quaternion quantities for specifying spline\n * boundary conditions (during initialization), and re-converting the\n * interpolated values back into Cartesian quantities that can be commanded\n * to robot control (upon sampling).\n *\n */\nclass CartesianTrajectorySegment : public QuinticSplineSegment\n{\npublic:\n  /**\n   * @brief Relative time in seconds\n   */\n  using Time = QuinticSplineSegment::Time;\n\n  using State = CartesianState;\n\n  /**\n   * @brief State of a 7-dimensional Spline\n   *\n   * The first three dimensions represent Cartesian translation and the last\n   * four represent the rotation quaternion (w, x, y, z).  Each dimension has its own\n   * position, velocity and acceleration value.\n   *\n   */\n  using SplineState = QuinticSplineSegment::State;\n\n  CartesianTrajectorySegment() = delete;\n\n  virtual ~CartesianTrajectorySegment(){};\n\n  /**\n   * @brief Construct a Cartesian trajectory segment from start and end state.\n   *\n   * This uses the quintic spline interpolation from the joint_trajectory_controller under the hood.\n   * The Cartesian states are converted to spline states to fit into their pipeline.\n   * Here is the essential part of the documentation that also applies here:\n   *\n   * <BLOCKQUOTE>\n   * The start and end states need not necessarily be specified all the way to the acceleration level:\n   * - If only \\b positions are specified, linear interpolation will be used.\n   * - If \\b positions and \\b velocities are specified, a cubic spline will be used.\n   * - If \\b positions, \\b velocities and \\b accelerations are specified, a quintic spline will be used.\n   *\n   * \\note If start and end states have different specifications\n   * (eg. start is positon-only, end is position-velocity), the lowest common specification will be used\n   * (position-only in the example).\n   * </BLOCKQUOTE>\n   *\n   * @param start_time Time in seconds when this segment starts\n   * @param start_state CartesianState at start time\n   * @param end_time Time in seconds when this segment ends\n   * @param end_state CartesianState at end time\n   */\n  CartesianTrajectorySegment(const Time& start_time, const CartesianState& start_state, const Time& end_time,\n                             const CartesianState& end_state);\n\n  /**\n   * @brief Sample the CartesianState at the given time\n   *\n   * \\note Will use \\ref QuinticSplineSegment::sample internally with the following characteristics: \\n\n   * Within the <tt>[start_time, end_time]</tt> interval, spline\n   * interpolation takes place, outside it this method will output the\n   * start/end states with zero velocity and acceleration.\n   *\n   *\n   * @param time The time at which to sample\n   * @param state The CartesianState at the requested \\b time\n   */\n  void sample(const Time& time, CartesianState& state) const;\n\nprivate:\n};\n\n/**\n * @brief Stream operator for testing and debugging\n *\n * @param os The output stream\n * @param state The SplineState\n *\n * @return Reference to the stream for chaining\n */\nstd::ostream& operator<<(std::ostream& os, const CartesianTrajectorySegment::SplineState& state);\n\n/**\n * @brief Convert a CartesianState into a ros_controllers_cartesian::SplineState\n *\n * The computation of quaternion velocities and accelerations from\n * Cartesian angular velocities and accelerations is based on\n * <a href=\"https://math.stackexchange.com/questions/1792826\">this blog post</a> and on <a\n * href=\"https://arxiv.org/abs/0811.2889v1\">this article</a>.\n * \\note SplineState has the velocities and accelerations\n * given in the body-local reference frame that is implicitly defined\n * by \\b state's pose.\n *\n * \\note By default, all-zero velocities and accelerations are interpreted as intended boundary conditions.\n * If used together with Cartesian trajectory execution, this will yield\n * smooth stops in the trajectory's waypoints (= cubic interpolation with\n * zero velocity/acceleration boundary conditions).\n * If users want linear interpolation, they should mark the velocities/accelerations as unspecified by setting\n * at least one of the field values to NaN, e.g. state.v.x() = std::nan(\"0\");\n *\n * @param state The CartesianState to convert\n *\n * @return The content of \\b state in ros_controllers_cartesian::SplineState notation\n */\nCartesianTrajectorySegment::SplineState convert(const CartesianState& state);\n\n/**\n * @brief Convert a ros_controllers_cartesian::SplineState into a CartesianState\n *\n * The computation of Cartesian angular velocities and accelerations from\n * quaternion velocities 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Tejasvi-13/ADS-tutorial/blob/e0338474f8574e09efddcbb7a7536aae0b0d3201/EMS_without_stack.cpp", "id": "cpp-0-e8003c1412cec1de20e06b7fb3417e1f", "text": "#include<iostream>\n\nusing namespace std;\nint c,c1,c2;\nclass Employee\n{\n    public:\n    int age,c=0;\n    string name;\n    long int salary;\n    Employee *lchild,*rchild;\n    void accept();\n    void search(Employee *root,long int n);\n    void display(Employee *root);\n    void height();\n    void displayLeafNodes(Employee *root);\n    void insert(Employee *root,Employee *next);\n    void calculate();\n    void find_min(Employee* root);\n    void find_max(Employee* root);\n} *root;\n\nvoid Employee::find_min(Employee* root)\n{\n    while(root->lchild!=NULL)\n    {\n        root=root->lchild;\n    }\n   cout<<\"\\nMin salary of a Employee:  \"<<root->salary;\n}\n\nvoid Employee::find_max(Employee* root)\n{\n    while(root->rchild!=NULL)\n    {\n        root=root->rchild;\n    }\n    cout<<\"\\nMax salary of an Employee:  \"<<root->salary;\n} \n\nvoid Employee :: display(Employee *root)\n{\n    if (root != nullptr)\n    {\n        display(root->lchild);\n        cout << \"\\n\" << root->name << \"\\t\" << root->salary << endl;\n        display(root->rchild);\n    }\n};\n\nvoid Employee::height()\n{\nif(c1>=c2)\n{\ncout<<\"Heigheight of tree is : \"<<c1;\n}\nelse\n{\ncout<<\"Heigheight of tree is : \"<<c2;\n}\n}\n\nvoid Employee::displayLeafNodes(Employee *root)\n{\n    if (root == nullptr)\n        return;\n\n    if (root->lchild == nullptr && root->rchild == nullptr)\n    {\n        cout << \"\\nLeaf Node: \" << root->name << \"\\t\" << root->salary << endl;\n    }\n\n    displayLeafNodes(root->lchild);\n    displayLeafNodes(root->rchild);\n}\n\n\n\nvoid Employee::search(Employee *root, long int n) {\n    if (root == NULL) {\n        cout << \"Tree is Employeety\";\n        return;\n    }\n\n    if (root->salary == n) {\n        cout << \"Employeeloyee is found:\" << endl;\n        cout << \"\\nNAME\\tSALARY\";\n        cout << \"\\n\" << root->name << \"\\t\" << root->salary;\n        return;\n    }\n\n    // Search lchild subtree if target name is less than current node'salary name\n    if (n < root->salary)\n        search(root->lchild, n);\n    // Search righeight subtree if target name is greater than current node'salary name\n    else\n        search(root->rchild, n);\n}\n\n\n\nvoid Employee::accept()\n{\n    Employee *next;\n    root=new Employee;\n    \n    int ch;\n    cout<<\"Enter the name: \";\n    cin>>root->name;\n    cout<<\"Enter salary : \";\n    cin>>root->salary;\n    root->lchild=root->rchild=NULL;\n    c++;\n    c1=c2=c;\n    do\n    {\n        cout << \"\\nu want to add more employee if yes then press 1 otherwise press any key : \" << endl;\n        cin >> ch;\n        if (ch == 1)\n        {\n            next = new Employee();\n            cout << \"enter the following Employee information :\" << endl;\n            cout<<\"Enter the name: \";\n            cin>>next->name;\n            cout<<\"Enter salary : \";\n            cin>>next->salary;\n            next->lchild = NULL;\n            next->rchild = NULL;\n            insert(root, next);\n        }\n    } while (ch == 1);\n}\n\n\nvoid Employee::insert(Employee *root,Employee *next)\n{ \n   string chr;\n    if (root->salary > next->salary)\n    {\n        if (root->lchild == NULL)\n        {c++;\n            c1++;\n            root->lchild = next;\n  cout<<\"Inserted successfully\";\n        }\n        else\n        {\n            insert(root->lchild, next);\n        }\n    }\n    if (root->salary < next->salary)\n    {\n        if (root->rchild == NULL)\n        {    c++;\n            c2++;\n            root->rchild = next;\n            cout<<\"Inserted successfully\";\n        }\n        else\n        {\n            insert(root->rchild, next);\n        }\n    }\n}\n\nvoid Employee::calculate()\n{\n    cout<<\"The total number of record : \"<<c;\n}\n\nint main()\n{\n    Employee f;\n    int ch;\n    long int n;\n    do\n    {\n        cout<<\"\\nWELCOME TO Employee TREE \";\n        cout<<\"\\n1.Add the Employee member\\n2.display \\n3.display the total generation\\n4.display the leaf node\\n5.calculate total record\\n6.find\\n7.find min salary\\n8.find max salary \\n9.exit \";\n        cout<<\"\\nEnter your choice : \";\n        cin>>ch;\n        switch(ch)\n        {\n            case 1:\n            f.accept();\n            break;\n            case 2:\n            cout<<\"\\nNAME\\tSALARY\";\n            f.display(root);\n            \n            break;\n            case 3:\n            f.height();\n            break;\n            case 4:\n            f.displayLeafNodes(root);\n            break;\n            case 5:\n            f.calculate();\n            break;\n            case 6:\n            cout<<\"Enter the salary for search : \";\n            cin>>n;\n            f.search(root,n);\n            break;\n            case 7:\n            f.find_min(root);\n            break;\n            case 8:\n            f.find_max(root);\n            break;\n            case 9:\n            cout<<\"Thanks for visiting...!!!\";\n            break;\n            default:\n            cout<<\"Wrong choice\";\n\n        }\n    } while (ch!=9);\n    \n}\n", "text_preview": "#include<iostream> using namespace std; int c,c1,c2; class Employee { public: int age,c=0; string name; long int salary; Employee *lchild,*rchild; void accept(); void search(Employee *root,long int n); void display(Employee *root); void hei", "input_sha256": "7ceea734f0040a4c021d56b03138e0e1024619c38dce91aa9ff127cc0358065e", "text_sha256": "e8003c1412cec1de20e06b7fb3417e1f0eeb2cbad1bed48dccc3e9b5eba3be33", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 7, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/pixelsuchter/sloppyEyeTracker/blob/b31f974f0adef9f53e46df5c978973a602cbef36/main.cpp", "id": "cpp-0-ddaaf5e0b351ad5ce9332bb68cb43ec4", "text": "#include <iostream>\n#include <opencv2/opencv.hpp>\n#include <tuple>\n#include <utility>\n\nusing coordinate = cv::Point;\n\n// find the pupil in the frame\ncoordinate findPupil(cv::Mat &frame, coordinate lastLocation, int x1, int y1,\n                     int x2, int y2);\n\ncoordinate spiral(cv::Mat &frame, coordinate lastLocation, int stepsize,\n                  cv::Rect bound, int threshhold);\n\ncv::Rect findPupilBox(cv::Mat &frame, coordinate pupilLocation, int threshhold);\n\nstatic int checks = 0;\nstatic long long checksum = 0;\nstatic int frames = 0;\nstatic cv::Mat displayframe;\n\n\nint main() {\n\n  // read the first frame of eye2.mp4\n  cv::VideoCapture cap(\"eye3.mp4\");\n\n  cv::Mat frame;\n  cap >> frame;\n\n  displayframe = frame.clone();\n\n  int width = frame.cols;\n  int height = frame.rows;\n\n  // create ouptut.mp4 with the same size as the input video\n  cv::VideoWriter outputVideo;\n  int ex = static_cast<int>(cv::VideoWriter::fourcc('X', '2', '6', '4'));\n  outputVideo.open(\"output.mp4\", ex, 30, cv::Size(width, height));\n\n  int centerX = width / 2;\n  int centerY = height / 2;\n\n  const int boundingx1 = (width * 25) / 100;\n  const int boundingx2 = (width * 75) / 100;\n  const int boundingy1 = (height * 40) / 100;\n  const int boundingy2 = (height * 90) / 100;\n\n  coordinate lastPupilLocation = coordinate(centerX, centerY);\n\n  // find the pupil\n  coordinate pupil = findPupil(frame, lastPupilLocation, boundingx1, boundingy1,\n                               boundingx2, boundingy2);\n\n  // draw a circle at the pupil\n  cv::circle(frame, pupil, 10, cv::Scalar(0, 0, 255), 2);\n  // show the frame\n  // cv::imshow(\"frame\", frame);\n  // cv::waitKey(0);\n\n  // loop through all frames and find the pupil based on the last location\n  while (true) {\n    checks = 0;\n    // get the next frame\n    cap >> frame;\n    frames++;\n\n    displayframe = frame.clone();\n\n    // if the frame is empty, break\n    if (frame.empty()) {\n      break;\n    }\n\n    cv::rectangle(displayframe,\n                  cv::Rect(boundingx1, boundingy1, boundingx2 - boundingx1,\n                           boundingy2 - boundingy1),\n                  cv::Scalar(100, 0, 0));\n    // find the pupil\n    pupil = findPupil(frame, lastPupilLocation, boundingx1, boundingy1,\n                      boundingx2, boundingy2);\n    spiral(frame, lastPupilLocation, 10,\n           cv::Rect(boundingx1, boundingy1, boundingx2 - boundingx1,\n                    boundingy2 - boundingy1),\n           35);\n    // draw a circle at the pupil\n    // cv::circle(frame, pupil, 10, cv::Scalar(0, 0, 255), 2);\n\n    // draw box around pupil\n    auto pupilBox = findPupilBox(frame, pupil, 50);\n    // print the pupil box\n    // std::cout << \"Pupil box: \" << pupilBox << std::endl;\n    // draw the pupil box\n    cv::rectangle(displayframe, pupilBox, cv::Scalar(0, 255, 0), 1);\n\n    // set lastpupillocation to the center of the pupil box\n    lastPupilLocation = cv::Point(pupilBox.x + pupilBox.width / 2,\n                                  pupilBox.y + pupilBox.height / 2);\n    // draw circle at center\n    cv::circle(displayframe, lastPupilLocation, 10, cv::Scalar(0, 0, 255), 2);\n\n    std::cout << \"Checks: \" << checks << std::endl;\n    checksum += checks;\n    checks = 0;\n\n    // show the frame\n    cv::imshow(\"frame\", displayframe);\n    cv::waitKey(0);\n\n    // Write the frame to output file\n    outputVideo.write(frame);\n  }\n\n  // release the video capture\n  cap.release();\n\n  // release the output video\n  outputVideo.release();\n\n  // destroy all windows\n  cv::destroyAllWindows();\n\n  std::cout << \"Average checks per frame: \" << checksum / frames << std::endl;\n  return 0;\n}\n\ncoordinate findPupil(cv::Mat &frame, coordinate lastLocation, int x1, int y1,\n                     int x2, int y2) {\n  // Get the size of the frame\n  int width = frame.cols;\n  int height = frame.rows;\n\n  // Get the center\n  int centerX = width / 2;\n  int centerY = height / 2;\n\n  return spiral(frame, lastLocation, 10, cv::Rect(x1, y1, x2 - x1, y2 - y1),\n                35);\n}\n\nbool isPupil(cv::Mat &frame, coordinate where, int threshhold) {\n  cv::Vec3b pixel = frame.at<cv::Vec3b>(where);\n  checks++;\n  cv::circle(displayframe, where, 1, cv::Scalar(250, 50, 0), 2);\n\n  if (pixel[0] < threshhold && pixel[1] < threshhold && pixel[2] < threshhold) {\n    return true;\n  } else {\n    return false;\n  }\n}\n\ncoordinate spiral(cv::Mat &frame, coordinate lastLocation, int stepsize,\n                  cv::Rect bound, int threshhold) {\n  // draw a square spiral with a circle every stepsize pixels until a bound is\n  // hit\n  int x = lastLocation.x;\n  int y = lastLocation.y;\n  int radius = 5;\n  int direction = 0;\n\n  // Print arguments:\n  std::cout << \"spiral: \" << x << \" \" << y << \" \" << stepsize << \" \" << bound.x\n            << \" \" << bound.y << \" \" << bound.width << \" \" << bound.height\n            << std::endl;\n\n  int range = 0;\n\n  while (x < bound.width + bound.x && y < bound.height + bound.y &&\n         x > bound.x && y > bound.y) {\n    // draw a circle at the current location\n    // cv::circle(frame, cv::Point(x, y), radius, cv::Scalar(255, 0, 255), 2);\n\n    switch (direction) {\n    case 0: {\n      range++;\n      direction++;\n      for (int i = 0; i < range; i++) {\n        x += i * stepsize;\n        // cv::circle(frame, cv::Point(x, y), radius, cv::Scalar(255, 0, 255),\n        // 2);\n        if (isPupil(frame, {x, y}, threshhold)) {\n          return coordinate(x, y);\n        }\n      }\n    } break;\n    case 1: {\n      direction++;\n      for (int i = 0; i < range; i++) {\n        y += i * stepsize;\n        // cv::circle(frame, cv::Point(x, y), radius, cv::Scalar(255, 0, 255),\n        // 2);\n        if (isPupil(frame, {x, y}, threshhold)) {\n          return coordinate(x, y);\n        }\n      }\n    } break;\n    case 2: {\n      range++;\n      direction++;\n      for (int i = 0; i < range; i++) {\n        x -= i * stepsize;\n        // cv::circle(frame, cv::Point(x, y), radius, cv::Scalar(255, 0, 255),\n        // 2);\n        if (isPupil(frame, {x, y}, threshhold)) {\n          return coordinate(x, y);\n        }\n      }\n    } break;\n    case 3: {\n      direction = 0;\n      for (int i = 0; i < range; i++) {\n        y -= i * stepsize;\n        // cv::circle(frame, cv::Point(x, y), radius, cv::Scalar(255, 0, 255),\n        // 2);\n        if (isPupil(frame, {x, y}, threshhold)) {\n          return coordinate(x, y);\n        }\n      }\n    } break;\n    }\n  }\n  return coordinate(lastLocation.x, lastLocation.y);\n}\n\ncv::Rect findPupilBox(cv::Mat &frame, coordinate pupilLocation,\n                      int threshhold) {\n  // March left, right, up, down and calculate the bounding area of the pupil\n  // using the isPupil function\n\n  auto constexpr steps = 25;\n  auto constexpr distance = 20;\n\n  // march left\n  auto x = pupilLocation.x;\n  auto y = pupilLocation.y;\n  while (true) {\n    int i = 0;\n    if (!isPupil(frame, {x, y}, threshhold)) {\n      if (!isPupil(frame, {x, y + distance}, threshhold) &&\n          !isPupil(frame, {x, y - distance}, threshhold)) {\n        for (; i < steps; i++) {\n          x++;\n          if (isPupil(frame, {x, y}, threshhold)) {\n            break;\n          }\n        }\n        break;\n      }\n    }\n    x -= steps;\n  }\n  int left = x;\n  x = pupilLocation.x;\n  // march right\n  while (true) {\n    int i = 0;\n    if (!isPupil(frame, {x, y}, threshhold)) {\n      if (!isPupil(frame, {x, y + distance}, threshhold) &&\n          !isPupil(frame, {x, y - distance}, threshhold)) {\n        for (; i < steps; i++) {\n          x--;\n          if (isPupil(frame, {x, y}, threshhold)) {\n            break;\n          }\n        }\n        break;\n      }\n    }\n    x += steps;\n  }\n  int right = x;\n  x = pupilLocation.x;\n  // march up\n  while (true) {\n    int i = 0;\n    if (!isPupil(frame, {x, y}, threshhold)) {\n      if (!isPupil(frame, {x + distance, y}, threshhold) &&\n          !isPupil(frame, {x - distance, y}, threshhold)) {\n        for (; i < steps; i++) {\n          y++;\n          if (isPupil(frame, {x, y}, threshhold)) {\n            break;\n          }\n        }\n        break;\n      }\n    }\n    y -= steps;\n  }\n  int top = y;\n  y = pupilLocation.y;\n  // march down\n  while (true) {\n    int i = 0;\n    if (!isPupil(frame, {x, y}, threshhold)) {\n      if (!isPupil(frame, {x + distance, y}, threshhold) &&\n          !isPupil(frame, {x - distance, y}, threshhold)) {\n        for (; i < steps; i++) {\n          y--;\n          if (isPupil(frame, {x, y}, threshhold)) {\n            break;\n          }\n        }\n        break;\n      }\n    }\n    y += steps;\n  }\n  int bottom = y;\n\n  return {left, top, right - left, bottom - top};\n}", "text_preview": "#include <iostream> #include <opencv2/opencv.hpp> #include <tuple> #include <utility> using coordinate = cv::Point; // find the pupil in the frame coordinate findPupil(cv::Mat &frame, coordinate lastLocation, int x1, int y1, int x2, int y2)", "input_sha256": "b0a115f0d2ad038bced6e56b4cdd8ddb99520cc5e2c19f7691ebce77fd450e93", "text_sha256": "ddaaf5e0b351ad5ce9332bb68cb43ec4fae98aeeaaf9f289b7329d8cd9c7de49", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 9, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/fishy15/competitive_programming/blob/739a10ba45bc0eb8ea5a83bf8ae455045dd233fa/icpc/brazil_subregional/2019/artwork.cpp", "id": "cpp-0-563b4ad98651acbebf89a58ebc6f7ac4", "text": "#include <iostream>\n#include <iomanip>\n#include <fstream>\n#include <vector>\n#include <array>\n#include <algorithm>\n#include <utility>\n#include <map>\n#include <queue>\n#include <set>\n#include <cmath>\n#include <cstdio>\n#include <cstring>\n#include <functional>\n#include <numeric>\n\n#define ll long long\n#define ld long double\n#define eps 1e-8\n#define MOD 1000000007\n\n#define INF 0x3f3f3f3f\n#define INFLL 0x3f3f3f3f3f3f3f3f\n\n#define rep(i, a, b) for(int i = a; i < (b); ++i)\n#define all(x) begin(x), end(x)\n#define sz(x) (int)(x).size()\n\nusing namespace std;\n\ntemplate <class T> int sgn(T x) { return (x > 0) - (x < 0); }\ntemplate<class T>\nstruct Point {\n    typedef Point P;\n    T x, y;\n    explicit Point(T _x=0, T _y=0) : x(_x), y(_y) {}\n    bool operator<(P p) const { return tie(x,y) < tie(p.x,p.y); }\n    bool operator==(P p) const { return tie(x,y)==tie(p.x,p.y); }\n    P operator+(P p) const { return P(x+p.x, y+p.y); }\n    P operator-(P p) const { return P(x-p.x, y-p.y); }\n    P operator*(T d) const { return P(x*d, y*d); }\n    P operator/(T d) const { return P(x/d, y/d); }\n    T dot(P p) const { return x*p.x + y*p.y; }\n    T cross(P p) const { return x*p.y - y*p.x; } // + => p on right\n    T cross(P a, P b) const { return (a-*this).cross(b-*this); }\n    bool half() const { return y < 0 || (y == 0 && x < 0); }\n    T dist2() const { return x*x + y*y; }\n    double dist() const { return sqrt((double)dist2()); }\n    // angle to x-axis in interval [-pi, pi]\n    double angle() const { return atan2(y, x); }\n    P unit() const { return *this/dist(); } // makes dist()=1\n    P perp() const { return P(-y, x); } // rotates +90 degrees\n    P normal() const { return perp().unit(); }\n    // returns point rotated 'a' radians ccw around the origin\n    P rotate(double a) const {\n        return P(x*cos(a)-y*sin(a),x*sin(a)+y*cos(a)); }\n    friend ostream& operator<<(ostream& os, P p) {\n        return os << \"(\" << p.x << \",\" << p.y << \")\"; }\n};\n\nusing P = Point<ll>;\n\nstruct DSU {\n    vector<int> dsu;\n    vector<int> size;\n    DSU(int n) : dsu(n), size(n, 1) {\n        iota(all(dsu), 0);\n    }\n\n    int find(int x) {\n        return x == dsu[x] ? x : dsu[x] = find(dsu[x]);\n    }\n\n    void join(int x, int y) {\n        if ((x = find(x)) != (y = find(y))) {\n            if (size[x] < size[y]) swap(x, y);\n            dsu[y] = x;\n            size[x] += size[y];\n        }\n    }\n};\n\nint main() {\n    cin.tie(0)->sync_with_stdio(0);\n\n    int m, n, k;\n    cin >> m >> n >> k;\n\n    vector<pair<P, int>> circles;\n    rep(i, 0, k) {\n        int x, y, r;\n        cin >> x >> y >> r;\n        circles.push_back({P(x, y), r});\n    }\n\n    int top = k;\n    int bottom = k+1;\n    int left = k+2;\n    int right = k+3;\n\n    DSU dsu(k + 4);\n\n    // join with ends\n    rep(i, 0, k) {\n        auto [o, r] = circles[i];\n        if (o.x <= r) {\n            dsu.join(i, left);\n        }\n        if (m - o.x <= r) {\n            dsu.join(i, right);\n        }\n        if (o.y <= r) {\n            dsu.join(i, top);\n        }\n        if (n - o.y <= r) {\n            dsu.join(i, bottom);\n        }\n    }\n\n    // join with each other\n    rep(i, 0, k) {\n        rep(j, i+1, k) {\n            auto dist2 = (circles[i].first - circles[j].first).dist2();\n            ll allowed = circles[i].second + circles[j].second;\n            if (dist2 <= allowed * allowed) {\n                dsu.join(i, j);\n            }\n        }\n    }\n\n    auto x_conn = dsu.find(left) == dsu.find(right);\n    auto y_conn = dsu.find(top) == dsu.find(bottom);\n    auto diag1 = dsu.find(left) == dsu.find(top);\n    auto diag2 = dsu.find(right) == dsu.find(bottom);\n    if (x_conn || y_conn || diag1 || diag2) {\n        cout << \"N\\n\";\n    } else {\n        cout << \"S\\n\";\n    }\n\n    return 0;\n}\n", "text_preview": "#include <iostream> #include <iomanip> #include <fstream> #include <vector> #include <array> #include <algorithm> #include <utility> #include <map> #include <queue> #include <set> #include <cmath> #include <cstdio> #include <cstring> #inclu", "input_sha256": "ed59ac27be969677d8ab0a4a8dd76e0e484a3ecb061eb497aa33ad854ba977b5", "text_sha256": "563b4ad98651acbebf89a58ebc6f7ac446b9234cdcd54336605ce40f456e4275", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 10, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0867677825471882, "noise_severity": 0.05930789250569578, "missing_content_severity": 0.0019672589134857835, "factual_density": 3.5565124816654854, "pretraining_utility": 0.6423076833241743, "math_density": 3.175256040562969, "code_density": 3.909819577051708, "reasoning_density": 1.9015640676732675, "education_level": 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0.0019731506378503636}, "worked_solution": {"false": 0.9392225699896655, "true": 0.06077743001033451}, "contextual_dialogue": {"false": 0.9999937782639958, "true": 6.221736004221122e-06}, "content_quality": {"-2": 0.005749706331859258, "-1": 0.015284933595236985, "0": 0.18792947384955652, "1": 0.4685196436405508, "2": 0.32251624258279643}, "noise_severity": {"0": 0.9475779083571669, "1": 0.04745065516591226, "2": 0.0036614136697864764, "3": 0.0007056812283267714, "4": 0.000604341578807564}, "missing_content_severity": {"0": 0.9989529939924185, "1": 0.0004332802605606492, "2": 0.00037598511654291314, "3": 0.00016895410207226914, "4": 6.878652840562519e-05}, "factual_density": {"0": 0.002661337367446456, "1": 0.005914522949408679, "2": 0.057458443584021364, "3": 0.3001817128484603, "4": 0.6337839832506633}, "pretraining_utility": {"-2": 0.008734351345278665, "-1": 0.0775713664560078, "0": 0.32866245269092237, "1": 0.432715906544843, "2": 0.15231592296294824}, "math_density": {"0": 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0.0006386801979068192, "Vehicles & Transportation": 0.0002611243739647765, "Travel & Tourism": 0.0004921592464664129, "Other / Uncovered Subject": 0.0011126793692473483, "NONE": 0.3445651576616487}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/zhangyiii/molecular_dynamics/blob/be56f8e78e662d45404761937bd2e722c6b8930e/Mol_dyn/openmp_implementation/md_cpu.cpp", "id": "cpp-0-26aa317305b7bf5a75167d295ec35828", "text": "#include <stdio.h>\n#include <math.h>\n#include <stdlib.h>\n#include <float.h>\n#include <time.h>\n#include <omp.h>\n#include <string.h>\n#include \"parameters.h\"\n\n#define NUM_THREADS 8\n\nstruct dim {\n    double x;\n    double y;\n    double z;\n};\ntypedef struct dim dim;\n\ndouble square_dist(dim first, dim second);\nint nearest_image(dim* initial_array, dim* new_array, int particle_count, int particle);\nvoid set_initial_state(dim *array, dim *velocity, dim *force);\ndouble fast_pow(double a, int n);\nvoid md(dim *array, dim *velocity, dim *force);\ndouble calculate_energy_force_lj(dim *array, dim *force);\nvoid motion(dim *array, dim *velocity, dim *force);\n\ndouble Urc = 4 * ( 1 / fast_pow(rc, 12) - 1 / fast_pow(rc, 6) );\n\nint main()\n{\n    time_t t;\n    time_t start_total_time = time(NULL);\n    srand((unsigned)time(&t));\n    dim *r = (dim*)malloc(sizeof(dim) * N);\n    dim *v = (dim*)malloc(sizeof(dim) * N);\n    dim *f = (dim*)malloc(sizeof(dim) * N);\n    set_initial_state(r,v,f);\n    md(r,v,f);\n    free(r);\n    free(v);\n    free(f);\n    time_t end_total_time = time(NULL);\n    printf(\"\\nTotal execution time in seconds =  %f\\n\", difftime(end_total_time, start_total_time));\n    return 0;\n}\n\n/////// HELPER FUNCTIONS ///////\n\nvoid set_initial_state(dim *array, dim *velocity, dim *force) {\n    int count = 0;\n    for (double i = -(box_size - initial_dist_to_edge)/2; i < (box_size - initial_dist_to_edge)/2; i += initial_dist_by_one_axis) {\n        for (double j = -(box_size - initial_dist_to_edge)/2; j < (box_size - initial_dist_to_edge)/2; j += initial_dist_by_one_axis) {\n            for (double l = -(box_size - initial_dist_to_edge)/2; l < (box_size - initial_dist_to_edge)/2; l += initial_dist_by_one_axis) {\n                if( count == N){\n                    return; //it is not balanced grid but we can use it\n                }\n                array[count] = { i,j,l };\n                velocity[count] = { 0, 0, 0 };\n                force[count] = { 0, 0, 0 };\n                count++;\n            }\n        }\n    }\n    if( count < N ){\n        printf(\"error decrease initial_dist parameter, count is %ld  N is %ld \\n\", count, N);\n        exit(1);\n    }\n}\n\nint nearest_image(dim* initial_array, dim* new_array, int particle_count, int particle) {\n    int count = 0;\n    for (int z_dif = -box_size; z_dif < box_size + 1; z_dif += box_size) {\n        for (int y_dif = -box_size; y_dif < box_size + 1; y_dif += box_size) {\n            for (int x_dif = -box_size; x_dif < box_size + 1; x_dif += box_size) {\n                for (int i = 0; i < particle_count; i++) {\n                    if (particle == i) {\n                        continue;\n                    }\n                    dim temp = { initial_array[i].x + x_dif, initial_array[i].y + y_dif, initial_array[i].z + z_dif };\n                    if (square_dist(temp, initial_array[particle]) < rc*rc) {\n                        new_array[count] = temp;\n                        count++;\n                    }\n                }\n            }\n        }\n    }\n    return count;\n}\n\ndouble square_dist(dim first, dim second) {\n    return (first.x - second.x)*(first.x - second.x) + (first.y - second.y)*(first.y - second.y) + (first.z - second.z)*(first.z - second.z);\n}\n\ndouble calculate_energy_force_lj(dim *array, dim *force){\n    for (int i = 0; i < N; i++){\n        force[i] = { 0, 0, 0};\n    }\n    double energy = 0;\n    #pragma omp parallel for reduction(+:energy) num_threads(NUM_THREADS)\n    for (int i = 0; i < N; i++) {\n        double force_x = 0;\n        double force_y = 0;\n        double force_z = 0;\n        dim *neighbors = (dim*)malloc(sizeof(dim) * N);\n        int count_near = nearest_image(array, neighbors, N, i);\n        for (int j = 0; j < count_near; j++) {\n            double dist = square_dist(array[i], neighbors[j]);\n            double r6 = fast_pow(dist, 3);\n            double r12 = r6 * r6;\n            double r8 = r6 * dist;\n            double r14 = r12 * dist;\n            double multiplier = (12 * (1 / r14 - 1 / r8));\n            force_x += (neighbors[j].x - array[i].x)  * multiplier;\n            force_y += (neighbors[j].y - array[i].y)  * multiplier;\n            force_z += (neighbors[j].z - array[i].z)  * multiplier;\n            energy += 4 * (1 / r12 - 1 / r6) - Urc;\n        }\n        free(neighbors);\n        force[i].x = force_x;\n        force[i].y = force_y;\n        force[i].z = force_z;\n    }\n    // now we consider each interaction twice, so we need to divide energy by 2\n    return energy / 2;\n}\n\nvoid md(dim *array, dim *velocity, dim *force) {\n    for (int n = 0; n < total_it; n ++){\n        double total_energy = calculate_energy_force_lj(array, force);\n        motion(array, velocity, force);\n        if (!(n % 1000)) {\n            printf(\"energy is %f \\n\", total_energy/N);\n        }\n    }\n}\n\nvoid motion(dim *array, dim *velocity, dim * force){\n    for (int i = 0; i < N; i++) {\n        velocity[i] = {velocity[i].x + force[i].x * dt,\n            velocity[i].y + force[i].y * dt,\n            velocity[i].z + force[i].z * dt};\n        array[i] = {array[i].x + velocity[i].x * dt,\n            array[i].y + velocity[i].y * dt,\n            array[i].z + velocity[i].z * dt};\n    }\n}\n\ninline double fast_pow(double a, int n) {\n    if (n == 0)\n        return 1;\n    if (n % 2 == 1)\n        return fast_pow(a, n - 1) * a;\n    else {\n        double b = fast_pow(a, n / 2);\n        return b * b;\n    }\n}\n", "text_preview": "#include <stdio.h> #include <math.h> #include <stdlib.h> #include 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/lms8839656/Coding-Test/blob/b46be5271142837fd02a8e59a9775c66938c47c2/%EB%B0%B1%EC%A4%80/%EC%82%AC%EB%8B%A4%EB%A6%AC%EC%A1%B0%EC%9E%91.cpp", "id": "cpp-0-eda520b30a1e92a74fbbe33a740265c6", "text": "#include <iostream>\n\nusing namespace std;\n\nbool MAP[30+1][10+1];\nbool CheckMAP[30+1][10+1];\nbool visited[30+1][10+1];\nbool Line[10+1];\nbool tempMAP[30+1][10+1];\n\nint N, M, H;\n\nint startNum;\nbool EndFlag = false;\n\nint dy[2] = {1, -1};\n\n\nvoid Input();\nvoid DFS(int x, int y);\nvoid SetLine(int cnt);\nvoid ResetVisted();\nvoid Run();\n\nvoid DebugOutput();\n\nint main(void)\n{\n    Input();\n    \n    \n//    cout << \"\\n\";\n//    for(int x = 1; x <= H; x++){\n//        for(int y = 1; y <= N; y++){\n//            cout << MAP[x][y] << \" \";\n//        }\n//        cout << \"\\n\";\n//    }\n    \n    for(int i = 0; i < 4; i++){\n        \n        SetLine(i);\n        if(EndFlag){\n            cout << i;\n            return 0;     \n        }\n    }\n    \n    if(EndFlag == false) cout << \"-1\";\n    return 0;\n}\n\nvoid Input(){\n    cin >> N >> M >> H;\n    int x, y;\n    for(int i = 0; i < M; i++){\n        if(M == 0) continue;\n        cin >> x >> y;\n        MAP[x][y] = true;\n        CheckMAP[x][y] = true;\n        MAP[x][y+1] = true;\n    }\n}\n\nvoid DebugOutput(){\n    \n    \n//    cout << \"\\n\";\n//    for(int x = 1; x <= H; x++){\n//        for(int y = 1; y <= N; y++){\n//            cout << MAP[x][y] << \" \";\n//        }\n//        cout << \"\\n\";\n//    }\n    \n    cout << \"\\n\";\n    for(int x = 1; x <= H; x++){\n        for(int y = 1; y <= N; y++){\n            cout << visited[x][y] << \" \";\n        }\n        cout << \"\\n\";\n    }\n    \n//    cout << \"\\n\";\n//    for(int i = 1; i <= N; i++){\n//        cout << Line[i] << \" \";\n//    }\n//    cout << \"\\n\";\n//    cout << EndFlag;\n    \n    \n//    cout << \"\\n\";\n//    for(int x = 1; x <= H; x++){\n//        for(int y = 1; y <= N; y++){\n//            cout << tempMAP[x][y] << \" \";\n//        }\n//        cout << \"\\n\";\n//    }\n}\n\nvoid DFS(int x, int y){\n    if(x > H){\n        //DebugOutput();\n        if(startNum == y) Line[startNum] = true;\n        else Line[startNum] = false;\n        return;\n    }\n    visited[x][y] = true;\n    \n    if(MAP[x][y] == true && CheckMAP[x][y] == true && !visited[x][y+1]) DFS(x, y+1);\n    else if(MAP[x][y] == true && CheckMAP[x][y] == false && !visited[x][y-1]) DFS(x, y-1);\n    else DFS(x+1, y);\n    \n    \n//    for(int i = 0; i < 2; i++){\n//        ny = y + dy[i];\n//        eny = y + 2*dy[i];\n//        nny = y - dy[i];\n//        if(ny > N || ny < 1 || visited[x][ny]) continue;\n//        \n//        if(MAP[x][y] == 1 && MAP[x][ny] == 1 && !visited[x][ny]){\n//            if(MAP[x][ny] == 1 && MAP[x][nny] == 1){\n//                if(MAP[x][eny] == 0){\n//                    DFS(x+1, ny);\n//                    Flag = true;\n//                }\n//                else continue;\n//            }\n//            else DFS(x+1, ny);   \n//            Flag = true;\n//        }\n//    }\n//    if(Flag == false) DFS(x+1, y);\n}\n\nvoid SetLine(int cnt){\n    if(EndFlag == true) return;\n    \n    if(cnt == 0){\n        Run();\n        return;\n    }\n    else{\n        for(int x = 1; x <= H; x++){\n            for(int y = 1; y <= N-1; y++){\n                if(MAP[x][y] || MAP[x][y+1]) continue;\n                MAP[x][y] = true;\n                MAP[x][y+1] = true;\n                CheckMAP[x][y] = true;\n                cnt--;\n                SetLine(cnt);\n                MAP[x][y] = false;\n                MAP[x][y+1] = false;\n                CheckMAP[x][y] = false;\n                cnt++;\n            }\n        }\n    }\n}\n\nvoid Run(){\n    for(int i = 1; i <= N; i++){\n        startNum = i;\n        DFS(1, i);\n        ResetVisted();\n    }\n    \n    \n    for(int i = 1; i <= N; i++){\n        if(!Line[i]) return;\n    }\n    \n    EndFlag = true;\n}\n\nvoid ResetVisted(){\n    for(int x = 1; x <= H; x++){\n        for(int y = 1; y <= N; y++){\n            visited[x][y] = false;\n        }\n    }\n}\n", "text_preview": "#include <iostream> using namespace std; bool MAP[30+1][10+1]; bool CheckMAP[30+1][10+1]; bool visited[30+1][10+1]; bool Line[10+1]; bool tempMAP[30+1][10+1]; int N, M, H; int startNum; bool EndFlag = false; int dy[2] = {1, -1}; void Input(", "input_sha256": "277fe556ec80e4e7d7ab40079c777de1ef85b4579d20e696d009326794742105", "text_sha256": "eda520b30a1e92a74fbbe33a740265c6639f0ef2330cfe8b67b758fb03e9fba0", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 13, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/cosmosapjw-quantum/omoknuni_small/blob/8be6bdd0a4515ad59ee2d9ed175e43906341f42d/include/nn/ddw_randwire_resnet.h", "id": "cpp-0-15aa9436b132d33aa1c552337e642455", "text": "#ifndef ALPHAZERO_NN_DDW_RANDWIRE_RESNET_H\n#define ALPHAZERO_NN_DDW_RANDWIRE_RESNET_H\n\n#include <torch/torch.h>\n#include <vector>\n#include <string>\n#include <random>\n#include <unordered_map>\n#include <memory>\n#include <tuple>\n#include \"nn/neural_network.h\"\n#include \"mcts/gpu_memory_pool.h\"\n#include \"core/export_macros.h\"\n\nnamespace alphazero {\nnamespace nn {\n\n/**\n * @brief Graph generation method for RandWire networks\n */\nenum class GraphGenMethod {\n    WATTS_STROGATZ,  // Small-world graph\n    ERDOS_RENYI,     // Random graph\n    BARABASI_ALBERT  // Scale-free graph\n};\n\n/**\n * @brief Squeeze-and-Excitation Block for attention mechanism\n */\nclass SEBlock : public torch::nn::Module {\npublic:\n    /**\n     * @brief Constructor\n     * \n     * @param channels Number of input and output channels\n     * @param reduction Reduction ratio for the bottleneck\n     */\n    SEBlock(int64_t channels, int64_t reduction = 16);\n    \n    /**\n     * @brief Forward pass\n     * \n     * @param x Input tensor\n     * @return Output tensor with channel attention applied\n     */\n    torch::Tensor forward(torch::Tensor x);\n\nprivate:\n    torch::nn::AdaptiveAvgPool2d squeeze{nullptr};\n    torch::nn::Sequential excitation{nullptr};\n};\n\n/**\n * @brief Residual block with batch normalization\n */\nclass ResidualBlock : public torch::nn::Module {\npublic:\n    /**\n     * @brief Constructor\n     * \n     * @param channels Number of input and output channels\n     */\n    ResidualBlock(int64_t channels);\n    \n    /**\n     * @brief Forward pass\n     * \n     * @param x Input tensor\n     * @return Output tensor with residual connection\n     */\n    torch::Tensor forward(torch::Tensor x);\n\nprivate:\n    torch::nn::Conv2d conv1{nullptr};\n    torch::nn::BatchNorm2d bn1{nullptr};\n    torch::nn::Conv2d conv2{nullptr};\n    torch::nn::BatchNorm2d bn2{nullptr};\n    std::shared_ptr<SEBlock> se{nullptr};\n};\n\n/**\n * @brief Router module for dynamic wiring\n */\nclass RouterModule : public torch::nn::Module {\npublic:\n    /**\n     * @brief Constructor\n     * \n     * @param in_channels Number of input channels\n     * @param out_channels Number of output channels\n     */\n    RouterModule(int64_t in_channels, int64_t out_channels);\n    \n    /**\n     * @brief Forward pass\n     * \n     * @param x Input tensor\n     * @return Output tensor with routing applied\n     */\n    torch::Tensor forward(torch::Tensor x);\n\nprivate:\n    torch::nn::Conv2d conv{nullptr};\n    torch::nn::BatchNorm2d bn{nullptr};\n};\n\n/**\n * @brief Dynamic routing gate for instance-aware connections\n */\nclass DynamicRoutingGate : public torch::nn::Module {\npublic:\n    /**\n     * @brief Constructor\n     * \n     * @param channels Number of channels\n     * @param num_nodes Number of nodes in the graph\n     */\n    DynamicRoutingGate(int64_t channels, int64_t num_nodes);\n    \n    /**\n     * @brief Compute dynamic routing weights\n     * \n     * @param x Input tensor\n     * @param edge_features Edge feature tensor\n     * @return Routing weights for each edge\n     */\n    torch::Tensor forward(torch::Tensor x, torch::Tensor edge_features);\n\nprivate:\n    torch::nn::Conv2d feature_extractor{nullptr};\n    torch::nn::Linear edge_scorer{nullptr};\n    torch::nn::BatchNorm2d bn{nullptr};\n};\n\n/**\n * @brief Directed graph edge for random wiring\n */\nstruct Edge {\n    int from;\n    int to;\n    \n    bool operator==(const Edge& other) const {\n        return from == other.from && to == other.to;\n    }\n};\n\n/**\n * @brief Custom hash for Edge struct\n */\nstruct EdgeHash {\n    std::size_t operator()(const Edge& edge) const {\n        return std::hash<int>()(edge.from) ^ std::hash<int>()(edge.to);\n    }\n};\n\n/**\n * @brief Simple directed graph implementation\n */\nclass DiGraph {\npublic:\n    /**\n     * @brief Add a node to the graph\n     * \n     * @param node Node identifier\n     */\n    void add_node(int node);\n    \n    /**\n     * @brief Add an edge to the graph\n     * \n     * @param from Source node\n     * @param to Target node\n     */\n    void add_edge(int from, int to);\n    \n    /**\n     * @brief Get all nodes in the graph\n     * \n     * @return Vector of node identifiers\n     */\n    std::vector<int> nodes() const;\n    \n    /**\n     * @brief Get predecessors of a node\n     * \n     * @param node Node identifier\n     * @return Vector of predecessor node identifiers\n     */\n    std::vector<int> predecessors(int node) const;\n    \n    /**\n     * @brief Get successors of a node\n     * \n     * @param node Node identifier\n     * @return Vector of successor node identifiers\n     */\n    std::vector<int> successors(int node) const;\n    \n    /**\n     * @brief Get in-degree of a node\n     * \n     * @param node Node identifier\n     * @return Number of incoming edges\n     */\n    int in_degree(int node) const;\n    \n    /**\n     * @brief Get out-degree of a node\n     * \n     * @param node Node identifier\n     * @return Number of outgoing edges\n     */\n    int out_degree(int node) const;\n    \n    /**\n     * @brief Get topological sort of nodes\n     * \n     * @return Vector of node identifiers in topological order\n     */\n    std::vector<int> topological_sort() const;\n    \n    /**\n     * @brief Get all edges in the graph\n     * \n     * @return Vector of edges\n     */\n    std::vector<Edge> edges() const;\n    \n    /**\n     * @brief Get the number of nodes\n     * \n     * @return Number of nodes\n     */\n    size_t size() const;\n\nprivate:\n    std::unordered_set<int> nodes_;\n    std::unordered_map<int, std::vector<int>> adjacency_list_;\n    std::unordered_map<int, std::vector<int>> reverse_adjacency_list_;\n    \n    /**\n     * @brief Depth-first topological sort helper\n     * \n     * @param node Current node\n     * @param visited Set of visited nodes\n     * @param temp_visited Set of temporarily visited nodes (for cycle detection)\n     * @param result Resulting topological sort\n     * @return true if successful, false if cycles detected\n     */\n    bool _dfs_topo_sort(int node, std::unordered_set<int>& visited, \n                        std::unordered_set<int>& temp_visited, \n                        std::vector<int>& result) const;\n};\n\n/**\n * @brief Configuration for RandWire block\n */\nstruct RandWireConfig {\n    int64_t num_nodes = 32;\n    GraphGenMethod method = GraphGenMethod::WATTS_STROGATZ;\n    double p = 0.75;  // For WS model\n    double edge_prob = 0.1;  // For ER model\n    int64_t m = 5;  // For BA model\n    int64_t k = 4;  // For WS model\n    int64_t seed = -1;\n    bool use_dynamic_routing = true;\n};\n\n/**\n * @brief Random wiring block with dynamic connections\n */\nclass RandWireBlock : public torch::nn::Module {\npublic:\n    /**\n     * @brief Constructor\n     * \n     * @param channels Number of input and output channels\n     * @param config Configuration for the block\n     */\n    RandWireBlock(int64_t channels, const RandWireConfig& config = RandWireConfig());\n    \n    /**\n     * @brief Forward pass\n     * \n     * @param x Input tensor\n     * @return Output tensor after graph processing\n     */\n    torch::Tensor forward(torch::Tensor x);\n    \n    /**\n     * @brief Forward pass with dynamic routing\n     * \n     * @param x Input tensor\n     * @param use_dynamic Whether to use dynamic routing for this forward pass\n     * @return Output tensor after graph processing\n     */\n    torch::Tensor forward(torch::Tensor x, bool use_dynamic);\n\nprivate:\n    int64_t channels_;\n    RandWireConfig config_;\n    DiGraph graph_;\n    std::vector<int> input_nodes_;\n    std::vector<int> output_nodes_;\n    std::vector<int> active_nodes_;  // Only nodes that are actually connected\n    std::unordered_map<std::string, std::shared_ptr<RouterModule>> routers_;  // Legacy, kept for compatibility\n    std::unordered_map<std::string, std::shared_ptr<RouterModule>> adaptive_routers_;  // Dynamic routers created on-demand\n    std::unordered_map<std::string, std::shared_ptr<ResidualBlock>> blocks_;\n    std::shared_ptr<RouterModule> output_router_{nullptr};\n    std::shared_ptr<DynamicRoutingGate> routing_gate_{nullptr};\n    \n    /**\n     * @brief Generate a graph based on the selected method\n     * \n     * @return Directed graph\n     */\n    DiGraph _generate_graph();\n    \n    /**\n     * @brief Generate Watts-Strogatz small-world graph\n     */\n    DiGraph _generate_ws_graph();\n    \n    /**\n     * @brief Generate Erdos-Renyi random graph\n     */\n    DiGraph _generate_er_graph();\n    \n    /**\n     * @brief Generate Barabasi-Albert scale-free graph\n     */\n    DiGraph _generate_ba_graph();\n    \n    /**\n     * @brief Find active nodes (nodes that are actually connected)\n     */\n    void _find_active_nodes();\n};\n\n/**\n * @brief Configuration for DDWRandWireResNet\n */\nstruct ALPHAZERO_API DDWRandWireResNetConfig {\n    int64_t input_channels;\n    int64_t output_size;\n    int64_t board_height;\n    int64_t board_width;\n    int64_t channels = 128;\n    int64_t num_blocks = 20;\n    RandWireConfig randwire_config;\n    bool use_dynamic_routing = true;\n};\n\n/**\n * @brief Dynamic Dense-Wired Random-Wire ResNet for AlphaZero\n */\nclass ALPHAZERO_API DDWRandWireResNet : public nn::NeuralNetwork, public torch::nn::Module {\npublic:\n    /**\n     * @brief Constructor\n     * \n     * @param config Configuration for the network\n     */\n    DDWRandWireResNet(const DDWRandWireResNetConfig& config);\n    \n    /**\n     * @brief Forward pass\n     * \n     * @param x Input tensor\n     * @return Tuple of policy and value tensors\n     */\n    std::tuple<torch::Tensor, torch::Tensor> forward(torch::Tensor x);\n    \n    /**\n     * @brief Forward pass with dynamic routing control\n     * \n     * @param x Input tensor\n     * @param use_dynamic Whether to use dynamic routing\n     * @return Tuple of policy and value tensors\n     */\n    std::tuple<torch::Tensor, torch::Tensor> forward(torch::Tensor x, bool use_dynamic);\n    \n    /**\n     * @brief Save the model to a file\n     * \n     * @param path File path\n     */\n    void save(const std::string& path) override;\n    \n    /**\n     * @brief Load the model from a file\n     * \n     * @param path File path\n     */\n    void load(const std::string& path) override;\n    \n    /**\n     * @brief Export the model to TorchScript format\n     * \n     * @param path File path\n     * @param input_shape Input tensor shape for tracing\n     */\n    void export_to_torchscript(const std::string& path, \n                              std::vector<int64_t> input_shape = {1, 0, 0, 0});\n\n    // Implement NeuralNetwork interface\n    std::vector<mcts::NetworkOutput> inference(\n        const std::vector<std::unique_ptr<core::IGameState>>& states) override;\n    \n    std::vector<int64_t> getInputShape() const override;\n    \n    int64_t getPolicySize() const override;\n    \n    // Tensor pool cleanup method removed - no longer using tensor pools\n    \n    /**\n     * @brief Set GPU memory pool for efficient tensor allocation\n     * @param pool Shared pointer to GPU memory pool\n     */\n    void setGPUMemoryPool(std::shared_ptr<mcts::GPUMemoryPool> pool) {\n        gpu_memory_pool_ = pool;\n    }\n    \n    /**\n     * @brief Override to method to track device changes\n     */\n    void to(torch::Device device, bool non_blocking = false) {\n        torch::nn::Module::to(device, non_blocking);\n        device_ = device;\n    }\n    \n    void to(torch::ScalarType dtype, bool non_blocking = false) {\n        torch::nn::Module::to(dtype, non_blocking);\n    }\n    \n    void to(torch::Device device, torch::ScalarType dtype, bool non_blocking = false) {\n        torch::nn::Module::to(device, dtype, non_blocking);\n        device_ = device;\n    }\n\nprivate:\n    DDWRandWireResNetConfig config_;\n    torch::nn::Conv2d input_conv_{nullptr};\n    torch::nn::BatchNorm2d input_bn_{nullptr};\n    torch::nn::ModuleList rand_wire_blocks_{nullptr};\n    \n    // Policy head\n    torch::nn::Conv2d policy_conv_{nullptr};\n    torch::nn::BatchNorm2d policy_bn_{nullptr};\n    torch::nn::Linear policy_fc_{nullptr};\n    \n    // Value head\n    torch::nn::Conv2d value_conv_{nullptr};\n    torch::nn::BatchNorm2d value_bn_{nullptr};\n    torch::nn::Linear value_fc1_{nullptr};\n    torch::nn::Linear value_fc2_{nullptr};\n    \n    /**\n     * @brief Initialize weights using Kaiming initialization\n     */\n    void _initialize_weights();\n    \n    /**\n     * @brief Prepare input tensor for batch inference\n     * @param states Vector of game states\n     * @return Prepared input tensor\n     */\n    torch::Tensor prepareInputTensor(\n        const std::vector<std::unique_ptr<core::IGameState>>& states);\n    \n    /**\n     * @brief Prepare input tensor with target device\n     * @param states Vector of game states\n     * @param target_device Target device for the tensor\n     * @return Prepared input tensor\n     */\n    torch::Tensor prepareInputTensor(\n        const std::vector<std::unique_ptr<core::IGameState>>& states,\n        torch::Device target_device);\n    \n    // Tensor pool removed - direct allocation is used instead\n    \n    // GPU memory pool for efficient tensor allocation\n    std::shared_ptr<mcts::GPUMemoryPool> gpu_memory_pool_;\n    \n    // Device for the model\n    torch::Device device_ = torch::kCPU;\n};\n\n} // namespace nn\n} // namespace alphazero\n\n#endif // ALPHAZERO_NN_DDW_RANDWIRE_RESNET_H", "text_preview": "#ifndef ALPHAZERO_NN_DDW_RANDWIRE_RESNET_H #define ALPHAZERO_NN_DDW_RANDWIRE_RESNET_H #include <torch/torch.h> #include <vector> #include <string> #include <random> #include <unordered_map> #include <memory> #include <tuple> #include \"nn/ne", "input_sha256": "87b68bd54066f7efadf0a7ea7ca34efa7efa06ddb7941bc249a95e66b288a7c9", "text_sha256": "15aa9436b132d33aa1c552337e642455ff3a880761531e130250df30509c2ff8", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 12, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/k4lizen/bouncing-lines/blob/41924f223f4f9f6fec6d157560566f0c21976452/src/main.cpp", "id": "cpp-0-16c2d7d34193bc088fbafae11166b538", "text": "#include \"blines.h\"\n\n#include \"bvh.h\"\n#include \"color.h\"\n#include \"hittable.h\"\n#include \"sphere.h\"\n#include \"camera.h\"\n#include \"material.h\"\n#include \"texture.h\"\n#include \"quad.h\"\n#include \"constant_medium.h\"\n\n#include <iostream>\n#include <fstream>\n\nvoid fun_balls(){\n    hittable_list world;\n\n    auto checker = make_shared<checker_texture>(0.32, color(.2, .3, .1), color(.9, .9, .9));\n    world.add(make_shared<sphere>(point3(0, -1000, 0), 1000, make_shared<lambertian>(checker)));\n\n    // auto ground_material = make_shared<lambertian>(color(0.5, 0.5, 0.5));\n    // world.add(make_shared<sphere>(point3(0, -1000, 0), 1000, ground_material));\n\n    for(int a = -11; a < 11; ++a){\n        for(int b = -11; b < 11; ++b){\n            auto choose_mat = random_double();\n            point3 center(a + 0.9 * random_double(), 0.2, b + 0.9 * random_double());\n\n            if((center - point3(4, 0.2, 0)).length() > 0.9){\n                shared_ptr<material> sphere_mat;\n\n                if(choose_mat < 0.8){\n                    //difuse \n                    auto albedo = color::random() * color::random();\n                    sphere_mat = make_shared<lambertian>(albedo);\n                    auto center2 = center + vec3(0, random_double(0, 0.5), 0);\n                    world.add(make_shared<sphere>(center, center2, 0.2, sphere_mat));\n                } else if (choose_mat < 0.95){\n                    // metal\n                    auto albedo = color::random(0.5, 1);\n                    auto fuzz = random_double(0, 0.5);\n                    sphere_mat = make_shared<metal>(albedo, fuzz);\n                    world.add(make_shared<sphere>(center, 0.2, sphere_mat));\n                }else{\n                    // glass\n                    sphere_mat = make_shared<dielectric>(1.5);\n                    world.add(make_shared<sphere>(center, 0.2, sphere_mat));\n                }\n            }\n        }\n    }\n\n    auto material1 = make_shared<dielectric>(1.5);\n    world.add(make_shared<sphere>(point3(0, 1, 0), 1.0, material1));\n\n    auto material2 = make_shared<lambertian>(color(0.4, 0.2, 0.1));\n    world.add(make_shared<sphere>(point3(-4, 1, 0), 1.0, material2));\n\n    auto material3 = make_shared<metal>(color(0.7, 0.6, 0.5), 0.0);\n    world.add(make_shared<sphere>(point3(4, 1, 0), 1.0, material3));\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image2.ppm\");\n    cam.aspect_ratio = 16.0 / 9.0;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 200;\n    cam.max_depth = 50;\n    cam.background = color(0.7, 0.8, 1);\n\n    cam.vfov = 20;\n    cam.lookfrom = point3(13, 2, 3);\n    cam.lookat = point3(0, 0, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0.6;\n    cam.focus_dist = 10.0;\n\n    cam.render(world, world);\n}\n\nvoid the_trio(){\n    hittable_list world;\n\n    shared_ptr<material> material_ground = make_shared<lambertian>(color(0.8, 0.8, 0.0));\n    shared_ptr<material> material_center = make_shared<lambertian>(color(0.1, 0.2, 0.5));\n    shared_ptr<material> material_left = make_shared<dielectric>(1.5);\n    shared_ptr<material> material_right = make_shared<metal>(color(0.8, 0.6, 0.2), 1.0);\n\n    world.add(make_shared<sphere>(point3(0.0, -100.5, -1.0), 100.0, material_ground));\n    world.add(make_shared<sphere>(point3(0.0, 0.0, -1.0), 0.5, material_center));\n    world.add(make_shared<sphere>(point3(-1.0, 0.0, -1.0), 0.5, material_left));\n    world.add(make_shared<sphere>(point3(-1.0, 0.0, -1.0), -0.4, material_left));\n    world.add(make_shared<sphere>(point3(1.0, 0.0, -1.0), 0.5, material_right));\n    \n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image.ppm\");\n    cam.aspect_ratio = 16.0 / 9.0;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 100;\n    cam.max_depth = 50;\n    cam.background = color(0.7, 0.8, 1);\n\n    cam.vfov = 20;\n    cam.lookfrom = point3(-2, 2, 1);\n    cam.lookat = point3(0, 0, -1);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n    cam.focus_dist = 1;\n\n    cam.render(world, world);\n}\n\nvoid two_balls(){\n    hittable_list world;\n\n    auto checker = make_shared<checker_texture>(1 / pi, color(.2, .3, .1), color(.9, .9, .9));\n\n    world.add(make_shared<sphere>(point3(0, -10, 0), 10, make_shared<lambertian>(checker)));\n    world.add(make_shared<sphere>(point3(0, 10, 0), 10, make_shared<lambertian>(checker)));\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image3.ppm\");\n    cam.aspect_ratio = 16.0 / 9.0;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 50;\n    cam.max_depth = 10;\n    cam.background = color(0.7, 0.8, 1);\n\n    cam.vfov = 20;\n    cam.lookfrom = point3(13, 2, 3);\n    cam.lookat = point3(0, 0, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, world);\n}\n\nvoid earth(){\n    auto earth_texture = make_shared<image_texture>(\"earthmap.jpg\");\n    auto earth_surface = make_shared<lambertian>(earth_texture);\n    auto globe = make_shared<sphere>(point3(0, 0, 0), 2, earth_surface);\n\n    camera cam(\"images\\\\image4.ppm\");\n    cam.aspect_ratio = 16.0 / 9.0;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 50;\n    cam.max_depth = 10;\n    cam.background = color(0.7, 0.8, 1);\n\n    cam.vfov = 20;\n    cam.lookfrom = point3(0, 0, 12);\n    cam.lookat = point3(0, 0, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(hittable_list(globe), hittable_list(globe));\n}\n\nvoid two_perlin_spheres(){\n    hittable_list world;\n\n    auto pertext = make_shared<noise_texture>(4);\n\n    world.add(make_shared<sphere>(point3(0, -1000, 0), 1000, make_shared<lambertian>(pertext)));\n    world.add(make_shared<sphere>(point3(0, 2, 0), 2, make_shared<lambertian>(pertext)));\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image5.ppm\");\n    cam.aspect_ratio = 16.0 / 9.0;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 50;\n    cam.max_depth = 10;\n    cam.background = color(0.7, 0.8, 1);\n\n    cam.vfov = 20;\n    cam.lookfrom = point3(13, 2, 3);\n    cam.lookat = point3(0, 0, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, world);\n}\n\nvoid quads(){\n    hittable_list world;\n\n    auto left_red = make_shared<lambertian>(color(1, 0.2, 0.2));\n    auto back_green = make_shared<lambertian>(color(0.2, 1, 0.2));\n    auto right_blue = make_shared<lambertian>(color(0.2, 0.2, 1));\n    auto upper_orange = make_shared<lambertian>(color(1.0, 0.5, 0));\n    auto lower_teal = make_shared<lambertian>(color(0.2, 0.8, 0.8));\n\n    world.add(make_shared<quad>(point3(-3, -2, 5), vec3(0, 0, -4), vec3(0, 4, 0), left_red));\n    world.add(make_shared<quad>(point3(-2, -2, 0), vec3(4, 0, 0), vec3(0, 4, 0), back_green));\n    world.add(make_shared<quad>(point3(3, -2, 1), vec3(0, 0, 4), vec3(0, 4, 0), right_blue));\n    world.add(make_shared<quad>(point3(-2, 3, 1), vec3(4, 0, 0), vec3(0, 0, 4), upper_orange));\n    world.add(make_shared<quad>(point3(-2, -3, 5), vec3(4, 0, 0), vec3(0, 0, -4), lower_teal));\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image6.ppm\");\n    cam.aspect_ratio = 1;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 50;\n    cam.max_depth = 10;\n    cam.background = color(0.7, 0.8, 1);\n\n    cam.vfov = 80;\n    cam.lookfrom = point3(0, 0, 9);\n    cam.lookat = point3(0, 0, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, world);\n}\n\nvoid simple_light(){\n    hittable_list world;\n\n    auto pertext = make_shared<noise_texture>(4);\n    world.add(make_shared<sphere>(point3(0, -1000, 0), 1000, make_shared<lambertian>(pertext)));\n    world.add(make_shared<sphere>(point3(0, 2, 0), 2, make_shared<lambertian>(pertext)));\n\n    auto difflight = make_shared<diffuse_light>(color(4, 4, 4));\n    world.add(make_shared<sphere>(point3(0, 7, 0), 2, difflight));\n    world.add(make_shared<quad>(point3(3, 1, -2), vec3(2, 0, 0), vec3(0, 2, 0), difflight));\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image7.ppm\");\n    cam.aspect_ratio = 16.0 / 9;\n    cam.image_width = 400;\n    cam.samples_per_pixel = 400;\n    cam.max_depth = 50;\n    cam.background = color(0, 0, 0);\n\n    cam.vfov = 20;\n    cam.lookfrom = point3(26, 3, 6);\n    cam.lookat = point3(0, 2, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, world);\n}\n\nvoid cornell_box(std::string filename){\n    hittable_list world;\n\n    auto red = make_shared<lambertian>(color(0.65, .05, .05));\n    auto white = make_shared<lambertian>(color(0.73, .73, .73));\n    auto green = make_shared<lambertian>(color(0.12, .45, .15));\n    auto light = make_shared<diffuse_light>(color(15, 15, 15));\n\n    // Walls\n    world.add(make_shared<quad>(point3(555, 0, 0), vec3(0, 555, 0), vec3(0, 0, 555), green));\n    world.add(make_shared<quad>(point3(0, 0, 0), vec3(0, 555, 0), vec3(0, 0, 555), red));\n    world.add(make_shared<quad>(point3(343, 554, 332), vec3(-130, 0, 0), vec3(0, 0, -105), light));\n    world.add(make_shared<quad>(point3(0, 0, 0), vec3(555, 0, 0), vec3(0, 0, 555), white));\n    world.add(make_shared<quad>(point3(555, 555, 555), vec3(-555, 0, 0), vec3(0, 0, -555), white));\n    world.add(make_shared<quad>(point3(0, 0, 555), vec3(555, 0, 0), vec3(0, 555, 0), white));\n\n    // Light\n    // . Ceiling Light\n    world.add(make_shared<quad>(point3(213, 554, 227), vec3(130, 0, 0), vec3(0, 0, 105), light));\n\n    // . Glass sphere\n    auto glass = make_shared<dielectric>(1.5);\n    world.add(make_shared<sphere>(point3(190, 90, 190), 90, glass));\n\n    hittable_list lights;\n    auto m = shared_ptr<material>();\n    lights.add(make_shared<quad>(point3(343, 554, 332), vec3(-130, 0, 0), vec3(0, 0, -105), m));\n    lights.add(make_shared<sphere>(point3(190, 90, 190), 90, m));\n\n\n    // Box 1\n    // shared_ptr<material> aluminum = make_shared<metal>(color(0.8, 0.85, 0.88), 0.0);\n    shared_ptr<hittable> box1 = box(point3(0, 0, 0), point3(165, 330, 165), white);\n    box1 = make_shared<rotate_y>(box1, 15);\n    box1 = make_shared<translate>(box1, vec3(265, 0, 295));\n    world.add(box1);\n\n    /*\n    // Box 2\n    shared_ptr<hittable> box2 = box(point3(0, 0, 0), point3(165, 165, 165), white);\n    box2 = make_shared<rotate_y>(box2, -18);\n    box2 = make_shared<translate>(box2, vec3(130, 0, 65));\n    world.add(box2);\n    */\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\\" + filename);\n    cam.aspect_ratio = 1;\n    cam.image_width = 600;\n    cam.samples_per_pixel = 1000;\n    cam.max_depth = 50;\n    cam.background = color(0, 0, 0);\n\n    cam.vfov = 40;\n    cam.lookfrom = point3(278, 278, -800);\n    cam.lookat = point3(278, 278, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, lights);\n}\n\nvoid cornell_smoke(){\n    hittable_list world;\n\n    auto red = make_shared<lambertian>(color(0.65, .05, .05));\n    auto white = make_shared<lambertian>(color(0.73, .73, .73));\n    auto green = make_shared<lambertian>(color(0.12, .45, .15));\n    auto light = make_shared<diffuse_light>(color(15, 15, 15));\n\n    world.add(make_shared<quad>(point3(555, 0, 0), vec3(0, 555, 0), vec3(0, 0, 555), green));\n    world.add(make_shared<quad>(point3(0, 0, 0), vec3(0, 555, 0), vec3(0, 0, 555), red));\n    world.add(make_shared<quad>(point3(343, 554, 332), vec3(-130, 0, 0), vec3(0, 0, -105), light));\n    world.add(make_shared<quad>(point3(0, 0, 0), vec3(555, 0, 0), vec3(0, 0, 555), white));\n    world.add(make_shared<quad>(point3(555, 555, 555), vec3(-555, 0, 0), vec3(0, 0, -555), white));\n    world.add(make_shared<quad>(point3(0, 0, 555), vec3(555, 0, 0), vec3(0, 555, 0), white));\n\n    shared_ptr<hittable> box1 = box(point3(0, 0, 0), point3(165, 330, 165), white);\n    box1 = make_shared<rotate_y>(box1, 15);\n    box1 = make_shared<translate>(box1, vec3(265, 0, 295));\n    world.add(make_shared<constant_medium>(box1, 0.01, color(0, 0, 0)));\n\n    shared_ptr<hittable> box2 = box(point3(0, 0, 0), point3(165, 165, 165), white);\n    box2 = make_shared<rotate_y>(box2, -18);\n    box2 = make_shared<translate>(box2, vec3(130, 0, 65));\n    world.add(make_shared<constant_medium>(box2, 0.01, color(1, 1, 1)));\n\n    world.add(box(point3(265, 0, 295), point3(430, 330, 460), white));\n\n    world = hittable_list(make_shared<bvh_node>(world));\n\n    camera cam(\"images\\\\image9.ppm\");\n    cam.aspect_ratio = 1;\n    cam.image_width = 600;\n    cam.samples_per_pixel = 200;\n    cam.max_depth = 50;\n    cam.background = color(0, 0, 0);\n\n    cam.vfov = 40;\n    cam.lookfrom = point3(278, 278, -800);\n    cam.lookat = point3(278, 278, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, world);\n}\n\nvoid final_scene(int image_width, int samples_per_pixel, int max_depth){\n    hittable_list boxes1;\n    auto ground = make_shared<lambertian>(color(.48, .83, .53));\n\n    int boxes_per_side = 20;\n    for(int i = 0; i < boxes_per_side; ++i){\n        for(int j = 0; j < boxes_per_side; ++j){\n            auto w = 100.0;\n            auto x0 = -1000 + i * w;\n            auto z0 = -1000 + j * w;\n            auto y0 = 0.0;\n            auto x1 = x0 + w;\n            auto y1 = random_double(1, 101);\n            auto z1 = z0 + w;\n\n            boxes1.add(box(point3(x0, y0, z0), point3(x1, y1, z1), ground));\n        }\n    }\n\n    hittable_list world;\n\n    world.add(make_shared<bvh_node>(boxes1));\n\n    auto light = make_shared<diffuse_light>(color(7, 7, 7));\n    world.add(make_shared<quad>(point3(123, 554, 147), vec3(300, 0, 0), vec3(0, 0, 265), light));\n\n    auto center1 = point3(400, 400, 200);\n    auto center2 = center1 + vec3(30, 0, 0);\n    auto sphere_material = make_shared<lambertian>(color(0.7, 0.3, 0.1));\n    world.add(make_shared<sphere>(center1, center2, 50, sphere_material));\n\n    world.add(make_shared<sphere>(point3(260, 150, 45), 50, make_shared<dielectric>(1.5)));\n    world.add(make_shared<sphere>(point3(0, 150, 145), 50, make_shared<metal>(color(.8, .8, .8), 1.0)));\n\n    auto boundary = make_shared<sphere>(point3(360, 150, 145), 70, make_shared<dielectric>(1.5));\n    world.add(boundary);\n    world.add(make_shared<constant_medium>(boundary, 0.2, color(0.2, 0.4, 0.9)));\n    \n    boundary = make_shared<sphere>(point3(0, 0, 0), 5000, make_shared<dielectric>(1.5));\n    world.add(make_shared<constant_medium>(boundary, 0.0001, color(1, 1, 1)));\n\n    auto emat = make_shared<lambertian>(make_shared<image_texture>(\"earthmap.jpg\"));\n    world.add(make_shared<sphere>(point3(400, 200, 400), 100, emat));\n\n    auto pertext = make_shared<noise_texture>(0.1);\n    world.add(make_shared<sphere>(point3(220, 280, 300), 80, make_shared<lambertian>(pertext)));\n\n    hittable_list boxes2;\n    auto white = make_shared<lambertian>(color(.73, .73, .73));\n    int ns = 1000;\n    for(int j = 0; j < ns; ++j){\n        boxes2.add(make_shared<sphere>(point3::random(0, 165), 10, white));\n    }\n\n    world.add(make_shared<translate>(make_shared<rotate_y>(make_shared<bvh_node>(boxes2), 15), vec3(-100, 270, 395)));\n\n    camera cam(\"images\\\\image10.ppm\");\n    cam.aspect_ratio = 1.0;\n    cam.image_width = image_width;\n    cam.samples_per_pixel = samples_per_pixel;\n    cam.max_depth = max_depth;\n    cam.background = color(0, 0, 0);\n\n    cam.vfov = 40;\n    cam.lookfrom = point3(478, 278, -600);\n    cam.lookat = point3(278, 278, 0);\n    cam.vup = vec3(0, 1, 0);\n\n    cam.defocus_angle = 0;\n\n    cam.render(world, world);\n}\n\nint main(){\n    switch(11){\n        case 1: the_trio(); break; // book 1\n        case 2: fun_balls(); break;\n\n        case 3: two_balls(); break; // book 2\n        case 4: earth(); break;\n        case 5: two_perlin_spheres(); break;\n        case 6: quads(); break;\n        case 7: simple_light(); break;\n        case 8: cornell_box(\"image8.ppm\"); break;\n        case 9: cornell_smoke(); break;\n        case 10: final_scene(800, 1000, 40); break;\n\n        case 11: cornell_box(\"image1.ppm\"); break; // book3\n    }\n\n    return 0;\n}\n\n// TODO: check why perlin noise is different from example!!", "text_preview": "#include \"blines.h\" #include \"bvh.h\" #include \"color.h\" #include \"hittable.h\" #include \"sphere.h\" #include \"camera.h\" #include \"material.h\" #include \"texture.h\" #include \"quad.h\" #include \"constant_medium.h\" #include <iostream> #include <fs", "input_sha256": "f1ba8fa2c38e89c7023236216f9c655345c09ca0bae6824a5efa812ae923ceb0", "text_sha256": "16c2d7d34193bc088fbafae11166b538bf1064db3fc7ebe63e51a20f90d316bf", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 14, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8383057404641839, "noise_severity": 0.07859166449363802, "missing_content_severity": 0.056846149301639996, "factual_density": 3.0227915028791723, "pretraining_utility": 0.43423566494880195, "math_density": 2.7884001792807362, "code_density": 3.8698261259870343, "reasoning_density": 0.18804347653171616, "education_level": 2.439573405581531, "creative_writing_quality": -0.37393847457129475}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4147 below 0.70", "secondary_topic vs NONE margin 0.0117 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.007319493228996388, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9962720837158849, "secondary": 0.976420926403139, "tertiary": 0.9840586584681786, "structured_records": 0.023761837158954767, "procedural_steps": 0.0049406453172743696, "worked_solution": 0.010207204235187733, "contextual_dialogue": 4.611309929611234e-06, "content_quality": 0.5110690464108989, "noise_severity": 0.9345069462553016, "missing_content_severity": 0.9526282089153, "factual_density": 0.44062790772322025, "pretraining_utility": 0.3774460019791168, "math_density": 0.0, "code_density": 0.8915217716558618, "reasoning_density": 0.8432971028902365, "education_level": 0.5246010347032981, "creative_writing_quality": 0.9983890347850021, "primary_topic": 0.9200855507830674, "secondary_topic": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/dome420/Algorithmen-und-Datenstrukturen/blob/15ef492dba1ad117d086f8d53cf3f50204330639/ADS_SS22/%C3%9Cbungsblatt%203.2%20BST%20A1/BST.h", "id": "cpp-0-6cdeb03f9424cf82bd6ec9457a49866b", "text": "#pragma once\n\n#include \"Node.h\"\n#include <algorithm>\n#include <queue>\n\nclass BST \n{\nprivate:\n\tTreenode* head;\n\tint computeHeight(Treenode* p); // rekursive Methode,berechnet die Höhe zum Knoten p und gibt diese zurück\n\tvoid deleteBST(Treenode* p); // löscht den Teilbaum ab p // rekursive Methode zur Ausgabe aller Knoten zu einer Höhe\n\tint printHeight(Treenode* node, int height);\npublic:\n\tBST() \n\t{\n\t\thead = new Treenode;\n\t\thead->right = nullptr;  \n\t\thead->left = nullptr;\n\t};\n\t~BST();\n\tvoid insert(int key);\n\tint  getBSTHeight(); // Starterfunktion für die rekursive Methode und liefert die Höhe des Baumes zurück\n\tvoid printBST(); // Ausgabe des BST in Levelorder\n\tvoid printNiveau(int niveau); // Ausgabe aller Knoten zu einem Niveau\n\tvoid printHeight(int height); // Ausgabe aller Knoten zu einer Höhe\n\tvoid postOrder(Treenode* p); // Traversiert Rekursiv den BST Bottum up; möglichkeit Baum von unten nach oben zu löschen\n};\n/*\n*\tAufgabe 1.a) \n*\t\t- Was benötigen wir für das Löschen aller Knoten im BST\n*\t\n*/\nBST::~BST()\n{\n\tif (head->right != nullptr)deleteBST(head->right);\n}\nvoid BST::deleteBST(Treenode* p)\n{\n\tif (p->left) \n\t{\n\t\tdeleteBST(p->left);\n\t\tp->left = nullptr;\n\t}\n\tif (p->right) {\n\t\tdeleteBST(p->right);\n\t\tp->right = nullptr;\n\t}\n\tif (p->left == nullptr && p->right == nullptr)\n\t{\n\t\tdelete p;\n\t}\n}\n\n/*\n*\tInser-Methode Hinweis:\n*\t- Zu bachten sind Zwei Fälle 1) Baum Leer , 2) Baum hat einen oder mehr Knoten\n*   - Wichtig Es wird immer ab der Wurzel verglichen\n*\t- if( key < BST[key] ) -> Linkseinfügen\n*\t- if( key >= BST[Key] ) -> Rechtseinfügen\n*/\nvoid BST::insert(int key)\n{\n\tTreenode *newNode = new Treenode();  // Initialisierung eines neuen Knoten -> Wird in den Baum eingefügt\n\tnewNode->left = nullptr;\n\tnewNode->right = nullptr;\n\tnewNode->item = key;\n\n\tif (head->right == nullptr) \n\t{\n\t\thead->right = newNode;\n\t}\n\telse\n\t{\n\t\tTreenode* current = head->right; // Zeigt auf das Erste Element im Baum\n\t\tif (newNode->item < current->item) \n\t\t{\n\t\t\tif (current->left == nullptr)\n\t\t\t{\n\t\t\t\tcurrent->left = newNode; // Neuer Knoten wird als Blatt eingefügt,\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tcurrent = current->left;\n\t\t\t}\n\t\t}\n\t\telse if (newNode->item >= current->item) \n\t\t{\n\t\t\tif (current->right == nullptr)\n\t\t\t{\n\t\t\t\tcurrent->right = newNode; // Neuer Knoten wird als Blatt eingefügt,\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tcurrent = current->right;\n\t\t\t}\n\t\t}\n\t}\n}\n\nvoid BST::printBST()\n{\n\tTreenode* root = head->right;\n\tint current_niv = 0; // Damit merken wir uns auf welchen Niv wir gerade stehen. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ermig1979/Simd/blob/cb12098d72ac162e00f9fad348b9fa5804ff6675/src/Simd/SimdBaseResizerBicubic.cpp", "id": "cpp-0-ef6c1373f0c74c7ead88e192236d690c", "text": "/*\n* Simd Library (http://ermig1979.github.io/Simd).\n*\n* Copyright (c) 2011-2024 <NAME>.\n*\n* Permission is hereby granted, free of charge, to any person obtaining a copy\n* of this software and associated documentation files (the \"Software\"), to deal\n* in the Software without restriction, including without limitation the rights\n* to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n* copies of the Software, and to permit persons to whom the Software is\n* furnished to do so, subject to the following conditions:\n*\n* The above copyright notice and this permission notice shall be included in\n* all copies or substantial portions of the Software.\n*\n* THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n* IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n* FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n* AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n* LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n* OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n* SOFTWARE.\n*/\n#include \"Simd/SimdMemory.h\"\n#include \"Simd/SimdResizer.h\"\n#include \"Simd/SimdCopy.h\"\n\nnamespace Simd\n{\n    namespace Base\n    {\n        ResizerByteBicubic::ResizerByteBicubic(const ResParam & param)\n            : Resizer(param)\n        {\n        }\n        \n        void ResizerByteBicubic::EstimateIndexAlpha(size_t sizeS, size_t sizeD, size_t N, Array32i& index, Array32i& alpha)\n        {\n            index.Resize(sizeD);\n            alpha.Resize(sizeD * 4);\n            float scale = float(sizeS) / float(sizeD);\n            for (size_t i = 0; i < sizeD; ++i)\n            {\n                float pos = (float)((i + 0.5f) * scale - 0.5f);\n                int idx = (int)::floor(pos);\n                float d = pos - idx;\n                if (idx < 0)\n                {\n                    idx = 0;\n                    d = 0.0f;\n                }\n                if (idx > (int)sizeS - 2)\n                {\n                    idx = (int)sizeS - 2;\n                    d = 1.0f;\n                }\n                index[i] = idx * (int)N;\n                alpha[i * 4 + 0] = Round(BICUBIC_RANGE * (2.0f - d) * (1.0f - d) * d / 6.0f);\n                alpha[i * 4 + 1] = Round(BICUBIC_RANGE * (d - 2.0f) * (d + 1.0f) * (1.0f - d) / 2.0f);\n                alpha[i * 4 + 2] = Round(BICUBIC_RANGE * (d - 2.0f) * (d + 1.0f) * d / 2.0f);\n                alpha[i * 4 + 3] = Round(BICUBIC_RANGE * (1.0f + d) * (1.0f - d) * d / 6.0f);\n            }\n        } \n\n        void ResizerByteBicubic::Init(bool sparse)\n        {\n            if (_iy.data)\n                return;\n            EstimateIndexAlpha(_param.srcH, _param.dstH, 1, _iy, _ay);\n            EstimateIndexAlpha(_param.srcW, _param.dstW, _param.channels, _ix, _ax);\n            if (!sparse)\n            {\n                for (int i = 0; i < 4; ++i)\n                    _bx[i].Resize(_param.dstW * _param.channels);\n            }\n            _sxl = (_param.srcW - 2) * _param.channels;\n            for (_xn = 0; _ix[_xn] == 0; _xn++);\n            for (_xt = _param.dstW; _ix[_xt - 1] == _sxl; _xt--);\n        }\n\n        template<int N, int F, int L> SIMD_INLINE int32_t CubicSumX(const uint8_t* src, const int32_t* ax)\n        {\n            return ax[0] * src[F * N] + ax[1] * src[0 * N] + ax[2] * src[1 * N] + ax[3] * src[L * N];\n        }\n\n        template<int N, int F, int L> SIMD_INLINE void BicubicInt(const uint8_t* src0, const uint8_t* src1,\n            const uint8_t* src2, const uint8_t* src3, size_t sx, const int32_t* ax, const int32_t* ay, uint8_t * dst)\n        {\n            for (size_t c = 0; c < N; ++c)\n            {\n                int32_t rs0 = CubicSumX<N, F, L>(src0 + sx + c, ax);\n                int32_t rs1 = CubicSumX<N, F, L>(src1 + sx + c, ax);\n                int32_t rs2 = CubicSumX<N, F, L>(src2 + sx + c, ax);\n                int32_t rs3 = CubicSumX<N, F, L>(src3 + sx + c, ax);\n                int32_t sum = ay[0] * rs0 + ay[1] * rs1 + ay[2] * rs2 + ay[3] * rs3;\n                dst[c] = RestrictRange((sum + BICUBIC_ROUND) >> BICUBIC_SHIFT, 0, 255);\n            }\n        }\n\n        template<int N> void ResizerByteBicubic::RunS(const uint8_t* src, size_t srcStride, uint8_t* dst, size_t dstStride)\n        {\n            for (size_t dy = 0; dy < _param.dstH; dy++, dst += dstStride)\n            {\n                size_t sy = _iy[dy];\n                const uint8_t* src1 = src + sy * srcStride;\n                const uint8_t* src2 = src1 + srcStride;\n                const uint8_t* src0 = sy ? src1 - srcStride : src1;\n                const uint8_t* src3 = sy < _param.srcH - 2 ? src2 + srcStride : src2;\n                const int32_t* ay = _ay.data + dy * 4;\n                size_t dx = 0;\n                for (; dx < _xn; dx++)\n                    BicubicInt<N, 0, 2>(src0, src1, src2, src3, _ix[dx], _ax.data + dx * 4, ay, dst + dx * N);\n                for (; dx < _xt; dx++)\n                    BicubicInt<N, -1, 2>(src0, src1, src2, src3, _ix[dx], _ax.data + dx * 4, ay, dst + dx * N);\n                for (; dx < _param.dstW; dx++)\n                    BicubicInt<N, -1, 1>(src0, src1, src2, src3, _ix[dx], _ax.data + dx * 4, ay, dst + dx * N);\n            }\n        }\n\n        template<int N, int F, int L> SIMD_INLINE void PixelCubicSumX(const uint8_t* src, const int32_t* ax, int32_t* dst)\n        {\n            for (size_t c = 0; c < N; ++c)\n                dst[c] = CubicSumX<N, F, L>(src + c, ax);\n        }\n\n        template<int N> SIMD_INLINE void RowCubicSumX(const uint8_t* src, size_t nose, size_t body, size_t tail, const int32_t* ix, const int32_t* ax, int32_t* dst)\n        {\n            size_t dx = 0;\n            for (; dx < nose; dx++, ax += 4, dst += N)\n                PixelCubicSumX<N, 0, 2>(src + ix[dx], ax, dst);\n            for (; dx < body; dx++, ax += 4, dst += N)\n                PixelCubicSumX<N, -1, 2>(src + ix[dx], ax, dst);\n            for (; dx < tail; dx++, ax += 4, dst += N)\n                PixelCubicSumX<N, -1, 1>(src + ix[dx], ax, dst);\n        }\n\n        SIMD_INLINE void BicubicRowInt(const int32_t* src0, const int32_t* src1, const int32_t* src2, const int32_t* src3, size_t n, const int32_t* ay, uint8_t* dst)\n        {\n            for (size_t i = 0; i < n; ++i)\n            {\n                int32_t sum = ay[0] * src0[i] + ay[1] * src1[i] + ay[2] * src2[i] + ay[3] * src3[i];\n                dst[i] = RestrictRange((sum + BICUBIC_ROUND) >> BICUBIC_SHIFT, 0, 255);\n            }\n        }\n\n        template<int N> void ResizerByteBicubic::RunB(const uint8_t* src, size_t srcStride, uint8_t* dst, size_t dstStride)\n        {\n            int32_t prev = -1;\n            for (size_t dy = 0; dy < _param.dstH; dy++, dst += dstStride)\n            {\n                int32_t sy = _iy[dy], next = prev;\n                for (int32_t curr = sy - 1, end = sy + 3; curr < end; ++curr)\n                {\n                    if (curr < prev)\n                        continue;\n                    const uint8_t* ps = src + RestrictRange(curr, 0, (int)_param.srcH - 1) * srcStride;\n                    int32_t* pb = _bx[(curr + 1) & 3].data;\n                    RowCubicSumX<N>(ps, _xn, _xt, _param.dstW, _ix.data, _ax.data, pb);\n                    next++;\n                }\n                prev = next;\n\n                const int32_t* ay = _ay.data + dy * 4;\n                int32_t* pb0 = _bx[(sy + 0) & 3].data;\n                int32_t* pb1 = _bx[(sy + 1) & 3].data;\n                int32_t* pb2 = _bx[(sy + 2) & 3].data;\n                int32_t* pb3 = _bx[(sy + 3) & 3].data;\n                BicubicRowInt(pb0, pb1, pb2, pb3, _bx[0].size, ay, dst);\n            }\n        }\n\n        void ResizerByteBicubic::Run(const uint8_t * src, size_t srcStride, uint8_t * dst, size_t dstStride)\n        {\n            bool sparse = _param.dstH * 4.0 <= _param.srcH;\n            Init(sparse);\n            switch (_param.channels)\n            {\n            case 1: sparse ? RunS<1>(src, srcStride, dst, dstStride) : RunB<1>(src, srcStride, dst, dstStride); return;\n            case 2: sparse ? RunS<2>(src, srcStride, dst, dstStride) : RunB<2>(src, srcStride, dst, dstStride); return;\n            case 3: sparse ? RunS<3>(src, srcStride, dst, dstStride) : RunB<3>(src, srcStride, dst, dstStride); return;\n            case 4: sparse ? RunS<4>(src, srcStride, dst, dstStride) : RunB<4>(src, srcStride, dst, dstStride); return;\n            default:\n                assert(0);\n            }\n        }\n    }\n}\n\n", "text_preview": "/* * Simd Library (http://ermig1979.github.io/Simd). * * Copyright (c) 2011-2024 <NAME>. * * Permission is hereby granted, free of charge, to any person obtaining a copy * of this software and associated documentation files (the \"Software\")", "input_sha256": "110ea0413decb6ca3eda37a5b725f5f51c130548387dd7c7486bffd2fe9c7694", "text_sha256": "ef6c1373f0c74c7ead88e192236d690ca2fc7e32cd07c15ba0ab86d883bdb1c6", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 15, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 1, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.2382078556483047, "noise_severity": 0.6579444073739719, "missing_content_severity": 0.044911482630061915, "factual_density": 3.4043053986834892, "pretraining_utility": 0.5135830659618312, "math_density": 3.1157500318808573, "code_density": 3.6977256793341553, "reasoning_density": 0.7507805015015976, "education_level": 2.8756667991969014, "creative_writing_quality": -0.24988533433617943}, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/pkchen1129/Sensor_Fusion_Tutorial/blob/77a40af94e5da3364380be3a77694779aa4f0b52/Udacity_Lidar_Obstacle_Detection/src/quiz/ransac/ransac2d.cpp", "id": "cpp-0-5cb2ec5b3b52503bd0d5632e9e45d16b", "text": "/* \\author <NAME> */\n// Quiz on implementing simple RANSAC line fitting\n\n#include \"../../render/render.h\"\n#include <unordered_set>\n#include \"../../processPointClouds.h\"\n// using templates for processPointClouds so also include .cpp to help linker\n#include <math.h>\n#include \"../../processPointClouds.cpp\"\n\npcl::PointCloud<pcl::PointXYZ>::Ptr CreateData()\n{\n\tpcl::PointCloud<pcl::PointXYZ>::Ptr cloud(new pcl::PointCloud<pcl::PointXYZ>());\n  \t// Add inliers\n  \tfloat scatter = 0.6;\n  \tfor(int i = -5; i < 5; i++)\n  \t{\n  \t\tdouble rx = 2*(((double) rand() / (RAND_MAX))-0.5);\n  \t\tdouble ry = 2*(((double) rand() / (RAND_MAX))-0.5);\n  \t\tpcl::PointXYZ point;\n  \t\tpoint.x = i+scatter*rx;\n  \t\tpoint.y = i+scatter*ry;\n  \t\tpoint.z = 0;\n\n  \t\tcloud->points.push_back(point);\n  \t}\n  \t// Add outliers\n  \tint numOutliers = 10;\n  \twhile(numOutliers--)\n  \t{\n  \t\tdouble rx = 2*(((double) rand() / (RAND_MAX))-0.5);\n  \t\tdouble ry = 2*(((double) rand() / (RAND_MAX))-0.5);\n  \t\tpcl::PointXYZ point;\n  \t\tpoint.x = 5*rx;\n  \t\tpoint.y = 5*ry;\n  \t\tpoint.z = 0;\n\n  \t\tcloud->points.push_back(point);\n\n  \t}\n  \tcloud->width = cloud->points.size();\n  \tcloud->height = 1;\n\n  \treturn cloud;\n\n}\n\npcl::PointCloud<pcl::PointXYZ>::Ptr CreateData3D()\n{\n\tProcessPointClouds<pcl::PointXYZ> pointProcessor;\n\treturn pointProcessor.loadPcd(\"../../../sensors/data/pcd/simpleHighway.pcd\");\n}\n\n\npcl::visualization::PCLVisualizer::Ptr initScene()\n{\n\tpcl::visualization::PCLVisualizer::Ptr viewer(new pcl::visualization::PCLVisualizer (\"2D Viewer\"));\n\tviewer->setBackgroundColor (0, 0, 0);\n  \tviewer->initCameraParameters();\n  \tviewer->setCameraPosition(0, 0, 15, 0, 1, 0);\n  \tviewer->addCoordinateSystem (1.0);\n  \treturn viewer;\n}\nstd::unordered_set<int> Ransacplane(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, int maxIterations, float distanceTol){\n\tstd::unordered_set<int> inliersResult;\n\tsrand(time(NULL));\n\t// TODO: Fill in this function\n\n\t// For max iterations\n\n\tfor(int it = 0 ; it < maxIterations ; ++it){\n\t\t// Randomly sample subset and fit line\n\t\t\n\t\tstd::unordered_set<int> inliers; //indices\n\t\t\n\t\twhile(inliers.size() < 3)\n\t\t\tinliers.insert(rand()%(cloud->points.size()));\n\t\tfloat x1, x2, x3, y1, y2, y3, z1, z2, z3;\n\t\tauto itr = inliers.begin();\n\n\t\tx1 = cloud->points[*itr].x;\n\t\ty1 = cloud->points[*itr].y;\n\t\tz1 = cloud->points[*itr].z;\n\t\titr++;\n\t\tx2 = cloud->points[*itr].x;\n\t\ty2 = cloud->points[*itr].y;\n\t\tz2 = cloud->points[*itr].z;\n\t\titr++;\n\t\tx3 = cloud->points[*itr].x;\n\t\ty3 = cloud->points[*itr].y;\n\t\tz3 = cloud->points[*itr].z;\n\n\t\tfloat i = (y2 - y1) * (z3 - z1) - (z2 - z1) * (y3 - y1);\n\t\tfloat j = (z2 - z1) * (x3 - x1) - (x2 - x1) * (z3 - z1);\n\t\tfloat k = (x2 - x1) * (y3 - y1) - (y2 - y1) * (x3 - x1);\n\t\t\n\n\t\tfloat A = i;\n\t\tfloat B = j;\n\t\tfloat C = k;\n\t\tfloat D = -(i*x1 + j*y1 + k*z1);\n\n\t\t// Measure distance between every point and fitted line\n\t\tfor(int index = 0 ; index < cloud->points.size() ; index++){\n\t\t\tif(inliers.count(index) > 0) continue; //Same as the two that we've chosen before\n\t\t\t\n\t\t\tfloat x = cloud->points[index].x;\n\t\t\tfloat y = cloud->points[index].y;\n\t\t\tfloat z = cloud->points[index].z;\n\n\t\t\tfloat d = fabs( A * x + B * y + C * z + D) / sqrt(pow(A,2) + pow(B,2) + pow(C,2));\n\t\t\t// If distance is smaller than threshold count it as inlier\n\t\t\tif(d <= distanceTol) inliers.insert(index);\t\n\t\t}\n\t\tif(inliers.size() > inliersResult.size()){\n\t\t\tinliersResult = inliers;\n\t\t}\n\n\n\t}\n\n\t// Return indicies of inliers from fitted line with most inliers\n\t\n\treturn inliersResult;\n}\n\nstd::unordered_set<int> Ransac(pcl::PointCloud<pcl::PointXYZ>::Ptr cloud, int maxIterations, float distanceTol)\n{\n\tstd::unordered_set<int> inliersResult;\n\tsrand(time(NULL));\n\t\n\t// TODO: Fill in this function\n\n\t// For max iterations\n\n\tfor(int i = 0 ; i < maxIterations ; ++i){\n\t\t// Randomly sample subset and fit line\n\t\t\n\t\tstd::unordered_set<int> inliers; //indices\n\t\twhile(inliers.size() < 2)\n\t\t\tinliers.insert(rand()%(cloud->points.size())); // Will insert two numbers between 0~cloud.size(), ex: 14, 53.\n\t\t\t//Using these two points to make a Line\n\t\tfloat x1, x2, y1, y2;\n\t\tauto itr = inliers.begin();\n\n\t\tx1 = cloud->points[*itr].x;\n\t\ty1 = cloud->points[*itr].y;\n\t\titr++;\n\t\tx2 = cloud->points[*itr].x;\n\t\ty2 = cloud->points[*itr].y;\n\t\tfloat A = y1 - y2;\n\t\tfloat B = x2 - x1;\n\t\tfloat C = x1*y2 - x2*y1;\n\t\t\n\t\t// Measure distance between every point and fitted line\n\t\tfor(int index = 0 ; index < cloud->points.size() ; index++){\n\t\t\tif(inliers.count(index) > 0) continue; //Same as the two that we've chosen before\n\t\t\t\n\t\t\tfloat x = cloud->points[index].x;\n\t\t\tfloat y = cloud->points[index].y;\n\t\t\tfloat d = fabs( A * x + B * y + C) / sqrt(pow(A,2) + pow(B,2));\n\t\t\t// If distance is smaller than threshold count it as inlier\n\t\t\tif(d <= distanceTol) inliers.insert(index);\t\n\t\t}\n\t\tif(inliers.size() > inliersResult.size()){\n\t\t\tinliersResult = inliers;\n\t\t}\n\n\n\t}\n\n\t// Return indicies of inliers from fitted line with most inliers\n\t\n\treturn inliersResult;\n\n}\n\nint main ()\n{\n\n\t// Create viewer\n\tpcl::visualization::PCLVisualizer::Ptr viewer = initScene();\n\n\t// Create data\n\tpcl::PointCloud<pcl::PointXYZ>::Ptr cloud = CreateData3D();\n\t\n\n\t// TODO: Change the max iteration and distance tolerance arguments for Ransac function\n\t// std::unordered_set<int> inliers = Ransac(cloud, 20, 1.0);\n\tstd::unordered_set<int> inliers = Ransacplane(cloud, 50, 0.18);\n\n\n\tpcl::PointCloud<pcl::PointXYZ>::Ptr  cloudInliers(new pcl::PointCloud<pcl::PointXYZ>());\n\tpcl::PointCloud<pcl::PointXYZ>::Ptr cloudOutliers(new pcl::PointCloud<pcl::PointXYZ>());\n\n\tfor(int index = 0; index < cloud->points.size(); index++)\n\t{\n\t\tpcl::PointXYZ point = cloud->points[index];\n\t\tif(inliers.count(index))\n\t\t\tcloudInliers->points.push_back(point);\n\t\telse\n\t\t\tcloudOutliers->points.push_back(point);\n\t}\n\n\n\t// Render 2D point cloud with inliers and outliers\n\tif(inliers.size())\n\t{\n\t\trenderPointCloud(viewer,cloudInliers,\"inliers\",Color(0,1,0));\n  \t\trenderPointCloud(viewer,cloudOutliers,\"outliers\",Color(1,0,0));\n\t}\n  \telse\n  \t{\n  \t\trenderPointCloud(viewer,cloud,\"data\");\n  \t}\n\t\n  \twhile (!viewer->wasStopped ())\n  \t{\n  \t  viewer->spinOnce ();\n  \t}\n  \t\n}\n", "text_preview": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Dooweed/DS_project/blob/e92cb43a6df196c6b733c021fea31425b5d8bd0b/Heap.h", "id": "cpp-0-2c63f7dea1409f3baa1b76678f587b2f", "text": "#pragma once\n\n#include <string>\n#include \"Task.h\"\nusing namespace std;\n\ntemplate <typename T> class MaxBinaryHeap;\ntemplate <typename T> class MinBinaryHeap;\n\n// Exchanges values passed by reference\ntemplate <typename T>\nvoid exchange_values(T& value_1, T& value_2) {\n\tT temp = value_1;\n\tvalue_1 = value_2;\n\tvalue_2 = temp;\n}\n\n/*\n  Abstract class for MinBinaryHeap and MaxBinaryHeap\n  child classes must implement percolate_up, percolate_down, remove and build functions in a proper way\n\n  Heap can only accept comparable types ( <, >, == )\n*/\ntemplate <typename T>\nclass BinaryHeap {\nprotected:\n\tint MAX_LEN;\n\tint len; // Points to the next after the last non-empty value\n\tT** items;\n\nprotected:\n\t// Returns item array as a string\n\tstring get_string() {\n\t\tstring str(\"{\");\n\t\tfor (int i = 1; i < this->len; i++) {\n\t\t\tstr.append(to_string(*this->items[i]));\n\t\t\tif (i != this->len) {\n\t\t\t\tstr.append(\", \");\n\t\t\t}\n\t\t}\n\t\tstr.append(\" }\");\n\t\treturn str;\n\t}\n\n\t// Returns true if array has empty space and false otherwise\n\tbool has_available_space() {\n\t\treturn this->len < this->MAX_LEN;\n\t}\n\t// Returns true if element has no children\n\tbool is_leaf(int index) {\n\t\treturn index * 2 >= this->len;\n\t}\n\n\t// Returns index of the left child if it exists and -1 otherwise\n\tint left_child(int parent_index) {\n\t\tint child_index = parent_index * 2;\n\t\treturn child_index < this->len ? child_index : -1;\n\t}\n\t// Returns index of the right child if it exists and -1 otherwise\n\tint right_child(int parent_index) {\n\t\tint child_index = parent_index * 2 + 1;\n\t\treturn child_index < this->len ? child_index : -1;\n\t}\n\t// Returns index of the parent\n\tint parent(int child_index) {\n\t\treturn child_index / 2;\n\t}\n\n\t// Returns index of the smallest child and -1 if parent has no children\n\tint smallest_child(int parent_index) {\n\t\tint l_child = left_child(parent_index), r_child = right_child(parent_index);\n\t\tif (l_child == -1 || r_child == -1) // If node has less than 2 children\n\t\t\treturn l_child == -1 ? -1 : l_child;\n\t\telse\n\t\t\treturn *this->items[l_child] < *this->items[r_child] ? l_child : r_child;\n\t}\n\t// Returns index of the biggest child and -1 if parent has no children\n\tint biggest_child(int parent_index) {\n\t\tint l_child = left_child(parent_index), r_child = right_child(parent_index);\n\t\tif (l_child == -1 || r_child == -1) // If node has less than 2 children\n\t\t\treturn l_child == -1 ? -1 : l_child;\n\t\telse\n\t\t\treturn *this->items[l_child] > *this->items[r_child] ? l_child : r_child;\n\t}\n\n\t// Moves up an element to a proper position\n\tvirtual void percolate_up(int i) = 0;\n\t// Moves down an element to a proper position. Takes index of element as an argument\n\tvirtual void percolate_down(int i) = 0;\n\n\t// Default value calls\n\tvoid percolate_up() { percolate_up(this->len); }\n\tvoid percolate_down() { percolate_down(1); }\n\npublic:\n\t// Constructor\n\tBinaryHeap<T>(int max_len) {\n\t\tthis->items = new T* [max_len+1];\n\n\t\tthis->MAX_LEN = max_len+1;\n\t\tthis->len = 1;\n\t}\n\t~BinaryHeap() {\n\t\tdelete this->items;\n\t}\n\n\t// Returns true if heap is empty and false otherwise\n\tbool is_empty() {\n\t\treturn this->len <= 1;\n\t}\n\n\t// Print array\n\tvoid print() {\n\t\tcout << this->get_string() << endl;\n\t}\n\n\t// Insert new value to appropriate place in the heap\n\tvoid insert(T* number) {\n\t\tif (this->has_available_space()) {\n\t\t\tthis->items[this->len] = number;\n\t\t\tthis->percolate_up();\n\t\t\tthis->len += 1;\n\t\t}\n\t\telse {\n\t\t\tcout << \"Heap is full\" << endl;\n\t\t}\n\t}\n\n\t// Removes and returns root element\n\tT* remove_root() {\n\t\tif (this->is_empty()) {\n\t\t\tcout << \"Heap is empty, nothing to remove\" << endl;\n\t\t\treturn NULL;\n\t\t}\n\t\telse {\n\t\t\tT* element = this->get_root();\n\t\t\tthis->items[1] = this->items[this->len - 1];\n\t\t\tthis->len -= 1;\n\t\t\tpercolate_down();\n\t\t\treturn element;\n\t\t}\n\t}\n\n\t// Returns index of corresponding value\n\tint index_of(T value) {\n\t\tfor (int i = 1; i < this->len; i++) {\n\t\t\tif (this->items[i] == value) {\n\t\t\t\treturn i;\n\t\t\t}\n\t\t}\n\t\treturn NULL;\n\t}\n\n\t// Removes element by value and returns it\n\tT* remove_value(T value) {\n\t\treturn remove(index_of(value));\n\t}\n\n\t// Removes element by index and returns it\n\tvirtual T* remove(int index) = 0;\n\t// Sorting the heap\n\tvirtual void sort() = 0;\n\n\t// Returns root element without deleting it\n\tT* get_root() {\n\t\tif (this->is_empty()) {\n\t\t\tcout << \"Heap is empty\" << endl;\n\t\t\treturn NULL;\n\t\t}\n\t\telse\n\t\t\treturn this->items[1];\n\t}\n\n\t// Getters\n\tint get_len() {\n\t\treturn this->len - 1;\n\t}\n\tT** get_items() {\n\t\treturn this->items;\n\t}\n\n\t// Setters\n\tvoid set_len(int len) {\n\t\tthis->len = len;\n\t}\n\tvoid set_items(T** items) {\n\t\tthis->items = items;\n\t}\n\n\t// Insert array of new values in O(n). Works faster than calling insert() for every element of array ( O(nlogn) ). Works only for empty heap.\n\tvoid build(T* array[], int length, bool skip_first=false) {\n\t\tif (this->len == 1) {\n\t\t\tint starting_index = skip_first ? 1 : 0;\n\t\t\tfor (int i = starting_index; i < length; i++, this->len++) { // Append elements of list to this->items\n\t\t\t\tthis->items[this->len] = array[i];\n\t\t\t}\n\t\t\tfor (int i = length / 2; i > 0; i--) { // Move element to a proper position\n\t\t\t\tthis->percolate_down(i);\n\t\t\t}\n\t\t}\n\t\telse {\n\t\t\tcout << \"Heap is not empty\" << endl;\n\t\t}\n\t}\n};\n\ntemplate <typename T>\nclass MinBinaryHeap : public BinaryHeap<T> {\nprivate:\n\tvoid percolate_up(int i) {\n\t\tfor (; i / 2 > 0; i /= 2) {\n\t\t\tT& child = *this->items[i], & parent = *this->items[i / 2];\n\t\t\tif (child < parent) { // Exchange parent with child if child value is less than parent value\n\t\t\t\texchange_values(child, parent);\n\t\t\t}\n\t\t}\n\t}\npublic:\n\tMinBinaryHeap(int max_len) : BinaryHeap<T>(max_len) {};\n\n\tvoid percolate_down(int i) {\n\t\twhile (!this->is_leaf(i)) {\n\t\t\tint smallest_child_index = this->smallest_child(i);\n\t\t\tT& parent = *this->items[i], & child = *this->items[smallest_child_index];\n\t\t\tif (child < parent) // Exchange parent with child if child value is less than parent value\n\t\t\t\texchange_values(child, parent);\n\n\t\t\ti = smallest_child_index;\n\t\t}\n\t}\n\n\tT* remove(int index) {\n\t\tif (index < this->len && index > 0) {\n\t\t\tT &element = *this->items[index], &last_element = *this->items[this->len - 1], &parent_element = *this->items[this->parent(index)];\n\t\t\texchange_values(element, last_element);\n\t\t\tthis->len -= 1;\n\t\t\tif (element < parent_element)\n\t\t\t\tpercolate_up(index);\n\t\t\telse\n\t\t\t\tpercolate_down(index);\n\t\t\treturn &last_element;\n\t\t}\n\t\telse {\n\t\t\tcout << \"Index is out of bounds\" << endl;\n\t\t\treturn NULL;\n\t\t}\n\t}\n\n\tvoid sort() {\n\t\tMaxBinaryHeap<T>* max_heap = new MaxBinaryHeap<T>(this->MAX_LEN);\n\t\tmax_heap->build(this->items, this->len, true);\n\t\tT** max_heap_items = max_heap->get_items();\n\n\t\tfor (int i = this->len - 1; i > 1; i--) {\n\t\t\tT& last = *max_heap_items[i], & root = *max_heap_items[1];\n\t\t\texchange_values(last, root);\n\t\t\tmax_heap->set_len(max_heap->get_len());\n\t\t\tmax_heap->percolate_down(1);\n\t\t}\n\t\tdelete max_heap;\n\t}\n\n\t// Just another name to remove_root function\n\tT* remove_min() { return this->remove_root(); }\n\t// Just another name to get_root function\n\tT* get_min() { return this->get_root(); }\n};\n\ntemplate <typename T>\nclass MaxBinaryHeap : public BinaryHeap<T> {\nprivate:\n\tvoid percolate_up(int i) {\n\t\tfor (int i = this->len; i / 2 > 0; i /= 2) {\n\t\t\tT &child = *this->items[i], &parent = *this->items[i / 2];\n\n\t\t\tif (child > parent) // Exchange parent with child if child value is bigger than parent value\n\t\t\t\texchange_values(child, parent);\n\t\t}\n\t}\npublic:\n\tMaxBinaryHeap(int max_len) : BinaryHeap<T>(max_len) {};\n\n\tvoid percolate_down(int i) {\n\t\twhile (!this->is_leaf(i)) {\n\t\t\tint biggest_child_index = this->biggest_child(i);\n\t\t\tT& parent = *this->items[i], & child = *this->items[biggest_child_index];\n\t\t\tif (child > parent) // Exchange parent with child if child value is bigger than parent value\n\t\t\t\texchange_values(child, parent);\n\n\t\t\ti = biggest_child_index;\n\t\t}\n\t}\n\n\tT* remove(int index) {\n\t\tif (index < this->len && index > 0) {\n\t\t\tT &element = *this->items[index], &last_element = *this->items[this->len - 1], &parent_element = *this->items[this->parent(index)];\n\t\t\texchange_values(element, last_element);\n\t\t\tthis->len -= 1;\n\t\t\tif (element < parent_element)\n\t\t\t\tpercolate_down(index);\n\t\t\telse\n\t\t\t\tpercolate_up(index);\n\t\t\treturn &last_element;\n\t\t}\n\t\telse {\n\t\t\tcout << \"Index is out of bounds\" << endl;\n\t\t\treturn NULL;\n\t\t}\n\t}\n\n\tvoid sort() {\n\t\tMinBinaryHeap<T>* min_heap = new MinBinaryHeap<T>(this->MAX_LEN);\n\t\tmin_heap->build(this->items, this->len, true);\n\t\tT** min_heap_items = min_heap->get_items();\n\n\t\tfor (int i = this->len - 1; i > 1; i--) {\n\t\t\tT& last = *min_heap_items[i], & root = *min_heap_items[1];\n\t\t\texchange_values(last, root);\n\t\t\tmin_heap->set_len(min_heap->get_len());\n\t\t\tmin_heap->percolate_down(1);\n\t\t}\n\t\tdelete min_heap;\n\t}\n\n\t// Just another name to remove_root function\n\tT* remove_max() { return this->remove_root(); }\n\t// Just another name to get_root function\n\tT* get_max() { return this->get_root(); }\n};\n", "text_preview": "#pragma once #include <string> #include \"Task.h\" using namespace std; template <typename T> class MaxBinaryHeap; template <typename T> class MinBinaryHeap; // Exchanges values passed by reference template <typename T> void exchange_values(T", "input_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/lanl/vpic/blob/5d1aa3cceac48c5ff36e131cd26c89a85b118d75/src/species_advance/standard/pipeline/hydro_p_pipeline.cc", "id": "cpp-0-2fc14e9c76848e1c98b690a8ac5ed6b4", "text": "#define IN_spa\n\n#define HAS_V4_PIPELINE\n#define HAS_V8_PIPELINE\n#define HAS_V16_PIPELINE\n\n#include \"spa_private.h\"\n\n#include \"../../../util/pipelines/pipelines_exec.h\"\n\n// accumulate_hydro_p adds the hydrodynamic fields associated with the\n// supplied particle_list to the hydro array.  Trilinear interpolation\n// is used.  hydro is known at the nodes at the same time as particle\n// positions. No effort is made to fix up edges of the computational\n// domain.  All particles on the list must be inbounds.  Note, the\n// hydro jx,jy,jz are for diagnostic purposes only; they are not\n// accumulated with a charge conserving algorithm.\n\nvoid\naccumulate_hydro_p_pipeline_scalar( accumulate_hydro_p_pipeline_args_t * args,\n                                    int pipeline_rank,\n                                    int n_pipeline )\n{\n  const species_t      *              sp = args->sp;\n  /**/  hydro_t        * ALIGNED(128) h  = args->h + pipeline_rank * args->h_size;\n  const particle_t     * ALIGNED(128) p  = sp->p;\n  const interpolator_t * ALIGNED(128) f  = args->f;\n\n  // Constants.\n\n  const float qsp      = sp->q;\n  const float qdt_2mc  = args->qdt_2mc;\n  const float qdt_4mc2 = qdt_2mc / ( 2 * sp->g->cvac );\n  const float mspc     = sp->g->cvac * args->msp;\n  const float c        = sp->g->cvac;\n  const float r8V      = sp->g->r8V;\n\n  const float one            = 1.0;\n  const float one_third      = 1.0 / 3.0;\n\n  float dx, dy, dz, ux, uy, uz, w, vx, vy, vz, ke_mc;\n  float t, w0, w1, w2, w3, w4, w5, w6, w7;\n  int   i, n, n1, n0;\n\n  int   stride_10;\n  int   stride_21;\n  int   stride_43;\n\n  // Determine which particles this pipeline processes.\n\n  DISTRIBUTE( args->np, 16, pipeline_rank, n_pipeline, n0, n1 );\n\n  n1 += n0;\n\n  // c        = sp->g->cvac;\n  // qsp      = sp->q;\n  // mspc     = args->msp * c;\n  // qdt_2mc  = args->qdt_2mc;\n  // qdt_4mc2 = qdt_2mc / ( 2 * c );\n  // r8V      = sp->g->r8V;\n\n  stride_10 = VOXEL( 1, 0, 0, sp->g->nx, sp->g->ny, sp->g->nz ) -\n              VOXEL( 0, 0, 0, sp->g->nx, sp->g->ny, sp->g->nz );\n\n  stride_21 = VOXEL( 0, 1, 0, sp->g->nx, sp->g->ny, sp->g->nz ) -\n              VOXEL( 1, 0, 0, sp->g->nx, sp->g->ny, sp->g->nz );\n\n  stride_43 = VOXEL( 0, 0, 1, sp->g->nx, sp->g->ny, sp->g->nz ) -\n              VOXEL( 1, 1, 0, sp->g->nx, sp->g->ny, sp->g->nz );\n\n  for( n = n0; n < n1; n++ )\n  {\n    //--------------------------------------------------------------------------\n    // Load particle data.\n    //--------------------------------------------------------------------------\n\n    dx = p[n].dx;\n    dy = p[n].dy;\n    dz = p[n].dz;\n    i  = p[n].i;\n\n    ux = p[n].ux;\n    uy = p[n].uy;\n    uz = p[n].uz;\n    w  = p[n].w;\n\n    //--------------------------------------------------------------------------\n    // Load interpolation data for particles and half advance with E.\n    //--------------------------------------------------------------------------\n\n    ux += qdt_2mc * (      ( f[i].ex    + dy * f[i].dexdy    ) +\n\t\t      dz * ( f[i].dexdz + dy * f[i].d2exdydz ) );\n\n    uy += qdt_2mc * (      ( f[i].ey    + dz * f[i].deydz    ) +\n\t\t      dx * ( f[i].deydx + dz * f[i].d2eydzdx ) );\n\n    uz += qdt_2mc * (      ( f[i].ez    + dx * f[i].dezdx    ) +\n\t\t      dy * ( f[i].dezdy + dx * f[i].d2ezdxdy ) );\n\n    //--------------------------------------------------------------------------\n    // Load B interpolation data for particles.\n    //--------------------------------------------------------------------------\n\n    w5 = f[i].cbx + dx * f[i].dcbxdx;\n    w6 = f[i].cby + dy * f[i].dcbydy;\n    w7 = f[i].cbz + dz * f[i].dcbzdz;\n\n    //--------------------------------------------------------------------------\n    // Compute kinetic energy.\n    //--------------------------------------------------------------------------\n\n    // Boris rotation: curl scalars ( 0.5 in v0 for half rotate ) and\n    // kinetic energy computation. Note: gamma - 1 = |u|^2 / ( gamma + 1 )\n    // is the numerically accurate way to compute gamma - 1.\n    ke_mc  = ux * ux + uy * uy + uz * uz; // ke_mc = |u|^2   (invariant)\n    vz     = sqrt( one + ke_mc );         // vz    = gamma   (invariant)\n    ke_mc *= c / ( vz + one );            // ke_mc = c|u|^2 / (gamma+1) = c * (gamma-1)\n    vz     = c / vz;                      // vz    = c/gamma\n\n    //--------------------------------------------------------------------------\n    // Half Boris advance.\n    //--------------------------------------------------------------------------\n\n    w0  = qdt_4mc2 * vz;\n    w1  = w5 * w5 + w6 * w6 + w7 * w7;  // |cB|^2\n    w2  = w0 * w0 * w1;\n    w3  = w0 * ( one + ( one_third ) * w2 * ( one + 0.4f * w2 ) );\n    w4  = w3 / ( one + w1 * w3 * w3 );\n    w4 += w4;\n\n    // Boris rotation: uprime.\n    w0 = ux + w3 * ( uy * w7 - uz * w6 );\n    w1 = uy + w3 * ( uz * w5 - ux * w7 );\n    w2 = uz + w3 * ( ux * w6 - uy * w5 );\n\n    // Boris rotation: u.\n    ux += w4 * ( w1 * w7 - w2 * w6 );\n    uy += w4 * ( w2 * w5 - w0 * w7 );\n    uz += w4 * ( w0 * w6 - w1 * w5 );\n\n    //--------------------------------------------------------------------------\n    // Compute velocity.\n    //--------------------------------------------------------------------------\n\n    vx = ux * vz;\n    vy = uy * vz;\n    vz = uz * vz;\n\n    //--------------------------------------------------------------------------\n    // Compute the trilinear coefficients.\n    //--------------------------------------------------------------------------\n\n    w0  = r8V * w;  // w0 = (1/8) (w/V)\n    dx *= w0;       // dx = (1/8) (w/V) x\n    w1  = w0 + dx;  // w1 = (1/8) (w/V) + (1/8) (w/V) x = (1/8) (w/V) (1+x)\n    w0 -= dx;       // w0 = (1/8) (w/V) - (1/8) (w/V) x = (1/8) (w/V) (1-x)\n    w3  = one + dy; // w3 = 1+y\n    w2  = w0 * w3;  // w2 = (1/8) (w/V) (1-x) (1+y)\n    w3 *= w1;       // w3 = (1/8) (w/V) (1+x) (1+y)\n    dy  = one - dy; // dy = 1-y\n    w0 *= dy;       // w0 = (1/8) (w/V) (1-x) (1-y)\n    w1 *= dy;       // w1 = (1/8) (w/V) (1+x) (1-y)\n    w7  = one + dz; // w7 = 1+z\n    w4  = w0 * w7;  // w4 = (1/8) (w/V) (1-x) (1-y) (1+z) = (w/V) trilin_0 * Done\n    w5  = w1 * w7;  // w5 = (1/8) (w/V) (1+x) (1-y) (1+z) = (w/V) trilin_1 * Done\n    w6  = w2 * w7;  // w6 = (1/8) (w/V) (1-x) (1+y) (1+z) = (w/V) trilin_2 * Done\n    w7 *= w3;       // w7 = (1/8) (w/V) (1+x) (1+y) (1+z) = (w/V) trilin_3 * Done\n    dz  = one - dz; // dz = 1-z\n    w0 *= dz;       // w0 = (1/8) (w/V) (1-x) (1-y) (1-z) = (w/V) trilin_4 * Done\n    w1 *= dz;       // w1 = (1/8) (w/V) (1+x) (1-y) (1-z) = (w/V) trilin_5 * Done\n    w2 *= dz;       // w2 = (1/8) (w/V) (1-x) (1+y) (1-z) = (w/V) trilin_6 * Done\n    w3 *= dz;       // w3 = (1/8) (w/V) (1+x) (1+y) (1-z) = (w/V) trilin_7 * Done\n\n    //--------------------------------------------------------------------------\n    // Accumulate the hydro fields.\n    //--------------------------------------------------------------------------\n\n    #define ACCUM_HYDRO( wn )                                        \\\n    t         = qsp * wn;        /* t  = ( qsp   w / V ) trilin_n */ \\\n    h[i].jx  += t * vx;                                              \\\n    h[i].jy  += t * vy;                                              \\\n    h[i].jz  += t * vz;                                              \\\n    h[i].rho += t;                                                   \\\n    t         = mspc * wn;       /* t  = ( msp c w / V ) trilin_n */ \\\n    dx        = t * ux;          /* dx = ( px    w / V ) trilin_n */ \\\n    dy        = t * uy;                                              \\\n    dz        = t * uz;                                              \\\n    h[i].px  += dx;                                                  \\\n    h[i].py  += dy;                                                  \\\n    h[i].pz  += dz;                                                  \\\n    h[i].ke  += t * ke_mc;                                           \\\n    h[i].txx += dx * vx;                                             \\\n    h[i].tyy += dy * vy;                                             \\\n    h[i].tzz += dz * vz;                                             \\\n    h[i].tyz += dy * vz;                                             \\\n    h[i].tzx += dz * vx;                                             \\\n    h[i].txy += dx * vy\n\n    /**/            ACCUM_HYDRO( w0 ); // Cell i,   j,   k\n    i += stride_10; ACCUM_HYDRO( w1 ); // Cell i+1, j,   k\n    i += stride_21; ACCUM_HYDRO( w2 ); // Cell i,   j+1, k\n    i += stride_10; ACCUM_HYDRO( w3 ); // Cell i+1, j+1, k\n    i += stride_43; ACCUM_HYDRO( w4 ); // Cell i,   j,   k+1\n    i += stride_10; ACCUM_HYDRO( w5 ); // Cell i+1, j,   k+1\n    i += stride_21; ACCUM_HYDRO( w6 ); // Cell i,   j+1, k+1\n    i += stride_10; ACCUM_HYDRO( w7 ); // Cell i+1, j+1, k+1\n\n    #undef ACCUM_HYDRO\n  }\n}\n\n//----------------------------------------------------------------------------//\n// Top level function to select and call the proper accumulate_hydro_p\n// pipeline function.\n//----------------------------------------------------------------------------//\n\nvoid\naccumulate_hydro_p_pipeline( hydro_array_t * RESTRICT ha,\n                             const species_t * RESTRICT sp,\n                             const interpolator_array_t * RESTRICT ia )\n{\n  DECLARE_ALIGNED_ARRAY( accumulate_hydro_p_pipeline_args_t, 128, args, 1 );\n\n  if ( ! ha           ||\n       ! sp           ||\n       ! ia           ||\n       ha->g != sp->g ||\n       ha->g != ia->g )\n  {\n    ERROR( ( \"Bad args.\" ) );\n  }\n\n  args->sp      = sp;\n  args->f       = ia->i;\n  args->h       = ha->h;\n  args->h_size  = ha->stride;\n  args->qdt_2mc = ( sp->q * sp->g->dt ) / ( 2 * sp->m * sp->g->cvac );\n  args->msp     = sp->m;\n  args->np      = sp->np;\n\n  EXEC_PIPELINES( accumulate_hydro_p, args, 0 );\n\n  WAIT_PIPELINES();\n}\n", "text_preview": "#define IN_spa #define HAS_V4_PIPELINE #define HAS_V8_PIPELINE #define HAS_V16_PIPELINE #include \"spa_private.h\" #include \"../../../util/pipelines/pipelines_exec.h\" // accumulate_hydro_p adds the hydrodynamic fields associated with the // s", "input_sha256": "48c69a6d972ae54b46e0004dae8fa863df19bd01ad52076f9fd20d1d96e8692b", "text_sha256": "2fc14e9c76848e1c98b690a8ac5ed6b48176d67cc14d6395a50ab542bba7dbc4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 17, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.2706776101768575, "noise_severity": 0.021370548007773903, "missing_content_severity": 0.026723966504772566, "factual_density": 3.403356432688835, "pretraining_utility": 0.4920381882287853, "math_density": 3.4495781247318917, "code_density": 3.8672405021463128, "reasoning_density": 1.105533956917132, "education_level": 3.3589225033632286, "creative_writing_quality": -0.221518350109251}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/bizkut/warz-client/blob/5262ea65e0baaf3f37ffaede5f41c9b7eafee7c1/External/NaturalMotion/common/NMNumerics/include/NMNumerics/NMCSplineSmoother.h", "id": "cpp-0-0b1714de7cc4c7758ff1d7ce90268928", "text": "// Copyright (c) 2010 NaturalMotion.  All Rights Reserved.\n// Not to be copied, adapted, modified, used, distributed, sold,\n// licensed or commercially exploited in any manner without the\n// written consent of NaturalMotion.\n//\n// All non public elements of this software are the confidential\n// information of NaturalMotion and may not be disclosed to any\n// person nor used for any purpose not expressly approved by\n// NaturalMotion in writing.\n//----------------------------------------------------------------------------------------------------------------------\n#ifdef _MSC_VER\n  #pragma once\n#endif\n#ifndef NM_CSPLINE_SMOOTHER_H\n#define NM_CSPLINE_SMOOTHER_H\n\n//----------------------------------------------------------------------------------------------------------------------\n#include \"NMPlatform/NMPlatform.h\"\n#include \"NMPlatform/NMMatrix.h\"\n#include \"NMNumerics/NMBSplineCurve.h\"\n//----------------------------------------------------------------------------------------------------------------------\nnamespace NMP\n{\n\n//----------------------------------------------------------------------------------------------------------------------\n/// \\brief  Used to return the status of the smoothing spline solver\nclass CSplineSmootherStatus\n{\npublic:\n  enum eType {\n    Success, // Successfully computed smoothing spline\n    Divergent, // Roundoff error in vicinity of target tolerance\n    MaxIterations, // Max iterations reached\n    NotPosDef, // Symmetric system L L^T is not pos-def due to round-off error\n    Singular, // Poor system\n    Multiplicity, // Some data sites are repeated\n    BadParams // Spectacularly poor system\n  };\n};\n\n//----------------------------------------------------------------------------------------------------------------------\n/// \\brief  Cubic Smoothing B-Spline solver\n///\n/// This is an implementation of the paper:\n///\n/// @article{ Boor98smoothingspline,\n///   author = \"<NAME>\",\n///   title = \"Calculation of the smoothing spline with weighted roughness measure\",\n///   journal = \"Math. Models Methods Appl. Sci\",\n///   year = \"1998\",\n///   volume = \"11\",\n///   pages = \"33--41\" }\n///\n/// DESCRIPTION:\n///\n/// The smoothing spline C(t) minimizes the roughness measure:\n///\n/// F( D^(2)C(t) ) = \\integral || (D^(2)C)(t) ||^2 dt \n/// on the interval min(x) < t < max(x),\n/// where D^(2)f is the second derivative of the B-spline f.\n///\n/// over all B-spline functions C(t) for which the weighted fitting\n/// measurement error:\n///\n/// E( C(t) ) = sum_j w(j) * || (Y(:,j) - C(x(j))) ||^2\n/// j = [0:length(x)-1]\n///\n/// is equal (within a supplied +/- precision) to the given\n/// variance tolerance: tol. The specified precision eps is determined\n/// as a scalar factor of the variance tolerance eps = fac * tol.\n///\n/// This is achieved by minimizing a single smoothing parameter p\n/// of the functional:\n///\n/// p * E( C(t) ) + F( (D^(2)C)(t) )\n///\n/// so that E( C(t) ) equals the residual data variance tolerance: tol\n///\n/// When tol = 0 then the solver computes the interpolating cubic\n/// B-spline of the input data Y at sites x.\n///\n/// As tol --> \\infty then p --> 0 and the minimization is on the second\n/// derivative (D^(2)f) of the B-spline C(t) i.e. the acceleration of the\n/// curvature. This shapes the B-spline toward a straight line.\n///\n/// If the measurement weights are not given explicitly then they are\n/// chosen so that E( C(t) ) is the composite Trapezoid rule approximation\n/// for the integral F( Y - C(t) ).\n///\n/// The form of the linear system ensures that the smoothing parameter\n/// converges very quickly. The minimization is achieved by first performing\n/// a Newton iteration of the linear system at p=0, thereafter knowing the\n/// the current and last error estimates it is faster to use the secant\n/// method.\n///\ntemplate <typename _T>\nclass CSplineSmoother\n{\npublic:\n  //---------------------------------------------------------------------\n  /// \\name   Constructors\n  /// \\brief  Functions to compute a cubic B-Spline smoothing curve\n  //---------------------------------------------------------------------\n  //@{\n\n  //---------------------------------------------------------------------\n  /// \\brief  Constructor to initialize a system for solving\n  /// \\param  dims the number of dimensions of the data.\n  /// \\param  tol is the target residual data variance tolerance\n  CSplineSmoother(uint32_t dims, _T tol);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Constructor to compute the smoothing spline\n  /// \\param  x is a 1D data vector of sample sites (n - vector)\n  /// \\param  Y is a (n by dims) matrix of data samples at sites x\n  /// \\param  w is a 1D fitting weight vector for the data samples (n - vector)\n  /// \\param  tol is the target residual data variance tolerance\n  CSplineSmoother(const Vector<_T>& x, const Matrix<_T>& Y, const Vector<_T>& w, _T tol);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Constructor to compute the smoothing spline using the\n  /// composite trapezoid rule weighting strategy.\n  ///\n  /// \\param  x is a 1D data vector of sample sites (n - vector)\n  /// \\param  Y is a (n by dims) matrix of data samples at sites x\n  /// \\param  tol is the target residual data variance tolerance\n  CSplineSmoother(const Vector<_T>& x, const Matrix<_T>& Y, _T tol);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Constructor to compute the smoothing spline with uniformly spaced\n  /// samples x = [0:n-1] and the composite trapezoid rule weights.\n  ///\n  /// \\param  Y is a (n by dims) matrix of data samples at sites x\n  /// \\param  tol is the target residual data variance tolerance\n  CSplineSmoother(const Matrix<_T>& Y, _T tol);\n\n  //---------------------------------------------------------------------\n  /// Destructor\n  ~CSplineSmoother();\n  //@}\n\n  //---------------------------------------------------------------------\n  /// \\name   Solve functions\n  /// \\brief  Functions to fit a smoothing B-spline to the data\n  //---------------------------------------------------------------------\n  //@{\n\n  //---------------------------------------------------------------------\n  /// \\brief  Function to fit a smoothing B-spline to the data\n  /// \\param  x is a 1D data vector of sample sites (n - vector)\n  /// \\param  Y is a (n by dims) matrix of data samples at sites x\n  /// \\param  w is a 1D fitting weight vector for the data samples (n - vector)\n  CSplineSmootherStatus::eType\n  solve(const Vector<_T>& x, const Matrix<_T>& Y, const Vector<_T>& w);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Function to fit a smoothing B-spline to the data\n  /// \\param  x is a 1D data vector of sample sites (n - vector)\n  /// \\param  Y is a (n by dims) matrix of data samples at sites x\n  CSplineSmootherStatus::eType\n  solve(const Vector<_T>& x, const Matrix<_T>& Y);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Function to fit a smoothing B-spline to the data\n  /// \\param  Y is a (n by dims) matrix of data samples at sites x = [0:n-1]\n  CSplineSmootherStatus::eType\n  solve(const Matrix<_T>& Y);\n  //@}\n\n  //---------------------------------------------------------------------\n  /// \\name   Solver Data\n  /// \\brief  Functions get the status of the solve and the computed curve\n  //---------------------------------------------------------------------\n  //@{\n\n  //---------------------------------------------------------------------\n  /// \\brief  Get the computed curve\n  const BSplineCurve<_T>& getCurve() const;\n  BSplineCurve<_T>& getCurve();\n\n  //---------------------------------------------------------------------\n  /// \\brief  Get the computed smoothing parameter\n  _T getSmoothingParam() const;\n\n  //---------------------------------------------------------------------\n  /// \\brief  Error functional E( C(t) ), where\n  /// E( C(t) ) = sum_j w(j) * || (Y(:,j) - C(x(j))) ||^2\n  ///\n  /// \\return  The actual residual data variance between the sample data\n  /// and the computed smoothing spline. If the fit is good this should\n  /// be very close to the fitting tolerance tol\n  _T getError() const;\n\n  //---------------------------------------------------------------------\n  /// \\brief  Fitting tolerance parameter\n  _T getTol() const;\n  void setTol(_T tol);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Termination condition factor\n  _T getEpsFac() const;\n  void setEpsFac(_T fac);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Maximum number of iterations\n  uint32_t getMaxIterations() const;\n  void setMaxIterations(uint32_t n);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Get the status of the solve\n  CSplineSmootherStatus::eType getStatus() const;\n\n  //---------------------------------------------------------------------\n  /// \\brief  Tests if the solver completely failed to compute a smoothing spline\n  bool fail() const;\n  //@}\n\nprotected:\n  //---------------------------------------------------------------------\n  /// \\name   Utility solve functions\n  /// \\brief  Special case functions that perform the actual computation\n  //---------------------------------------------------------------------\n  //@{\n\n  //---------------------------------------------------------------------\n  /// \\brief  Main solve routine to compute the cubic smoothing spline\n  CSplineSmootherStatus::eType\n  doSolve(const Vector<_T>& x, const Matrix<_T>& Y, const Vector<_T>& w);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Fit single data point to a B-Spline of order 1\n  CSplineSmootherStatus::eType\n  doCurve1(const Vector<_T>& x, const Matrix<_T>& Y);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Fit two data points to a B-Spline of order 2\n  CSplineSmootherStatus::eType\n  doCurve2(const Vector<_T>& x, const Matrix<_T>& Y);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Fit three data points to a B-Spline of order 3\n  CSplineSmootherStatus::eType\n  doCurve3(const Vector<_T>& x, const Matrix<_T>& Y);\n\n  //---------------------------------------------------------------------\n  /// \\brief  When tol=0 then the curve we compute is the interpolating B-spline\n  CSplineSmootherStatus::eType\n  doInterpolate(const Vector<_T>& x, const Matrix<_T>& Y);\n\n  //---------------------------------------------------------------------\n  /// \\brief  Initialize default values for the solver\n  void init();\n\n  //---------------------------------------------------------------------\n  /// \\brief  Compute the composite Trapezoid rule weights\n  void weights(const Vector<_T>& x, Vector<_T>& w) const;\n  //@}\n\nprivate:\n  /// Target residual data variance tolerance\n  _T                              m_tol;\n\n  /// Termination condition tolerance multiplier. i.e. scaled factor of m_tol\n  _T                              m_epsFac;\n\n  /// Maximum number of iterations to perform\n  uint32_t                        m_maxIts;\n\n  /// Computed smoothing parameter\n  _T                              m_rho;\n\n  /// Computed error functional E( C(t) )\n  _T                              m_Ep;\n\n  /// Computation status\n  CSplineSmootherStatus::eType    m_status;\n\n  /// Fitted B-Spline curve\n  BSplineCurve<_T>                m_curve;\n};\n\n//----------------------------------------------------------------------------------------------------------------------\n} // namespace NMP\n\n#endif // NM_CSPLINE_SMOOTHER_H\n", "text_preview": "// Copyright (c) 2010 NaturalMotion. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/MihaiUdrea/aoc-2019/blob/b9c691cc060b21a14eb7243f80a0cec2b3f2cf22/day12.cpp", "id": "cpp-0-81483b6bf9436df9ac79379859c90188", "text": "// Aoc.cpp : This file contains the 'main' function. Program execution begins and ends there.\n// --- Day 12: The N-Body Problem ---\n#include \"stdafx.h\"\n#include \"catch.hpp\"\n#include \"Utils.h\"\n\nstruct Data\n{\n  Point position;\n  Point speed;\n\n  auto operator<=>(const Data&) const = default;\n};\nauto DimensionsRange = irange(0, 3);\nusing Input = array<Data, 4>;\n\nstruct Solve {\n  Input vec;\n\n  Solve(string inStr) {\n    int pos = 0;\n    forEachRxToken(inStr, lineRxToken, [&](string line) {\n      static const regex matchExp(R\"~(<x=([^,]+), y=([^,]+), z=([^>]+)>)~\");\n      auto res = match_rx(line, matchExp);\n\n      Point p{ stoi(res[2]), stoi(res[1]), stoi(res[3]) };\n      vec[pos].position = p;\n      pos++;\n      });\n  };\n\n  array<Point, 4> gravity;\n\n  void ApplyGravity()\n  {\n    fill(gravity.begin(), gravity.end(), Point());\n\n    for (auto i : DimensionsRange)\n    {\n      if (reset.find(i) != reset.end())\n        continue;\n\n      Input& v = vec;\n\n      for (auto moonIdx : irange(0, (int)v.size()))\n      {\n        auto smallerC = count_if(v.begin(), v.end(), [&](auto& l) {\n          return l.position.GetAxys(i) < v[moonIdx].position.GetAxys(i);\n          });\n        auto greaterC = count_if(v.begin(), v.end(), [&](auto& l) {\n          return l.position.GetAxys(i) > v[moonIdx].position.GetAxys(i);\n          });\n\n        gravity[moonIdx].IncAxyx(i, (int)(greaterC - smallerC));\n      }\n\n      // apply\n      for (auto moonIdx : irange(0, (int)vec.size()))\n      {\n        vec[moonIdx].position.IncAxyx(i, gravity[moonIdx].GetAxys(i));\n      }\n    }\n  }\n\n  void ApplySpeed()\n  {\n    // move moon\n    for (auto& moon : vec)\n      moon.position = moon.position + moon.speed;\n\n    // inc speed    \n    for (auto it = vec.begin(); it != vec.end(); ++it)\n      it->speed = it->speed + gravity[it - vec.begin()];\n  }\n\n  int GetEnergy()\n  {\n    return accumulate(vec.begin(), vec.end(), 0, [](auto s, auto moon) {\n      return s + ManhDist(moon.speed) * ManhDist(moon.position); });\n  }\n\n  string Do(int steps = 1000)\n  {\n    for (auto step : irange(0, steps))\n    {\n      ApplyGravity();\n      ApplySpeed();\n    }\n\n    return ToString(GetEnergy());\n  }\n\n  size_t GetDimHash(const Input& a, int dim)\n  {\n    return accumulate(a.begin(), a.end(), 0ui64, [=](auto s, auto e) {\n      return (s << 20) + (e.position.GetAxys(dim) << 10) + e.speed.GetAxys(dim);\n      });\n  }\n\n  array<set<size_t>, 4> history;\n  map<size_t, size_t> reset;\n\n  void AddToHistory(size_t step)\n  {\n    for (auto dim : DimensionsRange)\n    {\n      if (reset.find(dim) != reset.end())\n        continue;\n\n      if (!history[dim].insert(GetDimHash(vec, dim)).second)\n      {\n        reset[dim] = step;\n      }\n    }\n  }\n\n  string Do2()\n  {\n    for (size_t step = 0; reset.size() < 3; ++step)\n    {\n      AddToHistory(step);\n      ApplyGravity();\n      ApplySpeed();\n    }\n\n    return std::to_string(lcm(lcm(reset[0], reset[1]), reset[2]));\n  }\n};\n\nTEST_CASE(\"Sample 0\", \"[x]\") {\n  REQUIRE(Solve(\n    R\"(<x=-1, y=0, z=2>\n<x=2, y=-10, z=-7>\n<x=4, y=-8, z=8>\n<x=3, y=5, z=-1>)\").Do(10) == \"179\");\n}\n\nTEST_CASE(\"Part 2 Test\", \"[x.]\") {\n  REQUIRE(Solve(\n    R\"(<x=-1, y=0, z=2>\n<x=2, y=-10, z=-7>\n<x=4, y=-8, z=8>\n<x=3, y=5, z=-1>)\").Do2() == \"2772\");\n\n}\n\nTEST_CASE(\"Part One\", \"[x.]\") {\n  cout << endl << \"Part One ------------- \" << endl;\n  toClipboard(Solve(ReadFileToString(L\"input.txt\")).Do());\n}\n\nTEST_CASE(\"Part Two\", \"[x.]\") {\n  cout << endl << \"Part Two ------------- \" << endl;\n  toClipboard(Solve(ReadFileToString(L\"input.txt\")).Do2());\n}\n\n/* Input ---------------\n\n<x=-4, y=-9, z=-3>\n<x=-13, y=-11, z=0>\n<x=-17, y=-7, z=15>\n<x=-16, y=4, z=2>\n\n ------------Input End*/\n", "text_preview": "// Aoc.cpp : This file contains the 'main' function. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Yonghui56/ComponentialMultiphase/blob/47c019ac50e1a182eb7d3912776795f71ac2963a/ogsNumerics/FemLib/Core/Extrapolation/FemExtrapolationGaussLinear.cpp", "id": "cpp-0-269cd2227180d7f50f70e831b6fac585", "text": "/**\n * Copyright (c) 2012, OpenGeoSys Community (http://www.opengeosys.com)\n *            Distributed under a Modified BSD License.\n *              See accompanying file LICENSE.txt or\n *              http://www.opengeosys.com/LICENSE.txt\n *\n *\n * \\file FemExtrapolationGaussLinear.cpp\n *\n * Created on 2012-08-03 by <NAME>\n */\n\n#include \"FemExtrapolationGaussLinear.h\"\n\n#include <algorithm>\n#include <cmath>\n\n#include \"MathLib/Integration/GaussLegendre.h\"\n#include \"FemLib/Core/Element/IFemElement.h\"\n\nnamespace FemLib\n{\n\n// gauss linear\nvoid FeExtrapolationGaussLinear::extrapolate(IFiniteElement &fe, const std::vector<MathLib::LocalVector> &gp_values, std::vector<MathLib::LocalVector> &nodal_values)\n{\n    extrapolate<MathLib::LocalVector>(fe, gp_values, nodal_values);\n}\n\ntemplate<typename Tvalue>\nvoid FeExtrapolationGaussLinear::extrapolate(IFiniteElement &fe, const std::vector<Tvalue> &gp_values, std::vector<Tvalue> &nodal_values)\n{\n    const MeshLib::IElement* e = fe.getElement();\n    AbstractFemIntegrationGaussBase* gauss = static_cast<AbstractFemIntegrationGaussBase*>(fe.getIntegrationMethod());\n    const size_t e_nnodes = e->getNumberOfNodes();\n    // reorder gp values\n    std::vector<Tvalue> reordered_gp_values(gp_values.size());\n    switch (e->getShapeType()) {\n        case MeshLib::ElementShape::QUAD:\n        case MeshLib::ElementShape::HEXAHEDRON:\n            for (size_t j=0; j<gp_values.size(); j++) {\n                size_t nod_id = getLocalIndex(*e, *gauss, j);\n                reordered_gp_values[nod_id] = gp_values[j];\n            }\n            break;\n        default:\n            reordered_gp_values.assign(gp_values.begin(), gp_values.end());\n            break;\n    }\n\n    // calculate Xi_p\n    double Xi_p = calcXi_p(*e, *gauss);\n\n    // \n    size_t i_s = 0;\n    size_t i_e = e_nnodes;\n    size_t ish = 0;\n    if (e->getShapeType()==MeshLib::ElementShape::TETRAHEDRON) {\n        i_s = 1;\n        i_e = e_nnodes + 1;\n        ish = 1;\n    }\n\n    // \n    double x[3] = {};\n    for (size_t j=0; j<e_nnodes; j++) {\n        getExtropoGaussPoints(*e, j, Xi_p, x);\n        fe.computeBasisFunctions(x);\n        MathLib::LocalMatrix *N = fe.getBasisFunction();\n        //Tvalue EV(0);\n        Tvalue EV = reordered_gp_values[0];\n        EV *= .0;\n        for(size_t k=i_s; k<i_e; k++)\n            EV += reordered_gp_values[k] * (*N)(0,k - ish);\n        nodal_values[j] = EV;\n    }\n}\n\ndouble FeExtrapolationGaussLinear::calcXi_p(const MeshLib::IElement& e, AbstractFemIntegrationGaussBase& gauss)\n{\n    double Xi_p = 0.0;\n    if (e.getShapeType() == MeshLib::ElementShape::QUAD || e.getShapeType() == MeshLib::ElementShape::HEXAHEDRON) {\n        double r = .0;\n        const size_t nGauss = gauss.getSamplingLevel();\n        for (size_t gp=0; gp<nGauss; gp++) {\n            r = MathLib::GaussLegendre::getPoint(nGauss, gp);\n            if (fabs(r) > Xi_p)\n                Xi_p = fabs(r);\n        }\n        r = 1.0 / Xi_p;\n        Xi_p = r;\n    }\n\n    return Xi_p;\n}\n\nint FeExtrapolationGaussLinear::getLocalIndex(const MeshLib::IElement& e, AbstractFemIntegrationGaussBase& gauss, size_t igp)\n{\n    int LoIndex = -1;\n    double r,s,t;\n    size_t nGauss = gauss.getSamplingLevel();\n    size_t gp_r = 0;\n    size_t gp_s = 0;\n    size_t gp_t = 0;\n    const double MKleinsteZahl = std::numeric_limits<double>::epsilon();\n\n    switch (e.getShapeType()) {\n    case MeshLib::ElementShape::QUAD:\n        gp_r = igp/nGauss;\n        gp_s = igp%nGauss;\n        gp_t = 0;\n        r = MathLib::GaussLegendre::getPoint(nGauss, gp_r);\n        s = MathLib::GaussLegendre::getPoint(nGauss, gp_s);\n        if (r > 0.0 && s > 0.0)\n            LoIndex = 0;\n        else if (r < 0.0 && s > 0.0)\n            LoIndex = 1;\n        else if (r < 0.0 && s < 0.0)\n            LoIndex = 2;\n        else if (r > 0.0 && s < 0.0)\n            LoIndex = 3;\n        else if (fabs(r) < MKleinsteZahl && s > 0.0)\n            LoIndex = 4;\n        else if (r < 0.0 && fabs(s) < MKleinsteZahl)\n            LoIndex = 5;\n        else if (fabs(r) < MKleinsteZahl && s < 0.0)\n            LoIndex = 6;\n        else if (r > 0.0 && fabs(s) < MKleinsteZahl)\n            LoIndex = 7;\n        else if (fabs(r) < MKleinsteZahl && fabs(s) < MKleinsteZahl)\n            LoIndex = 8;\n        break;\n    case MeshLib::ElementShape::HEXAHEDRON:\n        gp_r = igp/(nGauss * nGauss);\n        gp_s = igp%(nGauss * nGauss);\n        gp_t = gp_s%nGauss;\n        r = MathLib::GaussLegendre::getPoint(nGauss, gp_r);\n        s = MathLib::GaussLegendre::getPoint(nGauss, gp_s);\n        t = MathLib::GaussLegendre::getPoint(nGauss, gp_t);\n\n        if (t > 0.0) {\n            if (r > 0.0 && s > 0.0)\n                LoIndex = 0;\n            else if (r < 0.0 && s > 0.0)\n                LoIndex = 1;\n            else if (r < 0.0 && s < 0.0)\n                LoIndex = 2;\n            else if (r > 0.0 && s < 0.0)\n                LoIndex = 3;\n            else if (fabs(r) < MKleinsteZahl && s > 0.0)\n                LoIndex = 8;\n            else if (r < 0.0 && fabs(s) < MKleinsteZahl)\n                LoIndex = 9;\n            else if (fabs(r) < MKleinsteZahl && s < 0.0)\n                LoIndex = 10;\n            else if (r > 0.0 && fabs(s) < MKleinsteZahl)\n                LoIndex = 11;\n            else if (fabs(r) < MKleinsteZahl && fabs(s) < MKleinsteZahl)\n                return -1;\n        } else if (fabs(t) < MKleinsteZahl) {\n            if (fabs(r) < MKleinsteZahl || fabs(s) < MKleinsteZahl)\n                return -1;\n            if (r > 0.0 && s > 0.0)\n                LoIndex = 16;\n            else if (r < 0.0 && s > 0.0)\n                LoIndex = 17;\n            else if (r < 0.0 && s < 0.0)\n                LoIndex = 18;\n            else if (r > 0.0 && s < 0.0)\n                LoIndex = 19;\n        } if (t < 0.0) {\n            if (r > 0.0 && s > 0.0)\n                LoIndex = 4;\n            else if (r < 0.0 && s > 0.0)\n                LoIndex = 5;\n            else if (r < 0.0 && s < 0.0)\n                LoIndex = 6;\n            else if (r > 0.0 && s < 0.0)\n                LoIndex = 7;\n            else if (fabs(r) < MKleinsteZahl && s > 0.0)\n                LoIndex = 12;\n            else if (r < 0.0 && fabs(s) < MKleinsteZahl)\n                LoIndex = 13;\n            else if (fabs(r) < MKleinsteZahl && s < 0.0)\n                LoIndex = 14;\n            else if (r > 0.0 && fabs(s) < MKleinsteZahl)\n                LoIndex = 15;\n            else if (fabs(r) < MKleinsteZahl && fabs(s) < MKleinsteZahl)\n                return -1;\n        }\n        break;\n    default:\n        std::cerr << \"CElement::GetLocalIndex invalid mesh element type given\" << std::endl;\n        break;\n    }\n    return LoIndex;\n}\n\nvoid FeExtrapolationGaussLinear::getExtropoGaussPoints(const MeshLib::IElement &e, const int i, double Xi_p, double* unit)\n{\n    int j = 0;\n    //\n    switch (e.getShapeType()) {\n    case MeshLib::ElementShape::LINE:\n        break;\n    case MeshLib::ElementShape::TRIANGLE:\n        switch (i) {\n        case 0:\n            unit[0] = -0.1666666666667;\n            unit[1] = -0.1666666666667;\n            break;\n        case 1:\n            unit[0] = 1.6666666666667;\n            unit[1] = -0.1666666666667;\n            break;\n        case 2:\n            unit[0] = -0.1666666666667;\n            unit[1] = 1.6666666666667;\n            break;\n        }\n        break;\n    case MeshLib::ElementShape::QUAD:\n        switch (i) {\n        case 0:\n            unit[0] = Xi_p;\n            unit[1] = Xi_p;\n            break;\n        case 1:\n            unit[0] = -Xi_p;\n            unit[1] = Xi_p;\n            break;\n        case 2:\n            unit[0] = -Xi_p;\n            unit[1] = -Xi_p;\n            break;\n        case 3:\n            unit[0] = Xi_p;\n            unit[1] = -Xi_p;\n            break;\n        }\n        break;\n    case MeshLib::ElementShape::HEXAHEDRON:\n        if (i < 4) {\n            j = i;\n            unit[2] = Xi_p;\n        } else  {\n            j = i - 4;\n            unit[2] = -Xi_p;\n        }\n        switch (j) {\n        case 0:\n            unit[0] = Xi_p;\n            unit[1] = Xi_p;\n            break;\n        case 1:\n            unit[0] = -Xi_p;\n            unit[1] = Xi_p;\n            break;\n        case 2:\n            unit[0] = -Xi_p;\n            unit[1] = -Xi_p;\n            break;\n        case 3:\n            unit[0] = Xi_p;\n            unit[1] = -Xi_p;\n            break;\n        }\n        break;\n    case MeshLib::ElementShape::TETRAHEDRON:\n        switch (i) {\n        case 0:\n            unit[0] = -0.166666666666667;\n            unit[1] = -0.166666666666667;\n            unit[2] = -0.166666666666667;\n            break;\n        case 1:\n            unit[0] = 1.5;\n            unit[1] = -0.166666666666667;\n            unit[2] = -0.166666666666667;\n            break;\n        case 2:\n            unit[0] = -0.166666666666667;\n            unit[1] = 1.5;\n            unit[2] = -0.166666666666667;\n            break;\n        case 3:\n            unit[0] = -0.166666666666667;\n            unit[1] = -0.166666666666667;\n            unit[2] = 1.5;\n            break;\n        }\n        break;\n    default:\n        unit[0] = unit[1] = unit[2] = 0.;\n        break;\n    }\n}\n\n\n} //end namespace\n\n", "text_preview": "/** * Copyright (c) 2012, OpenGeoSys Community (http://www.opengeosys.com) * Distributed under a Modified BSD License. * See accompanying file LICENSE.txt or * http://www.opengeosys.com/LICENSE.txt * * * \\file FemExtrapolationGaussLinear.cp", "input_sha256": "1ced6569cc08165e3921ead5fb6b8fd3aefac2e2edb17e301c16e53cfab7aeeb", "text_sha256": "269cd2227180d7f50f70e831b6fac5850a285a8a44335291d0256bfa032be183", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 22, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.8458124456161489, "noise_severity": 0.4104987896825049, "missing_content_severity": 0.04154431934271294, "factual_density": 3.2107143674790555, "pretraining_utility": 0.357148624919193, "math_density": 3.221528226982007, "code_density": 3.743402224733179, "reasoning_density": 1.2349211982896904, "education_level": 3.1865008949777374, "creative_writing_quality": -0.2978372279247988}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary probability 0.5147 below 0.70", "secondary vs NONE margin 0.0693 below 0.10", "secondary_topic probability 0.4665 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.3145902797587577, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9947098458251401, "secondary": 0.49365159549054494, "tertiary": 0.7496485294998029, "structured_records": 0.015698771233654114, "procedural_steps": 0.010387559323778883, "worked_solution": 0.008047574934915603, "contextual_dialogue": 6.959391818165053e-06, "content_quality": 0.48699085264341446, "noise_severity": 0.6579176752645791, "missing_content_severity": 0.9653797338810726, "factual_density": 0.3422619728992127, "pretraining_utility": 0.4493101149585056, "math_density": 0.35127352248500565, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/apache/tvm/blob/89f9573dd84758f3abb608fcf666c3d34a1c3489/src/arith/const_fold.h", "id": "cpp-0-b419deb6ae141c108d55a3493b37acfd", "text": "/*\n * Licensed to the Apache Software Foundation (ASF) under one\n * or more contributor license agreements.  See the NOTICE file\n * distributed with this work for additional information\n * regarding copyright ownership.  The ASF licenses this file\n * to you under the Apache License, Version 2.0 (the\n * \"License\"); you may not use this file except in compliance\n * with the License.  You may obtain a copy of the License at\n *\n *   http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing,\n * software distributed under the License is distributed on an\n * \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n * KIND, either express or implied.  See the License for the\n * specific language governing permissions and limitations\n * under the License.\n */\n\n/*!\n * \\file const_fold.h\n * \\brief Centralized location for constant folding.\n */\n#ifndef TVM_ARITH_CONST_FOLD_H_\n#define TVM_ARITH_CONST_FOLD_H_\n\n#include <tvm/ffi/optional.h>\n#include <tvm/tir/expr.h>\n#include <tvm/tir/op.h>\n\n#include <algorithm>\n#include <cmath>\n#include <limits>\n\n#include \"int_operator.h\"\n\nnamespace tvm {\nnamespace arith {\n\n/*!\n * \\brief Try to run binary compute with constant folding.\n *\n * \\param a The left operand.\n * \\param b The right operand.\n * \\tparam Op The operator type.\n *\n * \\note a and b Must already matched data types with each other.\n * \\return std::nullopt if constant fold fails, otherwise return folded result.\n */\ntemplate <typename Op>\ninline Optional<PrimExpr> TryConstFold(PrimExpr a, PrimExpr b);\n\n/*!\n * \\brief Try to run unary compute with constant folding.\n *\n * \\param a The left operand.\n * \\tparam Op The operator type.\n *\n * \\note a and b Must already matched data types with each other.\n * \\return std::nullopt if constant fold fails, otherwise return folded result.\n */\ntemplate <typename Op>\ninline Optional<PrimExpr> TryConstFold(PrimExpr a);\n\n/*!\n * \\brief Check whether type is used to represent index.\n *\n * Index types are frequently used in shape computation\n * and need to be aggressively constant-folded.\n *\n * \\param type The type to represent index.\n * \\return the checked result.\n */\ninline bool IsIndexType(const DataType& type) {\n  return type.is_int() && !type.is_scalable_or_fixed_length_vector() &&\n         (type.bits() == 32 || type.bits() == 64);\n}\n\n/*! \\brief Helper to get const folding result repr in int64. */\ninline int64_t GetFoldResultInt64Repr(int64_t x, const DataType& dtype) {\n  if (dtype.bits() < 64) {\n    x &= (1LL << dtype.bits()) - 1;\n  }\n  if (dtype.is_int()) {\n    // get sign extended value of integer with specified bits\n    int64_t m = 1LL << (dtype.bits() - 1);\n    x = (x ^ m) - m;\n  }\n  return x;\n}\n\n/*! \\brief Helper to get fp32 const folding result repr in double. */\ninline double GetFoldResultDoubleRepr(float x) {\n  double res = static_cast<double>(x);\n  if (std::isinf(res) || std::isnan(res)) {\n    return res;\n  }\n  // certain platform (eg, on gcc7-i386) do the folding arithmetic\n  // on float and write back to double is optimized to double\n  // precision arithmetic, this is legal and we check the output\n  // range thus to ensure consistency when the float result is inf.\n  if (res < std::numeric_limits<float>::lowest()) {\n    LOG(WARNING) << \"underlying float value overflow\";\n    return -std::numeric_limits<double>::infinity();\n  } else if (res > std::numeric_limits<float>::max()) {\n    LOG(WARNING) << \"underlying float value overflow\";\n    return std::numeric_limits<double>::infinity();\n  }\n  return res;\n}\n\n#define TVM_ARITH_CONST_PROPAGATION(BODY)        \\\n  using tir::FloatImmNode;                       \\\n  const IntImmNode* pa = a.as<IntImmNode>();     \\\n  const IntImmNode* pb = b.as<IntImmNode>();     \\\n  const FloatImmNode* fa = a.as<FloatImmNode>(); \\\n  const FloatImmNode* fb = b.as<FloatImmNode>(); \\\n  BODY;\n\n#define TVM_INDEX_CONST_PROPAGATION(BODY)                 \\\n  const IntImmNode* pa = a.as<IntImmNode>();              \\\n  const IntImmNode* pb = b.as<IntImmNode>();              \\\n  const DataType& ta = a.dtype();                         \\\n  const DataType& tb = b.dtype();                         \\\n  if (arith::IsIndexType(ta) && arith::IsIndexType(tb)) { \\\n    BODY;                                                 \\\n  }\n\n// specialization of constant folders.\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Add>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      int64_t res = pa->value + pb->value;\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pa && pa->value == 0) return b;\n    if (pb && pb->value == 0) return a;\n    if (fa && fb) {\n      if (rtype.bits() == 32) {\n        return FloatImm(rtype, GetFoldResultDoubleRepr(static_cast<float>(fa->value) +\n                                                       static_cast<float>(fb->value)));\n      } else if (rtype.bits() == 64) {\n        return FloatImm(rtype, fa->value + fb->value);\n      }\n    }\n    if (fa && fa->value == 0) return b;\n    if (fb && fb->value == 0) return a;\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Sub>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    ICHECK(!((pa && pa->dtype.is_uint() && pa->value == 0U) &&\n             (pb && pb->dtype.is_uint() && pb->value > 0U)))\n        << \"Checked failed. Minuend 's value is 0U and it's dtype is uint \"\n        << \"while Subtrahend's dtype is uint; which will cause a negative uint\";\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      int64_t res = pa->value - pb->value;\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pb && pb->value == 0) return a;\n    if (fa && fb) {\n      if (rtype.bits() == 32) {\n        return FloatImm(rtype, GetFoldResultDoubleRepr(static_cast<float>(fa->value) -\n                                                       static_cast<float>(fb->value)));\n      } else if (rtype.bits() == 64) {\n        return FloatImm(rtype, fa->value - fb->value);\n      }\n    }\n    if (fb && fb->value == 0) return a;\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Mul>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      int64_t res = pa->value * pb->value;\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pa) {\n      if (pa->value == 1) return b;\n      if (pa->value == 0) return a;\n    }\n    if (pb) {\n      if (pb->value == 1) return a;\n      if (pb->value == 0) return b;\n    }\n    if (fa && fb) {\n      if (rtype.bits() == 32) {\n        return FloatImm(rtype, GetFoldResultDoubleRepr(static_cast<float>(fa->value) *\n                                                       static_cast<float>(fb->value)));\n      } else if (rtype.bits() == 64) {\n        return FloatImm(rtype, fa->value * fb->value);\n      }\n    }\n    if (fa) {\n      if (fa->value == 1) return b;\n      if (fa->value == 0) return a;\n    }\n    if (fb) {\n      if (fb->value == 1) return a;\n      if (fb->value == 0) return b;\n    }\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Div>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      // due to division and mod can have different modes\n      // NOTE: this will assumes truc div.\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n      int64_t res = pa->value / pb->value;\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pa) {\n      if (pa->value == 0) return a;\n    }\n    if (pb) {\n      if (pb->value == 1) return a;\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n    }\n    if (fa && fb) {\n      ICHECK_NE(fb->value, 0) << \"Divide by zero\";\n      if (rtype.bits() == 32) {\n        return FloatImm(rtype, GetFoldResultDoubleRepr(static_cast<float>(fa->value) /\n                                                       static_cast<float>(fb->value)));\n      } else if (rtype.bits() == 64) {\n        return FloatImm(rtype, fa->value / fb->value);\n      }\n    }\n    if (fa && fa->value == 0) return a;\n    if (fb) {\n      if (fb->value == 1) return a;\n      ICHECK_NE(fb->value, 0) << \"Divide by zero\";\n    }\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Mod>(PrimExpr a, PrimExpr b) {\n  TVM_INDEX_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n      int64_t res = pa->value % pb->value;\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pa) {\n      if (pa->value == 0) return a;\n    }\n    if (pb) {\n      if (pb->value == 1) return tir::make_zero(rtype);\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n    }\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::FloorDiv>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n      int64_t res = arith::floordiv(pa->value, pb->value);\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pa) {\n      if (pa->value == 0) return a;\n    }\n    if (pb) {\n      if (pb->value == 1) return a;\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n    }\n    if (fa && fb && fb->value != 0) {\n      if (rtype.bits() == 32) {\n        return FloatImm(rtype, GetFoldResultDoubleRepr(std::floor(static_cast<float>(fa->value) /\n                                                                  static_cast<float>(fb->value))));\n      } else if (rtype.bits() == 64) {\n        return FloatImm(rtype, std::floor(fa->value / fb->value));\n      } else {\n        return std::nullopt;\n      }\n    }\n    if (fa && fa->value == 0) return a;\n    if (fb) {\n      if (fb->value == 1) return a;\n      ICHECK_NE(fb->value, 0) << \"Divide by zero\";\n    }\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::FloorMod>(PrimExpr a, PrimExpr b) {\n  TVM_INDEX_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) {\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n      int64_t res = arith::floormod(pa->value, pb->value);\n      return IntImm(rtype, GetFoldResultInt64Repr(res, rtype));\n    }\n    if (pa) {\n      if (pa->value == 0) return a;\n    }\n    if (pb) {\n      if (pb->value == 1) return tir::make_zero(rtype);\n      ICHECK_NE(pb->value, 0) << \"Divide by zero\";\n    }\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Min>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) return IntImm(rtype, std::min(pa->value, pb->value));\n    if (fa && fb) return FloatImm(rtype, std::min(fa->value, fb->value));\n  });\n  if (a.same_as(b)) return a;\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Max>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    const DataType& rtype = a.dtype();\n    if (pa && pb) return IntImm(rtype, std::max(pa->value, pb->value));\n    if (fa && fb) return FloatImm(rtype, std::max(fa->value, fb->value));\n  });\n  if (a.same_as(b)) return a;\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::GT>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    if (pa && pb) return IntImm(DataType::UInt(1), pa->value > pb->value);\n    if (fa && fb) return IntImm(DataType::UInt(1), fa->value > fb->value);\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::GE>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    if (pa && pb) return IntImm(DataType::UInt(1), pa->value >= pb->value);\n    if (fa && fb) return IntImm(DataType::UInt(1), fa->value >= fb->value);\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::LT>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    if (pa && pb) return IntImm(DataType::UInt(1), pa->value < pb->value);\n    if (fa && fb) return IntImm(DataType::UInt(1), fa->value < fb->value);\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::LE>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    if (pa && pb) return IntImm(DataType::UInt(1), pa->value <= pb->value);\n    if (fa && fb) return IntImm(DataType::UInt(1), fa->value <= fb->value);\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::EQ>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    if (pa && pb) return IntImm(DataType::UInt(1), pa->value == pb->value);\n    if (fa && fb) return IntImm(DataType::UInt(1), fa->value == fb->value);\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::NE>(PrimExpr a, PrimExpr b) {\n  TVM_ARITH_CONST_PROPAGATION({\n    if (pa && pb) return IntImm(DataType::UInt(1), pa->value != pb->value);\n    if (fa && fb) return IntImm(DataType::UInt(1), fa->value != fb->value);\n  });\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::And>(PrimExpr a, PrimExpr b) {\n  const IntImmNode* pa = a.as<IntImmNode>();\n  const IntImmNode* pb = b.as<IntImmNode>();\n  if (pa && pa->value) return b;\n  if (pa && !pa->value) return a;\n  if (pb && pb->value) return a;\n  if (pb && !pb->value) return b;\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Or>(PrimExpr a, PrimExpr b) {\n  const IntImmNode* pa = a.as<IntImmNode>();\n  const IntImmNode* pb = b.as<IntImmNode>();\n  if (pa && pa->value) return a;\n  if (pa && !pa->value) return b;\n  if (pb && pb->value) return b;\n  if (pb && !pb->value) return a;\n  return std::nullopt;\n}\n\ntemplate <>\ninline Optional<PrimExpr> TryConstFold<tir::Not>(PrimExpr a) {\n  const IntImmNode* pa = a.as<IntImmNode>();\n  if (pa) {\n    return IntImm(DataType::UInt(1), !(pa->value));\n  }\n  return std::nullopt;\n}\n\n/*! \\brief Helper namespace for symbolic value limits */\nstruct SymbolicLimits {\n  /*! \\brief positive infinity */\n  static PrimExpr pos_inf_;\n  /*! \\brief negative infinity */\n  static PrimExpr neg_inf_;\n};\n\n/*!\n * \\brief Opaque expression representing positive infinity.\n *\n *  It can only be used as parameter of by min/max\n *  for integer analysis and cannot be used in normal expressions.\n *\n * \\return positive infinity.\n */\ninline PrimExpr pos_inf() { return SymbolicLimits::pos_inf_; }\n\n/*!\n * \\brief Check if value is positive infinity.\n * \\param value The value to be checked.\n *\n * \\return The check result.\n */\ninline bool is_pos_inf(const PrimExpr& value) { return value.same_as(SymbolicLimits::pos_inf_); }\n\n/*!\n * \\brief Opaque expression representing negative infinity.\n *\n *  It can only be used as parameter of by min/max\n *  for integer analysis and cannot be used in normal expressions.\n *\n * \\return negative infinity.\n */\ninline PrimExpr neg_inf() { return SymbolicLimits::neg_inf_; }\n\n/*!\n * \\brief Check if value is negative infinity.\n * \\param value The value to be checked.\n *\n * \\return The check result.\n */\ninline bool is_neg_inf(const PrimExpr& value) { return value.same_as(SymbolicLimits::neg_inf_); }\n\n}  // namespace arith\n}  // namespace tvm\n#endif  // TVM_ARITH_CONST_FOLD_H_\n", "text_preview": "/* * Licensed to the Apache Software Foundation (ASF) under one * or more contributor license agreements. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Kontekst/SemestrIV-Laboratoria-Projekty/blob/47c1a271bf5f7f1cd68d282cdb079eaec5c6b39c/Metody%20Numeryczne%20Projekt%20Nr%203/Metody%20Numeryczne%20Projekt%20Nr%203/Metody%20Numeryczne%20Projekt%20Nr%203.cpp", "id": "cpp-0-a3661c66862d932b327f68c13bf4645b", "text": "/*----- <NAME> 160553 Grupa Numer 4 -----*/\n/*----- Metody Numeryczne Projekt Numer 3 -----*/\n/*----- Aproksymacja profilu wysokosciowego -----*/\n\n// test1.txt płaska pustynia solna Salar de Uyuni w Boliwii 100km długości minimalne różnice wysokości max = 8m\n// test2.txt przekroj Himalaji 100 km, ostre wachania wysokości do 4 km\n// test3.txt wyżyna Tybetańska 100 km duże wachania wysokości, różnica do 2,5km\n// test4.txt Alpy 100km, wachania do 2,5km\n\n#include <iostream>\n#include <fstream>\n#include <string>\n\nusing namespace std;\n\ndouble scoreResultWithLagrange(double dystanceX, double *measuresArray[2], int numberOfMeasurements);\nvoid initializeArray(double*** measurements,int* numberOfLines);\nvoid countNumberOfLinesInFile(int ptrCounter[]);\nvoid initializeMeasurementsArray(double*** measurements, int numberOfLines[4]);\nvoid fillMeasurementsArray(double*** measurements, int numberOfLines[4]);\nvoid calculateLagrange(double*** measurements, int numberOfLines[4], int interval);\ndouble calculateSingleLagrange(double ***measurements, int numberOfLines, int interval, int numberOfFile, int xCalculated);\n\nstring filesNames[] = { \"test1.txt\", \"test2.txt\", \"test3.txt\",\"test4.txt\" };\nstring lagrangeNames[] = { \"Lag1.txt\", \"Lag2.txt\", \"Lag3.txt\",\"Lag4.txt\" };\nstring gluedThirdDegreeNames[] = { \"Glue1.txt\", \"Glue2.txt\", \"Glue3.txt\",\"Glue4.txt\" };\n\nint main()\n{\n\n\tint numberOfLines[4] = { 0,0,0,0 };\n\tdouble ***measurements = new double**[4]; // ||measurements[4][512][2]|| a dokładniej ||measurements[4][countofLines[i]][2]||  [4 pliki][512 punktów pomiarowych][pary pomierzonych punktów (x,wysokość)]\n\tcountNumberOfLinesInFile(numberOfLines);\n\tinitializeMeasurementsArray(measurements, numberOfLines);\n\t\n\t/*for (int i = 0; i < 4; i++) {\n\t\tfor (int j = 0; j < 512; j++)\n\t\t\tmeasurements[i][j][1] /= 1000.0;\n\t}\n\t}*/\n\tcalculateLagrange(measurements,numberOfLines,8); \n\n\treturn 0;\n}\n\n//-------------------------// Zliczenie pomiarow\nvoid countNumberOfLinesInFile(int *ptrCounter) {\n\tfor (int i = 0; i < 4; i++) {\n\t\tstd::string line;\n\t\tstd::ifstream myfile(filesNames[i]);\n\t\twhile (getline(myfile, line))\n\t\t\tptrCounter[i]++;\n\t}\n}\n//-------------------------// Inicjalizacja tablicy dla pomiarow\nvoid initializeMeasurementsArray(double*** measurements,int numberOfLines[4]) {\n\tfor (int i = 0; i < 4; i++) {\n\t\tmeasurements[i] = new double*[numberOfLines[i]];\n\t}\n\n\tfor (int i = 0; i < 4; i++) {\n\t\tfor (int k = 0; k < numberOfLines[i]; k++) {\n\t\t\tmeasurements[i][k] = new double[2];\n\t\t}\n\t}\n\tfillMeasurementsArray(measurements, numberOfLines);\n}\n//-------------------------// Wczytanie pomiarow do tablicy\nvoid fillMeasurementsArray(double*** measurements, int numberOfLines[4]) {\n\tfor (int i = 0; i < 4; i++) {\n\t\tstd::ifstream myFile(filesNames[i]);\n\t\tfor (int j = 0; j < numberOfLines[i]; j++) {\n\t\t\tmyFile >> measurements[i][j][0];\n\t\t\tmyFile >> measurements[i][j][1];\n\t\t}\n\t}\n}\n//-------------------------// Procedura liczenia z Lagrange z zapisem do pliku\nvoid calculateLagrange(double*** measurements, int numberOfLines[4],int interval) {\n\tofstream myFile;\n\tfor (int i = 0; i < 4; i++) {\n\t\tmyFile.open(lagrangeNames[i]);\n\t\tfor (int j = 0; j < numberOfLines[i]; j++) {\n\t\t\tmyFile << measurements[i][j][0]<<\" \";\n\t\t\tif ( ( j % interval != 0 ) && ( j != 0) ) { // Liczenie i zapis wyniku obliczonego z Lagrange\n\t\t\t\tmyFile << calculateSingleLagrange(measurements, numberOfLines[i], interval, i, j) << endl;\n\t\t\t}\n\t\t\telse { // Zapis prawidlowej zmierzonej wartosci \n\t\t\t\tmyFile << measurements[i][j][1] << endl;\n\t\t\t}\n\t\t}\n\t\tmyFile.close();\n\t}\n}\n//-------------------------//\ndouble calculateSingleLagrange(double ***measurements,int numberOfLines, int interval,int numberOfFile, int xCalculated) {\n\tdouble result = 0.0;\n\tfor (int e = 0; e < (numberOfLines / interval); e++) {\n\t\tdouble Ln = 1.0;\n\t\tfor (int f = 0; f < (numberOfLines / interval); f++) { //Gora rownania\n\t\t\tif (f != e)\n\t\t\tLn *= (measurements[numberOfFile][xCalculated][0] - measurements[numberOfFile][f*interval][0]);\n\t\t//\tcout << measurements[numberOfFile][xCalculated][0] << \" - \" << measurements[numberOfFile][f*interval][0] << endl;\n\t\t}\n\t\tdouble temp = 1.0;\n\t\tfor (int f = /*numberOfLines*/ /*e*/0; f < (numberOfLines / interval); f++) { //Gora rownania\n\t\t\tif (f!=e)\n\t\t\ttemp *= ( measurements[numberOfFile][/*numberOfLines / (xCalculated + 1 )*/e*interval][0] - measurements[numberOfFile][f*interval][0]);\n\t\t//\tcout <<f << \" || \"<<temp << endl;\n\t\t\t//cout <<f<<\"||\"<< measurements[numberOfFile][e*interval][0] << \" - \" << measurements[numberOfFile][f*interval][0] << endl;\n\t\t\t\n\t\t}\n\t\t//cout << Ln << \"  |||  \" << temp<<endl;\n\t\tLn = Ln / temp; //CHECK skrocic\n\t\tresult = result + measurements[ numberOfFile ][ e * interval ][ 1 ] * Ln; //CHECK skrocic\n\t}\n\treturn result;\n}", "text_preview": "/*----- <NAME> 160553 Grupa Numer 4 -----*/ /*----- Metody Numeryczne Projekt Numer 3 -----*/ /*----- Aproksymacja profilu wysokosciowego -----*/ // test1.txt płaska pustynia solna Salar de Uyuni w Boliwii 100km długości minimalne różnice w", "input_sha256": "b9866d32a9a8e4ea916fb8d17a7ac9a47b12247d78ca83738607040378fee33a", "text_sha256": "a3661c66862d932b327f68c13bf4645be2e885cf5b5a8aeb6935ffaddee75c41", "run_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ishandutta2007/codeforces-n/blob/009f4ffe46e5eaf2be12da62043aa204b394aaa8/natsugiri/normal/1000/F.cpp", "id": "cpp-0-44b3b181e27e4dc37d1d71a2382b197b", "text": "#include<stdio.h>\n#include<iostream>\n#include<vector>\n#include<algorithm>\n#include<string>\n#include<string.h>\nusing namespace std;\n\ntypedef long long LL;\ntypedef vector<int> VI;\n\n#define REP(i,n) for(int i=0, i##_len=(n); i<i##_len; ++i)\n#define EACH(i,c) for(__typeof((c).begin()) i=(c).begin(),i##_end=(c).end();i!=i##_end;++i)\n#define eprintf(...) fprintf(stderr, __VA_ARGS__)\n\ntemplate<class T> inline void amin(T &x, const T &y) { if (y<x) x=y; }\ntemplate<class T> inline void amax(T &x, const T &y) { if (x<y) x=y; }\ntemplate<class Iter> void rprintf(const char *fmt, Iter begin, Iter end) {\n    for (bool sp=0; begin!=end; ++begin) { if (sp) putchar(' '); else sp = true; printf(fmt, *begin); }\n    putchar('\\n');\n}\n\n\n\ntemplate<class T> struct RMQ {\n    static const int npos = -1;\n\n    int n, m;\n    vector<T> d;\n    RMQ() {}\n    RMQ(int n_, const T init=T()) : n(n_), m(2<<__lg(max(1, n))), d(2*m, init) {}\n    template<class Iter> void build(Iter begin, Iter end, const T init=T()) {\n\tn = end - begin;\n\tm = 2<<__lg(max(1, n));\n\td.resize(2*m);\n\tfor (int i=0; begin!=end; ++begin, i++) d[i+m] = *begin;\n\tfor (int i=n; i<m; i++) d[i+m] = init;\n\tfor (int i=m; --i; ) d[i] = min(d[i*2+1], d[i*2]);\n    };\n\n    const T at(int i) const { return d[i+m]; }\n\n    void modify(int i, const T v) {\n\ti += m; d[i] = v;\n\tfor (i>>=1; i; i>>=1) d[i] = min(d[i*2+1], d[i*2]);\n    }\n\n    T min_v(int l, int r) const {\n\tl += m; r += m;\n\tT ret = d[l++];\n\tfor (; l<r; l>>=1, r>>=1) {\n\t    if (l & 1) { amin(ret, d[l]); l++; }\n\t    if (r & 1) { r--; amin(ret, d[r]); }\n\t}\n\treturn ret;\n    }\n\n    int min_i(int l, int r) const {\n\treturn first_less_equal(l, min_v(l, r));\n    }\n\n    // for i in [l .. n-1]:\n    //   if at(i) < v: return i;\n    // return npos;\n    int first_less_than(int l, const T v) const {\n\tint r = 2*m;\n\tfor (l+=m; l<r; l>>=1, r>>=1) {\n\t    if (l & 1) {\n\t\tif (d[l] < v) break;\n\t\tl++;\n\t    }\n\t}\n\tif (l >= r) return npos;\n\tfor (; l<m;) {\n\t    if (d[l*2] < v) l = l*2;\n\t    else l = l*2+1;\n\t}\n\treturn l - m;\n    }\n\n    // for i in [l .. n-1]:\n    //   if at(i) <= v: return i;\n    // return npos;\n    int first_less_equal(int l, const T v) const { \n\tint r = 2*m;\n\tfor (l+=m; l<r; l>>=1, r>>=1) {\n\t    if (l & 1) {\n\t\tif (d[l] <= v) break;\n\t\tl++;\n\t    }\n\t}\n\tif (l >= r) return npos;\n\tfor (; l<m;) {\n\t    if (d[l*2] <= v) l = l*2;\n\t    else l = l*2+1;\n\t}\n\treturn l - m;\n    }\n\n\n    // for i in [r-1 .. 0]:\n    //   if at(i) < v: return i;\n    // return npos;\n    int last_less_than(int r, const T v) const {\n\tif (r < m) {\n\t    for (r+=m; 1<r; r>>=1) {\n\t\tif (r & 1) {\n\t\t    r--;\n\t\t    if (d[r] < v) break;\n\t\t}\n\t    }\n\t    if (r <= 1) return npos;\n\t} else {\n\t    if (d[1] < v) r = 1;\n\t    else return npos;\n\t}\n\tfor (; r<m; ) {\n\t    if (d[r*2+1] < v) r = r*2+1;\n\t    else r = r*2;\n\t}\n\treturn r - m;\n    }\n\n    // for i in [r-1 .. 0]:\n    //   if at(i) < v: return i;\n    // return npos;\n    int last_less_equal(int r, const T v) const { \n\tif (r < m) { \n\t    for (r+=m; 1<r; r>>=1) {\n\t\tif (r & 1) {\n\t\t    r--;\n\t\t    if (d[r] <= v) break;\n\t\t}\n\t    }\n\t    if (r <= 1) return npos;\n\t} else {\n\t    if (d[1] <= v) r = 1;\n\t    else return npos;\n\t}\n\tfor (; r<m; ) {\n\t    if (d[r*2+1] <= v) r = r*2+1;\n\t    else r = r*2;\n\t}\n\treturn r - m;\n    }\n};\nconst int MAXN = 500011;\nint N;\nint A[MAXN];\nint Q;\n\nstruct Query {\n    int l, r;\n    int i;\n    bool operator<(const Query &y) const {\n\treturn r < y.r;\n    }\n} query[MAXN];\nint ans[MAXN];\nint last_pos[MAXN];\n\nconst int INF = 1<<29;\n\nvoid MAIN() {\n    scanf(\"%d\", &N);\n    REP (i, N) scanf(\"%d\", A+i);\n    scanf(\"%d\", &Q);\n    REP (i, Q) {\n\tint l, r;\n\tscanf(\"%d%d\", &l, &r);\n\tl--;\n\tquery[i].l = l;\n\tquery[i].r = r;\n\tquery[i].i = i;\n    }\n\n    memset(last_pos, -1, sizeof last_pos);\n\n    sort(query, query+Q);\n    RMQ<int> rmq(N, INF);\n    for (int i=0, qi=0; ; i++) {\n\twhile (qi < Q && query[qi].r <= i) {\n\t    int pos = rmq.min_i(query[qi].l, query[qi].r);\n\t    int val = rmq.at(pos);\n\t    if (val < query[qi].l) {\n\t\tans[query[qi].i] = A[pos];\n\t    }\n\t    qi++;\n\t}\n\n\tif (i == N) break;\n\n\tif (last_pos[A[i]] >= 0) {\n\t    rmq.modify(last_pos[A[i]], INF);\n\t}\n\trmq.modify(i, last_pos[A[i]]);\n\tlast_pos[A[i]] = i;\n    }\n\n    REP (i, Q) printf(\"%d\\n\", ans[i]);\n}\n\nint main() {\n    int TC = 1;\n//    scanf(\"%d\", &TC);\n    REP (tc, TC) MAIN();\n    return 0;\n}", "text_preview": "#include<stdio.h> #include<iostream> 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/tomstewart89/BasicLinearAlgebra/blob/a87a7fe310f5765ad3ca7d1924ac7e39e122d526/examples/HowToUse/HowToUse.ino", "id": "cpp-0-4cdb19e725d914651e196cb101f85825", "text": "#include <BasicLinearAlgebra.h>\n\n/*\n * This example sketch should show you everything you need to know in order to work with the Matrix library. It's a\n * faily long explantion but try to read through it carefully. Remember that everything is built around templates so the\n * compatibility of matrices used in all operations can be checked at compile time. For that reason though, if you try\n * to multiply matrices of the wrong dimensions etc you'll be met with some very cryptic compile errors so be careful!\n */\n\n// All the functions in BasicLinearAlgebra are wrapped up inside the namespace BLA, so specify that we're using it like\n// so:\nusing namespace BLA;\n\nvoid setup()\n{\n    Serial.begin(115200);\n\n    // Let's being by declaring a few matrices. Matrix is a template class so you'll have to specfify the dimensions as\n    // <row,column> after Matrix like so:\n    BLA::Matrix<3, 3> A;\n\n    // NOTE: Turns out that one of the libraries included when using an Arduino DUE also declares a class called Matrix,\n    // so to resolve the ambiguity you'll need to explicitly identify this library's Matrix class when declaring them by\n    // prepending the declaration with BLA:: If you're using other boards then just writing using namespace BLA; will\n    // suffice.\n\n    // The cols parameters has a default of 1 so to declare a vector of length 3 you can simply write:\n    BLA::Matrix<3> v;\n\n    // Just like any other variable, Matrices should be initialised to some value before you use them. To set every\n    // element of the Matrix to a certain value you can use the Fill function like so:\n    v.Fill(0);\n\n    // The matrix elements can be accessed individually using the brackets operator like so:\n    v(2, 0) = 5.3;\n    v(1) = 43.67;  // you can also just write v(2) = 5.3; since v has only one column\n\n    // Or you can set the entire matrix like so:\n    A = {3.25, 5.67, 8.67, 4.55, 7.23, 9.00, 2.35, 5.73, 10.56};\n\n    // You can also set the entire array on construction like so:\n    BLA::Matrix<3, 3> B = {6.54, 3.66, 2.95, 3.22, 7.54, 5.12, 8.98, 9.99, 1.56};\n\n    // Ask the matrix how many rows and columns it has:\n    v.Cols;\n    v.Rows;\n\n    // Now you can do some matrix math! The Matrix class supports addition and subtraction between matrices of the same\n    // size:\n    BLA::Matrix<3, 3> C = A + B;\n\n    // You can't do addition between matrices of a different size. Since the class is built around templates, the\n    // compiler will tell you if you've made a mistake. Try uncommenting the next line to see what I mean A + v;\n\n    // You can use the Matrix class's unary operators, like so:\n    C -= B;\n\n    // Or like so:\n    B += A;\n\n    // As well as addition and subtraction, we can also do matrix multiplication. Note that again, the matrices,\n    // including the one in which the result will stored must have the appropriate dimensions. The compiler will let you\n    // know if they aren't\n    BLA::Matrix<3, 1> D = A * v;\n\n    // As well as algebra, Matrix supports a few other matrix related operations including transposition:\n    BLA::Matrix<1, 3> D_T = ~D;\n\n    // And concatenation, both horizontally...\n    BLA::Matrix<3, 6> AleftOfB = A || B;\n\n    // And vertically\n    BLA::Matrix<6, 3> AonTopOfB = A && B;\n\n    // An inverse of a matrix can also be calculated for square matrices via the Invert function. This will invert\n    // the input matrix in-place.\n    BLA::Matrix<3, 3> C_inv = C;\n    bool is_nonsingular = Invert(C_inv);\n\n    // If the matrix is singular, the inversion won't work. In those cases Invert will return false.\n\n    // For convenience, there's also Inverse, which will return the inverse, leaving the input matrix unmodified.\n    BLA::Matrix<3, 3> also_C_inv = Inverse(C);\n\n    // If you want to print out the value of any element in the array you can do that with the Serial object:\n    Serial.print(\"v(1): \");\n    Serial.println(v(1));\n\n    // Alternatively, you can write the whole matrix to Serial, which works like so:\n    Serial.print(\"B: \");\n    Serial.println(B);\n\n    // You can even write some quite complex compound statements very succinctly. For example:\n    Serial.print(\"identity matrix: \");\n    Serial.print(AleftOfB * AonTopOfB - (A * A + B * B) + C * C_inv);\n\n    // Or as a more real life example, here's how you might calculate an updated state estimate for a third order state\n    // space model:\n    BLA::Matrix<3> state;\n    BLA::Matrix<2> input;\n    BLA::Matrix<3, 2> input_matrix;\n    BLA::Matrix<3, 3> state_transition_matrix;\n    float dt;\n    state += (state_transition_matrix * state + input_matrix * input) * dt;\n\n    // Enjoy!\n}\n\nvoid loop() {}\n", "text_preview": "#include <BasicLinearAlgebra.h> /* * This example sketch should show you everything you need to know in order to work with the Matrix library. It's a * faily long explantion but try to read through it carefully. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/PaddlePaddle/Paddle/blob/434a48d09dc70a0a71036d7b5f31ed104eec1232/paddle/phi/kernels/sparse/cpu/slice_kernel.cc", "id": "cpp-0-37d9e61b74fbfe9115300e2c4603408a", "text": "// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved.\n//\n// Licensed under the Apache License, Version 2.0 (the \"License\");\n// you may not use this file except in compliance with the License.\n// You may obtain a copy of the License at\n//\n//     http://www.apache.org/licenses/LICENSE-2.0\n//\n// Unless required by applicable law or agreed to in writing, software\n// distributed under the License is distributed on an \"AS IS\" BASIS,\n// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n// See the License for the specific language governing permissions and\n// limitations under the License.\n\n#include \"paddle/phi/kernels/sparse/unary_kernel.h\"\n\n#include \"paddle/common/ddim.h\"\n#include \"paddle/phi/backends/cpu/cpu_context.h\"\n#include \"paddle/phi/core/kernel_registry.h\"\n#include \"paddle/phi/kernels/empty_kernel.h\"\n#include \"paddle/phi/kernels/funcs/slice_utils.h\"\n\nnamespace phi::sparse {\n\ntemplate <typename T, typename Context>\nvoid SliceCooCompute(const Context& dev_ctx,\n                     const SparseCooTensor& x,\n                     const std::vector<int64_t>& axes,\n                     const std::vector<int64_t>& starts,\n                     const std::vector<int64_t>& ends,\n                     SparseCooTensor* out) {\n  const phi::DDim& x_dims = x.dims();\n\n  // Step1: Infer output dims\n  auto out_dims = funcs::GetSliceDims<int64_t>(\n      x_dims, axes, starts, ends, nullptr, nullptr);\n\n  // Step2: Get out_nnz (the number of non-zero elements in output)\n  const int64_t x_nnz = x.nnz();\n  int64_t out_nnz = 0;\n  const auto* x_indices_data = x.indices().data<int64_t>();\n  for (int64_t j = 0; j < x_nnz; ++j) {\n    bool hit = true;\n    for (size_t ii = 0; ii < axes.size(); ++ii) {\n      auto item = x_indices_data[axes[ii] * x_nnz + j];\n      if (!(starts[ii] <= item && item < ends[ii])) {\n        hit = false;\n        break;\n      }\n    }\n    if (!hit) continue;\n    out_nnz++;\n  }\n\n  // Step3: Get the values and indices of output\n  auto sparse_dim = static_cast<int64_t>(x.sparse_dim());\n  DenseTensor out_indices =\n      phi::Empty<int64_t, Context>(dev_ctx, {sparse_dim, out_nnz});\n  DenseTensor out_values = phi::Empty<T, Context>(dev_ctx, {out_nnz});\n\n  auto* out_indices_data = out_indices.data<int64_t>();\n  auto* out_values_data = out_values.data<T>();\n  const auto* x_values_data = x.values().data<T>();\n  int64_t index = 0;\n  for (int64_t j = 0; j < x_nnz && index < out_nnz; ++j) {\n    bool hit = true;\n    for (size_t ii = 0; ii < axes.size(); ++ii) {\n      auto item = x_indices_data[axes[ii] * x_nnz + j];\n      if (!(starts[ii] <= item && item < ends[ii])) {\n        hit = false;\n        break;\n      }\n    }\n    if (!hit) continue;\n    // set value\n    out_values_data[index] = x_values_data[j];\n    // set coordinate\n    for (int64_t i = 0; i < sparse_dim; ++i) {\n      out_indices_data[i * out_nnz + index] = x_indices_data[i * x_nnz + j];\n    }\n    for (size_t ii = 0; ii < axes.size(); ++ii) {\n      auto i = axes[ii];\n      out_indices_data[i * out_nnz + index] -= starts[ii];\n    }\n    index++;\n  }\n  out->SetMember(out_indices, out_values, out_dims, x.coalesced());\n}\n\ntemplate <typename T, typename Context>\nvoid SliceCooKernel(const Context& dev_ctx,\n                    const SparseCooTensor& x,\n                    const phi::IntArray& axes,\n                    const phi::IntArray& starts,\n                    const phi::IntArray& ends,\n                    SparseCooTensor* out) {\n  const phi::DDim& x_dims = x.dims();\n  std::vector<int64_t> axes_vec = axes.GetData();\n  std::vector<int64_t> starts_vec = starts.GetData();\n  std::vector<int64_t> ends_vec = ends.GetData();\n\n  // Check and update attr\n  funcs::CheckAndUpdateSparseSliceAttrs<int64_t>(\n      x_dims, &axes_vec, &starts_vec, &ends_vec);\n\n  SliceCooCompute<T, Context>(dev_ctx, x, axes_vec, starts_vec, ends_vec, out);\n}\n\nint64_t GetCsrNonZeroNumber(const SparseCsrTensor& x,\n                            const int64_t x_crows_start,\n                            const int64_t x_crows_end,\n                            const int64_t min_col,\n                            const int64_t max_col,\n                            const int64_t x_cols_offset = 0) {\n  const auto* x_crows_data = x.crows().data<int64_t>();\n  const auto* x_cols_data = x.cols().data<int64_t>();\n  int64_t out_nnz = 0;\n  for (int64_t i = x_crows_start; i < x_crows_end; ++i) {\n    int64_t st = x_crows_data[i] + x_cols_offset;\n    int64_t ed = x_crows_data[i + 1] + x_cols_offset;\n    for (int64_t jj = st; jj < ed; ++jj) {\n      if (x_cols_data[jj] >= min_col && x_cols_data[jj] < max_col) {\n        out_nnz++;\n      }\n    }\n  }\n  return out_nnz;\n}\n\ntemplate <typename T>\nvoid GetCsrSubMatrix(const SparseCsrTensor& x,\n                     const int64_t x_crows_start,\n                     const int64_t x_crows_end,\n                     const int64_t min_col,\n                     const int64_t max_col,\n                     DenseTensor* out_crows,\n                     DenseTensor* out_cols,\n                     DenseTensor* out_values,\n                     const int64_t x_cols_offset = 0,\n                     const int64_t out_crows_offset = 0,\n                     const int64_t out_cols_offset = 0) {\n  const auto* x_crows_data = x.crows().data<int64_t>();\n  const auto* x_cols_data = x.cols().data<int64_t>();\n  const auto* x_values_data = x.values().data<T>();\n\n  auto* out_crows_data = out_crows->data<int64_t>();\n  auto* out_cols_data = out_cols->data<int64_t>();\n  auto* out_values_data = out_values->data<T>();\n  out_crows_data[out_crows_offset] = 0;\n  int64_t index = 0, out_n_rows = x_crows_end - x_crows_start;\n  for (int i = 0; i < out_n_rows; ++i) {\n    int64_t st = x_crows_data[x_crows_start + i] + x_cols_offset;\n    int64_t ed = x_crows_data[x_crows_start + i + 1] + x_cols_offset;\n    for (int64_t jj = st; jj < ed; ++jj) {\n      if (x_cols_data[jj] >= min_col && x_cols_data[jj] < max_col) {\n        out_cols_data[out_cols_offset + index] = x_cols_data[jj] - min_col;\n        out_values_data[out_cols_offset + index] = x_values_data[jj];\n        index++;\n      }\n    }\n    out_crows_data[out_crows_offset + i + 1] = index;\n  }\n}\n\ntemplate <typename T, typename Context>\nvoid SliceCsrTensor2D(const Context& dev_ctx,\n                      const SparseCsrTensor& x,\n                      const std::vector<int64_t>& axes,\n                      const std::vector<int64_t>& starts,\n                      const std::vector<int64_t>& ends,\n                      const phi::DDim& out_dims,\n                      SparseCsrTensor* out) {\n  // Step1: Get nnz of out\n  int64_t out_nnz =\n      GetCsrNonZeroNumber(x, starts[0], ends[0], starts[1], ends[1], 0);\n  // Step2: Set out\n  int64_t out_n_rows = ends[0] - starts[0];\n  DenseTensor out_crows =\n      phi::Empty<int64_t, Context>(dev_ctx, {out_n_rows + 1});\n  DenseTensor out_cols = phi::Empty<int64_t, Context>(dev_ctx, {out_nnz});\n  DenseTensor out_values = phi::Empty<T, Context>(dev_ctx, {out_nnz});\n  GetCsrSubMatrix<T>(x,\n                     starts[0],\n                     ends[0],\n                     starts[1],\n                     ends[1],\n                     &out_crows,\n                     &out_cols,\n                     &out_values,\n                     0,\n                     0,\n                     0);\n  out->SetMember(out_crows, out_cols, out_values, out_dims);\n}\n\ntemplate <typename T, typename Context>\nvoid SliceCsrTensor3D(const Context& dev_ctx,\n                      const SparseCsrTensor& x,\n                      const std::vector<int64_t>& axes,\n                      const std::vector<int64_t>& starts,\n                      const std::vector<int64_t>& ends,\n                      const phi::DDim& out_dims,\n                      SparseCsrTensor* out) {\n  const auto* x_crows_data = x.crows().data<int64_t>();\n  // Step1: Get nnz of out\n  const int64_t x_dim0 = x.dims()[0], x_n_rows = x.dims()[1];\n  int64_t x_cols_offset = 0, out_nnz = 0;\n  // all_nnzs stores the nnz along with out_dim0, which will be used to set out.\n  std::vector<int64_t> all_nnzs(ends[0] - starts[0]);\n  for (int64_t i = 0; i < x_dim0; ++i) {\n    if (i >= starts[0] && i < ends[0]) {  // slice dim 0\n      int64_t x_crows_st = i * (x_n_rows + 1) + starts[1];\n      int64_t x_crows_ed = i * (x_n_rows + 1) + ends[1];\n      int64_t nnz = GetCsrNonZeroNumber(\n          x, x_crows_st, x_crows_ed, starts[2], ends[2], x_cols_offset);\n      out_nnz += nnz;\n      all_nnzs[i - starts[0]] = nnz;\n    }\n    // get the start index in non_zero_cols_\n    x_cols_offset += x_crows_data[(i + 1) * (x_n_rows + 1) - 1];\n  }\n\n  // Step2: Set out\n  const int64_t out_dim0 = out_dims[0], out_n_rows = out_dims[1];\n  DenseTensor out_crows =\n      phi::Empty<int64_t, Context>(dev_ctx, {out_dim0 * (out_n_rows + 1)});\n  DenseTensor out_cols = phi::Empty<int64_t, Context>(dev_ctx, {out_nnz});\n  DenseTensor out_values = phi::Empty<T, Context>(dev_ctx, {out_nnz});\n\n  x_cols_offset = 0;\n  int64_t out_crows_offset = 0, out_cols_offset = 0;\n  for (int64_t i = 0; i < x_dim0; ++i) {\n    if (i >= starts[0] && i < ends[0]) {  // slice dim 0\n      int64_t x_crows_start = i * (x_n_rows + 1) + starts[1];\n      int64_t x_crows_end = i * (x_n_rows + 1) + ends[1];\n      GetCsrSubMatrix<T>(x,\n                         x_crows_start,\n                         x_crows_end,\n                         starts[2],\n                         ends[2],\n                         &out_crows,\n                         &out_cols,\n                         &out_values,\n                         x_cols_offset,\n                         out_crows_offset,\n                         out_cols_offset);\n      out_crows_offset += (out_n_rows + 1);\n      out_cols_offset += all_nnzs[i - starts[0]];\n    }\n    x_cols_offset += x_crows_data[(i + 1) * (x_n_rows + 1) - 1];\n  }\n  out->SetMember(out_crows, out_cols, out_values, out_dims);\n}\n\ntemplate <typename T, typename Context>\nvoid SliceCsrCompute(const Context& dev_ctx,\n                     const SparseCsrTensor& x,\n                     const std::vector<int64_t>& axes,\n                     const std::vector<int64_t>& starts,\n                     const std::vector<int64_t>& ends,\n                     SparseCsrTensor* out) {\n  const phi::DDim& x_dims = x.dims();\n\n  // Step1: Infer output dims\n  auto out_dims = funcs::GetSliceDims<int64_t>(\n      x_dims, axes, starts, ends, nullptr, nullptr);\n\n  // Step2: Construct new axes, starts and ends.\n  std::vector<int64_t> new_axes(3), new_starts(3), new_ends(3);\n  funcs::ConstructNewSliceAttrs(\n      x_dims, axes, starts, ends, &new_axes, &new_starts, &new_ends);\n\n  // Setp3: Slice csr tensor according to its dimension\n  if (x_dims.size() == 2) {\n    SliceCsrTensor2D<T, Context>(\n        dev_ctx, x, new_axes, new_starts, new_ends, out_dims, out);\n  } else if (x_dims.size() == 3) {\n    SliceCsrTensor3D<T, Context>(\n        dev_ctx, x, new_axes, new_starts, new_ends, out_dims, out);\n  } else {\n    // throw exception\n    common::errors::InvalidArgument(\n        \"Slice for Sparse CSR Tensor only support 2-D or 3-D, but got %d-D.\",\n        x_dims.size());\n  }\n}\n\ntemplate <typename T, typename Context>\nvoid SliceCsrKernel(const Context& dev_ctx,\n                    const SparseCsrTensor& x,\n                    const phi::IntArray& axes,\n                    const phi::IntArray& starts,\n                    const phi::IntArray& ends,\n                    SparseCsrTensor* out) {\n  const phi::DDim& x_dims = x.dims();\n  std::vector<int64_t> axes_vec = axes.GetData();\n  std::vector<int64_t> starts_vec = starts.GetData();\n  std::vector<int64_t> ends_vec = ends.GetData();\n\n  // Check and update attr\n  funcs::CheckAndUpdateSparseSliceAttrs<int64_t>(\n      x_dims, &axes_vec, &starts_vec, &ends_vec);\n  SliceCsrCompute<T, Context>(dev_ctx, x, axes_vec, starts_vec, ends_vec, out);\n}\n}  // namespace phi::sparse\n\nPD_REGISTER_KERNEL(slice_coo,\n                   CPU,\n                   ALL_LAYOUT,\n                   phi::sparse::SliceCooKernel,\n                   float,\n                   double,\n                   int8_t,\n                   uint8_t,\n                   int16_t,\n                   int,\n                   int64_t,\n                   bool) {}\n\nPD_REGISTER_KERNEL(slice_csr,\n                   CPU,\n                   ALL_LAYOUT,\n                   phi::sparse::SliceCsrKernel,\n                   float,\n                   double,\n                   int8_t,\n                   uint8_t,\n                   int16_t,\n                   int,\n                   int64_t,\n                   bool) {}\n", "text_preview": "// Copyright (c) 2023 PaddlePaddle Authors. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/indianwhocodes/radxproj/blob/ba5e6ff179ed6ef9e33ec4a66a03ae7a7725cb83/apps/Radx/src/Radx2Grid/SatInterp.hh", "id": "cpp-0-6847a7be2e17236d0eaf03cea72c4f82", "text": "// *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=* \n// ** Copyright UCAR (c) 1990 - 2016                                         \n// ** University Corporation for Atmospheric Research (UCAR)                 \n// ** National Center for Atmospheric Research (NCAR)                        \n// ** Boulder, Colorado, USA                                                 \n// ** BSD licence applies - redistribution and use in source and binary      \n// ** forms, with or without modification, are permitted provided that       \n// ** the following conditions are met:                                      \n// ** 1) If the software is modified to produce derivative works,            \n// ** such modified software should be clearly marked, so as not             \n// ** to confuse it with the version available from UCAR.                    \n// ** 2) Redistributions of source code must retain the above copyright      \n// ** notice, this list of conditions and the following disclaimer.          \n// ** 3) Redistributions in binary form must reproduce the above copyright   \n// ** notice, this list of conditions and the following disclaimer in the    \n// ** documentation and/or other materials provided with the distribution.   \n// ** 4) Neither the name of UCAR nor the names of its contributors,         \n// ** if any, may be used to endorse or promote products derived from        \n// ** this software without specific prior written permission.               \n// ** DISCLAIMER: THIS SOFTWARE IS PROVIDED \"AS IS\" AND WITHOUT ANY EXPRESS  \n// ** OR IMPLIED WARRANTIES, INCLUDING, WITHOUT LIMITATION, THE IMPLIED      \n// ** WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE.    \n// *=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=*=* \n/////////////////////////////////////////////////////////////\n// SatInterp.hh\n//\n// SatInterp class - derived from Interp.\n// Handles satellite RADAR or LIDAR data\n//\n// <NAME>, RAP, NCAR, P.O.Box 3000, Boulder, CO, 80307-3000, USA\n//\n// March 2013\n//\n///////////////////////////////////////////////////////////////\n\n#ifndef SatInterp_HH\n#define SatInterp_HH\n\n#include \"Interp.hh\"\n#include <kd/kd.hh>\n#include <iostream>\n\nclass SatInterp : public Interp {\n  \npublic:\n\n  // constructor\n  \n  SatInterp(const string &progName,\n            const Params &params,\n            RadxVol &readVol,\n            vector<Field> &interpFields,\n            vector<Ray *> &interpRays);\n  \n  // destructor\n  \n  virtual ~SatInterp();\n  \n  // interpolate a volume\n  // assumes volume has been read\n  // and _interpFields and _interpRays vectors are populated\n  // returns 0 on succes, -1 on failure\n\n  virtual int interpVol();\n  \n  // get methods for threading\n\n  const Params &getParams() const { return _params; }\n  pthread_mutex_t *getDebugPrintMutex() { return &_debugPrintMutex; }\n\nprotected:\nprivate:\n\n   // threading\n  \n  deque<SatThread *> _activeThreads;\n  deque<SatThread *> _availThreads;\n  pthread_mutex_t _debugPrintMutex;\n  pthread_mutex_t _kdTreeMutex;\n  \n  // keeping track of points in instr space\n\n  typedef struct {\n    int index;\n    int rayStartIndex;\n    int rayEndIndex;\n    int iray;\n    int igate;\n    double xx;\n    double yy;\n    double zz;\n    double wt;\n    bool isTagPt;\n    const Ray *ray;\n    double distSq;\n  } instr_point_t;\n  vector<instr_point_t> _instrPoints;\n\n  typedef struct {\n    instr_point_t first;\n    instr_point_t second;\n  } ray_closest_t;\n  \n  // KD tree for instr points\n  \n  static const int KD_DIM = 3;\n  \n  vector<KD_real *> _kdVec;\n  KD_real **_kdMat;\n  KD_tree *_kdTree;\n\n  // tag gates - use to identify rays closest to grid points\n\n  vector<instr_point_t> _tagPoints;\n  vector<bool> _tagGates;\n  double _tagStartRangeKm, _tagGateSpacingKm;\n\n  // search criteria\n\n  double _maxSearchRadius;\n  double _maxZ;\n  double _zSearchRatio;\n\n  // class for point neighbors\n\n  class NeighborProps {\n  public:\n    const GridLoc *loc;\n    int iz, iy, ix;\n    vector<int> tagIndexes;\n    vector<double> distSq;\n  };\n  \n  void _initZLevels();\n  void _initGrid();\n  \n  void _initThreads();\n  static void *_computeInThread(void *thread_data);\n  \n  void _allocOutputArrays();\n  void _freeOutputArrays();\n  void _initOutputArrays();\n  \n  void _computeInstrPoints();\n  void _computeTagGates(int nGates);\n\n  void _computeGridRelative();\n  void _computeGridRelMultiThreaded();\n  void _computeGridRelRow(int iz, int iy);\n\n  void _buildKdTree();\n  void _freeKdTree();\n\n  void _doInterp();\n  void _interpSingleThreaded();\n  void _interpMultiThreaded();\n\n  void _interpPlane(int iz);\n\n  void _interpBlock(int blocky, int blockx);\n  void _interpPoint(const NeighborProps &neighborProps);\n  \n  void _findClosestGates(const GridLoc &loc,\n                         const instr_point_t &pt,\n                         ray_closest_t &closestPts);\n\n  double _computeDistSq(const GridLoc &loc, const instr_point_t &pt);\n\n  void _computeNearestGridPt(int ifield,\n                             int iz, int iy, int ix,\n                             const vector<instr_point_t> &interpPts);\n  void _computeFoldedGridPt(int ifield,\n                            int iz, int iy, int ix,\n                            const vector<instr_point_t> &interpPts);\n  void _computeInterpGridPt(int ifield,\n                            int iz, int iy, int ix,\n                            const vector<instr_point_t> &interpPts);\n\n  int _writeOutputFile();\n\n};\n\n#endif\n", "text_preview": "// 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ybouret/iics/blob/e9ccdfc0577d374e1369f8154f8136dc28ff9adc/sludge/src/segmenter-locate.cpp", "id": "cpp-0-50c2cc53a35ad037648111a12b043022", "text": "#include \"segmenter.hpp\"\n\n//==============================================================================\n// bissection to locate where a point is\n//==============================================================================\nunit_t Segmenter::locate_point(Real           x, \n                               const Array1D &a) throw()\n{\n    unit_t ilo = a.lower;\n    if( x <= a[ilo] )\n        return ilo;\n    \n    unit_t ihi = a.upper;\n    assert(ihi-ilo>=1);\n    if( x >= a[ihi] )\n        return ihi -1;\n    \n    while(ihi-ilo>1)\n    {\n        const int mid = (ilo+ihi)>>1;\n        if( x < a[mid] )\n        {\n            ihi = mid;\n        }\n        else \n        {\n            ilo = mid;\n        }\n    }\n    assert(a[ilo]<=x);\n    assert(x<a[ihi]);\n    return ilo;\n}\n\n\nvoid Segmenter:: process_spot(Spot *spot)\n{\n    assert(segments.size()>0);\n    assert(spot!=NULL);\n    assert(spot->handle!=NULL);\n    Tracer *p = spot->handle;\n    const Vertex P = p->vertex;\n    assert(P.y >= Y[Y.lower] );\n    assert(P.y <  Y[Y.upper] );\n    assert(P.x >= X[X.lower] );\n    assert(P.x <= X[X.upper] );\n    \n    //--------------------------------------------------------------------------\n    // find position on grid\n    //--------------------------------------------------------------------------\n    const unit_t i  = spot->gLower.x = locate_point( P.x, X );\n    const unit_t j  = spot->gLower.y = locate_point( P.y, Y );\n    const unit_t i1 = spot->gUpper.x = spot->gLower.x + 1;\n    const unit_t j1 = spot->gUpper.y = spot->gLower.y + 1;\n    \n    //--------------------------------------------------------------------------\n    // find grid boundaries\n    //--------------------------------------------------------------------------\n    const Vertex vmin( X[i],   Y[j]  );\n    const Vertex vmax( X[i1],  Y[j1] );\n    \n    //--------------------------------------------------------------------------\n    // compute bilinear interpolation coefficients\n    //--------------------------------------------------------------------------\n    spot->bary.x = (P.x-vmin.x)/dX[i];\n    spot->bary.y = (P.y-vmin.y)/dY[j];\n    \n    //--------------------------------------------------------------------------\n    // find potential intersections\n    //--------------------------------------------------------------------------\n    {\n        const Vertex Q = P + p->edge;\n        find_junctions(P, Q, vmin, vmax, spot);\n    }\n    \n    if( !p->prev->is_spot )\n    {\n        const Vertex Q = P - (p->prev->edge);\n        find_junctions(P, Q, vmin, vmax, spot);\n    }\n    \n}\n\n\n#define __NEW_INTER(SEGMENT,INDEX,COORD,ARR) \\\ndo  {                                \\\nJunction *I = junctions.append();    \\\nI->vertex.x = Ix;                    \\\nI->vertex.y = Iy;                    \\\nI->bubble   = bubble;                \\\nSEGMENT[INDEX].append()->handle = I; \\\nI->up = 1 + (I->lo = locate_point( I->vertex.COORD, ARR )); \\\n} while(false)\n\n\nvoid Segmenter:: find_junctions(const Vertex &P, \n                                const Vertex &Q, \n                                const Vertex &vmin, \n                                const Vertex &vmax, \n                                Spot         *spot )\n{\n    Tracer *p = spot->handle;\n    const unit_t i  = spot->gLower.x;\n    const unit_t j  = spot->gLower.y;\n    const unit_t i1 = spot->gUpper.x;\n    const unit_t j1 = spot->gUpper.y;\n    Bubble *bubble  = p->bubble; assert(bubble);\n    //--------------------------------------------------------------------------\n    // simplified Cohen-Sutherland\n    //--------------------------------------------------------------------------\n    if( Q.x < vmin.x )\n    {\n        assert( Q.x < P.x );\n        const Real Iy = P.y + ( vmin.x - P.x )  * (Q.y - P.y ) / (Q.x - P.x );\n        const Real Ix = vmin.x;\n        __NEW_INTER(vertical,i,y,Y);\n    }\n    \n    // special right case\n    if( P.x < vmax.x && Q.x >= vmax.x )\n    {\n        assert(Q.x>P.x);\n        const Real Iy = P.y + ( vmax.x - P.x )  * (Q.y - P.y ) / (Q.x - P.x );\n        const Real Ix = vmax.x;\n        __NEW_INTER(vertical,i1,y,Y);\n    }\n    \n    if( Q.y < vmin.y )\n    {\n        assert( Q.y < P.y );\n        const Real Ix = P.x + ( vmin.y - P.y)* (Q.x - P.x) /(Q.y - P.y );\n        const Real Iy = vmin.y;\n        __NEW_INTER(horizontal,j,x,X);\n    }\n    \n    if( Q.y >= vmax.y )\n    {\n        assert( Q.y > P.y );\n        const Real Ix = P.x + ( vmax.y - P.y)* (Q.x - P.x) / (Q.y - P.y ) ;\n        const Real Iy = vmax.y;\n        __NEW_INTER(horizontal,j1,x,X);\n    }\n    \n}\n", "text_preview": "#include \"segmenter.hpp\" //============================================================================== // bissection to locate where a point is //============================================================================== unit_t Segme", "input_sha256": "e3726210d16bcf1fe53f609546fba0848e14a51c72e95b73c9163ae033066186", "text_sha256": "50c2cc53a35ad037648111a12b0430224e2ea97ffe94835f96eb10bd8b2d4f7d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 30, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9768197505136147, "noise_severity": 0.02742336884069763, "missing_content_severity": 0.028525983387245404, "factual_density": 3.5254025267721523, "pretraining_utility": 0.5087742457559251, "math_density": 3.0160205371085715, "code_density": 3.7677258513497778, "reasoning_density": 1.9208065570400423, "education_level": 2.5278558443522496, "creative_writing_quality": -0.24369105956865394}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.005021700864492162, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9981456807948045, "secondary": 0.9923694287490781, "tertiary": 0.9976094644337657, "structured_records": 0.0014667042028713602, "procedural_steps": 0.007517728933059545, "worked_solution": 0.01820074070590678, "contextual_dialogue": 5.726156353980558e-06, "content_quality": 0.545418806295754, "noise_severity": 0.977147192632752, "missing_content_severity": 0.9762283471772955, "factual_density": 0.6045021056434604, "pretraining_utility": 0.39437793037095104, "math_density": 0.40739774956904307, "code_density": 0.8064382094581479, "reasoning_density": 0.0, "education_level": 0.4591437936176751, "creative_writing_quality": 0.999302356384282, "primary_topic": 0.9346227035075587, "secondary_topic": 0.526381132172197}, "probability_margin": {"primary": 0.997115417396888, "secondary": 0.9882292349405211, "tertiary": 0.9964268930911464, "education_level": 0.209235192540503, "creative_writing_quality": 0.9991481310694973, "primary_topic": 0.8811340727576907, "secondary_topic": 0.21443569336152424}, "raw_probabilities": {"primary": {"Academic Writing": 4.0442658944635636e-05, "Content Listing": 1.0795605977159318e-05, "Creative Writing": 9.95341501150786e-06, "Customer Support Page": 4.310391894326368e-06, "Q&A Forum": 6.9106734676197945e-06, "Discussion Thread": 4.209550065515324e-06, "Comment Section": 6.453809182919666e-06, "Missing Content": 1.9207815841109894e-05, "Knowledge Article": 7.376005047997733e-05, "Legal Notices": 2.151778613388874e-05, "News Article": 2.6195110383858808e-06, "Organizational Announcement": 5.914982166723597e-06, "Nonfiction Writing": 8.479517941551115e-06, "Organizational About Page": 6.064330839717356e-06, "Personal About Page": 6.053583257133847e-06, "Personal Blog": 4.043939869180949e-06, "Product Page": 3.6741673138075843e-06, "Spam / Ads": 5.461690999234894e-06, "Structured Data": 0.00023910977662417438, "Technical Writing": 0.998222944095021, "Transcript / Interview": 7.76022988528413e-06, "Tutorial / How-To Guide": 0.0001792125725880885, "User Reviews": 3.5731473239278835e-06, "Other / Unknown": 0.0011075266981330084}, "secondary": {"Academic Writing": 0.00018043876019781005, "Content Listing": 3.068062827594953e-05, "Creative Writing": 5.5818986057438867e-05, "Customer Support Page": 2.0919178436637926e-05, "Q&A Forum": 1.866811011864146e-05, "Discussion Thread": 1.583253665399531e-05, "Comment Section": 3.5257746931153055e-05, "Missing Content": 0.00015717149711286558, "Knowledge Article": 0.0007221488695569639, "Legal Notices": 0.00014883508412157436, "News Article": 2.5245838335077708e-05, "Organizational Announcement": 2.3140430410964118e-05, "Nonfiction Writing": 3.02128918300358e-05, "Organizational About Page": 3.152339946892544e-05, "Personal About Page": 2.44703306940133e-05, "Personal Blog": 2.0456073188878805e-05, "Product Page": 1.501443119263478e-05, "Spam / Ads": 2.500976131337589e-05, "Structured Data": 0.0007314045122155105, "Technical Writing": 0.00445813427734544, "Transcript / Interview": 3.072147220024819e-05, "Tutorial / How-To Guide": 0.000498464503041843, "User Reviews": 1.3061463433521983e-05, "NONE": 0.9926873692178665}, "tertiary": {"Academic Writing": 4.5541949578777795e-05, "Content Listing": 1.5995435783349966e-05, "Creative Writing": 3.058463415538458e-05, "Customer Support Page": 9.78804381423292e-06, "Q&A Forum": 7.84357404651996e-06, "Discussion Thread": 6.916908059785033e-06, "Comment Section": 2.156227154400717e-05, "Missing Content": 9.123703952224246e-05, "Knowledge Article": 0.0001883205690099433, "Legal Notices": 8.647306532376115e-05, "News Article": 1.4845028845181688e-05, "Organizational Announcement": 1.2369335655464447e-05, "Nonfiction Writing": 1.6736252898669753e-05, "Organizational About Page": 2.0626110209867422e-05, "Personal About Page": 1.1485530622018271e-05, "Personal Blog": 1.0624121917790537e-05, "Product Page": 8.784867948372434e-06, "Spam / Ads": 1.2832007839830874e-05, "Structured Data": 0.00018191736758109856, "Technical Writing": 0.0012821769912125028, "Transcript / Interview": 1.3457835857597494e-05, "Tutorial / How-To Guide": 0.00019399208010314328, "User Reviews": 6.818896111711007e-06, "NONE": 0.9977090700823589}, "structured_records": {"false": 0.9985332957971287, "true": 0.0014667042028713602}, "procedural_steps": {"false": 0.9924822710669404, "true": 0.007517728933059545}, "worked_solution": {"false": 0.9817992592940932, "true": 0.01820074070590678}, "contextual_dialogue": {"false": 0.999994273843646, "true": 5.726156353980558e-06}, "content_quality": {"-2": 0.008268751289541906, "-1": 0.02327267074564889, "0": 0.2129872456058167, "1": 0.49431274087963756, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/jrachels/Quadratic-Assignment-Problem-Solver/blob/566d5392666bf0868353493b2c7776dcb6d45e03/QuadraticAssignmentProblem/Node/node.h", "id": "cpp-0-8413c21c2dd47c4004ddf1a539b15e7a", "text": "//TO DO: When you compute the children of a node, you are computing all of the children at once. You should be able to use the intermediate values you compute from one child\n// to make computing the other children faster.\n// TO DO: to make it so that both Node and SparseFTOfMatrixFunc have the same YoungOrthogonalRepresentationConstants&, they should inherit from a Fourier Transform class that\n// contains only a static YOR_Constants_ member\n// TO DO: store ft_one_two_cycle_right_ as it's transpose so I don't have to compute the transpose in the svd algorithm. requires updating the svd algorithm\n// TO DO: every child of the same parent gets  the same row bound for twotwocycle and onethreecycle components. I don't need to recompute it. \n// TO DO: Improve time complexity of computing bound of OneTwo cycle by (1) computing QR decomposition of sums of dyadics as you move down the tree and (2) compute product of sum of dyadics if you get too many entries\n// ^ I don't think this is possible because dyadic matrices don't commute. reducing complexity requires that f^((n-1, 1)) matrices be low rank. Prove.\n\n\n#ifndef QAP_NODE_H_\n#define QAP_NODE_H_\n\n#include <stdexcept>\n#include <cmath>\n\n#include \"QuadraticAssignmentProblem/FTOfMatrixFunc/sparseftofmatrixfunc.h\"\n#include \"QuadraticAssignmentProblem/Matrices/squarematrix.h\"\n#include \"QuadraticAssignmentProblem/Matrices/matrixconcept.h\"\n#include \"QuadraticAssignmentProblem/SVD/tracenormsumofdyadicmatrices.h\"\n#include \"QuadraticAssignmentProblem/FourierTransform/youngorthogonalfouriertransform.h\"\n\nnamespace QAPSolver {\n\tnamespace Tests {\n\t\tclass NodeTester;\n\t}\n\tnamespace FourierTransform {\n\n\t\tnamespace internal {\n\t\t\t// maybe these should be in an unnamed namespace rather than an internal namespace\n\n\t\t\t// Modify vector in a way equivalent to multiplying by the representation matrix of \n\t\t\t// a left rotation of elements j through i>j.\n\t\t\ttemplate<class FTOfQAPObjectiveFuncComponent>\n\t\t\trequires requires (FTOfQAPObjectiveFuncComponent& component, const int size, const int k) {\n\t\t\t\tcomponent.ApplyAdjacentTranspositionTranspose(size, k);\n\t\t\t}\n\t\t\tvoid ApplyRotationTranspose(FTOfQAPObjectiveFuncComponent& component, const int size, const int j, const int i) { \n\t\t\t\tassert(j <= i);\n\n\t\t\t\tfor (int k = j; k < i; k++) {\n\t\t\t\t\tcomponent.ApplyAdjacentTranspositionTranspose(size, k);\n\t\t\t\t}\n\t\t\t}\n\t\t\tdouble factorial(int n);\n\t\t}\n\n\t\tclass Node : protected YoungOrthogonalFourierTransform {\n\t\t\tfriend class SparseFTOfMatrixFunc;\n\t\t\tfriend class QAPSolver::Tests::NodeTester;\n\t\tpublic:\n\t\t\ttemplate<QAPSolver::MatrixConcept M>\n\t\t\tNode(const M& matrix1, const M& matrix2, YoungOrthogonalRepresentationConstants& yor_constants);\n\n\t\t\tNode(const Node& extended_ft, int index_of_rotation);\n\n\t\t\tNode() = default;\n\n\t\t\tdouble Bound() const;\n\n\t\t\tdouble Value() const;\n\n\t\tprivate:\n\n\t\t\tint size;\n\n\t\t\t//static YoungOrthogonalRepresentationConstants& yor_constants_;\n\n\t\t\t/*******************************************************/\n\t\t\t// BEGIN TWO TWO CYCLE *******************************/\n\t\t\t/*******************************************************/\n\n\t\t\tclass FTAtTwoTwoCycleTableau {\n\t\t\t\ttemplate<class FTOfQAPObjectiveFuncComponent>\n\t\t\t\trequires requires (FTOfQAPObjectiveFuncComponent& component, const int size, const int k) {\n\t\t\t\t\tcomponent.ApplyAdjacentTranspositionTranspose(size, k);\n\t\t\t\t}\n\t\t\t\tfriend void internal::ApplyRotationTranspose(FTOfQAPObjectiveFuncComponent& component, const int size, const int j, const int i);\n\n\t\t\tpublic:\n\n\t\t\t\tFTAtTwoTwoCycleTableau(const SparseFTOfMatrixFunc::FFTAtTwoTwoCycleTableau& ft_matrix_two_two_cycle_1, const SparseFTOfMatrixFunc::FFTAtTwoTwoCycleTableau& ft_matrix_two_two_cycle_2, double missing_factor);\n\n\t\t\t\tstatic FTAtTwoTwoCycleTableau FindChildComponentFromRotations(FTAtTwoTwoCycleTableau& ft_parent, int parent_size);\n\n\t\t\t\tFTAtTwoTwoCycleTableau() = default;\n\n\t\t\t\tdouble Bound_Contribution() const;\n\n\t\t\t\tFTAtTwoTwoCycleTableau Rotate(const int index_of_rotation, const int size) const;\n\n\t\t\t\tstd::vector<double> ft_two_two_cycle_column_;\n\t\t\t\tstd::vector<double> ft_two_two_cycle_row_;\n\n\t\t\tprivate:\n\n\t\t\t\t// Issue: this code is partially duplicated from the fftofmatrixfunc class\n\t\t\t\tvoid ApplyAdjacentTranspositionTranspose(int size, int i);\n\t\t\t};\n\t\t\tNode::FTAtTwoTwoCycleTableau ft_at_two_two_cycle_tableau_;\n\n\t\t\t/*******************************************************/\n\t\t\t// BEGIN ONE THREE CYCLE *******************************/\n\t\t\t/*******************************************************/\n\n\t\t\tclass FTAtOneThreeCycleTableau {\n\t\t\t\ttemplate<class FTOfQAPObjectiveFuncComponent>\n\t\t\t\trequires requires (FTOfQAPObjectiveFuncComponent& component, const int size, const int k) {\n\t\t\t\t\tcomponent.ApplyAdjacentTranspositionTranspose(size, k);\n\t\t\t\t}\n\t\t\t\tfriend void internal::ApplyRotationTranspose(FTOfQAPObjectiveFuncComponent& component, const int size, const int j, const int i);\n\t\t\tpublic:\n\n\t\t\t\tFTAtOneThreeCycleTableau(const SparseFTOfMatrixFunc::FFTAtOneThreeCycleTableau& ft_matrix_one_three_cycle_1, const SparseFTOfMatrixFunc::FFTAtOneThreeCycleTableau& ft_matrix_one_three_cycle_2, double missing_factor);\n\n\t\t\t\tstatic FTAtOneThreeCycleTableau FindChildComponentFromRotations(FTAtOneThreeCycleTableau& ft_parent, int parent_size);\n\n\t\t\t\tFTAtOneThreeCycleTableau() = default;\n\n\t\t\t\tdouble Bound_Contribution() const;\n\n\t\t\t\tFTAtOneThreeCycleTableau Rotate(const int index_of_rotation, const int size) const;\n\n\t\t\t\tstd::vector<double> ft_one_three_cycle_column_;\n\t\t\t\tstd::vector<double> ft_one_three_cycle_row_;\n\n\t\t\t\t// Issue: this code is partially duplicated from the fftofmatrixfunc class\n\t\t\t\tvoid ApplyAdjacentTranspositionTranspose(int size, int i);\n\t\t\t};\n\t\t\tNode::FTAtOneThreeCycleTableau ft_at_one_three_cycle_tableau_;\n\n\t\t\t/*******************************************************/\n\t\t\t// BEGIN ONE TWO CYCLE *******************************/\n\t\t\t/*******************************************************/\n\n\t\t\t// TO DO: store ft_one_two_cycle_right_ as it's transpose so I don't have to compute the transpose in the svd algorithm. requires updating the svd algorithm\n\n\t\t\tclass FTAtOneTwoCycleTableau {\n\t\t\t\ttemplate<class FTOfQAPObjectiveFuncComponent>\n\t\t\t\trequires requires (FTOfQAPObjectiveFuncComponent& component, const int size, const int k) {\n\t\t\t\t\tcomponent.ApplyAdjacentTranspositionTranspose(size, k);\n\t\t\t\t}\n\t\t\t\tfriend void internal::ApplyRotationTranspose(FTOfQAPObjectiveFuncComponent& component, const int size, const int j, const int i);\n\t\t\tpublic:\n\n\t\t\t\tFTAtOneTwoCycleTableau(const SparseFTOfMatrixFunc::FFTAtOneTwoCycleTableau& ft_matrix_one_two_cycle_1, const SparseFTOfMatrixFunc::FFTAtOneTwoCycleTableau& ft_matrix_one_two_cycle_2, double missing_factor);\n\n\t\t\t\tFTAtOneTwoCycleTableau() = default;\n\n\n\t\t\t\tdouble Bound_Contribution() const;\n\n\t\t\t\tstatic FTAtOneTwoCycleTableau FindChildComponentFromRotations(FTAtOneTwoCycleTableau& ft_one_two_cycle_parent, Node::FTAtTwoTwoCycleTableau& ft_two_two_cycle_parent,\n\t\t\t\t\tNode::FTAtOneThreeCycleTableau& ft_one_three_cycle_parent, int parent_size);\n\n\t\t\t\t// Issue: may produce too many copies. may produce 1 less copy if i reorder the basis elements in fftofmatrixfunc\n\t\t\t\t// should produce a smaller ft?\n\t\t\t\tFTAtOneTwoCycleTableau Rotate(const int index_of_rotation, const int size) const;\n\n\t\t\t\tSquareMatrix ft_one_two_cycle_;\n\n\t\t\tprivate:\n\t\t\t\t// Issue: this code is partially duplicated from the fftofmatrixfunc class\n\t\t\t\tvoid ApplyAdjacentTranspositionTranspose(int size, int k);\n\t\t\t};\n\t\t\tNode::FTAtOneTwoCycleTableau ft_at_one_two_cycle_tableau_;\n\n\t\t\t/*******************************************************/\n\t\t\t// BEGIN IDENTITY COMPONENT ****************************/\n\t\t\t/*******************************************************/\n\n\t\t\tclass FTAtIdentityTableau {\n\t\t\t\ttemplate<class FTOfQAPObjectiveFuncComponent>\n\t\t\t\trequires requires (FTOfQAPObjectiveFuncComponent& component, const int size, const int k) {\n\t\t\t\t\tcomponent.ApplyAdjacentTranspositionTranspose(size, k);\n\t\t\t\t}\n\t\t\t\tfriend void internal::ApplyRotationTranspose(FTOfQAPObjectiveFuncComponent& component, const int size, const int j, const int i);\n\t\t\tpublic:\n\t\t\t\tFTAtIdentityTableau(const SparseFTOfMatrixFunc::FFTAtIdentityTableau& ft_matrix_identity_tableau_1, const SparseFTOfMatrixFunc::FFTAtIdentityTableau& ft_matrix_identity_tableau_2, double missing_factor) {\n\t\t\t\t\tft_identity_tableau_ = missing_factor * (ft_matrix_identity_tableau_1.ft_at_identity_tableau) * (ft_matrix_identity_tableau_2.ft_at_identity_tableau);\n\t\t\t\t}\n\t\t\t\tFTAtIdentityTableau() : ft_identity_tableau_(0) {};\n\n\n\t\t\t\tstatic FTAtIdentityTableau FindChildComponentFromRotations(FTAtIdentityTableau& ft_identity_tableau_parent, Node::FTAtOneTwoCycleTableau& ft_one_two_cycle_parent, int parent_size);\n\n\t\t\t\tdouble Bound_Contribution() const {\n\t\t\t\t\treturn std::abs(ft_identity_tableau_);\n\t\t\t\t}\n\n\t\t\t\t// Issue: may produce too many copies. may produce 1 less copy if i reorder the basis elements in fftofmatrixfunc\n\t\t\t\t// should produce a smaller ft?\n\t\t\t\tFTAtIdentityTableau Rotate(const int index_of_rotation, const int size) const;\n\n\t\t\t\tdouble ft_identity_tableau_;\n\n\t\t\tprivate:\n\t\t\t\t// Issue: this code is partially duplicated from the fftofmatrixfunc class\n\t\t\t\tvoid ApplyAdjacentTranspositionTranspose(int size, int k) {\n\t\t\t\t\t// for each 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Após disso, é possível identificar qual navio foi\n * atingido por um determinado tiro, e também é possível saber se um navio foi\n * completamente destruído (vendo se a quantidade de pedaços restantes do mesmo\n * é igual a zero).\n */\n\n#include <cstdio>\n\nint qtd_navios, n, m;\nint num_navio[100][100];\nint pedacos_restantes[10000];\n\n/* Este procedimento numera a célula atual com o código do último navio\n * caso ela não esteja numerada e tenta numerar todos os vizinhos recursiva-\n * mente. Ele é chamado na função main, e deve ser lido apenas depois da lei-\n * tura de tudo que antecede a chamada a ele na função main. */\nvoid numera_navios(int l, int c)\n{\n    /* Verifica se estamos fora do tabuleiro e pára caso sim. */\n    if(l < 0 || l > n-1 || c < 0 || c > m-1)\n       return;\n    \n    if(num_navio[l][c] != -2)\n        return;\n\n    num_navio[l][c] = qtd_navios;\n\n    pedacos_restantes[qtd_navios]++;\n\n    numera_navios(l-1, c);\n    numera_navios(l, c-1);\n    numera_navios(l+1, c);\n    numera_navios(l, c+1);\n}\n\nint main()\n{\n    scanf(\"%d %d\\n\", &n, &m);\n\n    /* Primeiro, ler a descrição do tabuleiro, guardando a informação na matriz\n     * num_navio. Os valores -1 e -2 significam 'água' e 'navio a ser numerado',\n     * respectivamente. */\n    for(int i = 0; i < n; i++) {\n        for(int j = 0; j < m; j++) {\n            char tmp;\n\n            scanf(\"%c\", &tmp);\n\n            if(tmp == '.')\n                num_navio[i][j] = -1;\n            else\n                num_navio[i][j] = -2;\n        }\n        scanf(\"\\n\");\n    }\n\n    /* Agora, vamos numerar todos os navios. Para cada quadrado não ainda \n     * numerado (com valor -2), iremos dar um novo número para ele e todos\n     * os outros quadrados acessíveis a partir dele. O contador de navios é\n     * então incrementado.\n     *\n     * O nome técnico deste procedimento é 'flood fill'. Apesar de ter\n     * usado recursão na implementação abaixo por motivos didáticos, \n     * recomenda-se aos competidores familiares com a estrutura de dados \n     * denominada 'pilha' que implementem a função numera_navios usando-a,\n     * pois esta é mais apropriada do que a recursão para o flood fill. */\n    for(int i = 0; i < n; i++)\n        for(int j = 0; j < m; j++)\n            if(num_navio[i][j] == -2) {\n                pedacos_restantes[qtd_navios] = 0;\n                numera_navios(i, j);\n                qtd_navios++;\n            }\n    \n    int k, resp = 0;\n    scanf(\"%d\", &k);    \n\n    /* Processa os tiros, um a um */\n    for(int i = 0; i < k; i++) {\n        int l, c;\n\n        scanf(\"%d %d\", &l, &c);\n\n        /* Os tiros começam do 1, logo precisamos subtrair. */\n        l--; c--;\n\n        int navio_atingido = num_navio[l][c];\n\n        /* O tiro atingiu algum navio? */\n        if(navio_atingido != -1) {\n            /* Se sim, o navio tem um pedaço a menos, agora. Note que aqui\n             * estamos explorando o fato de que não é possível atirar num\n             * lugar já destruído, já que não é possível dar dois tiros no\n             * mesmo lugar. */\n            pedacos_restantes[navio_atingido]--;\n\n            /* Se o navio não tem mais nenhum pedaço restante, precisamos\n             * incrementar a resposta, pois acabamos de destruir um navio\n             * por completo */\n            if(pedacos_restantes[navio_atingido] == 0) {\n                resp++;\n            }\n        }\n    }\n\n    printf(\"%d\\n\", resp);\n}\n", "text_preview": "/* * Solução do problema 'Batalha naval' (OBI 2010 - Nível 2, Fase 1) * * <NAME> <NAME> (<<EMAIL>>) * * Esta solução consiste em dar números aos navios e associar a cada célula o * número do navio correspondente a ela. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Albus-Tan/Chfs-File-System/blob/8ec9a690fb2d0ba59d8959888d32769ece8ba84c/mr_worker.cc", "id": "cpp-0-32e1d192bd834765c8c903ba21ca1e76", "text": "#include <iostream>\n#include <fstream>\n#include <sstream>\n#include <stdio.h>\n#include <stdlib.h>\n#include <unistd.h>\n#include <sys/types.h>\n#include <dirent.h>\n\n#include <mutex>\n#include <string>\n#include <vector>\n#include <map>\n\n#include \"rpc.h\"\n#include \"mr_protocol.h\"\n\nusing namespace std;\n\n#define WORKER_LOG(fmt, args...) \\\n    do {                       \\\n    } while (0);\n\n//#define WORKER_LOG(fmt, args...)                                                                                   \\\n//     do {                                                                                                         \\\n//         printf(\"[MR_WORKER_LOG][%s:%d:%s] \" fmt \"\\n\", __FILE__, __LINE__, __FUNCTION__ , ##args);                     \\\n//     } while (0);\n\nstruct KeyVal {\n  string key;\n  string val;\n};\n\nbool IsLetter(char c) {\n  return (c >= 'a' && c <= 'z') || (c >= 'A' && c <= 'Z');\n}\n\n//\n// The map function is called once for each file of input. The first\n// argument is the name of the input file, and the second is the\n// file's complete contents. You should ignore the input file name,\n// and look only at the contents argument. The return value is a slice\n// of key/value pairs.\n//\nvector<KeyVal> Map(const string &filename, const string &content) {\n  // Copy your code from mr_sequential.cc here.\n  // Hints: split contents into an array of words.\n  std::vector<char> vec_buf;\n  vec_buf.resize(content.size());\n  vec_buf.assign(content.begin(), content.end());\n\n  vector<KeyVal> kv_pairs;\n  std::vector<char> last_word;\n  for (auto c : vec_buf) {\n    if (IsLetter(c)) {\n      last_word.push_back(c);\n    } else {\n      if (last_word.empty()) continue;\n      KeyVal kv;\n      kv.key.assign(last_word.begin(), last_word.end());\n      kv.val = \"1\";\n      kv_pairs.push_back(kv);\n      last_word.clear();\n    }\n  }\n\n  return kv_pairs;\n}\n\n//\n// The reduce function is called once for each key generated by the\n// map tasks, with a list of all the values created for that key by\n// any map task.\n//\nstring Reduce(const string &key, const vector<string> &values) {\n  // Copy your code from mr_sequential.cc here.\n  // Hints: return the number of occurrences of the word.\n  return to_string(values.size());\n}\n\ntypedef vector<KeyVal> (*MAPF)(const string &key, const string &value);\ntypedef string (*REDUCEF)(const string &key, const vector<string> &values);\n\nclass Worker {\n public:\n  Worker(const string &dst, const string &dir, MAPF mf, REDUCEF rf);\n\n  void doWork();\n\n private:\n  void doMap(int index, const vector<string> &filenames);\n  void doReduce(int index);\n  void doSubmit(mr_tasktype taskType, int index);\n  void generateIntermediateFile(int map_task_num, int reduce_task_num, const string &buf);\n\n  mutex mtx;\n  int id;\n\n  rpcc *cl;\n  std::string basedir;\n  std::string resdir;\n  MAPF mapf;\n  REDUCEF reducef;\n};\n\nWorker::Worker(const string &dst, const string &dir, MAPF mf, REDUCEF rf) {\n  this->resdir = dir;\n  this->basedir = \"/tmp/\" + dir;\n  this->mapf = mf;\n  this->reducef = rf;\n\n  sockaddr_in dstsock;\n  make_sockaddr(dst.c_str(), &dstsock);\n  this->cl = new rpcc(dstsock);\n  if (this->cl->bind() < 0) {\n    printf(\"mr worker: call bind error\\n\");\n  }\n}\n\nvoid Worker::generateIntermediateFile(int map_task_num, int reduce_task_num, const string &buf) {\n  // A reasonable naming convention for intermediate files is mr-X-Y,\n  // where X is the Map task number, and Y is the reduce task number.\n  // The worker's map task code will need a way to store intermediate\n  // key/value pairs in files in a way that can be correctly read\n  // back during reduce tasks.\n  WORKER_LOG(\"generateIntermediateFile mr-%d-%d\", map_task_num, reduce_task_num)\n  ofstream out(basedir + \"mr-\" + to_string(map_task_num) + \"-\" + to_string(reduce_task_num));\n  out << buf;\n  out.close();\n}\n\nvoid Worker::doMap(int index, const vector<string> &filenames) {\n  // Lab4: Your code goes here.\n  // Each mapper only processes one file at one time\n  string filename = filenames.front();\n\n  WORKER_LOG(\"doMap filename %s\", filename.data())\n\n  string content;\n  // read file to content\n  getline(ifstream(filename), content, '\\0');\n  vector<KeyVal> kv_pairs = mapf(filename, content);\n\n  // The Map part of your workers can use a hash function\n  // to distribute the intermediate key-values to different\n  // files intended for different Reduce tasks.\n  std::hash<std::string> doHash;\n  string buf0, buf1, buf2, buf3;\n  for (auto kv_pair : kv_pairs) {\n    // hash by key\n    string kv_str = kv_pair.key + ' ' + kv_pair.val + '\\n';\n    switch (doHash(kv_pair.key) % REDUCER_COUNT) {\n      case 0: {\n        buf0 = buf0 + kv_str;\n        break;\n      }\n      case 1: {\n        buf1 = buf1 + kv_str;\n        break;\n      }\n      case 2: {\n        buf2 = buf2 + kv_str;\n        break;\n      }\n      case 3: {\n        buf3 = buf3 + kv_str;\n        break;\n      }\n    }\n  }\n\n  vector<string> bufs;\n  bufs.push_back(buf0);\n  bufs.push_back(buf1);\n  bufs.push_back(buf2);\n  bufs.push_back(buf3);\n\n  int reduce_task_num = 0;\n  while (reduce_task_num < REDUCER_COUNT) {\n    generateIntermediateFile(index, reduce_task_num, bufs[reduce_task_num]);\n    ++reduce_task_num;\n  }\n\n}\n\nvoid Worker::doReduce(int index) {\n  // Lab4: Your code goes here.\n\n  map<string, uint64_t> word_num_map;\n\n  WORKER_LOG(\"doReduce index %d\", index)\n\n  int map_task_num = 0;\n  string buf;\n  string key;\n  uint64_t num;\n  // check files\n  while (1) {\n    buf = \"\";\n    ifstream in(basedir + \"mr-\" + to_string(map_task_num) + \"-\" + to_string(index));\n    if (in.is_open()) {\n      while (getline(in, buf)) {\n        stringstream ss;\n        ss << buf;\n        ss >> key >> num;\n        if (word_num_map.count(key) == 0) {\n          word_num_map.emplace(key, num);\n        } else {\n          word_num_map[key] += num;\n        }\n//        auto res = word_num_map.try_emplace(key, num);\n//        if(!res.second){\n//          *(res.first)->second += num;\n//        }\n      }\n      in.close();\n      ++map_task_num;\n    } else {\n      // no larger map_task_num file\n      in.close();\n      break;\n    }\n  }\n\n  string res_content = \"\";\n  for (auto word_cnt_pair : word_num_map) {\n    res_content = res_content + word_cnt_pair.first + ' ' + to_string(word_cnt_pair.second) + '\\n';\n  }\n  ofstream out(resdir + \"/mr-out\" + to_string(index));\n  out << res_content;\n  WORKER_LOG(\"result file mr-out%d generated\", index)\n  out.close();\n\n}\n\nvoid Worker::doSubmit(mr_tasktype taskType, int index) {\n  bool b;\n  mr_protocol::status ret = this->cl->call(mr_protocol::submittask, taskType, index, b);\n  if (ret != mr_protocol::OK) {\n    fprintf(stderr, \"submit task failed\\n\");\n    exit(-1);\n  }\n}\n\nvoid Worker::doWork() {\n  for (;;) {\n\n    //\n    // Lab4: Your code goes here.\n    // Hints: send asktask RPC call to coordinator\n    int no_use;\n    mr_protocol::AskTaskResponse reply;\n    mr_protocol::status ret = this->cl->call(mr_protocol::asktask, no_use, reply);\n    WORKER_LOG(\"asktask RPC sent\")\n    if (ret == mr_protocol::OK) {\n      switch (reply.tasktype) {\n        case mr_tasktype::MAP: {\n          WORKER_LOG(\"mr_tasktype::MAP\")\n          // if mr_tasktype::MAP, then doMap and doSubmit\n          doMap(reply.index, {reply.filename});\n          doSubmit(mr_tasktype::MAP, reply.index);\n          break;\n        }\n        case mr_tasktype::REDUCE: {\n          WORKER_LOG(\"mr_tasktype::REDUCE\")\n          // if mr_tasktype::REDUCE, then doReduce and doSubmit\n          doReduce(reply.index);\n          doSubmit(mr_tasktype::REDUCE, reply.index);\n          break;\n        }\n        case mr_tasktype::NONE: {\n          WORKER_LOG(\"mr_tasktype::NONE\")\n          // if mr_tasktype::NONE, meaning currently no work is needed, then sleep\n          sleep(1);\n          break;\n        }\n      }\n    } else {\n      WORKER_LOG(\"asktask RPC failed\")\n    }\n    sleep(1);\n\n  }\n}\n\nint main(int argc, char **argv) {\n  if (argc != 3) {\n    fprintf(stderr, \"Usage: %s <coordinator_listen_port> <intermediate_file_dir> \\n\", argv[0]);\n    exit(1);\n  }\n\n  MAPF mf = Map;\n  REDUCEF rf = Reduce;\n\n  Worker w(argv[1], argv[2], mf, rf);\n  w.doWork();\n\n  return 0;\n}\n\n", "text_preview": "#include <iostream> #include <fstream> #include <sstream> #include <stdio.h> #include <stdlib.h> #include <unistd.h> #include <sys/types.h> #include <dirent.h> #include <mutex> #include <string> #include <vector> #include <map> #include \"rp", "input_sha256": "d9c78572d1f404e1b3144a26ec25b81592c92fcd39b043aa836b7ca9ca762488", "text_sha256": "32e1d192bd834765c8c903ba21ca1e76b61e13a20791f79f72781442fa30358e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 32, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.6553990826133747, "noise_severity": 0.16922745947267004, "missing_content_severity": 0.12847592274271818, "factual_density": 3.3257167113811077, "pretraining_utility": 0.6734462364999244, "math_density": 0.4464466852704108, "code_density": 3.8644820263193678, "reasoning_density": 0.7581491491016976, "education_level": 2.397033813666173, "creative_writing_quality": -0.2564564903712943}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.006746196676473512, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9931679795420029, "secondary": 0.9178255286026518, "tertiary": 0.923586121428296, "structured_records": 0.008983161625318646, "procedural_steps": 0.0632570203914253, "worked_solution": 0.06174011001896021, "contextual_dialogue": 5.807566705937858e-06, "content_quality": 0.4692316507970501, "noise_severity": 0.8589771171061082, "missing_content_severity": 0.8929367310477349, "factual_density": 0.4380972594842564, "pretraining_utility": 0.5388497793291624, "math_density": 0.6279610956079908, "code_density": 0.8870683552661397, "reasoning_density": 0.36820904241525176, "education_level": 0.5483687318003343, "creative_writing_quality": 0.9992438465336518, "primary_topic": 0.9926280513438018, "secondary_topic": 0.8564984900035856}, "probability_margin": {"primary": 0.988756860640387, "secondary": 0.8748060150578868, "tertiary": 0.880906748989345, "education_level": 0.2847683446917317, "creative_writing_quality": 0.9990432954632119, "primary_topic": 0.9868845061647378, "secondary_topic": 0.7806044135011105}, "raw_probabilities": {"primary": {"Academic Writing": 5.921577866440885e-05, "Content Listing": 3.022684215992626e-05, "Creative Writing": 2.882497302333643e-05, "Customer Support Page": 1.2917581551692994e-05, "Q&A Forum": 1.9849599114487783e-05, "Discussion Thread": 1.1834941468189154e-05, "Comment Section": 2.1164022186143696e-05, "Missing Content": 4.465944873357183e-05, "Knowledge Article": 9.648561671004967e-05, "Legal Notices": 3.190070789239768e-05, "News Article": 7.207446000337444e-06, "Organizational Announcement": 1.3838269473860235e-05, "Nonfiction Writing": 1.6394945144451895e-05, "Organizational About Page": 1.627771740207756e-05, "Personal About Page": 1.6261859969978382e-05, "Personal Blog": 1.0702889502576132e-05, "Product Page": 1.0087646513644383e-05, "Spam / Ads": 1.3694270416956366e-05, "Structured Data": 0.00019774768680861407, "Technical Writing": 0.993452647061086, "Transcript / Interview": 2.1723522930972228e-05, "Tutorial / How-To Guide": 0.004695786420699026, "User Reviews": 9.985565709363597e-06, "Other / Unknown": 0.001160565186837962}, "secondary": {"Academic Writing": 0.0004934738478920659, "Content Listing": 0.0001955270294496555, "Creative Writing": 0.000272536826186626, "Customer Support Page": 0.00017557921517605192, "Q&A Forum": 0.00016629602733503552, "Discussion Thread": 0.00011100594133570915, "Comment Section": 0.0004394033190933804, "Missing Content": 0.00764922711069874, "Knowledge Article": 0.0010922890003372365, "Legal Notices": 0.0002891783575872092, "News Article": 0.00016559376124329817, "Organizational Announcement": 0.00016131357599340687, "Nonfiction Writing": 0.00018535825797535014, "Organizational About Page": 0.0001755746942087816, "Personal About Page": 0.00015206559677269176, "Personal Blog": 0.0001293904913220669, "Product Page": 0.00010284475711086533, "Spam / Ads": 0.00016240893109581049, "Structured Data": 0.0007638937082559647, "Technical Writing": 0.019107673217885195, "Transcript / Interview": 0.00021876363863313703, "Tutorial / How-To Guide": 0.04644344985298781, "User Reviews": 9.768793054944782e-05, "NONE": 0.9212494649108746}, "tertiary": {"Academic Writing": 0.0004805546006864129, "Content Listing": 0.00014331127576474754, "Creative Writing": 0.00021316429363111338, "Customer Support Page": 0.0001793785211847796, "Q&A Forum": 0.00012852417778748767, "Discussion Thread": 8.418618837505809e-05, "Comment Section": 0.000255631807503484, "Missing Content": 0.009276586554797189, "Knowledge Article": 0.0019179354033783136, "Legal Notices": 0.0004566317728509285, "News Article": 0.00011933942256922079, "Organizational Announcement": 0.00011369932831286192, "Nonfiction Writing": 0.00017795229090709268, "Organizational About Page": 0.00014808215787445194, "Personal About Page": 0.00012382985465695702, "Personal Blog": 0.00010049914054128258, "Product Page": 9.111452010000269e-05, "Spam / Ads": 0.00013747396124835244, "Structured Data": 0.0006407342633527338, "Technical Writing": 0.012361476541411683, "Transcript / Interview": 0.00014887163903453323, "Tutorial / How-To Guide": 0.0458632840461054, "User Reviews": 6.770520247554576e-05, "NONE": 0.9267700330354504}, "structured_records": {"false": 0.9910168383746814, "true": 0.008983161625318646}, "procedural_steps": {"false": 0.9367429796085747, "true": 0.0632570203914253}, "worked_solution": {"false": 0.9382598899810398, "true": 0.06174011001896021}, "contextual_dialogue": {"false": 0.999994192433294, "true": 5.807566705937858e-06}, "content_quality": {"-2": 0.015112648747901017, "-1": 0.06498236272518046, "0": 0.3154587965210186, "1": 0.45828564117744264, "2": 0.14616055082845728}, "noise_severity": {"0": 0.8472072787965258, "1": 0.13982273575865994, "2": 0.010427059891733884, "3": 0.0016210982817795787, "4": 0.000921827271300907}, "missing_content_severity": {"0": 0.9020578750262885, "1": 0.07226376245347191, "2": 0.021615605275582744, "3": 0.0032700792405460054, "4": 0.0007926780041106891}, "factual_density": {"0": 0.004615868189338568, "1": 0.0110029590185224, "2": 0.10606913263855758, "3": 0.41067267352885567, "4": 0.4676393666247258}, "pretraining_utility": {"-2": 0.007851475767042346, "-1": 0.051174451245615975, "0": 0.31406368436018134, "1": 0.5134971379746958, "2": 0.11341325065246466}, "math_density": {"0": 0.728524451573607, "1": 0.16749335430677215, "2": 0.061257728542068866, "3": 0.014459988430707458, "4": 0.028264477146844668}, "code_density": {"0": 0.00019788430974014226, "1": 0.0003674333188057278, "2": 0.009336372948249343, "3": 0.11495139058875595, "4": 0.8751469188344488}, "reasoning_density": {"0": 0.6688740178858068, "1": 0.09560234964523895, "2": 0.10893045326835334, "3": 0.061686823882651905, "4": 0.06490635531794912}, "education_level": {"none": 0.0007716216580475992, "0": 0.0010861625232721735, "1": 0.003743722668380409, "2": 0.6236406098336119, "3": 0.33887226514188024, "4": 0.03188561817480755}, "creative_writing_quality": {"not_applicable": 0.9993698721113765, "-2": 6.247306130000225e-05, "-1": 0.0001512331986126444, "0": 0.0003265766481646047, "1": 6.511102668177503e-05, "2": 2.473395386468191e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 2.6423693406118752e-05, "Art & Design": 3.053227219389316e-05, "Law & Crime": 2.9184611832536183e-05, "Education & Careers": 6.028357443212485e-05, "Entertainment & Media": 1.9346096077433513e-05, "Fashion & Beauty": 1.210367230887064e-05, "Business, Economics & Finance": 2.8073138822518734e-05, "Food & Dining": 1.9608014928096038e-05, "Games & Gaming": 4.803435007300742e-05, "Electronics & Hardware": 8.457408364194727e-05, "Health, Healthcare & Wellness": 1.9391776464163176e-05, "History & Geography": 2.216144240313265e-05, "Home, Pets & Hobbies": 1.567295355187868e-05, "Industry & Production": 2.4572989351900417e-05, "Language, Literature & Philosophy": 4.1811126591089746e-05, "Politics & Government": 1.1252120869884953e-05, "Religion & Spirituality": 2.154071673851979e-05, "Science, Mathematics & Engineering": 0.006027081665840884, "Relationships & Community": 1.0638871734322017e-05, "Software Usage & Applications": 0.0002504055658645298, "Programming & Software Engineering": 0.9929115878305786, "Sports & Fitness": 2.5931858497428548e-05, "Vehicles & Transportation": 1.2945943812219626e-05, "Travel & Tourism": 2.7190158886563248e-05, "Other / Uncovered Subject": 0.00017803781224503594, "Unknown / Insufficient Content": 4.161365885315861e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.00045984338507491486, "Art & Design": 0.00033788420496089075, "Law & Crime": 0.0003984836598722098, "Education & Careers": 0.001154765757151852, "Entertainment & Media": 0.0003592691084327461, "Fashion & Beauty": 0.00020066925193117007, "Business, Economics & Finance": 0.00039800473810173206, "Food & Dining": 0.00017736557639256494, "Games & Gaming": 0.00030844819931059403, "Electronics & Hardware": 0.0005594492516875183, "Health, Healthcare & Wellness": 0.00023990376103090203, "History & Geography": 0.00022513146353026321, "Home, Pets & Hobbies": 0.00017874107400815773, "Industry & Production": 0.00019901309172553242, "Language, Literature & Philosophy": 0.00031270466142583166, "Politics & Government": 0.00019800047035214904, "Religion & Spirituality": 0.0002637141376171068, "Science, Mathematics & Engineering": 0.08141336534849099, "Relationships & Community": 0.00011894244014089087, "Software Usage & Applications": 0.0009157671381122941, "Programming & Software Engineering": 0.048355943243535635, "Sports & Fitness": 0.00018188467669821723, "Vehicles & Transportation": 0.00011016434812791682, "Travel & Tourism": 0.0002847292518638399, "Other / Uncovered Subject": 0.0006300329108226736, "NONE": 0.8620177788496015}}, "probabilities": {"primary": {"Academic Writing": 5.921577866440885e-05, "Content Listing": 3.022684215992626e-05, "Creative Writing": 2.882497302333643e-05, "Customer Support Page": 1.2917581551692994e-05, "Q&A Forum": 1.9849599114487783e-05, "Discussion Thread": 1.1834941468189154e-05, "Comment Section": 2.1164022186143696e-05, "Missing Content": 4.465944873357183e-05, "Knowledge Article": 9.648561671004967e-05, "Legal Notices": 3.190070789239768e-05, "News Article": 7.207446000337444e-06, "Organizational Announcement": 1.3838269473860235e-05, "Nonfiction Writing": 1.6394945144451895e-05, "Organizational About 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"Organizational Announcement": 0.00011369932831286192, "Nonfiction Writing": 0.00017795229090709268, "Organizational About Page": 0.00014808215787445194, "Personal About Page": 0.00012382985465695702, "Personal Blog": 0.00010049914054128258, "Product Page": 9.111452010000269e-05, "Spam / Ads": 0.00013747396124835244, "Structured Data": 0.0006407342633527338, "Technical Writing": 0.012361476541411683, "Transcript / Interview": 0.00014887163903453323, "Tutorial / How-To Guide": 0.0458632840461054, "User Reviews": 6.770520247554576e-05, "NONE": 0.9267700330354504}, "structured_records": {"false": 0.9910168383746814, "true": 0.008983161625318646}, "procedural_steps": {"false": 0.9367429796085747, "true": 0.0632570203914253}, "worked_solution": {"false": 0.9382598899810398, "true": 0.06174011001896021}, "contextual_dialogue": {"false": 0.999994192433294, "true": 5.807566705937858e-06}, "content_quality": {"-2": 0.015112648747901017, "-1": 0.06498236272518046, "0": 0.3154587965210186, "1": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/r-forge/abernethy/blob/58fe874919bff2f6b405a954a6c71606ffb9af64/pkg/debias/src/MLEln2p.cpp", "id": "cpp-0-f260b14421b56b6064d15f92c29f0f18", "text": "/* MLEln2p.cpp\n *\n * This program is free software; you can redistribute it and/or modify it\n * under the terms of the GNU General Public License as published by the\n * Free Software Foundation; either version 2, or (at your option) any\n * later version.\n *\n * These functions are distributed in the hope that they will be useful,\n * but WITHOUT ANY WARRANTY; without even the implied warranty of\n * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\t See the\n * GNU General Public License for more details.\n *\n *  You should have received a copy of the GNU General Public License\n *  along with this program; if not, a copy is available at\n *  http://www.r-project.org/Licenses/\n *\n * This is an implementation of the MLE optimization obtained by the direct Nelder-Meade simplex method\n * optimizing the negative log-likelihood function for the lognormal 2-parameter distribution consistent\n * with the R package survival. This code has been streamlined for the simplest 2 parameter case, rather  \n * than built in a generalized format for the solution of more than 2 parameters at one time. \n * Inspiraton for this code came from a GNU implementation in C by <NAME>.  \n * http://www.mikehutt.com/neldermead.html \n * His code is expository and generalized for multiple parameter solutions, however the logic was not as straight\n * forward as I would prefer.  Mike Hutt also addresses constraints, which were not applied here.\n *\n * An excellant Scholarpedia entry by <NAME> and <NAME> (2009) helped assure accuracy of the algorithm.  \n * http://www.scholarpedia.org/w/index.php?title=Nelder-Mead_algorithm&action=cite&rev=91557\n * \n * Botao Jia posted an excellant logic diagram at \n * http://www.codeguru.com/cpp/article.php/c17505/Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm\n * which was used to construct the final logic (although result was no different than the logic used by Mike Hutt).\n * Detail for this calculation was not presented in The Weibull Handbook, however the results are consistent with \n * SuperSMITH software.\n *\n * This function was developed using the RcppArmadillo library\n *\n *     Copyright (C) 2013 <NAME>\n */\n\n#include \"debias.h\"\n\n\ndouble nLL2(arma::colvec par, arma::colvec Data, int Nf);\n\ndouble nLL2(arma::colvec par, arma::colvec Data, int Nf)  {\t\t\ndouble failcomp=0.0,suscomp=0.0;\t\t\narma::colvec F=Data.rows(0,Nf-1);\t\t\nfor(int i=0; i<Nf; i++)  {\t\t\nfailcomp=failcomp+R::dlnorm(F(i),par(0),par(1),1);\t\t\n}\t\t\nfailcomp=-failcomp;\t\t\nint Nd=Data.n_rows;\t\t\narma::colvec S;\t\t\nif(Nd>Nf)  {\t\t\n\t S=Data.rows(Nf,Nd-1);\t\n\tfor(int i=0; i<(Nd-Nf); i++)  {\t\n\tsuscomp=suscomp+R::plnorm(S(i),par(0),par(1),0,1);\t\n\t}\t\n}\t\t\ndouble negLL=failcomp-suscomp;\t\t\nreturn negLL;\t\t\n}\t\t\n\n\nSEXP MLEln2p (SEXP arg1, SEXP arg2, SEXP arg3, SEXP arg4)  \n{\n    using namespace Rcpp ;\n\n\tarma::colvec Data=Rcpp::as<arma::colvec>(arg1);\t\t\t\n\tint Nf=Rcpp::as<int>(arg2);\t\t\t\n\tarma::colvec vstart=Rcpp::as<arma::colvec>(arg3);\t\t\t\n\tdouble limit=Rcpp::as<double>(arg4);\t\t\t\n\tint n=2, k=n+1;\t\t\t\n // coefficients for reflection, expansion, and contraction\t\t\t\t\n\tdouble ALPHA=1.0, BETA=0.5, GAMMA=2.0;\t\t\t\n // construct the initial simplex\t\t\t\t\n\tarma::mat v(2,3);\t\t\t\n\tfor(int i=0;i<k;i++)  {\t\t\t\n\tv.col(i)=vstart;\t\t\t\n\t}\t\t\t\n\tv(0,1)=v(0,1)*1.2;\t\t\t\n\tv(1,2)=v(1,2)*1.2;\t\t\t\n\t\t\t\t\n\tarma::colvec f(3);\t\t\t\n\tfor(int i=0;i<k;i++)  {\t\t\t\n\tf(i)=nLL2(v.col(i),Data,Nf);\t\t\t\n\t}\t\t\t\n // assign vertex order variables\t\t\t\t\n\tarma::uvec ndx=sort_index(f);\t\t\t\n\tint vs=(int) ndx(0);\t\t\t\n\tint vh=(int) ndx(1);\t\t\t\n\tint vg=(int) ndx(2);\t\t\t\n // generate the initial error measure\t\t\t\t\n\tdouble P2avg=arma::as_scalar(mean(v.row(1)));\t\t\t\n\tdouble error=arma::as_scalar(sum(pow((v.row(1)-P2avg),2.0)/n));\t\t\t\n // initialize the output dataframe for construction as a matrix\t\t\t\t\n\tarma::rowvec DFrow(4);\t\t\t\n\tDFrow(0)=v(0,vs);\t\t\t\n\tDFrow(1)=v(1,vs);\t\t\t\n\tDFrow(2)=f(vs);\t\t\t\n\tDFrow(3)=error;\t\t\t\n\tarma::mat DF=DFrow;\t\t\t\n\t\t\t\t\n // initialization of variables used in the loop\t\t\t\t\n\tarma::colvec vm, vr, ve, vc;\t\t\t\n\tdouble fr, fe, fc;\t\t\t\n\t\t\t\t\n // This is the main loop for the minimization\t\t\t\t\n\twhile(error>limit)  {\t\t\t\n // calculate the centroid\t\t\t\t\n\tvm=(v.col(vs)+v.col(vh))/2.0;\t\t\t\n // reflect vg to new vertex vr\t\t\t\t\n\tvr=(vm+ALPHA*(vm-v.col(vg)));\t\t\t\n\tfr=nLL2(vr,Data,Nf);\t\t\t\n // depending on success, save reflected vertex in place of vg\t\t\t\t\n\tif (fr < f(vh) && fr >= f(vs)) {\t\t\t\n\tv.col(vg)=vr;\t\t\t\n\tf(vg)=fr;\t\t\t\n\t}else{\t\t\t\n\t\tif(f[vs]<fr)  {\t\t\n // test for an outside contraction\t\t\t\t\n // test for an outside contraction\t\t\t\t\n\t\tif (fr < f(vg) && fr >= f(vh))   {\t\t\n\t\t\tvc=vm+BETA*(vr-vm);\t\n\t\t\t}else{\t\n // this is an inside contraction\t\t\t\t\n\t\t\t\tvc=vm-BETA*(vr-vm);\n\t\t\t}\t\n\t\t\tfc=nLL2(vc,Data,Nf);\t\n // upon acceptance replace vg with contracton\t\t\t\t\n\t\t\tif (fc < f(vg)) {\t\n\t\t\t\tv.col(vg) = vc;\n\t\t\t\tf(vg)=fc;\n\t\t\t}else{\t\n // upon rare-to-never case shrink the simplex\t\t\t\t\n\t\t\t\tv.col(vh)=v.col(vs)+(v.col(vh)-v.col(vs))/2.0;\n\t\t\t\tv.col(vg)=v.col(vs)+(v.col(vg)-v.col(vs))/2.0;\n // This case results in two function calculations and simply replacing vh and vg points\t\t\t\t\n\t\t\t\tf(vh)=nLL2(v.col(vh),Data,Nf);\n\t\t\t\tf(vg)=nLL2(v.col(vg),Data,Nf);\n\t\t\t}\t\n\t\t}else{\t\t\n// now we make an expansion\t\t\t\t\n\t\t\tve=vm+GAMMA*(vr-vm);\t\n\t\t\tfe=nLL2(ve,Data,Nf);\t\n // store the better of reflection or expansion in place of vg\t\t\t\t\n\t\t\tif (fe < fr) {\t\n\t\t\t\tv.col(vg) =ve;\n\t\t\t\tf(vg)=fe;\n\t\t\t}else{\t\n\t\t\t\tv.col(vg) = vr;\n\t\t\t\tf(vg)=fr;\n\t\t\t}\t\n\t\t}\t\t\n\t}\t\t\t\n //re-assign vertex order variables \t\t\t\t\n\tndx=sort_index(f);\t\t\t\n\tvs=(int) ndx(0);\t\t\t\n\tvh=(int) ndx(1);\t\t\t\n\tvg=(int) ndx(2);\t\t\t\n\t\t\t\t\n\tP2avg=arma::as_scalar(mean(v.row(1)));\t\t\t\n\terror=arma::as_scalar(sum(pow((v.row(1)-P2avg),2.0)/n));\t\t\t\n\tDFrow(0)=v(0,vs);\t\t\t\n\tDFrow(1)=v(1,vs);\t\t\t\n\tDFrow(2)=-f(vs);\t\t\t\n\tDFrow(3)=error;\t\t\t\n\tDF=join_cols(DF,DFrow);\t\t\t\n\t\t\t\t\n// then close main iteration loop\t\t\t\t\n\t}\t\t\t\t\t\n\t\t\t\t\n\treturn wrap(DF);\t\t\t\t\n\t\n}\n", "text_preview": "/* MLEln2p.cpp * * This program is free software; you can redistribute it and/or modify it * under the terms of the GNU General Public License as published by the * Free Software Foundation; either version 2, or (at your option) any * later", "input_sha256": "399f900f941ed87f55c91de3dfe93176fda27e2ff8d8a44843100fc02a8a9dd4", "text_sha256": "f260b14421b56b6064d15f92c29f0f18b1ee7a2cba134bccbf8d6bac7c248c4e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 33, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Jinqiang/demo_lidar/blob/f4c60328dd2b7750ab682f917131a2d8da696a32/include/isam/util.h", "id": "cpp-0-3711943a2c6144d9d3a9f4953812de86", "text": "/**\n * @file util.h\n * @brief Basic utility functions that are independent of iSAM.\n * @author <NAME>\n * @version $Id: util.h 8263 2013-04-10 14:02:19Z carlevar $\n *\n * Copyright (C) 2009-2013 Massachusetts Institute of Technology.\n * <NAME>, <NAME>, <NAME>,\n * <NAME> and <NAME>\n *\n * This file is part of iSAM.\n *\n * iSAM is free software; you can redistribute it and/or modify it under\n * the terms of the GNU Lesser General Public License as published by the\n * Free Software Foundation; either version 2.1 of the License, or (at\n * your option) any later version.\n *\n * iSAM is distributed in the hope that it will be useful, but WITHOUT\n * ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or\n * FITNESS FOR A PARTICULAR PURPOSE.  See the GNU Lesser General Public\n * License for more details.\n *\n * You should have received a copy of the GNU Lesser General Public License\n * along with iSAM.  If not, see <http://www.gnu.org/licenses/>.\n *\n */\n\n#pragma once\n\n#include <stdlib.h>\n#include <stdio.h>\n#include <cmath>\n#include <string>\n#include <Eigen/Dense>\n\nnamespace isam {\n\n// some math constants\n#ifndef PI\nconst double PI = 3.14159265358979323846;\n#endif\nconst double TWOPI = 2.0*PI;\nconst double HALFPI = PI/2.0;\n\n// values up to this constant are considered zero and removed from the matrix\nconst double NUMERICAL_ZERO = 1e-12;\n\n/**\n * Return current system time in seconds.\n */\ndouble tic();\n\n/**\n * Remember and return system time in seconds.\n * @param id Name of time slot.\n */\ndouble tic(std::string id);\n\n/**\n * Return difference between current system time and t in seconds.\n * @param t0 Start time as returned by tic();\n */\ndouble toc(double t0);\n\n/**\n * Return difference between current system time and remembered time in\n * seconds, and add to accumulated time.\n * @param id Name of time slot.\n */\ndouble toc(std::string id);\n\n/**\n * Print a list of accumulated times and additional statistics\n * for each name used in tic/toc.\n */\nvoid tictoc_print();\n\n/**\n * Return the accumulated time.\n * @param id Name of time slot.\n */\ndouble tictoc(std::string id);\n\n/**\n * Return identity matrix.\n */\nEigen::MatrixXd eye(int num);\n\n/**\n * Calculate Givens rotation so that a specific entry becomes 0.\n * @param a Diagonal entry from above.\n * @param b Entry to be zeroed out.\n * @param c Resulting cosine part.\n * @param s Resulting sine part.\n */\nvoid givens(const double a, const double b, double& c, double& s);\n\n/**\n * Normalize angle to be within the interval [-pi,pi].\n */\ninline double standardRad(double t) {\n  if (t >= 0.) {\n    t = fmod(t+PI, TWOPI) - PI;\n  } else {\n    t = fmod(t-PI, -TWOPI) + PI;\n  }\n  return t;\n}\n\ninline double deg_to_rad(double d) {\n  return (d/180.*PI);\n}\n\ninline double rad_to_deg(double r) {\n  return (r/PI*180.);\n}\n\n/**\n * Calculate the pseudo inverse of an arbitrary matrix using the SVD\n * @input a Eigen matrix to invert\n * @input eps Numerical epsilon to determine zero singular values (defaults to std::numeric_limits::eps)\n * @return Eigen matrix of same type as a\n */\ntemplate<typename T>\nT pinv(const T &a, double eps = std::numeric_limits<typename T::Scalar>::epsilon()) {\n\n  bool m_lt_n = (a.rows() < a.cols());\n\n  Eigen::JacobiSVD<T> svd;\n  if (m_lt_n) {\n      T tmp = a.transpose();\n      svd = tmp.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV);\n  } else {\n      svd = a.jacobiSvd(Eigen::ComputeThinU | Eigen::ComputeThinV);\n  }\n\n  typename T::Scalar tolerance = eps * std::max(a.cols(), a.rows()) * svd.singularValues().array().abs().maxCoeff();\n\n  T result = svd.matrixV() *\n             T( (svd.singularValues().array().abs() > tolerance).select(svd.singularValues().array().inverse(), 0) ).asDiagonal() *\n             svd.matrixU().adjoint();\n\n  if (m_lt_n) {\n      return result.transpose();\n  } else {\n      return result;\n  }\n}\n\n/**\n * Calculate the pseudo inverse of an positive semidefinite matrix using the eigenvalue decomposition\n * @input a Eigen matrix to invert\n * @input eps Numerical epsilon to determine zero singular values (defaults to std::numeric_limits::eps)\n * @return Eigen matrix of same type as a\n */\ntemplate<typename T>\nT posdef_pinv(const T &a, double eps = std::numeric_limits<typename T::Scalar>::epsilon()) {\n\n  Eigen::SelfAdjointEigenSolver<T> eig(a);\n  if (eig.info() != Eigen::Success) {\n    return T();\n  }\n\n  typename T::Scalar tolerance = eps * std::max(a.cols(), a.rows()) * eig.eigenvalues().array().abs().maxCoeff();\n\n  T result = eig.eigenvectors() *\n             T( (eig.eigenvalues().array() > tolerance).select(eig.eigenvalues().array().inverse(), 0) ).asDiagonal() *\n             eig.eigenvectors().transpose();\n\n  return result;\n}\n\n\n#ifdef NDEBUG\n// Release mode\n// remove requirements in inner loops for speed, but keep standard require functional\n#define requireDebug(req,msg)\n#define require(req,msg) if (!(req)) {fputs(msg, stderr);fputs(\"\\n\\n\", stderr); exit(1);}\n#else\n// Debug mode\n// cause a crash to allow backtracing\n#define requireDebug(req,msg) if (!(req)) {fputs(msg, stderr);fputs(\"\\n\\n\", stderr); abort();}\n#define require(req,msg) requireDebug(req,msg)\n#endif\n\n}\n", "text_preview": "/** * @file util.h * @brief Basic utility functions that are independent of iSAM. * @author <NAME> * @version $Id: util.h 8263 2013-04-10 14:02:19Z carlevar $ * * Copyright (C) 2009-2013 Massachusetts Institute of Technology. * <NAME>, <NAM", "input_sha256": "0f5600591568f4ebed9ddaadd47d87f37865c054f344bba07a2d0ca56b415dfa", "text_sha256": "3711943a2c6144d9d3a9f4953812de8689116f056ce54647e3f68aab797ed679", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 34, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/AnqiNie/Erasure-Coding-based-Caching-Replacement/blob/1e73eb5c18b346df40c3cce1205d4b085ae5da82/%E9%9A%8F%E5%8D%B3%E6%9B%BF%E6%8D%A2.cpp", "id": "cpp-0-83f95b2461b18134fa3b1a499bfc2ad4", "text": "//5个服务器 6个文件 每个服务器缓存为3 每个文件需要3块恢复\n//每个服务器最多存1个文件的1个块 \n//替换策略为 在尽量不破坏完整性的前提下 按照贪心\n//如果不得不破坏完整性 则严格按照贪心\n//被破坏完整性的文件选择从云端下载\n\n#include<stdio.h>\n#include<iostream>\n#include <vector>\n#include <algorithm>\n#include <cmath>\n#include<cstdlib>\n#include<ctime>\nusing namespace std;\nint servern = 5;\nint fn = 6;\nint n = 6;\nint m = 3;\nint r = 3;\n\nint lij[5][5];\nint lix[5][6];\nint llimit = 300;\nint pix[5][6] = { 0 };\nint qix[5][6] = { 0 };\nint lambda[5][6] = { 0 };\nint VD[5] = { 0 };\n\nvoid request() {\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tcin >> qix[i][j];\n\t\t}\n\t}\n\treturn;\n}\nvoid calculate_pq() {\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tpix[i][j] = qix[i][j] + pix[i][j];\n\t\t}\n\t}\n\treturn;\n}\n\nvoid calculate_lix() {\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tfor (int t = 0; t < 5; t++) {\n\t\t\t\tlix[i][j] = max(lix[i][j], lambda[t][j] * lij[t][j]);\n\t\t\t}\n\t\t}\n\t}\n\treturn;\n}\n\n\n\nbool checkforfile(int x) {\n\tint cnt = 0;\n\tfor (int i = 0; i < 5; i++) {\n\t\tif (lambda[i][x] == 1) cnt += 1;\n\t}\n\tif (cnt >= m) return true;\n\telse return false;\n}\n\nbool checkforserver(int v) {\n\tint cnt = 0;\n\tfor (int i = 0; i < 6; i++) {\n\t\tif (lambda[v][i] == 1) cnt += 1;\n\t}\n\tif (cnt > r) return false;\n\telse return true;\n}\n\nint calculate_cost() {\n\tint cost = 0;\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tif (checkforfile(j)) {\n\t\t\t\tcost += pix[i][j] * lix[i][j];\n\t\t\t}\n\t\t\telse {\n\t\t\t\tcost += 10000;\t\t\t\t//此处为了突出缓存快 云下载慢 故放大\n\t\t\t}\n\t\t}\n\t}\n\treturn cost;\n}\n\nvoid showout() {\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tcout << lambda[i][j] << \" \";\n\t\t\tif (j == 5) cout << endl;\n\t\t}\n\t}\n\treturn;\n}\n\n\nint main() {\n\tsrand((unsigned)time(NULL));\n\t//为了计算方便 先对服务器的网络延迟情况进行一次手动输入\n\tcout << \"手动输入延迟情况\" << endl;\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 5; j++) {\n\t\t\tcin >> lij[i][j];\n\t\t}\n\t}\n\t//为了计算方便 先对服务器上的文件缓存情况进行一次手动输入\n\tcout << \"手动输入初始缓存情况\" << endl;\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tcin >> lambda[i][j];\n\t\t}\n\t}\n\t//为了计算方便 先对服务器上的文件热度情况进行一次手动输入\n\tcout << \"手动输入初始热度情况\" << endl;\n\tfor (int i = 0; i < 5; i++) {\n\t\tfor (int j = 0; j < 6; j++) {\n\t\t\tcin >> pix[i][j];\n\t\t}\n\t}\n\n\n\t//接收要求\n\tcout << \"接受请求\" << endl;\n\trequest();\n\n\t//随机替换 随机生成三个数对应 服务器 然后替换掉其中的某一块\n\tfor (int i = 0; i < 6; i++) {\n\t\tint vrandom1 = (rand()%5);\n\t\tint vrandom2 = (rand() % 5);\n\t\tint vrandom3 = (rand() % 5);\n\t\twhile (vrandom2 == vrandom1) {\n\t\t\tvrandom2 = (rand() % 5);\n\t\t}\n\t\twhile (vrandom3 == vrandom1||vrandom3==vrandom2) {\n\t\t\tvrandom3 = (rand() % 5);\n\t\t}\n\t\tfor (int t = 0; t < 6; t++) {\n\t\t\tif (lambda[vrandom1][t] == 1) {\n\t\t\t\tlambda[vrandom1][t] = 0;\n\t\t\t\tlambda[vrandom1][i] = 1;\n\t\t\t}\n\t\t\tif (lambda[vrandom2][t] == 1) {\n\t\t\t\tlambda[vrandom2][t] = 0;\n\t\t\t\tlambda[vrandom2][i] = 1;\n\t\t\t}\n\t\t\tif (lambda[vrandom3][t] == 1) {\n\t\t\t\tlambda[vrandom3][t] = 0;\n\t\t\t\tlambda[vrandom3][i] = 1;\n\t\t\t}\n\t\t}\n\t}\n\tcout << \"全部替换后\" << endl;\n\tshowout();\n\tcalculate_pq();\n\tcalculate_lix();\n\tint costend = calculate_cost();\n\tcout << \"随机替换后总成本为：\" << costend << endl;\n\treturn 0;\n}\n", "text_preview": "//5个服务器 6个文件 每个服务器缓存为3 每个文件需要3块恢复 //每个服务器最多存1个文件的1个块 //替换策略为 在尽量不破坏完整性的前提下 按照贪心 //如果不得不破坏完整性 则严格按照贪心 //被破坏完整性的文件选择从云端下载 #include<stdio.h> #include<iostream> #include <vector> #include <algorithm> #include <cmath> #include<cstdlib> #include<", "input_sha256": "c0f3f7a73cb368bb7187199ec085ef30800f4f259166c80c5934c178fa371305", "text_sha256": "83f95b2461b18134fa3b1a499bfc2ad4fb250278e2f96f08d5dcf04cfdaf1e68", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 35, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ishandutta2007/codeforces-e/blob/f5c5831c8589d284e013592e04fbe26d0909c889/eim_ron/normal/1637/F.cpp", "id": "cpp-0-9c10864f06284846a6ca89377077bd2e", "text": "#ifndef CPL_TEMPLATE\n#define CPL_TEMPLATE\n/*\n\tNormie's Template v2.5\n\tChanges:\n    Added warning against using pragmas on USACO.\n*/\n// Standard library in one include.\n#include <bits/stdc++.h>\nusing namespace std;\n \n// ordered_set library.\n// #include <ext/pb_ds/assoc_container.hpp>\n// #include <ext/pb_ds/tree_policy.hpp>\n// using namespace __gnu_pbds;\n// #define ordered_set(el) tree<el,null_type,less<el>,rb_tree_tag,tree_order_statistics_node_update>\n \n// AtCoder library. (Comment out these two lines if you're not submitting in AtCoder.) (Or if you want to use it in other judges, run expander.py first.)\n//#include <atcoder/all>\n//using namespace atcoder;\n\n//Pragmas (Comment out these three lines if you're submitting in szkopul or USACO.)\n// #pragma comment(linker, \"/stack:200000000\")\n// #pragma GCC optimize(\"Ofast,unroll-loops,tree-vectorize\")\n// #pragma GCC target(\"sse,sse2,sse3,ssse3,sse4,popcnt,abm,mmx,avx,avx2,tune=native\")\n \n//File I/O.\n#define FILE_IN \"cseq.inp\"\n#define FILE_OUT \"cseq.out\"\n#define ofile freopen(FILE_IN,\"r\",stdin);freopen(FILE_OUT,\"w\",stdout)\n \n//Fast I/O.\n#define fio ios::sync_with_stdio(0);cin.tie(0)\n#define nfio cin.tie(0)\n#define endl \"\\n\"\n \n//Order checking.\n#define ord(a,b,c) ((a>=b)and(b>=c))\n \n//min/max redefines, so i dont have to resolve annoying compile errors.\n#define min(a,b) (((a)<(b))?(a):(b))\n#define max(a,b) (((a)>(b))?(a):(b))\n\n// Fast min/max assigns to use with AVX.\n// Requires g++ 9.2.0.\n// template<typename T>\n// __attribute__((always_inline)) void chkmin(T& a, const T& b) {\n//     a=(a<b)?a:b;\n// }\n\n// template<typename T>\n// __attribute__((always_inline)) void chkmax(T& a, const T& b) {\n//     a=(a>b)?a:b;\n// }\n \n//Constants.\n#define MOD (ll(998244353))\n#define MAX 300001\n#define mag 320\nconst long double PI=3.14159265358979;\n \n//Pairs and 3-pairs.\n#define p1 first\n#define p2 second.first\n#define p3 second.second\n#define fi first\n#define se second\n#define pii(element_type) pair<element_type,element_type>\n#define piii(element_type) pair<element_type,pii(element_type)>\n \n//Quick power of 2.\n#define pow2(x) (ll(1)<<x)\n \n//Short for-loops.\n#define ff(i,__,___) for(int i=__;i<=___;i++)\n#define rr(i,__,___) for(int i=__;i>=___;i--)\n \n//Typedefs.\n#define bi BigInt\ntypedef long long ll;\ntypedef long double ld;\ntypedef short sh;\n\n// Binpow and stuff\nll BOW(ll a, ll x, ll p)\n{\n\tif (!x) return 1;\n\tll res=BOW(a,x/2,p);\n\tres*=res;\n\tres%=p;\n\tif (x%2) res*=a;\n\treturn res%p;\n}\nll INV(ll a, ll p)\n{\n\treturn BOW(a,p-2,p);\n}\n//---------END-------//\n#endif\nvector<int> gt[200001],vec;\nint arr[200001],dep[200001];\nint par[200001][20];\nint vert[200001];\nint ed[200001],ch[200001];\nint n,m,i,j,k,t,t1,u,v,a,b,root,rootdeg;\nll res;\nvoid dfs(int x, int p) {\n\tpar[x][0]=p;\n\tdep[x]=dep[p]+1;\n\tfor (int i=1;i<20;i++) {\n\t\tpar[x][i]=par[par[x][i-1]][i-1];\n\t}\n\tfor (auto g : gt[x]) if (g-p) dfs(g,x);\n}\nint lca(int u, int v) {\n\tfor (int i=19;i>=0;i--) if (dep[u]-(1<<i)>=dep[v]) u=par[u][i];\n\tfor (int i=19;i>=0;i--) if (dep[v]-(1<<i)>=dep[u]) v=par[v][i];\n\tif (u==v) return u;\n\tfor (int i=19;i>=0;i--) if (par[u][i]-par[v][i]) {\n\t\tu=par[u][i];\n\t\tv=par[v][i];\n\t}\n\treturn par[u][0];\n}\nint main()\n{\n\tfio;\n\tcin>>n;\n\tres=0;\n\tfor (i=1;i<=n;i++) {\n\t\tcin>>arr[i];\n\t\tvec.push_back(i);\n\t}\n\tsort(vec.begin(), vec.end(), [](int a, int b){\n\t\treturn (arr[a]>arr[b]);\n\t});\n\tfor (i=1;i<n;i++) {\n\t\tcin>>a>>b;\n\t\tgt[a].push_back(b);\n\t\tgt[b].push_back(a);\n\t}\n\tdfs(1,1);\n\troot=-1;\n\tfor (auto g : vec) {\n\t\t// cout<<g<<' '<<root<<endl;\n\t\tif (root==-1) {\n\t\t\tvert[g]=1;\n\t\t\troot=g;\n\t\t\tres+=arr[g];\n\t\t}\n\t\telse if (g==root) {\n\t\t\tvert[g]=1;\n\t\t}\n\t\telse {\n\t\t\tvert[g]=1;\n\t\t\tu=lca(g,root);\n\t\t\t// cout<<u<<endl;\n\t\t\tif (u!=root) {\n\t\t\t\tv=g;\n\t\t\t\twhile(v!=u) {\n\t\t\t\t\ted[v]=1;\n\t\t\t\t\tv=par[v][0];\n\t\t\t\t\tch[v]++;\n\t\t\t\t}\n\t\t\t\tv=root;\n\t\t\t\twhile(v!=u) {\n\t\t\t\t\ted[v]=1;\n\t\t\t\t\tv=par[v][0];\n\t\t\t\t\tch[v]++;\n\t\t\t\t}\n\t\t\t\t// cout<<\"add1\\n\";\n\t\t\t\tif (ch[root]!=1) res+=arr[g];\n\t\t\t\troot=u;\n\t\t\t}\n\t\t\telse {\n\t\t\t\tv=g;\n\t\t\t\twhile (v!=root && !ed[v]) {\n\t\t\t\t\ted[v]=1;\n\t\t\t\t\tv=par[v][0];\n\t\t\t\t\tch[v]++;\n\t\t\t\t}\n\t\t\t\tif ((vert[v] && ed[v]+ch[v]==2) || v==g) {\n\t\t\t\t\t\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tres+=arr[g];\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\tres+=arr[vec[0]]-arr[vec[1]];\n\tcout<<res;\n}", "text_preview": "#ifndef CPL_TEMPLATE #define CPL_TEMPLATE /* Normie's Template v2.5 Changes: Added warning against using pragmas on USACO. */ // Standard library in one include. #include <bits/stdc++.h> using namespace std; // ordered_set library. // #incl", "input_sha256": "9d2c3f6198e5135a35fe6b872debf1ab948fdaaff981c4b03f40ab7717934916", "text_sha256": "9c10864f06284846a6ca89377077bd2ea31c359bf95866d07e138a42454247c8", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 36, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Peteysoft/isoline_retrieval/blob/d2021dd734195a7722a1406c873fdddc9d5e9e00/src/amsu_interp_base.cc", "id": "cpp-0-e0811435b7afb41f28201ca9f81b7faa", "text": "#include <stdio.h>\n#include <stdlib.h>\n#include <string.h>\n\n#include \"peteys_tmpl_lib.h\"\n#include \"amsu_interp_base.h\"\n\n#include \"global_metric.h\"\n\n#define MAX_LL 200\n#define SWAP_ENDIAN 1\n\n#define COMMENT_LENGTH 200\n\n//#define CHECK_INDEX_FILE \n\n//#define PRINT_FLOC\n\n//once a point has been located, find three others and compute the weights:\nvoid derive_amsu_int_weights(amsu_1c_data *data,//amsu data\n                        long inds,              //the scan index\n                        long indp,              //swath index\n                        float lon,              //longitude of point\n                        float lat,              //latitude of the point\n                        float *val[4],           //values at each point\n                        float weight[4]) {     //returned weights\n\n  float d;\n  float d1, d2, d3, d4;         //distances to each of the four points\n  float s;\n  float s1, s2, s3, s4;\t\t//side lengths\n  long inds1, indp1;           //indices of adjacent points\n\n  d1=sdist(lon, lat, data->data[inds].lon[indp], data->data[inds].lat[indp]);\n\n  //get four pixels that enclose the test point (if possible):\n  if (inds == data->nscan-1) {\n    inds1=inds-1;\n    d2=sdist(lon, lat, data->data[inds1].lon[indp], data->data[inds1].lat[indp]);\n    s1=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds1].lon[indp], data->data[inds1].lat[indp]);\n  } else if (inds==0) {\n    inds1=1;\n    d2=sdist(lon, lat, data->data[inds1].lon[indp], data->data[inds1].lat[indp]);\n    s1=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds1].lon[indp], data->data[inds1].lat[indp]);\n  } else {\n    //check distance:\n    d2=sdist(lon, lat, data->data[inds+1].lon[indp], data->data[inds+1].lat[indp]);\n    s1=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds+1].lon[indp], data->data[inds+1].lat[indp]);\n    d=sdist(lon, lat, data->data[inds-1].lon[indp], data->data[inds-1].lat[indp]);\n    s=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds-1].lon[indp], data->data[inds-1].lat[indp]);\n    if (d1-d2+s1 >= 0) {\n      inds1=inds+1;\n    } else {\n      inds1=inds-1;\n      d2=d;\n      s1=s;\n    }\n  }\n\n  if (indp == data->np-1) {\n    indp1=indp-1;\n    d4=sdist(lon, lat, data->data[inds].lon[indp1], data->data[inds].lat[indp1]);\n    s4=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds].lon[indp1], data->data[inds].lat[indp1]);\n  } else if (indp == 0) {\n    indp1=1;\n    d4=sdist(lon, lat, data->data[inds].lon[indp1], data->data[inds].lat[indp1]);\n    s4=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds].lon[indp1], data->data[inds].lat[indp1]);\n  } else {\n    d4=sdist(lon, lat, data->data[inds].lon[indp+1], data->data[inds].lat[indp+1]);\n    s4=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds].lon[indp+1], data->data[inds].lat[indp+1]);\n    d=sdist(lon, lat, data->data[inds].lon[indp-1], data->data[inds].lat[indp-1]);\n    s=sdist(data->data[inds].lon[indp], data->data[inds].lat[indp],\n\t\t  data->data[inds].lon[indp-1], data->data[inds].lat[indp-1]);\n    if (d1-d4+s4 >= 0) {\n      indp1=indp+1;\n    } else {\n      indp1=indp-1;\n      d4=d;\n      s4=s;\n    }\n  }\n\n  d3=sdist(lon, lat, data->data[inds1].lon[indp1], data->data[inds1].lat[indp1]);\n/*\n  printf(\"\\n(%10.2f, %10.2f)\\n\\n\", lon, lat);\n  printf(\"(%10.2f, %10.2f)\\n\", data->data[inds].lon[indp], data->data[inds].lat[indp]);\n  printf(\"(%10.2f, %10.2f)\\n\", data->data[inds1].lon[indp], data->data[inds1].lat[indp]);\n  printf(\"(%10.2f, %10.2f)\\n\", data->data[inds1].lon[indp1], data->data[inds1].lat[indp1]);\n  printf(\"(%10.2f, %10.2f)\\n\\n\", data->data[inds].lon[indp1], data->data[inds].lat[indp1]);\n*/\n  //calculate the side lengths:\n  s2=sdist(data->data[inds1].lon[indp], data->data[inds1].lat[indp],\n\t\t  data->data[inds1].lon[indp1], data->data[inds1].lat[indp1]);\n  s3=sdist(data->data[inds].lon[indp1], data->data[inds].lat[indp1],\n\t\t  data->data[inds1].lon[indp1], data->data[inds1].lat[indp1]);\n\n  //use the distances to calculate the weights:\n  weight[0]=(d1-d2-s1)*(d1-d4-s4)/s1/s4/4;\n  weight[1]=(d2-d1-s1)*(d2-d3-s2)/s1/s2/4;\n  weight[2]=(d3-d2-s2)*(d3-d4-s3)/s2/s3/4;\n  weight[3]=(d4-d3-s3)*(d4-d1-s4)/s3/s4/4;\n  \n  //printf(\"%8.3g %8.3g %8.3g %8.3g\\n\", weight[0], weight[1], weight[2], weight[3]);\n\n  //can't forget the values:\n  val[0]=data->data[inds].bt[indp];\n  val[1]=data->data[inds1].bt[indp];\n  val[2]=data->data[inds1].bt[indp1];\n  val[3]=data->data[inds].bt[indp1];\n\n#ifdef PRINT_FLOC \n  printf(\"scan indices:  %d %d\\n\", inds, inds1);\n  printf(\"track indices: %d %d\\n\", indp, indp1);\n#endif\n\n  return;\n\n  printf(\"distances:\\n\\n\");\n  printf(\"%f\\n\", sqrt(d1));\n  printf(\"%f\\n\", sqrt(d2));\n  printf(\"%f\\n\", sqrt(d3));\n  printf(\"%f\\n\\n\", sqrt(d4));\n\n  printf(\"side lengths:\\n\\n\");\n  printf(\"%f\\n\", sqrt(s1));\n  printf(\"%f\\n\", sqrt(s2));\n  printf(\"%f\\n\", sqrt(s3));\n  printf(\"%f\\n\\n\", sqrt(s4));\n\n  printf(\"s11=%f\\n\", -(d2-d1-s1)/sqrt(s1)/2);\n  printf(\"s12=%f\\n\", -(d1-d2-s1)/sqrt(s1)/2);\n\n}\n\namsu_1c_data * amsu_interp_base::get_data_forward(long index) {\n  if (amsu_data[index] != NULL) return amsu_data[index];\n\n  //search from zero-point until index for data to delete:\n  if (nloaded >= maxload) for (long i=0; i<index; i++) {\n    if (amsu_data[i] != NULL) {\n      delete_amsu_1c_data(amsu_data[i]);\n      amsu_data[i]=NULL;\n      nloaded--;\n      break;\n    }\n  }\n\n  //if none found, search backwards from top:\n  if (nloaded >= maxload) for (long i=nfiles-1; i>index; i--) {\n    if (amsu_data[i] != NULL) {\n      delete_amsu_1c_data(amsu_data[i]);\n      amsu_data[i]=NULL;\n      nloaded--;\n      break;\n    }\n  }\n\n\n  amsu_data[index]=(*amsu_reader)(filelist[index], SWAP_ENDIAN);\n  nloaded++;\n\n  return amsu_data[index];\n\n}\n\namsu_1c_data *amsu_interp_base::get_data_backward(long index) {\n  if (amsu_data[index] != NULL) return amsu_data[index];\n\n  if (nloaded >= maxload) for (long i=nfiles-1; i>index; i--) {\n    if (amsu_data[i] != NULL) {\n      delete_amsu_1c_data(amsu_data[i]);\n      amsu_data[i]=NULL;\n      nloaded--;\n      break;\n    }\n  }\n\n  if (nloaded >= maxload) for (long i=0; i<index; i++) {\n    if (amsu_data[i] != NULL) {\n      delete_amsu_1c_data(amsu_data[i]);\n      amsu_data[i]=NULL;\n      nloaded--;\n      break;\n    }\n  }\n\n  amsu_data[index]=(*amsu_reader)(filelist[index], SWAP_ENDIAN);\n  nloaded++;\n\n  return amsu_data[index];\n\n}\n\n", "text_preview": "#include <stdio.h> #include <stdlib.h> #include <string.h> #include \"peteys_tmpl_lib.h\" #include \"amsu_interp_base.h\" #include \"global_metric.h\" #define MAX_LL 200 #define SWAP_ENDIAN 1 #define COMMENT_LENGTH 200 //#define CHECK_INDEX_FILE ", "input_sha256": "6681e97c9e6c38a8496c8519241be93c57b328c59abf466f5e779c4e38d801c8", "text_sha256": "e0811435b7afb41f28201ca9f81b7faa65cc18c69325caa4747d08c7ff5d648f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 37, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5256320929785744, "noise_severity": 0.07218400949826861, "missing_content_severity": 0.054586121587192164, "factual_density": 3.1437217152743058, "pretraining_utility": 0.2890763193988804, "math_density": 2.7569969974032507, "code_density": 3.8507653342641377, "reasoning_density": 1.8001158687199026, "education_level": 2.631471282934807, "creative_writing_quality": -0.37909839076147916}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0038291777870019805, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9968716124228554, "secondary": 0.9923793251613332, "tertiary": 0.9963749889390743, "structured_records": 0.0008502553131984604, "procedural_steps": 0.01009162987151538, "worked_solution": 0.013007003565136774, "contextual_dialogue": 6.60510931113394e-06, "content_quality": 0.39174710722491246, "noise_severity": 0.9398466587514428, "missing_content_severity": 0.9545115653440065, "factual_density": 0.2864347627285879, "pretraining_utility": 0.46920820752572245, "math_density": 0.37907464694380133, "code_density": 0.8756377785534483, "reasoning_density": 0.0, "education_level": 0.3532043690759304, "creative_writing_quality": 0.9991112083216143, "primary_topic": 0.9201687833669203, "secondary_topic": 0.5796155808041983}, "probability_margin": {"primary": 0.9948597540353957, "secondary": 0.9877627036759667, "tertiary": 0.9945964034884399, "education_level": 0.04421958617996086, "creative_writing_quality": 0.9989344435873789, "primary_topic": 0.8535084794080984, "secondary_topic": 0.29834361261610043}, "raw_probabilities": {"primary": {"Academic Writing": 5.3745528325406774e-05, "Content Listing": 1.9682643215151117e-05, "Creative Writing": 1.8040780508578782e-05, "Customer Support Page": 7.043185896935099e-06, "Q&A Forum": 9.94575901700353e-06, "Discussion Thread": 7.22329029063005e-06, "Comment Section": 1.1668572033296246e-05, "Missing Content": 3.8444370368975506e-05, "Knowledge Article": 8.04922650057333e-05, "Legal Notices": 3.537777572466594e-05, "News Article": 4.251788354387996e-06, "Organizational Announcement": 8.574724637106233e-06, "Nonfiction Writing": 1.3000149626063571e-05, "Organizational About Page": 1.0889018754139365e-05, "Personal About Page": 1.158631017336909e-05, "Personal Blog": 6.877219725032923e-06, "Product Page": 6.572184450909136e-06, "Spam / Ads": 8.495140801485033e-06, "Structured Data": 0.0002697350425474061, "Technical Writing": 0.9970019619052364, "Transcript / Interview": 1.555400864034693e-05, "Tutorial / How-To Guide": 0.00021268144458199916, "User Reviews": 5.949022244370781e-06, "Other / Unknown": 0.0021422078698406545}, "secondary": {"Academic Writing": 0.00016607190205035336, "Content Listing": 3.669788451063854e-05, "Creative Writing": 4.762434100550148e-05, "Customer Support Page": 1.932745597599568e-05, "Q&A Forum": 1.731492730411644e-05, "Discussion Thread": 1.5233787017022929e-05, "Comment Section": 3.2594918835176286e-05, "Missing Content": 0.0002353558148180938, "Knowledge Article": 0.00030405197354924714, "Legal Notices": 8.247285350316787e-05, "News Article": 2.0475220266188918e-05, "Organizational Announcement": 2.113715403795155e-05, "Nonfiction Writing": 2.285138918970447e-05, "Organizational About Page": 2.889291019509491e-05, "Personal About Page": 2.42967526795122e-05, "Personal Blog": 1.715018346507642e-05, "Product Page": 1.4038513245857922e-05, "Spam / Ads": 2.2440391692956988e-05, "Structured Data": 0.0007395724356370031, "Technical Writing": 0.004934149603644276, "Transcript / Interview": 2.906792654256072e-05, "Tutorial / How-To Guide": 0.0004604059001550515, "User Reviews": 1.192248106846135e-05, "NONE": 0.992696853279611}, "tertiary": {"Academic Writing": 8.271115493309653e-05, "Content Listing": 3.154375753107418e-05, "Creative Writing": 4.345346494242791e-05, "Customer Support Page": 1.4988850285846216e-05, "Q&A Forum": 1.31579496980689e-05, "Discussion Thread": 1.1083454719383641e-05, "Comment Section": 2.7148239346118007e-05, "Missing Content": 0.00018605928960447162, "Knowledge Article": 0.00018958003496035903, "Legal Notices": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Chenyichen12/cppFinal/blob/e1aea923d03b3cc2a2539396b1777116b722ef2f/client/src/decomposer/decompose_mat.cpp", "id": "cpp-0-9f0dabb8e8c446edf6cc69ca0552931e", "text": "/*\n * @Author: Chenyichen12 <EMAIL>\n * @Date: 2024-05-07 21:24:01\n */\n#include \"decompose_mat.h\"\n#include <qdebug.h>\n#include <utility>\nvoid composer_mat::initMat() {\n  for (int i = 0; i < require; i++) {\n    for (int j = 0; j < require; j++) {\n      (*this)(i + topLeft.first, j + topLeft.second) = 1;\n    }\n  }\n}\n\nbool composer_mat::checkIfNegative(const Eigen::Matrix<int, 6, 6> &mat) {\n  for (int i = 0; i < mat.size(); i++) {\n    if (mat(i) < 0) {\n      return true;\n    }\n  }\n  return false;\n}\n\ncomposer_mat::composer_mat(Eigen::Matrix<int, 6, 6> mat, int require)\n    : Eigen::Matrix<int, 6, 6>(std::move(mat)) {\n  this->topLeft.first = 0;\n  this->topLeft.second = 0;\n  this->require = require;\n  initMat();\n}\ncomposer_mat::composer_mat(int require) {\n  this->setConstant(0);\n  this->topLeft.first = 0;\n  this->topLeft.second = 0;\n  this->require = require;\n  initMat();\n}\n\nvoid composer_mat::setRequire(int r) { this->require = r; }\n\nvoid composer_mat::resetTopLeft() {\n  this->topLeft = std::pair<int, int>(0, 0);\n  this->setConstant(0);\n  for (int i = 0; i < require; i++) {\n    for (int j = 0; j < require; j++) {\n      (*this)(i + topLeft.first, j + topLeft.second) = 1;\n    }\n  }\n}\nbool composer_mat::checkOk(const Eigen::Matrix<int, 6, 6> &mat) {\n  auto newMat = mat - (*this);\n  if (checkIfNegative(newMat)) {\n    return false;\n  }\n  return true;\n}\nbool composer_mat::moveNext() {\n  if (topLeft.second == 6 - require) {\n    topLeft.second = 0;\n    if (topLeft.first == 6 - require) {\n      return false;\n    } else {\n      topLeft.first++;\n    }\n  } else {\n    topLeft.second++;\n  }\n\n  this->setConstant(0);\n  for (int i = 0; i < require; i++) {\n    for (int j = 0; j < require; j++) {\n      (*this)(i + topLeft.first, j + topLeft.second) = 1;\n    }\n  }\n  return true;\n}\n\ndecomposer::decomposer(const Eigen::Matrix<int, 6, 6> &mat) {\n  this->ans_mat = std::make_unique<Eigen::Matrix<int, 6, 6>>(mat);\n  auto needArray = std::array<int, 7>{4, 3, 3, 2, 2, 1, 1};\n  for (int i = 0; i < 7; i++) {\n    mats[i] = std::make_unique<composer_mat>(needArray[i]);\n  }\n  this->ifDecomposer = false;\n}\ndecomposer::decomposer(const QList<int> &array) {\n  if (array.size() != 36) {\n    throw std::invalid_argument(\"array size must be 36\");\n  }\n  this->ans_mat =\n      std::make_unique<Eigen::Matrix<int, 6, 6>>(Eigen::Matrix<int, 6, 6>());\n\n  for (int i = 0; i < array.size(); ++i) {\n    (*this->ans_mat)(i) = array[i];\n  }\n  auto needArray = std::array<int, 7>{4, 3, 3, 2, 2, 1, 1};\n  for (int i = 0; i < 7; i++) {\n    mats[i] = std::make_unique<composer_mat>(needArray[i]);\n  }\n  this->ifDecomposer = false;\n}\n\nbool decomposer::ifGo(int c, const Eigen::Matrix<int, 6, 6> &currentMat) {\n  this->mats[c]->resetTopLeft();\n  auto mat_ptr = this->mats[c].get();\n\n  while (true) {\n    if (!mat_ptr->checkOk(currentMat)) {\n      auto res = mat_ptr->moveNext();\n      if (!res) {\n        return false;\n      }\n      continue;\n    }\n    if (c == 6) {\n      return true;\n    }\n    if (ifGo(c + 1, currentMat - (*mat_ptr))) {\n      this->ifDecomposer = true;\n      return true;\n    } else {\n      auto res = mat_ptr->moveNext();\n      if (!res)\n        return false;\n    }\n  }\n}\nvoid decomposer::Decompose() { ifGo(0, *this->ans_mat); }\nbool decomposer::ifDecompose() const { return this->ifDecomposer; }\nQList<Eigen::Matrix<int, 6, 6>> decomposer::get_ans_mat() {\n  auto res = QList<Eigen::Matrix<int, 6, 6>>();\n\n  for (const auto &item : this->mats) {\n    res.append(*item);\n  }\n  // 翻转这个列表\n  std::reverse(res.begin(), res.end());\n  return std::move(res);\n}\n\n#ifdef TREE_TEST\n#include <gtest/gtest.h>\nclass tree_test {\npublic:\n  static std::array<std::unique_ptr<composer_mat>, 7> getMat() {\n    auto data = decomposer(Eigen::Matrix<int, 6, 6>());\n    return std::move(data.mats);\n  }\n\n  static Eigen::Matrix<int, 6, 6> *get_mat_origin(decomposer *model) {\n    return model->ans_mat.get();\n  }\n};\n\nTEST(decomposer, test_move) {\n  auto s = tree_test::getMat();\n  ASSERT_EQ((*s[0])(0, 0), 1);\n  ASSERT_EQ((*s[0])(3, 3), 1);\n  s[0]->moveNext();\n\n  ASSERT_EQ((*s[0])(3, 4), 1);\n  ASSERT_EQ((*s[0])(0, 0), 0);\n\n  int i = 0;\n  while (s[0]->moveNext()) {\n    i++;\n  }\n  ASSERT_EQ(i, 7);\n}\n\nvoid matEqual(Eigen::Matrix<int, 6, 6> m1, Eigen::Matrix<int, 6, 6> m2) {\n  for (int i = 0; i < 6; i++) {\n    for (int j = 0; j < 6; j++) {\n      ASSERT_EQ(m1(i, j), m2(i, j));\n    }\n  }\n}\nTEST(decomposer, test_complete) {\n\n  auto data = Eigen::Matrix<int, 6, 6>();\n  data << 0, 1, 2, 3, 3, 1, 0, 1, 3, 4, 3, 1, 1, 2, 3, 3, 1, 0, 1, 1, 1, 1, 0,\n      0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0;\n  ASSERT_EQ(data(0, 0), 0);\n  ASSERT_EQ(data(0, 1), 1);\n  auto composer = decomposer(data);\n  composer.Decompose();\n  ASSERT_TRUE(composer.ifDecompose());\n\n  auto matRes = Eigen::Matrix<int, 6, 6>();\n  matRes.setConstant(0);\n  for (const auto &item : composer.get_ans_mat()) {\n    matRes += item;\n  }\n  matEqual(matRes, data);\n}\n\nTEST(decomposer, test_array_construtor) {\n  auto data = QList<int>();\n  for (int i = 0; i < 36; ++i) {\n    data.append(i);\n  }\n  auto model = decomposer(data);\n  auto mat = tree_test::get_mat_origin(&model);\n  for (int i = 0; i < 36; i++) {\n    ASSERT_EQ((*mat)(i), i);\n  }\n\n  ASSERT_EQ((*mat)(1, 1), 7);\n}\n\n#include \"auto_make_question.h\"\nTEST(decompser, test_decoder) {\n  for (int i = 0; i < 100; i++) {\n\n    auto question = auto_make_question::make_one_question();\n    auto model = decomposer(question);\n\n    model.Decompose();\n    if(!model.ifDecompose()){\n      std::cout<<i<<'\\n';\n    }\n  }\n}\n#endif", "text_preview": "/* * @Author: Chenyichen12 <EMAIL> * @Date: 2024-05-07 21:24:01 */ #include \"decompose_mat.h\" #include <qdebug.h> #include <utility> void composer_mat::initMat() { for (int i = 0; i < require; i++) { for (int j = 0; j < require; j++) { (*th", "input_sha256": "0f238b5c80283de628b2aa55472540e6a5e70ef26c95ff8ac3373ab3e267278a", "text_sha256": "9f0dabb8e8c446edf6cc69ca0552931ee03bf59b6fdfe86a7327a6a152155859", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 41, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Mikimikio373/myproject/blob/966a4aa58f1b20c9e834d03d2005e689f5acd526/GrainMatching_in_Stage/GrainMatchig_in_Stage.hpp", "id": "cpp-0-b27d742a6d865f0392fc141b5dfa9714", "text": "#pragma once\n#include <iostream>\n#include <string>\n#include <sstream>\n#include <fstream>\n\n//外部インクルード\n#include <nlohmann/json.hpp> //C:\\Users\\flab\\Documents\\json-develop\\include\n\nusing std::cout;\nusing std::endl;\nusing std::cerr;\nusing std::vector;\nusing std::string;\n\nclass grain\n{\npublic:\n    double x = 0;\n    double y = 0;\n    int flg = 0;\n};\nclass grain_match\n{\npublic:\n    double x = 0;\n    double y = 0;\n    int flg = 0;\n    double x1, x2, y1, y2;\n};\n\n\nvector<string> split(string str, char del) {\n    int first = 0;\n    int last = str.find_first_of(del);\n    vector<string> result;\n    while (first < str.size()) {\n        string subStr(str, first, last - first);\n        result.push_back(subStr);\n        first = last + 1;\n        last = str.find_first_of(del, first);\n        if (last == string::npos) {\n            last = str.size();\n        }\n    }\n    return result;\n}\n\n//optionを探して代入して返す. 1: optionあり, -1: optionエラー\nint getopt(vector<string>& input, double &tar_x, double &tar_y, double &cutrange) {\n\n    int flg = 0;\n\n    for (int i = 0; i < input.size(); i++)\n    {\n        if (input[i] == \"-cutmode\")\n        {\n            try\n            {\n                tar_x = std::stod(input.at(i + 1));\n                tar_y = std::stod(input.at(i + 2));\n                cutrange = std::stod(input.at(i + 3));\n                flg = 1;\n            }\n            catch (const std::exception& tmp)\n            {\n                cerr << \"throw exception in -cutmode\" << endl;\n                return -1;\n            }\n        }\n    }\n    if (flg == 0) { return -1; }\n    else { return 1; }\n}\n\nvoid get_affparam(string path, double& a, double& b, double& c, double& d) {\n    if (std::filesystem::exists(path) == false)\n    {\n        std::cerr << \"threre no file: \" << path << endl;\n        exit(-1);\n    }\n\n    std::ifstream ifs(path);\n    string line;\n    vector<vector<string>> data;\n    while (std::getline(ifs, line))\n    {\n        vector<string> tmp = split(line, ',');\n        data.emplace_back(tmp);\n    }\n    //abcdの代入\n    a = stod(data[1][0]);\n    b = stod(data[1][1]);\n    c = stod(data[1][2]);\n    d = stod(data[1][3]);\n}\n\nvoid get_stage(string path, double& sx, double& sy) {\n    if (std::filesystem::exists(path) == false)\n    {\n        std::cerr << \"threre no file: \" << path << endl;\n        exit(-1);\n    }\n\n    std::ifstream json_f(path, std::ios::in);\n    nlohmann::json j;\n    json_f >> j;\n    json_f.close();\n    sx = j[\"Images\"][0][\"x\"];\n    sy = j[\"Images\"][0][\"y\"];\n}\n\nstring int2string_0set(int input, int digit) {\n    std::ostringstream ss;\n    ss << std::setw(digit) << std::setfill('0') << input;\n    string s(ss.str());\n\n    return s;\n}\n\nvoid read_csv(string path, vector<grain>& input) {\n    std::ifstream ifs(path);\n    string buf;\n    cout << path << \" now reading\" << endl;\n    int num = 0;\n    while (std::getline(ifs, buf))\n    {\n        if (num == 0) { num++; continue; }\n        vector<string> line_data = split(buf, ',');\n        grain tmp;\n        tmp.x = std::stod(line_data[0]);\n        tmp.y = std::stod(line_data[1]);\n        tmp.flg = std::stoi(line_data[2]);\n        input.push_back(tmp);\n    }\n}\n\n\nvoid GrainMatching(const vector<grain>& input1, const vector<grain>& input2, vector<grain>& out, int type, double centerX, double centerY, double cut_pixel)\n{\n    /*type = 0 flag=3をpass, type=1 カット, type=2 全部入れる*/\n\n    for (int i = 0; i < input1.size(); i++)\n    {\n        for (int j = 0; j < input2.size(); j++)\n        {\n            int flg1 = input1.at(i).flg;\n            int flg2 = input2.at(j).flg;\n            grain dist;\n            if (type != 2)\n            {\n                if (flg1 == 3 || flg2 == 3) continue;\n            }\n            //このフラグ判断ベン図的に怪しい\n            if (flg1 == 1 || flg2 == 1)\n            {\n                dist.flg = 1;\n            }\n            if (flg1 == 2 || flg2 == 2)\n            {\n                dist.flg = 2;\n            }\n            if (flg1 == 3 || flg2 == 3)\n            {\n                dist.flg = 3;\n            }\n            dist.x = input1.at(i).x - input2.at(j).x;\n            dist.y = input1.at(i).y - input2.at(j).y;\n            if (type == 1)\n            {\n                if (fabs(dist.x - centerX) > cut_pixel) continue;\n                if (fabs(dist.y - centerY) > cut_pixel) continue;\n            }\n            out.push_back(dist);\n        }\n        if (i == input1.size() - 1)\n        {\n            //cout << \"\\r\" << i << \"/\" << input1.size() << string(12, ' ') << endl;\n        }\n        else\n        {\n            //cout << \"\\r\" << i << \"/\" << input1.size() << string(12, ' ');\n        }\n\n    }\n}\n\n//注意 :input1 - input2を計算\nvoid GrainMatching_in_stage(const vector<grain>& input1, const vector<grain>& input2, vector<grain_match>& out, double tar_x, double tar_y, double cutrange)\n{\n\n    for (int i = 0; i < input1.size(); i++)\n    {\n        for (int j = 0; j < input2.size(); j++)\n        {\n            grain_match dist;\n            dist.x = input1.at(i).x - input2.at(j).x;\n            dist.y = input1.at(i).y - input2.at(j).y;\n            if (cutrange != 0)\n            {\n                if (fabs(dist.x - tar_x) > cutrange || fabs(dist.y - tar_y) > cutrange)\n                {\n                    continue;\n                }\n            }\n            dist.x1 = input1[i].x;\n            dist.y1 = input1[i].y;\n            dist.x2 = input2[j].x;\n            dist.y2 = input2[j].y;\n            out.push_back(dist);\n        }\n    }\n}\n\nvoid change_coordinate(vector<grain>& input, double a, double b, double c, double d)\n{\n    for (int i = 0; i < input.size(); i++)\n    {\n        double x1 = a * input[i].x + b * input[i].y;\n        double y1 = c * input[i].x + d * input[i].y;\n        input[i].x = x1;\n        input[i].y = y1;\n    }\n}\nvoid change_coordinate_yflip(vector<grain>& input, double a, double b, double c, double d)\n{\n    for (int i = 0; i < input.size(); i++)\n    {\n        double x1 = a * input[i].x + b * (double)(input[i].y - 1088.0);\n        double y1 = c * input[i].x + d * (double)(input[i].y - 1088.0);\n        input[i].x = x1;\n        input[i].y = y1;\n    }\n}\n\nvoid pixel2stage(vector<grain>& input, double a, double b, double c, double d, double p, double q, double sx, double sy)\n{\n    double h_width = 1024.0;\n    double h_height = 544.0;\n    for (int i = 0; i < input.size(); i++)\n    {\n        double x = a * (input[i].x - h_width) + b * (input[i].y - h_height) + p + sx;\n        double y = c * (input[i].x - h_width) + d * (input[i].y - h_height) + q + sy;\n        input[i].x = x;\n        input[i].y = y;\n    }\n}\n\nvoid calc_max_bin(const vector<grain>& input, vector<grain>& output, int xbin, double xmin, double xmax, int ybin, double ymin, double ymax)\n{\n    double x_wbin = (xmax - xmin) / xbin;\n    double y_wbin = (ymax - ymin) / ybin;\n    vector<vector<vector<grain>>> box(xbin, vector<vector<grain>>(ybin));\n    for (int i = 0; i < input.size(); i++)\n    {\n        //範囲外にあったらpass\n        if (input[i].x < xmin || input[i].x > xmax || input[i].y < ymin || input[i].y > ymax) { continue; }\n        grain tmp;\n        tmp.x = input[i].x;\n        tmp.y = input[i].y;\n        tmp.flg = input[i].flg;\n        int a = (int)floor((input[i].x - xmin) / x_wbin); //代入するbinの計算\n        int b = (int)floor((input[i].y - ymin) / y_wbin);\n        box[a][b].push_back(tmp); //bin詰め\n    }\n    int maxbinsize = 0;\n    int maxbinx, maxbiny;\n    for (int i = 0; i < xbin; i++)\n    {\n        for (int j = 0; j < ybin; j++)\n        {\n            int size = box[i][j].size();\n            if (size > maxbinsize)\n            {\n                maxbinsize = size;\n                maxbinx = i;\n                maxbiny = j;\n            }\n        }\n    }\n    output = box[maxbinx][maxbiny];\n    for (int i = -1; i < 2; i++)\n    {\n        for (int j = -1; j < 2; j++)\n        {\n            if (i == 0 && j == 0) { continue; }\n            output.insert(output.end(), box[maxbinx + i][maxbiny + j].begin(), box[maxbinx + i][maxbiny + j].end());\n        }\n    }\n}\n\nvoid grain2csv(string filepath, const vector<grain_match>& input, vector<string> label)\n{\n    string file = filepath + \".csv\";\n    FILE* fp = std::fopen(file.c_str(), \"wb\");\n    //一行目(ラベルの記述)\n    for (int i = 0; i < label.size() - 1; i++)\n    {\n        fputs((label.at(i) + \",\").c_str(), fp);\n    }\n    fputs((label.at(label.size() - 1) + \"\\r\\n\").c_str(), fp);\n\n    auto sz = input.size();\n    for (int i = 0; i < sz; i++)\n    {\n        fprintf(fp, \"%.6f,%.6f,%.6f,%.6f,%.6f,%.6f\\r\\n\", input[i].x, input[i].y, input[i].x1, input[i].y1, input[i].x2, input[i].y2);\n    }\n    fclose(fp);\n    cout << \"all out number of grain: \" << input.size() << endl;\n}\n\nvoid grain2csv_skip(string filepath, const vector<grain>& input, vector<string> label)\n{\n    string file = filepath + \".csv\";\n    std::ofstream ofs;\n    FILE* fp = std::fopen(file.c_str(), \"wb\");\n    //一行目(ラベルの記述)\n    for (int i = 0; i < label.size() - 1; i++)\n    {\n        fputs((label.at(i) + \",\").c_str(), fp);\n    }\n    fputs((label.at(label.size() - 1) + \"\\r\\n\").c_str(), fp);\n\n    auto sz = input.size();\n    int cnt = 0;\n    for (int i = 0; i < sz; i++)\n    {\n        if (input[i].flg != 0) { continue; }\n        fprintf(fp, \"%.6f,%.6f,%d\\r\\n\", input[i].x, input[i].y, input[i].flg);\n        cnt++;\n    }\n    fclose(fp);\n    cout << \"skipped object number: \" << cnt << endl;\n}\n\n\nvector<vector<double>> MatchingCount(const vector<vector<double>>& input, const vector<vector<double>>& pair, double thr_pixel)\n{\n    vector<vector<double>> output;\n    for (int i = 0; i < input.size(); i++)\n    {\n        for (int j = 0; j < pair.size(); j++)\n        {\n            double distx = input.at(i).at(0) - pair.at(j).at(0);\n            double disty = input.at(i).at(1) - pair.at(j).at(1);\n            if (fabs(distx) < thr_pixel && fabs(disty) < thr_pixel)\n            {\n                vector<double> out = { input.at(i).at(0), input.at(i).at(1) };\n                output.push_back(out);\n                break;\n            }\n            else\n            {\n                continue;\n            }\n        }\n    }\n    return output;\n}\n\nvector<vector<string>> csv2vector(string filename, int ignore_line_num = 0) {\n    //csvファイルの読み込み\n    std::ifstream reading_file;\n    reading_file.open(filename, std::ios::in);\n    if (!reading_file) {\n        std::vector<std::vector<std::string> > data;\n        return data;\n    }\n    std::string reading_line_buffer;\n    //最初のignore_line_num行を空読みする\n    for (int line = 0; line < ignore_line_num; line++) {\n        getline(reading_file, reading_line_buffer);\n        if (reading_file.eof()) break;\n    }\n\n    //二次元のvectorを作成\n    std::vector<std::vector<std::string> > data;\n    while (std::getline(reading_file, reading_line_buffer)) {\n        if (reading_line_buffer.size() == 0) break;\n        std::vector<std::string> temp_data;\n        temp_data = split(reading_line_buffer, ',');\n        data.push_back(temp_data);\n    }\n    return data;\n}\n", "text_preview": "#pragma once #include <iostream> #include <string> #include <sstream> #include <fstream> //外部インクルード #include <nlohmann/json.hpp> //C:\\Users\\flab\\Documents\\json-develop\\include using std::cout; using std::endl; using std::cerr; using 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Kitware/VTK/blob/41a7329277713cf7c7aa4492133e5d0fb02d57b7/Filters/Modeling/vtkQuadRotationalExtrusionFilter.cxx", "id": "cpp-0-cfbf0cd1b493e14578cf47936f0ee964", "text": "// SPDX-FileCopyrightText: Copyright (c) <NAME>, <NAME>, <NAME>\n// SPDX-License-Identifier: BSD-3-Clause\n#include \"vtkQuadRotationalExtrusionFilter.h\"\n\n#include \"vtkCellArray.h\"\n#include \"vtkCellData.h\"\n#include \"vtkCompositeDataIterator.h\"\n#include \"vtkCompositeDataSet.h\"\n#include \"vtkGenericCell.h\"\n#include \"vtkIdList.h\"\n#include \"vtkInformation.h\"\n#include \"vtkInformationVector.h\"\n#include \"vtkMath.h\"\n#include \"vtkMultiBlockDataSet.h\"\n#include \"vtkObjectFactory.h\"\n#include \"vtkPointData.h\"\n#include \"vtkPolyData.h\"\n\nVTK_ABI_NAMESPACE_BEGIN\nvtkStandardNewMacro(vtkQuadRotationalExtrusionFilter);\n\n//------------------------------------------------------------------------------\n// Create object with capping on, angle of 360 degrees, resolution = 12, and\n// no translation along z-axis.\n// vector (0,0,1), and point (0,0,0).\nvtkQuadRotationalExtrusionFilter::vtkQuadRotationalExtrusionFilter()\n{\n  this->Axis = 2;\n  this->Capping = 1;\n  this->DefaultAngle = 360.0;\n  this->DeltaRadius = 0.0;\n  this->Translation = 0.0;\n  this->Resolution = 12; // 30 degree increments\n}\n\n//------------------------------------------------------------------------------\nint vtkQuadRotationalExtrusionFilter::FillInputPortInformation(int, vtkInformation* info)\n{\n  info->Set(vtkAlgorithm::INPUT_REQUIRED_DATA_TYPE(), \"vtkCompositeDataSet\");\n\n  return 1;\n}\n\n//------------------------------------------------------------------------------\nint vtkQuadRotationalExtrusionFilter::RotateAroundAxis(double blockAngle, vtkIdType numPts,\n  vtkPoints* inPts, vtkPoints* newPts, vtkPointData* pd, vtkPointData* outPD)\n{\n  // Calculate quantities necessary to the sweep operation\n  int idx1;\n  int idx2;\n  if (!this->Axis)\n  {\n    idx1 = 1;\n    idx2 = 2;\n  }\n  else if (this->Axis == 1)\n  {\n    idx1 = 0;\n    idx2 = 2;\n  }\n  else if (this->Axis == 2)\n  {\n    idx1 = 0;\n    idx2 = 1;\n  }\n  else\n  {\n    vtkErrorMacro(<< \"Invalid axis number: \" << this->Axis << \"\\n\");\n    return 0;\n  }\n\n  double radIncr = this->DeltaRadius / this->Resolution;\n  double transIncr = this->Translation / this->Resolution;\n  double angleIncr = vtkMath::RadiansFromDegrees(blockAngle) / this->Resolution;\n  bool abort = false;\n\n  // Sweep over set resolution\n  for (int i = 1; i <= this->Resolution && !abort; ++i)\n  {\n    this->UpdateProgress(.1 + .5 * (i - 1) / this->Resolution);\n    for (vtkIdType ptId = 0; ptId < numPts; ++ptId)\n    {\n      if (this->CheckAbort())\n      {\n        abort = true;\n        break;\n      }\n      // Get point coordinates\n      double x[3];\n      inPts->GetPoint(ptId, x);\n\n      // Convert to cylindrical coordinates\n      double newX[3];\n      double radius = sqrt(x[idx1] * x[idx1] + x[idx2] * x[idx2]);\n      if (radius > 0.)\n      {\n        double tempd = x[idx1] / radius;\n        if (tempd < -1.)\n        {\n          tempd = -1.;\n        }\n        if (tempd > 1.)\n        {\n          tempd = 1.;\n        }\n        double theta = acos(tempd);\n\n        tempd = x[idx2] / radius;\n        if (tempd < -1.)\n        {\n          tempd = -1.;\n        }\n        if (tempd > 1.)\n        {\n          tempd = 1.;\n        }\n        double psi = asin(tempd);\n\n        if (psi < 0.)\n        {\n          if (theta < (vtkMath::Pi() / 2.))\n          {\n            theta = 2. * vtkMath::Pi() + psi;\n          }\n          else\n          {\n            theta = vtkMath::Pi() - psi;\n          }\n        }\n\n        // increment angle\n        radius += i * radIncr;\n        newX[this->Axis] = x[this->Axis] + i * transIncr;\n        newX[idx1] = radius * cos(i * angleIncr + theta);\n        newX[idx2] = radius * sin(i * angleIncr + theta);\n      }\n      else // radius is zero\n      {\n        newX[this->Axis] = x[this->Axis] + i * transIncr;\n        newX[idx1] = 0.;\n        newX[idx2] = 0.;\n      }\n\n      // Update swept mesh\n      newPts->InsertPoint(ptId + i * numPts, newX);\n      outPD->CopyData(pd, ptId, ptId + i * numPts);\n    }\n  }\n\n  return 1;\n}\n\n//------------------------------------------------------------------------------\nint vtkQuadRotationalExtrusionFilter::RequestData(vtkInformation* vtkNotUsed(request),\n  vtkInformationVector** inputVector, vtkInformationVector* outputVector)\n{\n  // get the info objects\n  vtkInformation* inInfo = inputVector[0]->GetInformationObject(0);\n  vtkInformation* outInfo = outputVector->GetInformationObject(0);\n\n  // Get composite input\n  vtkCompositeDataSet* compositeInput =\n    vtkCompositeDataSet::SafeDownCast(inInfo->Get(vtkDataObject::DATA_OBJECT()));\n\n  // Get typed output\n  vtkMultiBlockDataSet* compositeOutput =\n    vtkMultiBlockDataSet::SafeDownCast(outInfo->Get(vtkDataObject::DATA_OBJECT()));\n\n  if (compositeInput == nullptr || compositeOutput == nullptr)\n  {\n    vtkErrorMacro(<< \"Invalid algorithm connection\\n\");\n    return 0;\n  }\n\n  vtkDebugMacro(<< \"input=\" << compositeInput->GetClassName() << \"\\n\");\n\n  // we want the output to have the same structure than the input\n  compositeOutput->CopyStructure(compositeInput);\n\n  // allocate composite iterator\n  vtkCompositeDataIterator* inputIterator = compositeInput->NewIterator();\n  inputIterator->SkipEmptyNodesOff();\n  inputIterator->InitTraversal();\n\n  vtkCompositeDataIterator* outputIterator = compositeOutput->NewIterator();\n  outputIterator->SkipEmptyNodesOff();\n  outputIterator->InitTraversal();\n\n  while (inputIterator->IsDoneWithTraversal() == 0)\n  {\n    if (this->CheckAbort())\n    {\n      break;\n    }\n    // get the input and output\n    int blockId = inputIterator->GetCurrentFlatIndex();\n    vtkPolyData* input = vtkPolyData::SafeDownCast(inputIterator->GetCurrentDataObject());\n    inputIterator->GoToNextItem();\n    if (!input)\n    {\n      outputIterator->GoToNextItem();\n      continue;\n    }\n\n    vtkPolyData* output = vtkPolyData::New();\n    compositeOutput->SetDataSet(outputIterator, output);\n    outputIterator->GoToNextItem();\n    output->Delete();\n\n    vtkIdType numPts, numCells;\n    numPts = input->GetNumberOfPoints();\n    numCells = input->GetNumberOfCells();\n\n    if (numPts > 0 && numCells > 0)\n    {\n      // Retrieve angle for each block, or angle by default\n      double blockAngle = this->GetDefaultAngle();\n      vtkDebugMacro(<< \"DefaultAngle=\" << blockAngle << \"\\n\");\n\n      std::map<vtkIdType, double>::iterator amit = this->PerBlockAngles.find(blockId);\n      if (amit != this->PerBlockAngles.end())\n      {\n        vtkDebugMacro(<< \"Found angle \" << amit->second << \" for block \" << blockId << \"\\n\");\n        blockAngle = amit->second;\n      }\n\n      vtkDebugMacro(<< \"process block \" << blockId << \", angle=\" << blockAngle\n                    << \", resolution=\" << this->Resolution << \", obj=\" << output << \"\\n\");\n\n      vtkPointData* pd = input->GetPointData();\n      vtkCellData* cd = input->GetCellData();\n      vtkPolyData* mesh;\n      vtkPoints* inPts;\n      vtkCellArray *inVerts, *inLines, *inPolys, *inStrips;\n      int numEdges;\n      const vtkIdType* pts = nullptr;\n      vtkIdType ptId, ncells;\n      vtkPoints* newPts;\n      vtkCellArray *newLines = nullptr, *newPolys, *newStrips = nullptr;\n      vtkCell* edge;\n      vtkIdList* cellIds;\n      vtkIdType p1, p2;\n      vtkPointData* outPD = output->GetPointData();\n      vtkCellData* outCD = output->GetCellData();\n      int abort = 0;\n\n      // Initialize / check input\n      vtkDebugMacro(<< \"Rotationally extruding data\");\n\n      // Build cell data structure.\n      mesh = vtkPolyData::New();\n      inPts = input->GetPoints();\n      inVerts = input->GetVerts();\n      inLines = input->GetLines();\n      inPolys = input->GetPolys();\n      inStrips = input->GetStrips();\n      mesh->SetPoints(inPts);\n      mesh->SetVerts(inVerts);\n      mesh->SetLines(inLines);\n      mesh->SetPolys(inPolys);\n      mesh->SetStrips(inStrips);\n      if (inPolys || inStrips)\n      {\n        mesh->BuildLinks();\n      }\n\n      // Allocate memory for output. We don't copy normals because surface geometry\n      // is modified.\n      outPD->CopyNormalsOff();\n      outPD->CopyAllocate(pd, (this->Resolution + 1) * numPts);\n      newPts = vtkPoints::New();\n      newPts->Allocate((this->Resolution + 1) * numPts);\n      if ((ncells = inVerts->GetNumberOfCells()) > 0)\n      {\n        newLines = vtkCellArray::New();\n        newLines->AllocateEstimate(ncells, this->Resolution + 1);\n      }\n      // arbitrary initial allocation size\n      ncells = inLines->GetNumberOfCells() + inPolys->GetNumberOfCells() / 10 +\n        inStrips->GetNumberOfCells() / 10;\n      ncells = (ncells < 100 ? 100 : ncells);\n      newPolys = vtkCellArray::New();\n      newPolys->AllocateEstimate(ncells, 2 * (this->Resolution + 1));\n      outCD->CopyNormalsOff();\n      outCD->CopyAllocate(cd, ncells);\n\n      // copy points\n      for (ptId = 0; ptId < numPts; ptId++) // base level\n      {\n        newPts->InsertPoint(ptId, inPts->GetPoint(ptId));\n        outPD->CopyData(pd, ptId, ptId);\n      }\n      this->UpdateProgress(0.1);\n\n      // Rotate around selected axis\n      if (!this->RotateAroundAxis(blockAngle, numPts, inPts, newPts, pd, outPD))\n      {\n        return 0;\n      }\n\n      // To ensure that cell attributes are in consistent order with the\n      // cellId's, we process the verts, lines, polys and strips in order.\n      vtkIdType newCellId = 0;\n      int type;\n      vtkIdType npts = 0;\n      if (newLines) // there are verts which produce lines\n      {\n        for (vtkIdType cellId = 0; cellId < numCells && !abort; ++cellId)\n        {\n          if (this->CheckAbort())\n          {\n            abort = true;\n            break;\n          }\n          type = mesh->GetCellType(cellId);\n          if (type == VTK_VERTEX || type == VTK_POLY_VERTEX)\n          {\n            mesh->GetCellPoints(cellId, npts, pts);\n            for (int i = 0; i < npts; ++i)\n            {\n              ptId = pts[i];\n              newLines->InsertNextCell(this->Resolution + 1);\n              for (int j = 0; j <= this->Resolution; ++j)\n              {\n                newLines->InsertCellPoint(ptId + j * numPts);\n              }\n              outCD->CopyData(cd, cellId, newCellId++);\n            }\n          } // if a vertex or polyVertex\n        }   // for all cells\n      }     // if there are verts generating lines\n      this->UpdateProgress(0.25);\n      abort = this->CheckAbort();\n\n      // If capping is on, copy 2D cells to output ( plus create cap ). Notice\n      // that polygons are done first, then strips.\n      //\n      if (this->Capping &&\n        (blockAngle != 360.0 || this->DeltaRadius != 0.0 || this->Translation != 0.0))\n      {\n        if (inPolys->GetNumberOfCells() > 0)\n        {\n          // newPolys = vtkCellArray::New();\n          // newPolys->Allocate( inPolys->GetSize() );\n\n          for (vtkIdType cellId = 0; cellId < numCells && !abort; ++cellId)\n          {\n            if (this->CheckAbort())\n            {\n              abort = true;\n              break;\n            }\n            type = mesh->GetCellType(cellId);\n            if (type == VTK_TRIANGLE || type == VTK_QUAD || type == VTK_POLYGON)\n            {\n              mesh->GetCellPoints(cellId, npts, pts);\n              newPolys->InsertNextCell(npts, pts);\n              outCD->CopyData(cd, cellId, newCellId++);\n              newPolys->InsertNextCell(npts);\n              for (int i = 0; i < npts; ++i)\n              {\n                vtkIdType idx = pts[i] + this->Resolution * numPts;\n                newPolys->InsertCellPoint(idx);\n              }\n              outCD->CopyData(cd, cellId, newCellId++);\n            }\n          }\n        }\n\n        if (inStrips->GetNumberOfCells() > 0)\n        {\n          newStrips = vtkCellArray::New();\n          newStrips->AllocateCopy(inStrips);\n\n          for (vtkIdType cellId = 0; cellId < numCells && !abort; ++cellId)\n          {\n            if (this->CheckAbort())\n            {\n              abort = true;\n              break;\n            }\n            type = mesh->GetCellType(cellId);\n            if (type == VTK_TRIANGLE_STRIP)\n            {\n              mesh->GetCellPoints(cellId, npts, pts);\n              newStrips->InsertNextCell(npts, pts);\n              outCD->CopyData(cd, cellId, newCellId++);\n              newStrips->InsertNextCell(npts);\n              for (int i = 0; i < npts; ++i)\n              {\n                vtkIdType idx = pts[i] + this->Resolution * numPts;\n                newStrips->InsertCellPoint(idx);\n              }\n              outCD->CopyData(cd, cellId, newCellId++);\n            }\n          }\n        }\n      } // if capping\n      this->UpdateProgress(0.5);\n      abort = this->CheckAbort();\n\n      // Now process lines, polys and/or strips to produce strips\n      //\n      if (inLines->GetNumberOfCells() || inPolys->GetNumberOfCells() ||\n        inStrips->GetNumberOfCells())\n      {\n        cellIds = vtkIdList::New();\n        cellIds->Allocate(VTK_CELL_SIZE);\n        vtkGenericCell* cell = vtkGenericCell::New();\n\n        for (vtkIdType cellId = 0; cellId < numCells && !abort; ++cellId)\n        {\n          if (this->CheckAbort())\n          {\n            abort = true;\n            break;\n          }\n          type = mesh->GetCellType(cellId);\n          if (type == VTK_LINE || type == VTK_POLY_LINE)\n          {\n            mesh->GetCellPoints(cellId, npts, pts);\n            for (int i = 0; i < (npts - 1); ++i)\n            {\n              p1 = pts[i];\n              p2 = pts[i + 1];\n              vtkIdType idxPt;\n              for (int k = 0; k < this->Resolution; ++k)\n              {\n                newPolys->InsertNextCell(4);\n                idxPt = p1 + k * numPts;\n                newPolys->InsertCellPoint(idxPt);\n                idxPt = p2 + k * numPts;\n                newPolys->InsertCellPoint(idxPt);\n                idxPt = p2 + (k + 1) * numPts;\n                newPolys->InsertCellPoint(idxPt);\n                idxPt = p1 + (k + 1) * numPts;\n                newPolys->InsertCellPoint(idxPt);\n                outCD->CopyData(cd, cellId, newCellId++);\n              }\n            }\n          } // if a line\n\n          else if (type == VTK_TRIANGLE || type == VTK_QUAD || type == VTK_POLYGON ||\n            type == VTK_TRIANGLE_STRIP)\n          { // create strips from boundary edges\n            mesh->GetCell(cellId, cell);\n            numEdges = cell->GetNumberOfEdges();\n            for (int i = 0; i < numEdges; ++i)\n            {\n              edge = cell->GetEdge(i);\n              for (int j = 0; j < (edge->GetNumberOfPoints() - 1); ++j)\n              {\n                p1 = edge->PointIds->GetId(j);\n                p2 = edge->PointIds->GetId(j + 1);\n                mesh->GetCellEdgeNeighbors(cellId, p1, p2, cellIds);\n\n                if (cellIds->GetNumberOfIds() < 1) // generate strip\n                {\n                  vtkIdType idxPt;\n                  for (int k = 0; k < this->Resolution; ++k)\n                  {\n                    newPolys->InsertNextCell(4);\n                    idxPt = p1 + k * numPts;\n                    newPolys->InsertCellPoint(idxPt);\n                    idxPt = p2 + k * numPts;\n                    newPolys->InsertCellPoint(idxPt);\n                    idxPt = p2 + (k + 1) * numPts;\n                    newPolys->InsertCellPoint(idxPt);\n                    idxPt = p1 + (k + 1) * numPts;\n                    newPolys->InsertCellPoint(idxPt);\n                    outCD->CopyData(cd, cellId, newCellId++);\n                  }\n                } // if boundary edge\n              }   // for each sub-edge\n            }     // for each edge\n          }       // for each polygon or triangle strip\n        }         // for all cells\n\n        cellIds->Delete();\n        cell->Delete();\n      } // if strips are being generated\n      this->UpdateProgress(1.00);\n\n      // Update ourselves and release memory\n      output->SetPoints(newPts);\n      newPts->Delete();\n      mesh->Delete();\n\n      if (newLines)\n      {\n        output->SetLines(newLines);\n        newLines->Delete();\n      }\n\n      if (newPolys)\n      {\n        output->SetPolys(newPolys);\n        newPolys->Delete();\n      }\n\n      if (newStrips)\n      {\n        output->SetStrips(newStrips);\n        newStrips->Delete();\n      }\n\n      output->Squeeze();\n\n    } /* if numPts>0 && numCells>0 */\n\n  } /* Iterate over input blocks */\n\n  inputIterator->Delete();\n  outputIterator->Delete();\n\n  return 1;\n}\n\n//------------------------------------------------------------------------------\nvoid vtkQuadRotationalExtrusionFilter::RemoveAllPerBlockAngles()\n{\n  vtkDebugMacro(<< \"RemoveAllPerBlockAngles\\n\");\n  this->PerBlockAngles.clear();\n  this->Modified();\n}\n\n//------------------------------------------------------------------------------\nvoid vtkQuadRotationalExtrusionFilter::AddPerBlockAngle(vtkIdType blockId, double angle)\n{\n  vtkDebugMacro(<< \"PerBlockAngles[\" << blockId << \"]=\" << angle << \"\\n\");\n  this->PerBlockAngles[blockId] = angle;\n  this->Modified();\n}\n\n//------------------------------------------------------------------------------\nvoid vtkQuadRotationalExtrusionFilter::PrintSelf(ostream& os, vtkIndent indent)\n{\n  this->Superclass::PrintSelf(os, indent);\n\n  os << indent << \"Axis: \" << this->Axis << \"\\n\";\n  os << indent << \"Resolution: \" << this->Resolution << \"\\n\";\n  os << indent << \"Capping: \" << (this->Capping ? \"On\\n\" : \"Off\\n\");\n  os << indent << \"DefaultAngle: \" << this->DefaultAngle << \"\\n\";\n  os << indent << \"Translation: \" << this->Translation << \"\\n\";\n  os << indent << \"Delta Radius: \" << this->DeltaRadius << \"\\n\";\n  os << indent << \"PerBlockAngles:\\n\";\n  for (std::map<vtkIdType, double>::iterator it = this->PerBlockAngles.begin();\n       it != this->PerBlockAngles.end(); ++it)\n  {\n    os << indent.GetNextIndent() << \"Block #\" << it->first << \" -> \" << it->second << \"\\n\";\n  }\n}\nVTK_ABI_NAMESPACE_END\n", "text_preview": "// SPDX-FileCopyrightText: Copyright (c) <NAME>, <NAME>, <NAME> // SPDX-License-Identifier: BSD-3-Clause #include \"vtkQuadRotationalExtrusionFilter.h\" #include \"vtkCellArray.h\" #include \"vtkCellData.h\" #include \"vtkCompositeDataIterator.h\" ", "input_sha256": "1fc32c736bc1066560edf267d0ef0ff757a4d36d7de3568ef0b246d570c8ad1c", "text_sha256": "cfbf0cd1b493e14578cf47936f0ee9641e97b7c50129d8e75b6ad91d10bbecaf", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 39, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.21198562471458, "noise_severity": 0.16552966861625934, "missing_content_severity": 0.02712871953826972, "factual_density": 3.2803425740708683, "pretraining_utility": 0.5378874839105591, "math_density": 2.65059151371768, "code_density": 3.875651233313236, "reasoning_density": 1.5298847970325533, "education_level": 3.105943028745884, "creative_writing_quality": -0.2711966622033253}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.4951 below 0.70", "secondary vs NONE margin 0.0135 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.34444759094453814, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.997850887188463, "secondary": 0.4731927226559, "tertiary": 0.8185174718949777, "structured_records": 0.006739765239383091, "procedural_steps": 0.01376155411594248, "worked_solution": 0.009656562924687348, "contextual_dialogue": 4.820316033984559e-06, "content_quality": 0.5350287431200698, "noise_severity": 0.8620586094864506, "missing_content_severity": 0.9773927337181085, "factual_density": 0.4002854783923897, "pretraining_utility": 0.4340312774919789, "math_density": 0.3851651243287507, "code_density": 0.89637602776103, "reasoning_density": 0.0, "education_level": 0.5031642841655528, "creative_writing_quality": 0.9992253908126991, "primary_topic": 0.9329023772949518, "secondary_topic": 0.4581898201573885}, "probability_margin": {"primary": 0.9969339903092732, "secondary": 0.013511372923755482, "tertiary": 0.6680673360384638, "education_level": 0.3237203784231138, "creative_writing_quality": 0.9990423318043874, "primary_topic": 0.8749872010156994, "secondary_topic": 0.06742402481891135}, "raw_probabilities": {"primary": {"Academic Writing": 3.428990637112533e-05, "Content Listing": 1.4196047773494476e-05, "Creative Writing": 9.326479998517767e-06, "Customer Support Page": 6.57358263866832e-06, "Q&A Forum": 6.489458804371867e-06, "Discussion Thread": 4.426327562321428e-06, "Comment Section": 9.188123094302872e-06, "Missing Content": 1.9465694126161288e-05, "Knowledge Article": 6.06675011438441e-05, "Legal Notices": 0.0004544778464841003, "News Article": 3.0418817195788266e-06, "Organizational Announcement": 8.520935297356514e-06, "Nonfiction Writing": 5.368705277862191e-06, "Organizational About Page": 7.170992706368219e-06, "Personal About Page": 4.7479864500221235e-06, "Personal Blog": 3.628101839394844e-06, "Product Page": 4.874349798740428e-06, "Spam / Ads": 5.475256605945134e-06, "Structured Data": 0.00021408354759728404, "Technical Writing": 0.9979404335556105, "Transcript / Interview": 9.383511271772125e-06, "Tutorial / How-To Guide": 0.00016387327225244136, "User Reviews": 3.853689238635384e-06, "Other / Unknown": 0.0010064432463372892}, "secondary": {"Academic Writing": 0.0006710355714760657, "Content Listing": 0.0005231949560985659, "Creative Writing": 0.0004944264538184051, "Customer Support Page": 0.0003000611693911549, "Q&A Forum": 0.00028774419936294606, "Discussion Thread": 0.00031137269915482065, "Comment Section": 0.00047697130602236427, "Missing Content": 0.0006604044133934532, "Knowledge Article": 0.0010011754200468963, "Legal Notices": 0.4951430258785709, "News Article": 0.0005253418728941559, "Organizational Announcement": 0.0005510026555588271, "Nonfiction Writing": 0.00045064174094088834, "Organizational About Page": 0.0005257979853795618, "Personal About Page": 0.0003210604037206449, "Personal Blog": 0.00023569153211091295, "Product Page": 0.000357795187556817, "Spam / Ads": 0.00036234158389768684, "Structured Data": 0.0008909841464102927, "Technical Writing": 0.012865220210058044, "Transcript / Interview": 0.0005555890853148282, "Tutorial / How-To Guide": 0.0006015496869231346, "User Reviews": 0.0002559188870831071, "NONE": 0.4816316529548154}, "tertiary": {"Academic Writing": 0.0003705225107936308, "Content Listing": 0.00022711967870078624, "Creative Writing": 0.0002883824748484509, "Customer Support Page": 0.00016406938447027848, "Q&A Forum": 0.00012444668661071968, "Discussion Thread": 0.00011769056462729116, "Comment Section": 0.00024323340221956148, "Missing Content": 0.00029459689956671815, "Knowledge Article": 0.0007769937401320868, "Legal Notices": 0.15801190786088984, "News Article": 0.0002460146754648314, "Organizational Announcement": 0.000241943395843812, "Nonfiction Writing": 0.0001912292317449167, "Organizational About Page": 0.0002818230213851793, "Personal About Page": 0.00014643894367357823, "Personal Blog": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ifedorov17/LU-decomposition/blob/aa892fefd7d46860c606e2e52cfbb3ccd4bf293e/LU_Master.cpp", "id": "cpp-0-5464d93ae383ac27bbcff35d441c417b", "text": "#include \"LU_Master.h\"\n\nvoid LU_Master::LU(CSR A, std::vector<std::vector<double>>& L, std::vector<std::vector<double>>& U)\n{\n\tunsigned size = A.getSize();\n\n\tL.resize(size);\n\tU.resize(size);\n\tfor (int i = 0; i < size; i++) {\n\t\tL[i].resize(size);\n\t\tU[i].resize(size);\n\t}\n\n\tfor (int i = 0; i < size; i++) {\n\t\tfor (int j = 0; j < size; j++) {\n\t\t\tU[i][j] = A.get(i, j);\n\t\t\tL[j][i] = U[j][i] / U[i][i];\n\t\t}\n\t}\n\t\n\tfor (int k = 1; k < size; k++)\n\t{\n#pragma omp parallel for schedule(static)\n\t\tfor (int i = k - 1; i < size; i++) {\n\t\t\tfor (int j = i; j < size; j++) {\n\t\t\t\tL[j][i] = U[j][i] / U[i][i];\n\t\t\t}\n\t\t}\n\n#pragma omp parallel for schedule(static)\n\t\tfor (int i = k; i < size; i++) {\n\t\t\tfor (int j = k - 1; j < size; j++) {\n\t\t\t\tU[i][j] = U[i][j] - L[i][k - 1] * U[k - 1][j];\n\t\t\t}\n\t\t}\n\t}\n}\n\nvoid LU_Master::proisv(std::vector<std::vector <double>> A, std::vector<std::vector <double>> B,\n\tstd::vector<std::vector<double>>& R, unsigned size)\n{\n    #pragma omp parallel for schedule(static)\n\tfor (int i = 0; i < size; i++) {\n\t\tfor (int j = 0; j < size; j++) {\n\t\t\tfor (int k = 0; k < size; k++) {\n\t\t\t\tR[i][j] += A[i][k] * B[k][j];\n\t\t\t}\n\t\t}\n\t}\n}\n\nCSR LU_Master::parse_matrix(std::string path)\n{\n\tstd::vector<double> aelem;\n\tstd::vector<int> jptr;\n\tstd::vector<int> iptr;\n\tstd::vector<std::tuple<int, int, double>> tmpMatrix;\n\n\tstd::string tmp;\n\tdouble num;\n\n\tstd::ifstream file_stream(path);\n\tif (!file_stream.is_open())\n\t{\n\t\tstd::cout << \"ERROR\" << std::endl;\n\t\tthrow(std::exception()); \n\t}\n\telse\n\t{\n\t\twhile (std::getline(file_stream, tmp))\n\t\t{\n\t\t\tstd::regex num(\"[+-]?([0-9]+([.][0-9]*)?|[.][0-9]+)\");\n\t\t\tstd::sregex_token_iterator tokenIt(tmp.cbegin(), tmp.cend(), num);\n\t\t\tint row = std::stoi(*tokenIt);\n\t\t\ttokenIt++;\n\t\t\tint col = std::stoi(*tokenIt);\n\t\t\ttokenIt++;\n\t\t\tdouble value = std::stod(*tokenIt);\n\t\t\ttmpMatrix.push_back(std::make_tuple(row, col, value));\n\t\t}\n\t\tstd::sort(\n\t\t\ttmpMatrix.begin(),\n\t\t\ttmpMatrix.end(),\n\t\t\t[](const auto& a, const auto& b)\n\t\t\t{\n\t\t\t\tif (std::get<0>(a) == std::get<0>(b)) return (std::get<1>(a) < std::get<1>(b));\n\t\t\t\treturn\t(std::get<0>(a) < std::get<0>(b));\n\t\t\t}\n\t\t);\n\n\t\tint prev_row = -1;\n\n\t\tfor (int i = 0; i <  tmpMatrix.size(); i++)\n\t\t{\n\t\t\taelem.push_back(std::get<2>(tmpMatrix[i]));\n\t\t\tjptr.push_back(std::get<1>(tmpMatrix[i]));\n\t\t\tif (std::get<0>(tmpMatrix[i]) != prev_row)\n\t\t\t{\n\t\t\t\tiptr.push_back(i+1);\n\t\t\t\tprev_row = std::get<0>(tmpMatrix[i]); \n\t\t\t}\n\n\t\t}\n\t\tiptr.push_back(aelem.size()+1); \n\t}\n\t\n\tCSR result(aelem, jptr, iptr, iptr.size() - 1);\n\n\treturn result;\n}\n\ndouble LU_Master::sumY(unsigned size, std::vector<std::vector<double>> L, std::vector<double> b, std::vector<double> y, int i)\n{\n\tdouble result = 0.;\n\tfor (int k = 0; k < i; k++)\n\t{\n\t\tresult += L[i][k] * y[k];\n\t}\n\treturn result;\n}\n\ndouble LU_Master::sumX(unsigned size, std::vector<std::vector<double>> U, std::vector<double> x, int i)\n{\n\tdouble result = 0.;\n\tfor (int k = size - 1; k > i; k--)\n\t{\n\t\tresult += U[i][k] * x[k];\n\t}\n\treturn result;\n}\n\nvoid LU_Master::solver(unsigned size, std::vector<std::vector<double>> L, std::vector<std::vector<double>> U, std::vector<double> b, std::vector<double>& y, std::vector<double>& x)\n{\n\tfor (int i = 0; i < size; i++)\n\t{\n\t\ty[i] = (b[i] - LU_Master::sumY(size, L, b, y, i)) / L[i][i];\n\t}\n\tfor (int i = size - 1; i >= 0; i--)\n\t{\n\t\tx[i] = (y[i] - LU_Master::sumX(size, U, x, i)) / U[i][i];\n\t}\n}\n", "text_preview": "#include \"LU_Master.h\" void LU_Master::LU(CSR A, std::vector<std::vector<double>>& L, std::vector<std::vector<double>>& U) { unsigned size = A.getSize(); L.resize(size); U.resize(size); for (int i = 0; i < size; i++) { L[i].resize(size); U[", "input_sha256": "6a86b6d649c74609b9646f8562a07fa5a3f24f5e625036a35bcb6b3a139110c9", "text_sha256": "5464d93ae383ac27bbcff35d441c417b1c46731527283c452c65ce97606c3834", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 42, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5180198161992854, "noise_severity": 0.01574581178057859, "missing_content_severity": 0.06734592199506113, "factual_density": 3.3689354395989994, "pretraining_utility": 0.5785366433235721, "math_density": 3.2892391257613083, "code_density": 3.8552426979097687, "reasoning_density": 0.472537765771912, "education_level": 2.4263874181192797, "creative_writing_quality": -0.45021703055326034}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5964 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0037504803637758988, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9967139658341235, "secondary": 0.9935397042201011, "tertiary": 0.9974532489475193, "structured_records": 0.0012853176880885378, "procedural_steps": 0.017124094310023767, "worked_solution": 0.006193453867128381, "contextual_dialogue": 4.686431532440972e-06, "content_quality": 0.411045969431429, "noise_severity": 0.9868784901828512, "missing_content_severity": 0.943878398337449, "factual_density": 0.47411286633249927, "pretraining_utility": 0.4630206303816615, "math_density": 0.40769927146775686, "code_density": 0.8793689149248073, "reasoning_density": 0.6062185285234067, "education_level": 0.5266670377368204, "creative_writing_quality": 0.9995789789278704, "primary_topic": 0.8785737314358544, "secondary_topic": 0.5802761469354173}, "probability_margin": {"primary": 0.9951816361883301, "secondary": 0.9902953548839593, "tertiary": 0.9961962901124256, "education_level": 0.25694209818637087, "creative_writing_quality": 0.9995065757236641, "primary_topic": 0.7703075062556126, "secondary_topic": 0.39609139576967933}, "raw_probabilities": {"primary": {"Academic Writing": 6.523777015623144e-05, "Content Listing": 1.7913161428209788e-05, "Creative Writing": 2.1761316387787436e-05, "Customer Support Page": 7.491236455929899e-06, "Q&A Forum": 1.1634559894007768e-05, "Discussion Thread": 7.783244980715003e-06, "Comment Section": 1.2646514580681296e-05, "Missing Content": 3.683707895175908e-05, "Knowledge Article": 0.00013103499702238482, "Legal Notices": 2.8536674687351302e-05, "News Article": 4.750311217998906e-06, "Organizational Announcement": 1.1906439550500031e-05, "Nonfiction Writing": 1.3335603497091212e-05, "Organizational About Page": 1.0536053964043526e-05, "Personal About Page": 1.287289945109078e-05, "Personal Blog": 7.720587615979567e-06, "Product Page": 7.514756285560985e-06, "Spam / Ads": 1.1824428907277273e-05, "Structured Data": 0.00048019942648809343, "Technical Writing": 0.9968508839243684, "Transcript / Interview": 2.1273967686437824e-05, "Tutorial / How-To Guide": 0.0005504915027858338, "User Reviews": 6.565807598516381e-06, "Other / Unknown": 0.0016692477360382416}, "secondary": {"Academic Writing": 0.00040564374343203687, "Content Listing": 3.173151815981765e-05, "Creative Writing": 4.661033968999035e-05, "Customer Support Page": 1.656945093479422e-05, "Q&A Forum": 1.4841644700151392e-05, "Discussion Thread": 1.2759874347652422e-05, "Comment Section": 2.51179327149383e-05, "Missing Content": 0.0001781942867503058, "Knowledge Article": 0.00020325812863864997, "Legal Notices": 4.884544642335997e-05, "News Article": 1.9724957861533725e-05, "Organizational Announcement": 2.4613064075683852e-05, "Nonfiction Writing": 1.981815161034859e-05, "Organizational About Page": 2.4698453497661464e-05, "Personal About Page": 2.158349175832599e-05, "Personal Blog": 1.5979120859770773e-05, "Product Page": 1.2298278209554867e-05, "Spam / Ads": 2.0365986861560846e-05, "Structured Data": 0.0009976399312316715, "Technical Writing": 0.003513528326970931, "Transcript / Interview": 2.9007542117647653e-05, "Tutorial / How-To Guide": 0.0004979147938313517, "User Reviews": 1.0372324392089525e-05, "NONE": 0.9938088832109302}, "tertiary": {"Academic Writing": 0.00011384756439510903, "Content Listing": 1.7982173585426915e-05, "Creative Writing": 2.970218311195981e-05, "Customer Support Page": 9.94863407259568e-06, "Q&A Forum": 7.723588421361829e-06, "Discussion Thread": 6.401049624524328e-06, "Comment Section": 1.4825556183770519e-05, "Missing Content": 9.34476751075147e-05, "Knowledge 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4.686431532440972e-06}, "content_quality": {"-2": 0.024638739457012983, "-1": 0.08714531438965813, "0": 0.3461556630911447, "1": 0.4296779566213989, "2": 0.1123823264407853}, "noise_severity": {"0": 0.9865032148158858, "1": 0.012037574095970336, "2": 0.0009054021340700114, "3": 0.0003178023998269491, "4": 0.00023600655424684487}, "missing_content_severity": {"0": 0.9659992281444473, "1": 0.006515661491763702, "2": 0.022227655232322115, "3": 0.004654870487213748, "4": 0.0006025846442529855}, "factual_density": {"0": 0.008162155906557342, "1": 0.01574891725629317, "2": 0.1107385447043023, "3": 0.3296920955972874, "4": 0.5356582865355597}, "pretraining_utility": {"-2": 0.009474820603625004, "-1": 0.0731253401399334, "0": 0.3582441580184752, "1": 0.4476997378051771, "2": 0.11145594343278917}, "math_density": {"0": 0.009968874090133315, "1": 0.016653092392502778, "2": 0.12106483694710628, "3": 0.3787964268064375, "4": 0.47351676976382}, "code_density": {"0": 0.0002597880128783971, "1": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Sameh20200218AI/Problem4_Tree_Data_Structure/blob/04271280e00a31078e575fd707edaf060790e037/main.cpp", "id": "cpp-0-0b9cd4367e0bfc3e599f71cde93ff223", "text": " /*\n                                             problem 4\n                                            ------------\n\n   Members                                          ID\n  <NAME>                          20200548\n  <NAME>                             20200060\n  <NAME>                               20200343\n  <NAME>                              20200218\n\n */\n#include <iostream>\n\nusing namespace std;\nstruct TreeNode\n{\n    int val;\n    TreeNode * left ;\n    TreeNode * right;\n    TreeNode()\n    {\n        val = 0;\n        left = right = 0;\n    }\n     TreeNode(int x)\n    {\n        val = x;\n        left = right = 0;\n    }\n     TreeNode(int x,TreeNode*left,TreeNode*right)\n    {\n        val = x;\n        left =  left ;\n        right =right ;\n    }\n};\nTreeNode * newnode(int data)\n {\n     TreeNode * temp_node = new TreeNode;\n     temp_node->left = 0;\n     temp_node->right = 0;\n     temp_node->val = data;\n     return temp_node;\n }\n class Solution\n {\n public:\n    bool fliping(TreeNode * root1 , TreeNode*root2)\n    {\n        if(root1==0 && root2==0) // tree is empty\n            return true;\n        if( root1->val ==root2->val)\n            return fliping(root1->left , root2->right ) && fliping( root1->right , root2->left);\n        return false;\n    }\n    bool issymmetric(TreeNode *root)\n    {\n        return fliping(root,root);\n    }\n };\nint main()\n{\n  cout <<\"                                      First Test Case \" <<endl;\n    cout << \"                                     ----------------\";\n    cout << endl;\n    TreeNode * p1 = newnode(1);\n    p1->left = newnode(2);\n    p1->right = newnode(3);\n\n    Solution s1;\n     cout <<\" The data in nodes in the tree by preorder traversal is : 1  2  3 \" << endl;\n\n     if (s1.issymmetric(p1)==1){\n     cout << \" TRUE \\n\";\n        cout << \" Tree is Symmetric\"<<endl;}\n        else\n        {\n             cout << \" FALSE \\n\";\n            cout << \" Tree is  not Symmetric \"<<endl;\n        }\n\n         cout << \"------------------------------------------------------------\";\n    cout << endl;\n     cout <<\"                                      Second Test Case \" <<endl;\n    cout << \"                                     ----------------\";\n    cout << endl;\n    TreeNode * p2 = newnode(1);\n    p2->left = newnode(2);\n    p2->right = newnode(2);\n    p2->left->left = newnode(3);\n    p2->left->right = newnode(4);\n    p2->right->left = newnode(4);\n    p2->right->right = newnode(3);\n\n    Solution s2;\n     cout <<\" The data in nodes in the tree by preorder traversal is : 1 2 2 3 4 4 3 \" << endl;\n\n     if (s2.issymmetric(p2)==1){\n     cout << \" TRUE \\n\";\n        cout << \" Tree is Symmetric\"<<endl;}\n        else\n        {\n             cout << \" FALSE \\n\";\n            cout << \" Tree is  not Symmetric \"<<endl;\n        }\n\n         cout << \"------------------------------------------------------------\";\n    cout << endl;\n     cout <<\"                                      Third Test Case \" <<endl;\n    cout << \"                                     ----------------\";\n    cout << endl;\n    TreeNode * p3 = newnode(1);\n    p3->left = newnode(NULL);\n    p3->right = newnode(NULL);\n\n    Solution s3;\n     cout <<\" The data in nodes in the tree by preorder traversal is : 1 NULL NULL \" << endl;\n\n     if (s3.issymmetric(p3)==1){\n     cout << \" TRUE \\n\";\n        cout << \" Tree is Symmetric\"<<endl;}\n        else\n        {\n             cout << \" FALSE \\n\";\n            cout << \" Tree is  not Symmetric \"<<endl;\n        }\n\n         cout << \"------------------------------------------------------------\";\n    cout << endl;\n     cout <<\"                                      Fourth Test Case \" <<endl;\n    cout << \"                                     ----------------\";\n    cout << endl;\n    TreeNode * p4 = newnode(NULL);\n\n    Solution s4;\n     cout <<\" The data in nodes in the tree by preorder traversal is : NULL \" << endl;\n\n     if (s4.issymmetric(p4)==1){\n     cout << \" TRUE \\n\";\n        cout << \" Tree is Symmetric\"<<endl;}\n        else\n        {\n             cout << \" FALSE \\n\";\n            cout << \" Tree is  not Symmetric \"<<endl;\n        }\n\n         cout << \"------------------------------------------------------------\";\n    cout << endl;\n     cout <<\"                                      Fifth Test Case \" <<endl;\n    cout << \"                                     ----------------\";\n    cout << endl;\n    TreeNode * p5 = newnode(1);\n    p5->left = newnode(2);\n    p5->right = newnode(2);\n    p5->left->left = newnode(NULL);\n    p5->left->right = newnode(3);\n    p5->right->left = newnode(NULL);\n    p5->right->right = newnode(3);\n\n    Solution s5;\n     cout <<\" The data in nodes in the tree by preorder traversal is : 1  2  2 NULL 3 NULL 3\" << endl;\n\n     if (s5.issymmetric(p5)==1){\n     cout << \" TRUE \\n\";\n        cout << \" Tree is Symmetric\"<<endl;}\n        else\n        {\n             cout << \" FALSE \\n\";\n            cout << \" Tree is  not Symmetric \"<<endl;\n        }\n\n         cout << \"------------------------------------------------------------\";\n    cout << endl;\n\n    return 0;\n}\n", "text_preview": "/* problem 4 ------------ Members ID <NAME> 20200548 <NAME> 20200060 <NAME> 20200343 <NAME> 20200218 */ #include <iostream> using namespace std; struct TreeNode { int val; TreeNode * left ; TreeNode * right; TreeNode() { val = 0; left = rig", "input_sha256": "d8fc26639cf4795e5d22c710a66b5b119e79f23284cee4ced7b2e8b54d125e00", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Hecksher/software-basico/blob/ab34df903ece54eea3d7851a0f3f2f091f473d7d/code/simulador1.cpp", "id": "cpp-0-1b287ae6db99187f73904a772068e653", "text": "\n/*  MONTADOR\n *  <NAME> - 10/0129323\n *  <NAME> - 10/0119140\n */\n\n\n\n#include <iostream>\n#include <fstream>\n#include <vector>\n\nint main(int argc, char * argv[])\n{\n    std::fstream myfile(argv[1], std::ios_base::in);\n    std::vector<int> obj;\n\n    int num;\n    int i = 0, pc =0, acc = 0;\n    bool stop = false;\n\n\n    while (myfile >> num)\n    {\n        obj.push_back(num);\n    }\n\n\n    while ( stop == false )\n    {\n    \t\n\n    \tswitch (obj[pc])\n        {\n            case 0x1:\n\t\t    \t//std::cout << \"ADD \" << obj[obj[pc+1]] << std::endl;\n\t\t    \tacc = acc + obj[obj[pc+1]];\n   \t\t    \tpc += 2;\n                break;\n\n            case 0x2:\n\t\t    \t//std::cout << \"SUB \" << obj[obj[pc+1]] << std::endl;\n\t\t    \tacc = acc - obj[obj[pc+1]];\n   \t\t    \tpc += 2;\n   \t\t    \t//std::cout << \"Resultado \" << acc << std::endl;\n\n                break;\n\n            case 0x3:\n   \t\t    \t//std::cout << \"MUL \" << obj[obj[pc+1]] << std::endl;\n   \t\t    \tacc = acc*obj[obj[pc+1]];\n   \t\t    \t//std::cout << \"Resultado \" << acc << std::endl;\n\n   \t\t    \tpc += 2;\n                break;\n\n            case 0x4:\n   \t\t    \t//std::cout << \"DIV \" << obj[obj[pc+1]] << std::endl;\n   \t\t    \tif ( obj[obj[pc+1]] == 0 )\n   \t\t    \t{\n   \t\t    \t\tstd::cout << \"Erro: divisão por zero.\" << std::endl;\n   \t\t    \t\tstop = true;\n   \t\t    \t}\n   \t\t    \telse\n   \t\t    \t{\n   \t\t    \t\tacc = acc/obj[obj[pc+1]];\n   \t\t    \t\t//std::cout << \"Resultado \" << acc << std::endl;\n   \t\t    \t\tpc += 2;\n  \t\t    \t}\n                break;\n\n            case 0x5:\n   \t\t    \t//std::cout << \"JMP \" << obj[pc+1] << std::endl;\n   \t\t    \tpc=obj[pc+1];\n                break;\n\n            case 0x6:\n   \t\t    \t//std::cout << \"JMPN \" << obj[pc+1] << std::endl;\n   \t\t    \tif (acc<0)\n   \t\t    \t\tpc=obj[pc+1];\n   \t\t    \telse\n   \t\t    \t\tpc += 2;\n                break;\n\n            case 0x7:\n   \t\t    \t//std::cout << \"JMPP \" << obj[pc+1] << \" \" << acc << std::endl;\n   \t\t    \tif ( acc > 0){ \n   \t\t    \t\tpc=obj[pc+1];\n   \t\t    \t\t   \t\t    \t\t//stop = true;\n   \t\t    \t}\n   \t\t    \telse\n   \t\t    \t\tpc += 2;\n                break;\n\n            case 0x8:\n   \t\t    \tif (acc==0){\n   \t\t    \t\t//std::cout << \"JMPZ \" << obj[pc+1] << std::endl;\n\n   \t\t    \t\tpc=obj[pc+1];\n   \t\t    \t}\n   \t\t    \telse\n   \t\t    \t\tpc += 2;\n                break;\n\n            case 0x9:\n   \t\t    \t//std::cout << \"COPY \" << obj[obj[pc+1]] << \" em \" << obj[obj[pc+2]] << std::endl;\n   \t\t    \tobj[obj[pc+2]] = obj[obj[pc+1]];\n   \t\t    \tpc += 3;\n                break;\n\n            case 0xA:\n                //std::cout << \"LOAD \" << obj[obj[pc+1]] << std::endl;\n                acc = obj[obj[pc+1]];\n                pc += 2;\n\n                break;\n\n            case 0xB:\n                //std::cout << \"STORE \" << obj[obj[pc+1]] << std::endl;\n                obj[obj[pc+1]] = acc;\n                pc += 2;\n                break;\n\n            case 0xC:\n                std::cout << \"INPUT: \";\n                std::cin >> obj[obj[pc+1]];\n                pc += 2;\n\n                break;\n\n            case 0xD:\n                std::cout << \"OUTPUT: \" << obj[obj[pc+1]] << std::endl;\n\n                pc += 2;\n                break;\n\n            case 0xE:\n                \n                stop = true;\n\n                break;\n\n            default:\n                std::cout << \"Programa sem STOP.\" << std::endl;\n                stop= true;\n                break;\n        }\n\n\n 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/huhaoo/code/blob/3ba5bb024db1be4bfd6067f2f64fb9dccce01026/template/3_ntt.cpp", "id": "cpp-0-26ed85521fffbb6bdc66e0599b5293c4", "text": "/***************************************************************\n\tFile name: 3_ntt.cpp\n\tAuthor: huhao\n\tCreate time: Wed 06 Nov 2019 11:46:41 AM CST\n***************************************************************/\n#include<stdio.h>\n#include<string.h>\n#include<math.h>\n#include<algorithm>\nusing namespace std;\n#define fr(i,a,b) for(int i=(a),end_##i=(b);i<=end_##i;i++)\n#define fd(i,a,b) for(int i=(a),end_##i=(b);i>=end_##i;i--)\nint read()\n{\n\tint r=0,t=1,c=getchar();\n\twhile(c<'0'||c>'9')\n\t{\n\t\tt=c=='-'?-1:1;\n\t\tc=getchar();\n\t}\n\twhile(c>='0'&&c<='9')\n\t{\n\t\tr=r*10+c-48;\n\t\tc=getchar();\n\t}\n\treturn r*t;\n}\n#define i64 long long\ni64 power(i64 a,i64 b,i64 p)\n{\n\ti64 r=1;\n\twhile(b)\n\t{\n\t\tif(b&1)\n\t\t\tr=r*a%p;\n\t\ta=a*a%p;\n\t\tb>>=1;\n\t}\n\treturn r;\n}\nint check(i64 p)\n{\n\tfr(i,2,sqrt(p))\n\t\tif(p%i==0)\n\t\t\treturn 0;\n\treturn 1;\n}\ni64 getg(i64 n)\n{\n\ti64 a[20],c=0;\n\ti64 k=n-1;\n\tfr(i,2,sqrt(k))\n\t\tif(k%i==0)\n\t\t{\n\t\t\ta[++c]=i;\n\t\t\twhile(k%i==0)\n\t\t\t\tk/=i;\n\t\t\tend_i=sqrt(k);\n\t\t}\n\tif(k!=1)\n\t\ta[++c]=k;\n\tfr(g,2,n)\n\t{\n\t\tint flag=1;\n\t\tfr(i,1,c)\n\t\t\tif(power(g,(n-1)/a[i],n)==1)\n\t\t\t{\n\t\t\t\tflag=0;\n\t\t\t\tbreak;\n\t\t\t}\n\t\tif(flag)\n\t\t\treturn g;\n\t}\n\treturn -1;\n}\nvoid find_ntt_mod(i64 p,i64 cnt,i64 g)//2^p|mod-1\n{\n\ti64 i=1;\n\twhile(cnt)\n\t{\n\t\tif(check((i<<p)+1)&&getg((i<<p)+1)==g)\n\t\t{\n\t\t\tprintf(\"%lld*2^%lld=%lld,g=%lld\\n\",i,p,(i<<p)+1,getg((i<<p)+1));\n\t\t\tcnt--;\n\t\t}\n\t\ti++;\n\t}\n}\nconst i64 N=(1<<19)|10;\ni64 l,r[N];\nvoid init(int n)\n{\n\tl=0;\n\twhile((1<<l)<n)\n\t\tl++;\n\tfr(i,0,(1<<l)-1)\n\t\tr[i]=(r[i>>1]>>1)|((i&1)<<(l-1));\n}\nstruct calcNTT\n{\n\ti64 mod,g,ig;\n\tvoid set(i64 Mod=0,i64 G=0)\n\t{\n\t\tmod=Mod;\n\t\tg=G;\n\t\tig=power(g,mod-2,mod);\n\t}\n\tvoid NTT(i64 *a,i64 opt)\n\t{\n\t\tfr(i,0,(1<<l)-1)\n\t\t\tif(i<r[i])\n\t\t\t\tswap(a[i],a[r[i]]);\n\t\tfr(i,0,l-1)\n\t\t{\n\t\t\ti64 wn=power(opt==1?g:ig,(mod-1)>>(i+1),mod);\n\t\t\tfor(int j=0;j<(1<<l);j+=(1<<(i+1)))\n\t\t\t{\n\t\t\t\ti64 w=1;\n\t\t\t\tfr(k,0,(1<<i)-1)\n\t\t\t\t{\n\t\t\t\t\ti64 x=a[j+k],y=w*a[j+k+(1<<i)]%mod;\n\t\t\t\t\ta[j+k]=(x+y)%mod;\n\t\t\t\t\ta[j+k+(1<<i)]=(x-y+mod)%mod;\n\t\t\t\t\tw=w*wn%mod;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif(opt==-1)\n\t\t{\n\t\t\ti64 I=power(1<<l,mod-2,mod);\n\t\t\tfr(i,0,(1<<l)-1)\n\t\t\t\ta[i]=a[i]*I%mod;\n\t\t}\n\t}\n\ti64 _a[N],_b[N];\n\tvoid mul(i64 *o,i64 *a,i64 *b,i64 n,i64 m)\n\t{\n\t\tinit(n+m+1);\n\t\tfr(i,0,(1<<l)-1)\n\t\t\t_a[i]=_b[i]=o[i]=0;\n\t\tfr(i,0,n)\n\t\t\t_a[i]=a[i];\n\t\tfr(i,0,m)\n\t\t\t_b[i]=b[i];\n\t\tNTT(_a,1);NTT(_b,1);\n\t\tfr(i,0,(1<<l)-1)\n\t\t\to[i]=_a[i]*_b[i]%mod;\n\t\tNTT(o,-1);\n\t}\n}NTT1,NTT2,NTT3;\ni64 o1[N],o2[N],o3[N];\nvoid mul(i64 *o,i64 *a,i64 *b,i64 n,i64 m,i64 mod)\n{\n\tNTT1.mul(o1,a,b,n,m);\n\tNTT2.mul(o2,a,b,n,m);\n\tNTT3.mul(o3,a,b,n,m);\n\t// o = o1 + m1 k1 = o2 + m2 k2 = o3 + m3 k3\n\ti64 m1=NTT1.mod,m2=NTT2.mod,m3=NTT3.mod;\n\tfr(i,0,n+m)\n\t{\n\t\ti64 k1=(o2[i]-o1[i]%m2+m2)%m2*power(m1,m2-2,m2)%m2;\n\t\ti64 _o=o1[i]+k1*m1;\n\t\ti64 _k=(o3[i]-_o%m3+m3)%m3*power(m1*m2%m3,m3-2,m3)%m3;\n\t\to[i]=(_o%mod+_k%mod*m1%mod*m2%mod)%mod;\n\t}\n}\ni64 o[N],a[N],b[N],n,m,mod;\nint main()\n{\n/*\tprintf(\"%lld\\n\",getg(998244353));\n\tfind_ntt_mod(21,30,3);*/\n\tNTT1.set(469762049,3);\n\tNTT2.set(998244353,3);\n\tNTT3.set(1004535809,3);\n\tn=read();\n\tm=read();\n\tmod=read();\n\tfr(i,0,n)\n\t\ta[i]=read();\n\tfr(i,0,m)\n\t\tb[i]=read();\n\tmul(o,a,b,n,m,mod);\n\tfr(i,0,n+m)\n\t\tprintf(\"%lld%c\",o[i],i==n+m?'\\n':' ');\n\treturn 0;\n}\n", "text_preview": "/*************************************************************** File name: 3_ntt.cpp Author: huhao Create time: Wed 06 Nov 2019 11:46:41 AM CST ***************************************************************/ #include<stdio.h> #include<str", "input_sha256": "e8bc292fbb425a2efa7b5a8b5f81edbecb5afa593226759a715a5f7c83fa1a34", "text_sha256": "26ed85521fffbb6bdc66e0599b5293c4770f132d53d84bbba8d3d9208565b75c", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 38, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7357996216346625, "noise_severity": 0.13248817360556933, "missing_content_severity": 0.016181814629272262, "factual_density": 3.277723138040034, "pretraining_utility": 0.6078818717691921, "math_density": 3.2036069896867096, "code_density": 3.8618168439442115, "reasoning_density": 0.8216193007626053, "education_level": 2.5312880125250987, "creative_writing_quality": -0.1418346515909268}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.009246430729781041, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9961005857103271, "secondary": 0.9821309199275847, "tertiary": 0.9917793693847476, "structured_records": 0.0012007584651886794, "procedural_steps": 0.00595935098202189, "worked_solution": 0.01360544297921127, "contextual_dialogue": 5.179462853946919e-06, "content_quality": 0.4086423598468536, "noise_severity": 0.8895931886620256, "missing_content_severity": 0.9865151544756064, "factual_density": 0.39810261503336175, "pretraining_utility": 0.45761268225211393, "math_density": 0.3363391580722581, "code_density": 0.8848473699535097, "reasoning_density": 0.31531724936449557, "education_level": 0.43591879912251247, "creative_writing_quality": 0.9986977303545461, "primary_topic": 0.9393761075587411, "secondary_topic": 0.4623976301620581}, "probability_margin": {"primary": 0.9942200464068995, "secondary": 0.9738900654135937, "tertiary": 0.9886656406392023, "education_level": 0.17786100005638827, "creative_writing_quality": 0.9982932889896113, "primary_topic": 0.8859501862599355, "secondary_topic": 0.07802006713661652}, "raw_probabilities": {"primary": {"Academic Writing": 5.384511238374614e-05, "Content Listing": 2.4102931314587326e-05, "Creative Writing": 2.5680533566775674e-05, "Customer Support Page": 8.40934029393944e-06, "Q&A Forum": 1.3161692896213514e-05, "Discussion Thread": 8.889923094005938e-06, "Comment Section": 1.3628452787261587e-05, "Missing Content": 7.569835823109246e-05, "Knowledge Article": 0.00018050609357942677, "Legal Notices": 2.899044354010036e-05, "News Article": 5.608919378559814e-06, "Organizational Announcement": 1.10375363123219e-05, "Nonfiction Writing": 1.760163717994795e-05, "Organizational About Page": 1.4161060804504476e-05, "Personal About Page": 1.9732337584659185e-05, "Personal Blog": 1.146476704858762e-05, "Product Page": 6.766193455958708e-06, "Spam / Ads": 1.3874036044477752e-05, "Structured Data": 0.0002445610531132957, "Technical Writing": 0.9962630613057302, "Transcript / Interview": 1.579784258122556e-05, "Tutorial / How-To Guide": 0.0008925288113555143, "User Reviews": 7.876718892900948e-06, "Other / Unknown": 0.0020430148988306645}, "secondary": {"Academic Writing": 0.00033828352962006074, "Content Listing": 9.965365102802696e-05, "Creative Writing": 0.00014715015813643152, "Customer Support Page": 4.823625575104146e-05, "Q&A Forum": 4.565932701346568e-05, 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2.6000755298636665e-05, "Q&A Forum": 2.3261726802422682e-05, "Discussion Thread": 2.245017404299962e-05, "Comment Section": 4.7262581566882504e-05, "Missing Content": 0.0007641008735753818, "Knowledge Article": 0.0006481887497653411, "Legal Notices": 0.000327407225781018, "News Article": 3.5746813084939485e-05, "Organizational Announcement": 3.92505723540104e-05, "Nonfiction Writing": 5.7988341492419996e-05, "Organizational About Page": 6.869343545174507e-05, "Personal About Page": 0.00016774810994420408, "Personal Blog": 9.769695203842103e-05, "Product Page": 2.502742981538746e-05, "Spam / Ads": 4.038991279824608e-05, "Structured Data": 0.0005247878548528773, "Technical Writing": 0.0034562550211808497, "Transcript / Interview": 3.736614254014721e-05, "Tutorial / How-To Guide": 0.001091256479633397, "User Reviews": 2.0404023134763697e-05, "NONE": 0.9921218956603831}, "structured_records": {"false": 0.9987992415348114, "true": 0.0012007584651886794}, "procedural_steps": {"false": 0.9940406490179781, "true": 0.00595935098202189}, "worked_solution": {"false": 0.9863945570207887, "true": 0.01360544297921127}, "contextual_dialogue": {"false": 0.9999948205371461, "true": 5.179462853946919e-06}, "content_quality": {"-2": 0.02003656423378566, "-1": 0.08395511730815243, "0": 0.2588948459568947, "1": 0.41439907759194805, "2": 0.22271439490921907}, "noise_severity": {"0": 0.8723481403263769, "1": 0.12478905129320557, "2": 0.0015724945965887786, "3": 0.0006071220161286496, "4": 0.0006831917677000578}, "missing_content_severity": {"0": 0.9867292958301952, "1": 0.011243446251364003, "2": 0.0013741688328349452, "3": 0.00042232563018605333, "4": 0.00023076345542005257}, "factual_density": {"0": 0.011085480728066376, "1": 0.024994408687957805, "2": 0.11876363663607764, "3": 0.36542443971167166, "4": 0.4797320342362265}, "pretraining_utility": {"-2": 0.009764237116405278, "-1": 0.08092170380393982, "0": 0.3303553358070401, "1": 0.44958539673928694, "2": 0.1293733265333277}, "math_density": {"0": 0.017856964765576328, "1": 0.029573163195886557, "2": 0.13368453326927393, "3": 0.3688765951247774, "4": 0.4500087436444857}, "code_density": {"0": 0.0002696316753130402, "1": 0.0004967481460416546, "2": 0.011689580486023856, "3": 0.1122352239443636, "4": 0.8753088157482578}, "reasoning_density": {"0": 0.7101928878815695, "1": 0.04254583002784433, "2": 0.0696856782785013, "3": 0.07060030107058081, "4": 0.10697530274150396}, "education_level": {"none": 0.001996579994292699, "0": 0.0022529404037115596, "1": 0.014943942379530575, "2": 0.5299323326020937, "3": 0.35207133254570544, "4": 0.09880287207466595}, "creative_writing_quality": {"not_applicable": 0.9989147752954551, "-2": 9.285697888858317e-05, "-1": 0.00019596783058114918, "0": 0.0006214863058437932, "1": 0.00012206785797155606, "2": 5.284573125987921e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.027509987765962e-05, "Art & Design": 0.00012231214137181109, "Law & Crime": 0.00011517002068699407, 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4.4354999112955816e-05, "Travel & Tourism": 8.689286859667818e-05, "Other / Uncovered Subject": 0.00034227899742545327, "Unknown / Insufficient Content": 0.00015298571443694118}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006208781065548199, "Art & Design": 0.0005790179312050238, "Law & Crime": 0.0005924273833038191, "Education & Careers": 0.00045702138600959863, "Entertainment & Media": 0.00038625020061545793, "Fashion & Beauty": 0.00031431790526781047, "Business, Economics & Finance": 0.0004829792434210716, "Food & Dining": 0.00027032474477343635, "Games & Gaming": 0.0004932388867838482, "Electronics & Hardware": 0.000560061654425193, "Health, Healthcare & Wellness": 0.00039191952975688995, "History & Geography": 0.00038273120768599674, "Home, Pets & Hobbies": 0.00029302190958864054, "Industry & Production": 0.00039044221727024483, "Language, Literature & Philosophy": 0.0002465523627788865, "Politics & Government": 0.00023161997722557346, "Religion & Spirituality": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ishandutta2007/codeforces-l/blob/fb4b845b2415d4ddb3f4be2083ba023aba44fb73/ludo/normal/877/E.cpp", "id": "cpp-0-16e64a9e6e7c7749ae429275907af1ed", "text": "#include<bits/stdc++.h>\n\nusing namespace std;\n\n#define x first\n#define y second\n#define pb push_back\n#define eb emplace_back\n#define rep(i,a,b) for(auto i=(a);i!=(b); ++i)\n#define all(v) (v).begin(), (v).end()\n#define rs resize\n\ntypedef long long ll;\ntypedef pair<int, int> pii;\ntypedef vector<int> vi;\ntypedef vector<vi> vvi;\n\nconst int INF = 2147483647; // (1 << 30) - 1 + (1 << 30)\nconst ll LLINF = (1LL << 62) - 1 + (1LL << 62); // = 9.223.372.036.854.775.807\nconst double PI = acos(-1.0);\n\n// lambda-expression: [] (args) -> retType { body }\n\n#define DBG(x) cerr << __LINE__ << \": \" << #x << \" = \" << (x) << endl\n\nconst bool LOG = false;\nvoid Log() {\n\tif(LOG) cerr << \"\\n\\n\";\n}\ntemplate<class T, class... S>\nvoid Log(T t, S... s) {\n\tif(LOG) cerr << t << \"\\t\", Log(s...);\n}\n\ntemplate<class T>\nusing min_queue = priority_queue<T, vector<T>, greater<T>>;\n\nstruct ST {\n\tvi val, lazy; int n;\n\tST(int _n, vi init) : val(2 * _n), lazy(2 * _n), n(_n) {\n\t\tfor (int i = 0; i < n; i++)\n\t\t\tval[i + n] = init[i];\n\t\tfor (int i = n; --i > 0; )\n\t\t\tval[i] = val[2*i] + val[2*i+1];\n\t}\n\n\tvoid push(int idx, int nl, int nr) {\n\t\tif (lazy[idx]) {\n\t\t\tval[idx] = (nr - nl) - val[idx];\n\t\t\t// cerr << \"EVAL LAZY [\" << nl << \", \" << nr << \"): \" << val[idx] << endl;\n\t\t\tif (idx < n) {\n\t\t\t\tlazy[2*idx] ^= 1;\n\t\t\t\tlazy[2*idx+1] ^= 1;\n\t\t\t}\n\t\t\tlazy[idx] = 0;\n\t\t}\n\t}\n\n\tvoid update(int idx, int nl, int nr, int ql, int qr) {\n\t\tif (ql <= nl && nr <= qr) {\n\t\t\t// update this now\n\t\t\tlazy[idx] ^= 1;\n\t\t\tthis->push(idx, nl, nr);\n\t\t\t// cerr << \"LAZY [\" << nl << \", \" << nr << \"): \" << val[idx] << endl;\n\t\t\treturn;\n\t\t} else if (ql >= nr || qr <= nl) {\n\t\t\tthis->push(idx, nl, nr);\n\t\t\treturn;\n\t\t}\n\t\tint nm = (nl + nr) / 2;\n\t\tthis->push(idx, nl, nr);\n\t\tthis->update(2*idx, nl, nm, ql, qr);\n\t\tthis->update(2*idx+1, nm, nr, ql, qr);\n\t\tval[idx] = val[2*idx]+val[2*idx+1];\n\t\t// cerr << \"SET [\" << nl << \", \" << nr << \"): \" << val[idx] << endl;\n\t}\n\n\tvoid update(int l, int r) {\n\t\tthis->update(1, 0, n, l, r);\n\t}\n\n\tint query(int idx, int nl, int nr, int ql, int qr) {\n\t\tthis->push(idx, nl, nr);\n\t\tif (ql <= nl && nr <= qr) {\n\t\t\t// update this now\n\t\t\t// cerr << \"QUERY [\" << nl << \", \" << nr << \"): \" << val[idx] << endl;\n\t\t\treturn val[idx];\n\t\t} else if (ql >= nr || qr <= nl) return 0;\n\t\tint nm = (nl + nr) / 2;\n\t\treturn this->query(2*idx, nl, nm, ql, qr)\n\t\t\t + this->query(2*idx+1, nm, nr, ql, qr);\n\t}\n\n\tint query(int l, int r) {\n\t\treturn this->query(1, 0, n, l, r);\n\t}\n};\n\nconst int maxn = 1 << 18;\nint N, lidx[maxn], ridx[maxn], order[maxn], age = 0;\nvi E[maxn];\n\nvoid dfs(int u)\n{\n\torder[age] = u;\n\tlidx[u] = age++;\n\tfor (int v : E[u]) dfs(v);\n\tridx[u] = age;\n}\n\nint main() {\n\tios_base::sync_with_stdio(false);\n\tcin.tie(NULL);\n\n\tcin >> N;\n\tfor (int i = 2; i <= N; i++) {\n\t\tint p;\n\t\tcin >> p;\n\t\tE[p].pb(i);\n\t}\n\n\tdfs(1);\n\tvi init(maxn, 0);\n\tfor (int i = 1; i <= N; i++) {\n\t\tint t; cin >> t;\n\t\tinit[lidx[i]] = t;\n\t}\n\tST tree(maxn, init);\n\n\t/*for (int i = 1; i <= N; i++) {\n\t\tcerr << i << \": \" << lidx[i] << \", \" << ridx[i] << endl;\n\t}\n\tfor (int i = 0; i < N; i++) \n\t\tcerr << init[i] << \" \";\n\tcerr << endl;\n\t*/\n\n\tint Q;\n\tcin >> Q;\n\twhile (Q--) {\n\t\tint v; string s;\n\t\tcin >> s >> v;\n\t\tint l = lidx[v], r = ridx[v];\n\t\tif (s[0] == 'g') {\n\t\t\tcout << tree.query(l, r) << endl;\n\t\t} else {\n\t\t\ttree.update(l, r);\n\t\t}\n\t}\n\treturn 0;\n}", "text_preview": "#include<bits/stdc++.h> using namespace std; #define x first #define y second #define pb 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If the source is\n// shorter than the destination, the destination is padded with blank\n// characters.\nvoid copyCStrToCharArray (const char* source, char* dest, int ndest) {\n  \n  // Get the length of the source C string.\n  int nsource = strlen(source);\n  \n  // Only perform the copy if the source can fit into the destination.\n  if (nsource < ndest) {\n\n    // Copy the string.\n    strcpy(dest,source);\n\n    // Fill in the rest of the string with blanks.\n    for (int i = nsource; i < ndest; i++)\n      dest[i] = ' ';\n  }\n}\n\n// Return true if the two strings are the same. The second input\n// argument must be a C-style string (a null-terminated character\n// array), but the first input argument need not be one. We only\n// compare the two strings up to the length of \"cstr\".\nbool strIsEqualToCStr (const char* str, const char* cstr) {\n\n  // Get the length of the C string.\n  int n = strlen(cstr);\n\n  return !strncmp(str,cstr,n);\n}\n\n// Function definitions for class Program.\n// -----------------------------------------------------------------\nProgram::Program (int n, double* x, double* lb, double* ub, int* btype, \n\t\t  int m, int maxiter, double factr, double pgtol) {\n  this->n       = n;\n  this->x       = x;\n  this->lb      = lb;\n  this->ub      = ub;\n  this->btype   = btype;\n  this->m       = m;\n  this->maxiter = maxiter;\n  this->factr   = factr;\n  this->pgtol   = pgtol;\n  this->iprint  = defaultprintlevel;\n  this->owner   = false;\n\n  initStructures();\n}\n\nProgram::Program (int n, int m, int maxiter, double factr, double pgtol) {\n  this->n       = n;\n  this->x       = new double[n];\n  this->lb      = new double[n];\n  this->ub      = new double[n];\n  this->btype   = new int[n];\n  this->m       = m;\n  this->maxiter = maxiter;\n  this->factr   = factr;\n  this->pgtol   = pgtol;\n  this->iprint  = defaultprintlevel;\n  this->owner   = true;\n\n  initStructures();\n}\n\nProgram::~Program() { \n  if (owner) {\n    delete[] x;\n    delete[] lb;\n    delete[] ub;\n    delete[] btype;\n  }\n\n  delete[] g;\n  delete[] wa;\n  delete[] iwa;\n}\n\nSolverExitStatus Program::runSolver() {\n  SolverExitStatus status = success;  // The return value.\n\n  // Initialize the objective function and gradient to zero.\n  f = 0;\n  for (int i = 0; i < n; i++)\n    g[i] = 0;\n\n  // This initial call sets up the structures for L-BFGS.\n  callLBFGS(\"START\");\n\n  // Repeat until we've reached the maximum number of iterations.\n  int t = 0;\n  while (true) {\n\n    // Do something according to the \"task\" from the previous call to\n    // L-BFGS.\n    if (strIsEqualToCStr(task,\"FG\")) {\n\n      // Evaluate the objective function and the gradient of the\n      // objective at the current point.\n      f = computeObjective(n,x);\n      computeGradient(n,x,g);\n    } else if (strIsEqualToCStr(task,\"NEW_X\")) {\n\n      // Go to the next iteration and call the iterative callback\n      // routine.\n      t++;\n      iterCallback(t,x,f);\n\n      // If we've reached the maximum number of iterations, terminate\n      // the optimization.\n      if (t == maxiter) {\n\tcallLBFGS(\"STOP\");\n\tbreak;\n      }\n    } else if (strIsEqualToCStr(task,\"CONV\"))\n      break;\n    else if (strIsEqualToCStr(task,\"ABNO\")) {\n      status = abnormalTermination;\n      break;\n    } else if (strIsEqualToCStr(task,\"ERROR\")) {\n      status = errorOnInput;\n      break;\n    }\n\n    // Call L-BFGS again.\n    callLBFGS();\n  }\n\n  return status;\n}\n\nvoid Program::callLBFGS (const char* cmd) {\n  if (cmd)\n    copyCStrToCharArray(cmd,task,60);\n  setulb_(&n,&m,x,lb,ub,btype,&f,g,&factr,&pgtol,wa,iwa,task,&iprint,\n\t  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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/hazydazyart/OSU/blob/9ff0fc7bc185a4526c81af8fe82fdfdcbd45b9ec/CS162/homework9/maze.cpp", "id": "cpp-0-4a79dd676b0ed123d53a06067165c9c5", "text": "/********************************************************************* \n * ** Program Filename: maze.cpp\n * ** Author: <NAME>\n * ** Date: 3/14/2014\n * ** Description: Assignment 9\n * ** Input: Directions as char to navigate a maze\n * ** Output: Current location in maze; list of rooms already visited if\n * requested\n * *********************************************************************/\n\n#include <iostream>\n#include <algorithm>\n#include <string>\n#include <stack>\n\nclass MazeNode {\n\npublic:\n\n/********************************************************************* \n * ** Function: MazeNode constructors/deconstructor\n * ** Description: Default and non-default constructor and deconstructor\n * for MazeNode class objects\n * ** Parameters: Non-default cosntructor take a char and a string\n * ** Pre-Conditions: None\n * ** Post-Conditions: Constructs/deconstructs MazeNode object\n * *********************************************************************/\n\n\tMazeNode() {}\n\tMazeNode(char theLoc, std::string theChoices) {\n\t\tthis->location = theLoc;\n\t\tthis->choices = theChoices;\n\t}\n\n\t~MazeNode() {}\n\n/********************************************************************* \n * ** Function: getLocation\n * ** Description: Accessor function for location variable\n * ** Parameters: None\n * ** Pre-Conditions: Object exists and location has been set to some value\n * ** Post-Conditions: Returns location as a char\n * *********************************************************************/\n\n\tchar getLocation() {\n\t\treturn this->location;\n\t}\n\n/********************************************************************* \n * ** Function: getChoices\n * ** Description: Accessor function for choices variable\n * ** Parameters: None\n * ** Pre-Conditions: Object exists and choices has been set to some value\n * ** Post-Conditions: Returns choices as a string\n * *********************************************************************/\n\n\tstd::string getChoices() {\n\t\treturn this->choices;\n\t}\n\n/********************************************************************* \n * ** Function: getLink\n * ** Description: Overloaded function - default returns MazeNode object;\n * function with int parameter returns one of up to four MazeNode objects\n * ** Parameters: none/int\n * ** Pre-Conditions: Object exists and has at least one link variable set\n * to equal another object\n * ** Post-Conditions: Returns MazeNode object\n * *********************************************************************/\n\n\tMazeNode* getLink() {\n\t\treturn this->link;\n\t}\n\tMazeNode* getLink(int theLink) {\n\t\tif(theLink == 1) {\n\t\t\treturn this->link;\n\t\t}\n                if(theLink == 2) {\n                        return this->link2;\n                }\n                if(theLink == 3) {\n                        return this->link3;\n                }\n                if(theLink == 4) {\n                        return this->link4;\n                }\n\t}\n\n/********************************************************************* \n * ** Function: setLocation\n * ** Description: Mutator function for location variable\n * ** Parameters: Char\n * ** Pre-Conditions: Object exists\n * ** Post-Conditions: Location set to parameter value\n * *********************************************************************/\n\n\tvoid setLocation(char theLoc) {\n\t\tthis->location = theLoc;\n\t}\n\n/*********************************************************************\n * ** Function: setChoices\n * ** Description: Mutator function for choices variable\n * ** Parameters: String\n * ** Pre-Conditions: Object exists\n * ** Post-Conditions: Choices set to parameter value\n * *********************************************************************/\n\n\tvoid setChoices(std::string theChoices) {\n\t\tthis->choices = theChoices;\n\t}\n\n/********************************************************************* \n * ** Function: setLink\n * ** Description: Overloaded function sets up to four MazeNode variables\n * equal to the parameter, depending on how many links are needed\n * ** Parameters: 1-4 MazeNode objects\n * ** Pre-Conditions: Object exists and objects passed as parameter exist\n * ** Post-Conditions: link/link2/link3/link4 set to parameter values\n * *********************************************************************/\n\n\tvoid setLink(MazeNode* p) {\n\t\tthis->link = p;\n\t}\n        void setLink(MazeNode* p, MazeNode* p2) {\n                this->link = p;\n\t\tthis->link2 = p2;\n        }\n        void setLink(MazeNode* p, MazeNode* p2, MazeNode* p3) {\n                this->link = p;\n\t\tthis->link2 = p2;\n\t\tthis->link3 = p3;\n        }\n        void setLink(MazeNode* p, MazeNode* p2, MazeNode* p3, MazeNode* p4) {\n                this->link = p;\n\t\tthis->link2 = p2;\n\t\tthis->link3 = p3;\n\t\tthis->link4 = p4;\n        }\n\n/********************************************************************* \n * ** Function: printNode\n * ** Description: Prints information about MazeNode object\n * ** Parameters: None\n * ** Pre-Conditions: Object exists and location/choices have been\n * initialized\n * ** Post-Conditions: Prints information to console\n * *********************************************************************/\n\n\tvoid printNode() {\n        \tstd::cout << \"You are in room \" << this->getLocation()\n\t                  << \" of a maze of twisty little passages, \"\n                  \t  << \"all alike.\" << std::endl;\n        \tstd::cout << \"You can go \"\n\t\t\t  << this->getChoices() << std::endl;\n\t}\n\n\nprivate:\n\tchar location;\n\tstd::string choices;\n\tMazeNode* link;\n\tMazeNode* link2;\n\tMazeNode* link3;\n\tMazeNode* link4;\n};\n\n/********************************************************************* \n * ** Function: buildMaze\n * ** Description: Initializes MazeNode objects and sets links between\n * them in a list\n * ** Parameters: None \n * ** Pre-Conditions: None\n * ** Post-Conditions: Returns the \"head\" MazeNode object\n * *********************************************************************/\n\nMazeNode* buildMaze() { \n\n\tMazeNode *head = new MazeNode('A', \"east or south.\");\n\n        MazeNode *E = new MazeNode('E', \"south or north.\");\n\n        MazeNode *I = new MazeNode('I',\"north.\");\n\n        MazeNode *B = new MazeNode('B', \"west or south.\");\n\n\tMazeNode *J = new MazeNode('J', \"west.\");\n\t\n\tMazeNode *F = new MazeNode('F', \"north or east.\");\n\n\tMazeNode *G = new MazeNode('G', \"north, east, west, or south.\");\n\n\tMazeNode *K = new MazeNode('K', \"north.\");\n\n\tMazeNode *C = new MazeNode('C', \"south or east\");\t\n\t\n\tMazeNode *D = new MazeNode('D', \"west.\");\n\n\tMazeNode *H = new MazeNode('H', \"south or west.\");\n\n\tMazeNode *L = new MazeNode('L', \"north\");\n\n\thead->setLink(E, B);\n\tE->setLink(head, I);\n\tI->setLink(E, J);\n\tJ->setLink(I);\n\tB->setLink(head, F);\n\tF->setLink(B, G);\n\tG->setLink(F, C, K, H);\n\tD->setLink(C);\n\tC->setLink(G, D);\n\tH->setLink(G, L);\n\tK->setLink(G);\n\tL->setLink(H);\n\n\treturn head;\n}\n\n/********************************************************************* \n * ** Function: breadcrumbs\n * ** Description: Prints a list of rooms visited in reverse order and\n * prompts user for input for next direction\n * ** Parameters: std::stack<char>\n * ** Pre-Conditions: Stack passed as parameter has at least one value\n * ** Post-Conditions: Returns a string\n * *********************************************************************/\n\nstd::string breadcrumbs(std::stack<char> st) {\n\n        std::stack<char> temp = st;\n        std::string input;\n\n        std::cout << \"You have been through rooms: \" << std::endl;\n\n        while(!temp.empty()) {\n                std::cout << temp.top() << \" \";\n                temp.pop();\n        }\n\n\tstd::cout << std::endl;\n\n        std::cout << \"Choose a direction. \";\n        std::cin >> input;\n\n        return input;\n}\n\n/********************************************************************* \n * ** Function: getInput\n * ** Description: Prompts user for input; calls breadcrumbs function if\n * first input is \"C\"\n * ** Parameters: std::stack<char>\n * ** Pre-Conditions: Stack contains at least one value if input is \"C\"\n * ** Post-Conditions: Returns user input as a string \n * *********************************************************************/\n\nstd::string getInput(std::stack<char> st) {\n\n\tstd::string input;\n\t\n\tstd::cout << \"Choose a direction or enter C to see \" << std::endl;\n\tstd::cout << \"the list of rooms to return to the beginning. \";\n\tstd::cin >> input;\n\n\tif(input == \"C\") {\n\t\tinput = breadcrumbs(st);\n\t}\n\n\treturn input;\n}\n/********************************************************************* \n * ** Function: reverseStack\n * ** Description: Reverses stack of chars to show user which rooms they\n * visted in order during the maze\n * ** Parameters: Two std::stack<char>\n * ** Pre-Conditions: First stack parameter has at least one value\n * ** Post-Conditions: Prints reversed stack\n * *********************************************************************/\n\nvoid reverseStack(std::stack<char> st, std::stack<char> rev) {\n\n\twhile(!st.empty()) {\n\t\trev.push(st.top());\n\t\tst.pop();\n\t}\n\n\tstd::cout << \"During the maze, you passed through rooms:\" << std::endl;\n\n\twhile(!rev.empty()) {\n\t\tstd::cout << rev.top() << \" \";\n\t\trev.pop();\n\t}\n\n\tstd::cout << std::endl;\n}\n\n/********************************************************************* \n * ** Function: deleteList\n * ** Description: Deletes the linked list\n * ** Parameters: MazeNode pointer as reference\n * ** Pre-Conditions: Linked list exists\n * ** Post-Conditions: Deletes all nodes in the linked list\n * *********************************************************************/\n\n/*\nvoid deleteList(MazeNode*& head) {\n\tMazeNode* current = head;\n\tMazeNode* temp = current;\n\n\ttemp = current->getLink(1);\n\ttemp = temp->getLink(2);\n\ttemp = temp->getLink(2);\n\tcurrent = temp->getLink();\n\tdelete temp;\n\t\n\tMazeNode* temp2 = current;\n\tcurrent = current->getLink(1);\n\tdelete temp2;\n\n\tMazeNode* temp3 = current;\n\tcurrent = current->getLink(1);\n\tdelete temp3;\n\n        MazeNode* temp4 = current;\n\tcurrent = current->getLink(2);\n\tdelete temp4;\n\n\tMazeNode* temp5 = current;\n\tcurrent = current->getLink(2);\n\tdelete temp5;\n\n\tcurrent = current->getLink(2);\n\tMazeNode* temp6 = current->getLink(2);\n\tdelete temp6;\n\n\tMazeNode* temp7 = current;\n\tcurrent = current->getLink(1);\n\tdelete temp7;\n\n\tMazeNode* temp8 = current->getLink(3);\n\tdelete temp8;\n\n\tcurrent = current->getLink(4);\n\tMazeNode* temp9 = current->getLink(2);\n\tdelete temp9;\n\n\tdelete current;\n}\n*/\n\nint main() {\n\n\tbool solved = false;\n\tstd::string input;\n\tstd::stack<char> crumbs; // rooms visited in reverse\n\tstd::stack<char> rooms; // stack to be reversed in the end\n\n\tMazeNode *head = buildMaze(); // Initialize nodes and list\n\n\tMazeNode *conductor; // Initialize object to traverse the list\n\tconductor = head; // Set it equal to the first node\n\n\t/* For each room entered, push the current location into the crumbs stack.\n \t* then prompt for input. If input is \"C\", print the stack. Otherwise,\n \t* set the conductor equal to the appropriate node. */\n\n\twhile(!solved) {\n\t\tif(conductor->getLocation() == 'A') {\n\t\t\tcrumbs.push('A');\n\t\t\tconductor->printNode();\n\t\t\tinput = getInput(crumbs);\n/*\n\t\t\twhile((input != \"E\") || (input != \"S\")) {\n\t\t\t\tstd::cout << \"Invalid choice.\" << std::endl;\n\t\t\t\tstd::cin >> input;\n\t\t\t}\n*/\n\t\t\tif(input == \"E\") {\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t} else {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'E') {\n\t\t\tcrumbs.push('E');\n\t\t\tconductor->printNode();\n\t\t\tinput = getInput(crumbs);\n/*\n                        while(input != \"N\" || input != \"S\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"N\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else {\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'I') {\n\t\t\tcrumbs.push('I');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        while(input != \"N\" || input != \"E\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"N\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else {\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'J') {\n\t\t\tcrumbs.push('J');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        if(input != \"E\") {\n                                std::cout << \"But thou must.\" << std::endl;\n                        }\n*/\n\t\t\tconductor = conductor->getLink();\n\t\t}\n\t\tif(conductor->getLocation() == 'B') {\n\t\t\tcrumbs.push('B');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        while(input != \"W\" || input != \"S\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"W\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else {\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'F') {\n\t\t\tcrumbs.push('F');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        while(input != \"E\" || input != \"N\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"N\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else {\t\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'G') {\n\t\t\tcrumbs.push('G');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        while(input != \"E\" || input != \"S\" || input != \"N\" || input != \"W\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"W\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else if(input == \"N\") {\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t} else if(input == \"S\") {\n\t\t\t\tconductor = conductor->getLink(3);\n\t\t\t} else {\n\t\t\t\tconductor = conductor->getLink(4);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'C') {\n\t\t\tcrumbs.push('C');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        while(input != \"E\" || input != \"S\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"S\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else {\n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'D') {\n\t\t\tcrumbs.push('D');\n\t\t\tconductor->printNode();\n\t\t\tinput = getInput(crumbs);\n/*\n\t\t\tif(input != \"W\") {\n\t\t\t\tstd::cout << \"But thou must.\" << std::endl;\n\t\t\t}\n*/\n\t\t\tconductor = conductor->getLink();\n\t\t}\n\t\tif(conductor->getLocation() == 'K') {\n\t\t\tcrumbs.push('K');\n\t\t\tconductor->printNode();\n\t\t\tinput = getInput(crumbs);\n/*\n\t\t\tif(input != \"N\") {\n\t\t\t\tstd::cout << \"But thou must.\" << std::endl;\n\t\t\t}\n*/\n\t\t\tconductor = conductor->getLink();\n\t\t}\n\t\tif(conductor->getLocation() == 'H') {\n\t\t\tcrumbs.push('H');\n\t\t\tconductor->printNode();\n                        input = getInput(crumbs);\n/*\n                        while(input != \"W\" || input != \"S\") {\n                                std::cout << \"Invalid choice.\" << std::endl;\n                                input = getInput(crumbs);\n                        }\n*/\n\t\t\tif(input == \"W\") {\n\t\t\t\tconductor = conductor->getLink(1);\n\t\t\t} else { \n\t\t\t\tconductor = conductor->getLink(2);\n\t\t\t}\n\t\t}\n\t\tif(conductor->getLocation() == 'L') {\n\t\t\tcrumbs.push('L');\n\t\t\tstd::cout << \"You have reached room L.\" << std::endl;\n\t\t\tsolved = true; // Exit while loop\n\t\t}\n\n\t}\n\n\tstd::cout << \"You have escaped the maze!\" << std::endl;\n\treverseStack(crumbs, rooms); // Print reversed stack\n\n//\tdeleteList(head);\n\n\treturn 0;\n}\n", "text_preview": "/********************************************************************* * ** Program Filename: maze.cpp * ** Author: <NAME> * ** Date: 3/14/2014 * ** Description: Assignment 9 * ** Input: Directions as char to navigate a 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Transportation": 4.199845394841132e-05, "Travel & Tourism": 9.925687053069987e-05, "Other / Uncovered Subject": 0.0003451428651597055, "Unknown / Insufficient Content": 9.618925082834626e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0010709924098448599, "Art & Design": 0.0010471266649669693, "Law & Crime": 0.0007057075922558718, "Education & Careers": 0.01987417597803071, "Entertainment & Media": 0.001436845311751656, "Fashion & Beauty": 0.000357722866509608, "Business, Economics & Finance": 0.0006413602499844209, "Food & Dining": 0.00030561437879685934, "Games & Gaming": 0.13806520872980999, "Electronics & Hardware": 0.0004995063938832067, "Health, Healthcare & Wellness": 0.00039663850008482115, "History & Geography": 0.0005047167237458859, "Home, Pets & Hobbies": 0.0004125086040737876, "Industry & Production": 0.0002687255525576718, "Language, Literature & Philosophy": 0.0005072711147706383, "Politics & Government": 0.00025114782762380495, "Religion & Spirituality": 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"factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/stden/dm-judge/blob/2dbf7429694441c8447aad09a3d528283d034dfc/private/tasks/1066/solve.cpp", "id": "cpp-0-f2386660dcf986d23ad0231a2790f24b", "text": "#include <conio.h>\n#include <stdio.h>\n#include <string.h>\n\n  int c=2,i,cm=2;\n  char te[256], stack[256], rez[256];\n  FILE *in, *out;\nvoid add()\n{\n  strcpy (&stack[c],&te[i]);\n  c++;\n  if (c>cm) cm=c;\n//  printf (\"%d\",cm);\n}\n\nint vernost()\n{  int e2=0, e=1, u=0, ret;\n  for (int v=0;v<strlen(te);v++)\n\tswitch (te[v])\n\t{\n\t  case '^':\n\t  case '*':\n\t  case '/':\n\t  case '+':\n\t  case '-':e++; break;\n\t  case '&': break;\n\t  case '(':u++; break;\n\t  case ')':\n\t\t//if (te[v-\n\t\tu--;\n\t\tbreak;\n\n\t  default:\n\t\t\t   e2++; break;\n\t}\nif ((e==e2)&&(u==0))\n{ for (v=0;v<strlen(rez);v++)\n\tif (rez[v]=='(') return 0; else ret=1;\n  for (v=0;v<strlen(te);v++)\n\tswitch (te[v])\n\t {\n\t   case '+':\n\t   case '-':\n\t   case '*':\n\t   case '/':\n\t   case '^':\n\t   case '&':\n\t   case '(':\n\t\t switch (te[v+1])\n\t\t {\n\t\t   case '+':\n\t\t   case '-':\n\t\t   case '*':\n\t\t   case '/':\n\t\t   case '^':\n\t\t\t return 0;\n\t\t  // break;\n\t\t }\n\t\t break;\n\t   case ')':switch (te[v-1])\n\t\t {\n\t\t   case '(':\n\t\t   case '+':\n\t\t   case '-':\n\t\t   case '*':\n\t\t   case '/':\n\t\t   case '^':\n\t\t   case '&': return 0;\n\t\t   //break;\n\t\t }\n\t\t break;\n\t   default: \t switch (te[v+1])\n\t\t {\n\t\t   case '\\0':\n\t\t   case '(':\n\t\t   case ')':\n\t\t   case '+':\n\t\t   case '-':\n\t\t   case '*':\n\t\t   case '/':\n\t\t   case '^':\n\t\t   case '&': break;\n\t\t\t default: return 0;\n\t\t }\n\n\t }\n}\nelse return 0;\nreturn ret;\n}\n\nvoid main()\n{\n  unlink (\"out.txt\");\n  in=fopen(\"in.txt\",\"r\");\n  while (!feof(in))\n  {\n  //rez='\\0';\n  //rez =new char[256];\n  fscanf(in,\"%s/n\",&te[0]);\n  //scanf(\"%s\",&te);\n  //clrscr();\n  for (i=0;i<strlen(te);i++)\n  {\n\tswitch (te[i])\n\t{\n\t  case '(':\n\t\tadd();\n\t\tbreak;\n\t  case '^':\n\t\tadd();\n\t\tfor(;c>2;c--)\n\t\t  {\n\t\t\tif (stack[c-2]=='&')\n\t\t\t{\n\t\t\t  //printf (\"%c\",stack[c-2]);\n\t\t\t  //rez= new char;\n\t\t\t  rez[strlen(rez)]=stack[c-2];\n\t\t\t  strcpy (&stack[c-2],&stack[c-1]);\n\t\t\t}\n\t\t\telse break;\n\t\t  }\n\n\t\tbreak;\n\t  case '*':\n\t  case '/':\n\t\tadd();\n\t\tfor(;c>2;c--)\n\t\t  {\n\t\t\tif ((stack[c-2]=='^')||(stack[c-2]=='&'))\n\t\t\t{\n\t\t\t  //printf (\"%c\",stack[c-2]);\n\t\t\t  //rez=new char;\n\t\t\t  rez[strlen(rez)]=stack[c-2];\n\t\t\t  strcpy (&stack[c-2],&stack[c-1]);\n\t\t\t}\n\t\t\telse break;\n\t\t  }\n\t\tbreak;\n\t  case '+':\n\t  case '-':\n\t\tadd();\n\t\tfor(;c>2;c--)\n\t\t  {\n\t\t\tif ((stack[c-2]=='*')||(stack[c-2]=='/')||(stack[c-2]=='^')||(stack[c-2]=='&'))\n\t\t\t{\n\t\t\t  //printf (\"%c\",stack[c-2]);\n\t\t\t  rez[strlen(rez)]=stack[c-2];\n\t\t\t  strcpy (&stack[c-2],&stack[c-1]);\n\t\t\t}\n\t\t\telse break;\n\t\t  }\n\t\tbreak;\n\t  case ')':\n\t\tfor (;c>2;c--)\n\t\t  {\n\t\t\t//printf (\"%c\",stack[c-1]);\n\t\t\trez[strlen(rez)]=stack[c-1];\n\t\t\tif (stack[c-2]=='(') {c-=2; break; }\n\t\t\t//  if (strlen(stack)>cm) cm=strlen(stack);\n\t\t  }\n\t\tbreak;\n\t  case '&':\n\t\tadd();\n\t\tbreak;\n\n\t  default:\n\t\t\t  rez[strlen(rez)]=te[i];\n\t\t\t  //printf (\"%c\",te[i]);\n\t\t\t   break;\n\t}\n\n  }\n  for (;c>2;c--)\n\t{\n\t\t\t  //printf (\"%c\",stack[c-1]);\n\t\t\t  rez[strlen(rez)]=stack[c-1];\n\t}\n\n\t if (!vernost())\n\t  { //printf (\"HenpaBu/\\\\bHo\\n\");\n\t\tout=fopen(\"out.txt\",\"a\");\n\t\tfprintf (out,\"Error!\\n\");\n\t\tfclose (out);\n\t  }\n\t else\n\t  { //printf  (\"%s\",rez);\n\t\tout=fopen(\"out.txt\",\"a\");\n\t\tfprintf (out,\"%s\\t%d\\n\",rez,cm-2);\n\t\tfclose (out);\n\t  }\n   for (i=0;i<256;i++)\n   { rez[i]='\\0';\n\t te[i]='\\0';\n\t stack[i]='\\0';\n   }\n   cm=2;\n   //delete rez;\n  }\n  fclose (in);\n}", "text_preview": "#include <conio.h> #include <stdio.h> #include <string.h> int c=2,i,cm=2; char te[256], stack[256], rez[256]; FILE *in, *out; void add() { strcpy (&stack[c],&te[i]); c++; if (c>cm) cm=c; // printf (\"%d\",cm); } int vernost() { int e2=0, e=1,", "input_sha256": "06fc3368a045cd55f47210a462ea5eb46f7ced4210ed45ab37bdc4a5aaaea698", "text_sha256": "f2386660dcf986d23ad0231a2790f24b6e911d7dccb3033d4f24dcb54931ec7f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 49, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.026514022153396266, "noise_severity": 0.09480193793779282, "missing_content_severity": 0.0823322173755933, "factual_density": 2.914329701791949, "pretraining_utility": 0.11022342639765118, "math_density": 0.8950877726769679, "code_density": 3.9328515470739283, "reasoning_density": 1.5017495544324302, "education_level": 1.9860188452675147, "creative_writing_quality": -0.9524680207291029}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.004603836132303418, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9952360870051951, "secondary": 0.9918339738233629, "tertiary": 0.9966379767440278, "structured_records": 0.0005876544382908037, "procedural_steps": 0.002268989313298777, "worked_solution": 0.01277646172045066, "contextual_dialogue": 7.980301824226937e-06, "content_quality": 0.36405709935594066, "noise_severity": 0.9209983850518393, "missing_content_severity": 0.9313898188536722, "factual_density": 0.09527475149329057, "pretraining_utility": 0.4912004759768963, "math_density": 0.2540935227691934, "code_density": 0.9440429558949401, "reasoning_density": 0.0, "education_level": 0.7742351138873815, "creative_writing_quality": 0.9989674896538063, "primary_topic": 0.9760494023931494, "secondary_topic": 0.7760106543654628}, "probability_margin": {"primary": 0.9923115234777147, "secondary": 0.9868097100367468, "tertiary": 0.9950109282462053, "education_level": 0.710060811194218, "creative_writing_quality": 0.9988148876307774, "primary_topic": 0.9573652698124715, "secondary_topic": 0.6284300236781677}, "raw_probabilities": {"primary": {"Academic Writing": 4.387162638707215e-05, "Content Listing": 2.1042410059630053e-05, "Creative Writing": 3.098500244527445e-05, "Customer Support Page": 6.81026768098783e-06, "Q&A Forum": 1.593396932101285e-05, "Discussion Thread": 1.0191284259829755e-05, "Comment Section": 1.425338592405917e-05, "Missing Content": 4.04232590240126e-05, "Knowledge Article": 8.746208445332965e-05, "Legal Notices": 2.9712472221596833e-05, "News Article": 4.793310543343789e-06, "Organizational Announcement": 8.842927846485276e-06, "Nonfiction Writing": 1.574729669377515e-05, "Organizational About Page": 9.707449429599104e-06, "Personal About Page": 1.1372051097199552e-05, "Personal Blog": 6.282207881632428e-06, "Product Page": 5.923793642555524e-06, "Spam / Ads": 1.1056364347412604e-05, "Structured Data": 0.0001996641541501582, "Technical Writing": 0.9954345833799786, "Transcript / Interview": 1.4953740127319133e-05, "Tutorial / How-To Guide": 0.0008472262874776883, "User Reviews": 6.1013727433165985e-06, "Other / Unknown": 0.0031230599022638843}, "secondary": {"Academic Writing": 8.679866102983636e-05, "Content Listing": 3.3202196238225144e-05, "Creative Writing": 6.529631642364451e-05, "Customer Support Page": 2.1050999747079034e-05, "Q&A Forum": 2.4702560255723374e-05, "Discussion Thread": 1.6823190048582948e-05, "Comment Section": 3.329222824493722e-05, "Missing Content": 0.0002918340234772526, "Knowledge Article": 0.00019150983712935157, "Legal Notices": 6.0742741177623794e-05, "News Article": 2.4139440723716755e-05, "Organizational Announcement": 2.0820704421933897e-05, "Nonfiction Writing": 2.4422917379350308e-05, "Organizational About Page": 2.878897786760552e-05, "Personal About Page": 2.619053595322959e-05, "Personal Blog": 1.7566908995863363e-05, "Product Page": 1.3889135979384086e-05, "Spam / Ads": 2.405388490190894e-05, "Structured Data": 0.0004611647266999654, "Technical Writing": 0.0053645148773094065, "Transcript / Interview": 2.625654642879332e-05, "Tutorial / How-To Guide": 0.0009549542545709321, "User Reviews": 1.3759420939360367e-05, "NONE": 0.9921742249140562}, "tertiary": {"Academic Writing": 5.254569514642372e-05, "Content Listing": 2.4734273322940775e-05, "Creative Writing": 4.839789986392009e-05, "Customer Support Page": 1.3231703812059496e-05, "Q&A Forum": 1.6875434275695557e-05, "Discussion Thread": 1.1190270394861012e-05, "Comment Section": 2.2396095154594057e-05, "Missing Content": 0.00021098649871965385, "Knowledge Article": 0.00010306979659683244, "Legal Notices": 5.4001130971705425e-05, "News Article": 1.9230399331822747e-05, "Organizational Announcement": 1.5412521745958775e-05, "Nonfiction Writing": 2.4190193401038108e-05, "Organizational About Page": 2.9288844326912342e-05, "Personal About Page": 1.6392061733468254e-05, "Personal Blog": 1.448114776365483e-05, "Product Page": 1.196976191807105e-05, "Spam / Ads": 2.0793673810556486e-05, "Structured Data": 0.00022080219226124146, "Technical Writing": 0.0017671328001546036, "Transcript / Interview": 1.795625282208515e-05, "Tutorial / How-To Guide": 0.0004960200271020356, "User Reviews": 1.0840279010103237e-05, "NONE": 0.9967780610463599}, "structured_records": {"false": 0.9994123455617092, "true": 0.0005876544382908037}, "procedural_steps": {"false": 0.9977310106867012, "true": 0.002268989313298777}, "worked_solution": {"false": 0.9872235382795493, "true": 0.01277646172045066}, "contextual_dialogue": {"false": 0.9999920196981757, "true": 7.980301824226937e-06}, "content_quality": {"-2": 0.07023860668202203, "-1": 0.25434553809908966, "0": 0.3782945256904489, "1": 0.22593392974714105, "2": 0.07118739978129822}, "noise_severity": {"0": 0.9210694499544175, "1": 0.06714180013358942, "2": 0.008836988589834616, "3": 0.0018208846640992962, "4": 0.0011308766580590643}, "missing_content_severity": {"0": 0.9327357587607509, "1": 0.054924843154496535, "2": 0.010227614999965086, "3": 0.0014949881179836155, "4": 0.0006167949668039387}, "factual_density": {"0": 0.055496946231971385, "1": 0.06337025480401869, "2": 0.1748121006079819, "3": 0.3239475476521458, "4": 0.3823731507038823}, "pretraining_utility": {"-2": 0.023904662804814025, "-1": 0.20235867560540854, "0": 0.47098555980813, "1": 0.2451107759506069, "2": 0.05764032583104043}, "math_density": {"0": 0.6573776164672062, "1": 0.11148970865847074, "2": 0.04264828909890055, "3": 0.05563605728099386, "4": 0.13284832849442862}, "code_density": {"0": 0.00023305169662004926, "1": 0.00044838599284999665, "2": 0.00874481676259907, "3": 0.04738145463584347, "4": 0.9431922909120873}, "reasoning_density": {"0": 0.4988092959420003, "1": 0.049529226945947774, "2": 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5.056475411006354e-05, "History & Geography": 6.912114103180885e-05, "Home, Pets & Hobbies": 4.385073880991026e-05, "Industry & Production": 4.664998625337576e-05, "Language, Literature & Philosophy": 0.00033649644613187004, "Politics & Government": 2.952758061729045e-05, "Religion & Spirituality": 4.8865878832095524e-05, "Science, Mathematics & Engineering": 0.019605309411710663, "Relationships & Community": 3.395271796603894e-05, "Software Usage & Applications": 0.0002456048407476936, "Programming & Software Engineering": 0.9769705792241822, "Sports & Fitness": 8.855516548056269e-05, "Vehicles & Transportation": 3.351260679865124e-05, "Travel & Tourism": 7.635468728553166e-05, "Other / Uncovered Subject": 0.0007927866019952542, "Unknown / Insufficient Content": 0.0005593294425011412}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006297135204758181, "Art & Design": 0.00045824431276803577, "Law & Crime": 0.0005047886670892389, "Education & Careers": 0.00045911042158337995, "Entertainment & Media": 0.00041609695233920703, "Fashion & Beauty": 0.0002361340159497767, "Business, Economics & Finance": 0.00044238536822409634, "Food & Dining": 0.00020006004235194753, "Games & Gaming": 0.0006438989893187034, "Electronics & Hardware": 0.0006036723111236783, "Health, Healthcare & Wellness": 0.00029418015474446155, "History & Geography": 0.0003330739580623574, "Home, Pets & Hobbies": 0.00021071911251250016, "Industry & Production": 0.0001925382061778299, "Language, Literature & Philosophy": 0.0007493488964820457, "Politics & Government": 0.00020181150466052044, "Religion & Spirituality": 0.0003382686193222296, "Science, Mathematics & Engineering": 0.15619560551939274, "Relationships & Community": 0.00015722537731725932, "Software Usage & Applications": 0.0007031027650253408, "Programming & Software Engineering": 0.04942598238096969, "Sports & Fitness": 0.000279497645446613, "Vehicles & Transportation": 0.00013895887774455036, "Travel & Tourism": 0.00039715969484246624, "Other / Uncovered Subject": 0.001162793488514996, "NONE": 0.7846256291975604}}, "probabilities": {"primary": {"Academic Writing": 4.3871626387072156e-05, "Content Listing": 2.1042410059630057e-05, "Creative Writing": 3.0985002445274454e-05, "Customer Support Page": 6.810267680987831e-06, "Q&A Forum": 1.5933969321012855e-05, "Discussion Thread": 1.0191284259829757e-05, "Comment Section": 1.4253385924059173e-05, "Missing Content": 4.042325902401261e-05, "Knowledge Article": 8.746208445332966e-05, "Legal Notices": 2.971247222159684e-05, "News Article": 4.79331054334379e-06, "Organizational Announcement": 8.842927846485278e-06, "Nonfiction Writing": 1.5747296693775153e-05, "Organizational About Page": 9.707449429599106e-06, "Personal About Page": 1.1372051097199553e-05, "Personal Blog": 6.282207881632429e-06, "Product Page": 5.923793642555526e-06, "Spam / Ads": 1.1056364347412606e-05, "Structured Data": 0.00019966415415015825, "Technical Writing": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/zwkcoding/data_structure/blob/5704d036e180fa23e577c08ab5a37e358747aeb2/c%2B%2B/job_interview/Changxing/A%2A/main.cpp", "id": "cpp-0-be3dad2876c3a777af29d42e52048796", "text": "#include <iostream>\n#include <cstring>   // memset\n#include <queue>    // priority queue\n#include <cmath>    // sqrt, pow\n#include <utility>  // pair\n#include <stack> \n\nusing namespace std;\ntypedef pair<int ,int> Pair;\nconst int ROW = 5;  // map row\nconst int COL = 5;  // map column\n\nconst int INT_MAX = 10000;\n\nstruct Node {\n  int y;\n  int x;\n  int parent_x;\n  int parent_y;\n  double g;\n  double h;\n  double f;\n};\n\n/// compator\nclass Compare\n{\n  public:\n    bool operator() (const Node& l, const Node& r)  {\n      // min-heap\n      return l.g > r.g;\n    }\n};\n\n\n/// judge inside map\nbool isVaild(int x, int y)  {\n  if(x < 0 || x >= ROW || y < 0 || y >= COL)  {\n    return false;\n  }\n  return true;\n}\n\n/// judeg block\nbool isUnBlock(int grid[][COL], int row, int col)  {\n  if(grid[row][col] == 1)\n    return true;\n  else\n    return false;\n}\n\nbool isGoal(int x, int y, Node dest)  {\n  if(x == dest.x && y == dest.y)\n    return true;\n  else\n    return false;\n}\n\n/// trace path\nvoid tracePath(Node node_map[][COL], Node dest)  {\n  cout << \"found goal \\n The path is \" << endl;\n\n  int row = dest.x;\n  int col = dest.y;\n\n  stack<Pair> path;\n  vector<Node> result_path;\n  while(!( (node_map[row][col].x != -1) && (node_map[row][col].y != -1) &&\n  (node_map[row][col].parent_x == row) && (node_map[row][col].parent_y == col)))  {\n    path.push(make_pair(row, col));\n    int temp_row = node_map[row][col].parent_x;\n    int temp_col = node_map[row][col].parent_y;\n    row = temp_row;\n    col = temp_col;\n    // printf(\"(%d, %d)\", row, col);\n\n  }\n\n\n  path.push(make_pair(row, col));   // start node of path\n\n  // reverse path : from start to end\n  while(!path.empty())  {\n    Pair tmp = path.top();\n    path.pop();\n    printf(\"(%d, %d)\", tmp.first, tmp.second);\n  }\n\n  return ;\n}\n\n/// calc heuristic value\ndouble calcHeurist(int x, int y, Node dest)  {\n  // use euid distacne\n  double h = (sqrt(pow((x - dest.x),2) + pow((y - dest.y),2)));\n  return h;\n}\n\nvoid  aStar(int grid[][COL], Node src, Node dest)  {\n  vector<Node> open;\n  // judge start and dest vaildness\n  if(isVaild(src.x, src.y) == false)  {\n    printf(\"source is invaild\");\n    return;\n  }\n  if(isVaild(dest.x, dest.y) == false)  {\n    printf(\"destination is invaild\");\n    return;\n  }\n\n  // check start and dest  block\n  if(isUnBlock(grid, src.x, src.y) == false ||\n      isUnBlock(grid, dest.x, dest.y) == false)  {\n    printf(\"source or destination is blocked!\");\n    return;\n  }\n\n  if(isGoal(src.x, src.y, dest))  {\n    printf(\" destination is reached at source!\");\n    return;\n  }\n\n  // record expanded\n  bool closed_list[ROW][COL];\n  // memset(closed_list, false, sizeof(closed_list));\n\n  // init  map\n  Node node_map[ROW][COL];\n  for(int x = 0; x < ROW; x++)  {\n    for(int y = 0; y < COL; y++)  {\n      node_map[x][y].x = x;\n      node_map[x][y].y = y;\n      node_map[x][y].parent_x = -1;\n      node_map[x][y].parent_y = -1;\n      node_map[x][y].g = INT_MAX;\n      node_map[x][y].f = INT_MAX;\n      node_map[x][y].h = INT_MAX;\n      closed_list[x][y] = false;\n    }\n  }\n\n  // initial start node \n  int x = src.x;\n  int y = src.y;\n node_map[x][y].f = 0;\n node_map[x][y].g = 0;\n node_map[x][y].h = 0;\n node_map[x][y].parent_x = x;\n node_map[x][y].parent_y = y;\n\n  priority_queue<Node,vector<Node>, Compare> open_list;\n  open_list.push(node_map[x][y]);\n  bool isReach = false;\n\n  // start of A* process\n  while(!open_list.empty())  {\n    Node tmp;\n    // to erase from open\n    tmp = open_list.top();\n    open_list.pop();\n    int i = tmp.x;\n    int j = tmp.y;\n    closed_list[i][j] = false;\n\n\n    // is goal\n    if(isGoal(i , j , dest) == true)  {\n        // found goal\n        isReach = true;\n        tracePath(node_map, dest);\n        return;\n    }\n    \n    // neighber child  for  4-connect\n    for(int move_x = -1; move_x <= 1; move_x++)  {\n      for(int move_y = -1; move_y <= 1; move_y++)  {\n        double g_new, f_new, h_new;\n        if(isVaild(i + move_x, j + move_y) && isUnBlock(grid,i + move_x, j + move_y))  {\n          // not inside closed && isvaild && not blocked; \n          // not reopen node , considering if the heursitc is consistent\n          if(closed_list[i + move_x][j + move_y] == false)  {\n            g_new = tmp.g + 1.0;\n            h_new = calcHeurist(i + move_x, j + move_y, dest);\n            f_new = g_new + h_new;\n\n            // update f cost\n            if(node_map[i + move_x][j + move_y].f > f_new)  {\n                node_map[i + move_x][j + move_y].g = g_new;\n                node_map[i + move_x][j + move_y].h = h_new;\n                node_map[i + move_x][j + move_y].f = f_new;\n                node_map[i + move_x][j + move_y].parent_x = i;\n                node_map[i + move_x][j + move_y].parent_y = j;\n                open_list.push(node_map[i + move_x][j + move_y]);\n            }\n          }\n\n\n\n        }\n      }\n    }\n  }   // end of A* process\n\n  if(isReach == false)  {\n    cout << \"goal is not reached!\" << endl;\n    return; \n  }\n}\n\n\n\nint main() {\n  int map[ROW][COL] = \n  {\n    {1,0,1,1,1},\n    {1,0,1,1,1},\n    {1,0,1,1,1},\n    {1,1,1,1,1}\n  };\n\n  Node src, dest;\n  // todo class init\n  src.x = 0;\n  src.y = 0;\n  dest.x = 1;\n  dest.y = 3;\n\n  aStar(map, src, dest);\n\n  return 0;\n}\n", "text_preview": "#include <iostream> #include <cstring> // memset #include <queue> // priority queue #include <cmath> // sqrt, pow #include <utility> // pair #include <stack> using namespace std; typedef pair<int ,int> Pair; 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/han811/MCR_cpp/blob/a4e500b9d6300a172fff30c3cad8559c0d7e7ece/others/others2/mcr/MotionPlanning/MilestonePath.cpp", "id": "cpp-0-a7879e53dd1bc5399988f5a259b19be8", "text": "/*\nThis file is part of LMPL.\n\n    LMPL is free software: you can redistribute it and/or modify\n    it under the terms of the Lesser GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    LMPL is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the Lesser\n    GNU General Public License for more details.\n\n    You should have received a copy of the Lesser GNU General Public License\n    along with Foobar.  If not, see <http://www.gnu.org/licenses/>.\n*/\n#include \"MilestonePath.h\"\n#include \"misc/Random.h\"\n#include \"misc/Miscellany.h\"\n#include <assert.h>\n#include <math.h>\n\nMilestonePath::MilestonePath()\n{}\n\nMilestonePath::~MilestonePath()\n{}\n\nvoid MilestonePath::Concat(const MilestonePath& path)\n{\n  edges.reserve(edges.size()+path.edges.size());\n  for(size_t i=0;i<path.edges.size();i++) {\n    edges.push_back(path.edges[i]);\n  }\n}\n\nbool MilestonePath::InitializeEdgePlans()\n{\n  bool res=true;\n  for(size_t i=0;i<edges.size();i++) {\n    if(!edges[i]->IsVisible()) res=false;\n  }\n  return res;\n}\n\nvoid MilestonePath::CreateEdgesFromMilestones(CSpace* space,const vector<Config>& milestones)\n{\n  assert(!milestones.empty());\n  edges.resize(milestones.size()-1);\n  for(size_t i=0;i+1<milestones.size();i++) {\n    edges[i] = space->LocalPlanner(milestones[i],milestones[i+1]);\n  }\n}\n\nbool MilestonePath::IsFeasible()\n{\n  if(edges.empty()) return true;\n  //first check endpoints \n  CSpace* space=Space();\n  if(!space->IsFeasible(edges[0]->Start())) return false;\n  for(size_t i=0;i<edges.size();i++) {\n    if(!space->IsFeasible(edges[i]->Goal())) return false;\n  }\n  //then check edges\n  for(size_t i=0;i<edges.size();i++) \n    if(!edges[i]->IsVisible()) return false;\n  return true;\n}\n\nint MilestonePath::Eval(double t, Config& c) const\n{\n\tif(t <= ConstantHelper::Zero) { c = edges.front()->Start(); return 0; }\n  else if(t >= ConstantHelper::One) { c = edges.back()->Goal(); return edges.size()-1; }\n  else {\n    double u=t*(double)edges.size();\n    double u0=floor(u);\n    int index = (int)u0;\n    assert(index >= 0 && index < (int)edges.size());\n    edges[index]->Eval(u-u0,c);\n    return index;\n  }\n}\n\nint MilestonePath::Shortcut()\n{\n  int numShortcuts=0;\n  size_t i=0;\n  while(i+1 < edges.size()) {\n    //try to connect milestone i to i+2\n    const Config& x1=GetMilestone(i);\n    const Config& x2=GetMilestone(i+2);\n    EdgePlanner* e= IsVisible(edges[i]->Space(),x1,x2);\n    if(e) {\n      edges[i]=e;\n      edges.erase(edges.begin()+i+1);\n      numShortcuts++;\n      //don't advance to next milestone\n    }\n    else //advance to next milestone\n      i++; \n  }\n  return numShortcuts;\n}\n\nint MilestonePath::Reduce(int numIters)\n{\n  CSpace* space=Space();\n  //pick random points on the path, connect them if they're visible\n  Config x1,x2;\n  int i1,i2;\n  int numsplices=0;\n  for(int iters=0;iters<numIters;iters++) {\n    i1 = rand()%edges.size();\n    i2 = rand()%edges.size();\n    if(i2 < i1) swap(i1,i2);\n    else if(i1 == i2) continue;  //if they're on the same segment, forget it\n\n\tdouble t1=RandHelper::rand();\n    double t2=RandHelper::rand();\n    edges[i1]->Eval(t1,x1);\n    edges[i2]->Eval(t2,x2);\n    const Config& a=edges[i1]->Start();\n    const Config& b=edges[i2]->Goal();\n    EdgePlanner* e_x1x2=space->LocalPlanner(x1,x2);\n    if(e_x1x2->IsVisible()) {\n      EdgePlanner* e_ax1=space->LocalPlanner(a,x1);\n      EdgePlanner* e_x2b=space->LocalPlanner(x2,b);\n      if(e_ax1->IsVisible() && e_x2b->IsVisible()) {\n\tnumsplices++;\n\tcout<<\"Visible subsegment \"<<i1<<\"->\"<<i2<<endl;\n\t//replace edges a->a',...,b'->b with a->x1,x1->x2,x2->b\n\tedges.erase(edges.begin()+i1,edges.begin()+i2+1);\n\n\tedges.insert(edges.begin()+i1,e_ax1);\n\tedges.insert(edges.begin()+i1+1,e_x1x2);\n\tedges.insert(edges.begin()+i1+2,e_x2b);\n      }\n      else {\n\tdelete e_ax1;\n\tdelete e_x2b;\n      }\n    }\n    else {\n      delete e_x1x2;\n    }\n  }\n  return numsplices;\n}\n\nvoid MilestonePath::Discretize(double h)\n{\n  for(size_t i=0;i<edges.size();i++) {\n    int n=DiscretizeEdge(i,h);\n    i+=n-1;\n  }\n}\n\nint MilestonePath::DiscretizeEdge(int i,double h)\n{\n  EdgePlanner* e=edges[i];\n  const Config& a=e->Start();\n  const Config& b=e->Goal();\n  CSpace* space=e->Space();\n  int numDivs = (int)ceil(space->Distance(a,b)/h);\n  //don't do anything...\n  if(numDivs <= 1) return 1;\n\n  //create a bunch of replacement edges\n  double du=ConstantHelper::One/numDivs;\n  double u=0;\n  Config x1,x2;\n  MilestonePath replacement;\n  for(int k=0;k<numDivs;k++) {\n    if(k==0) x1=a;\n    else e->Eval(u,x1);\n    if(k+1==numDivs) x2=b;\n    else e->Eval(u+du,x2);\n    EdgePlanner* e2 = space->LocalPlanner(x1,x2);\n    if(e2->IsVisible()) \n      replacement.edges.push_back(e2);\n    else {\n      cerr<<\"Warning, reparameterized edge \"<<i<<\" is infeasible\"<<endl;\n      replacement.edges.push_back(e2);\n    }\n    u += du;\n  }\n  assert(!replacement.edges.empty());\n  Splice(i,i+1,replacement);\n  return replacement.edges.size();\n}\n\nvoid MilestonePath::DiscretizeEdge(int i,const vector<double>& u)\n{\n  assert(u.front()==0 && u.back()==1);\n  EdgePlanner* e=edges[i];\n  CSpace* space=e->Space();\n  if(u.size()==2) return;\n\n  Config x1,x2;\n  MilestonePath replacement;\n  x1 = e->Start();\n  for(size_t k=1;k<u.size();k++) {\n    e->Eval(u[k],x2);\n    EdgePlanner* e2 = space->LocalPlanner(x1,x2);\n    if(e2->IsVisible()) \n      replacement.edges.push_back(e2);\n    else {\n      cerr<<\"Warning, reparameterized edge \"<<i<<\" is infeasible\"<<endl;\n      replacement.edges.push_back(e2);\n    }\n    x1 = x2;\n  }\n  Splice(i,i+1,replacement);\n}\n\nbool MilestonePath::IsValid()\n{\n  if(edges.empty()) return false;\n  CSpace* space=Space();\n  for(size_t i=0;i<edges.size();i++) {\n    if(Space(i) != space) return false;\n    if(i!=0) \n      if(edges[i]->Start() != edges[i-1]->Goal()) return false;\n  }\n  return true;\n}\n\ndouble MilestonePath::Length() const\n{\n  double len=0;\n  for(size_t i=0;i<edges.size();i++)\n    len += Space(i)->Distance(edges[i]->Start(),edges[i]->Goal());\n  return len;\n}\n\nconst Config& MilestonePath::GetMilestone(int i) const\n{\n  assert(i>=0 && i<=(int)edges.size());\n  if(i==(int)edges.size())\n    return edges.back()->Goal();\n  return edges[i]->Start();\n}\n\nvoid MilestonePath::SetMilestone(int i,const Config& x)\n{\n  if(i == 0) {\n    //first milestone\n    const Config& b=edges[i]->Goal();\n    edges[i] = Space(i)->LocalPlanner(x,b);\n  }\n  else if(i==(int)edges.size()) {\n    assert(!edges.empty());\n    //last milestone\n    const Config& a=edges[i-1]->Start();\n    edges[i-1] = Space(i-1)->LocalPlanner(a,x);\n  }\n  else {\n    const Config& a=edges[i-1]->Start();\n    const Config& b=edges[i]->Goal();\n    edges[i-1] = Space(i-1)->LocalPlanner(a,x);\n    edges[i] = Space(i)->LocalPlanner(x,b);\n  }\n}\n\nbool MilestonePath::CheckSetMilestone(int i,const Config& x)\n{\n  if(i == 0) {\n    //first milestone\n    const Config& b=edges[i]->Goal();\n    EdgePlanner* e2=IsVisible(Space(i),x,b);\n    if(!e2) return false;\n    edges[i] = e2;\n    return true;\n  }\n  else if(i==(int)edges.size()) {\n    assert(!edges.empty());\n    //last milestone\n    const Config& a=edges[i-1]->Start();\n    EdgePlanner* e1=IsVisible(Space(i-1),a,x);\n    if(!e1) return false;\n    edges[i-1] = e1;\n    return true;\n  }\n  else {\n    const Config& a=edges[i-1]->Start();\n    const Config& b=edges[i]->Goal();\n    EdgePlanner* e1=IsVisible(Space(i-1),a,x);\n    if(!e1) return false;\n    EdgePlanner* e2=IsVisible(Space(i),x,b);\n    if(!e2) { delete e1; return false; }\n    \n    edges[i-1] = e1;\n    edges[i] = e2;\n    return true;\n  }\n}\n\nvoid MilestonePath::Splice(int start,int goal,const MilestonePath& path)\n{\n  if(start >= 0) assert(start <= (int)edges.size());\n  if(goal >= 0) assert(goal <= (int)edges.size());\n  if(start < 0) start=0;\n  if(goal < 0) goal=(int)edges.size();\n  edges.erase(edges.begin()+start,edges.begin()+goal);\n  edges.insert(edges.begin()+start,path.edges.begin(),path.edges.end());\n}\n\nbool MilestonePath::Load(istream& in,CSpace* space)\n{\n  assert(space != NULL);\n  vector<Config> configs;\n  int n;\n  in>>n;\n  if(in.bad()) return false;\n  assert(n > 0);\n  configs.reserve(n);\n  Config temp;\n  for(int i=0;i<n;i++) {\n    in>>temp;\n    configs.push_back(temp);\n  }\n  CreateEdgesFromMilestones(space,configs);\n  return true;\n}\n\nbool MilestonePath::Save(ostream& out)\n{\n  assert(!edges.empty());\n  out<<edges.size()+1<<endl;\n  for(size_t i=0;i<edges.size();i++)\n    out<<edges[i]->Start()<<endl;\n  out<<edges.back()->Goal()<<endl;\n  return true;\n}\n", "text_preview": "/* This file is part of LMPL. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/0x4d52/ugen/blob/590c03ba6e1e9caa20992327189dfcd74438ac86/UGen/filters/ugen_BEQ.h", "id": "cpp-0-aaf99f776ba7def5fa9c982a210de0f8", "text": "// $Id$\n// $HeadURL$\n\n/*\n ==============================================================================\n \n This file is part of the UGEN++ library\n Copyright 2008-11 The University of the West of England.\n by <NAME>\n \n ------------------------------------------------------------------------------\n \n UGEN++ can be redistributed and/or modified under the terms of the\n GNU General Public License, as published by the Free Software Foundation;\n either version 2 of the License, or (at your option) any later version.\n \n UGEN++ is distributed in the hope that it will be useful,\n but WITHOUT ANY WARRANTY; without even the implied warranty of\n MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n GNU General Public License for more details.\n \n You should have received a copy of the GNU General Public License\n along with UGEN++; if not, visit www.gnu.org/licenses or write to the\n Free Software Foundation, Inc., 59 Temple Place, Suite 330,\n Boston, MA 02111-1307 USA\n \n The idea for this project and code in the UGen implementations is\n derived from SuperCollider which is also released under the \n GNU General Public License:\n \n SuperCollider real time audio synthesis system\n Copyright (c) 2002 <NAME>. All rights reserved.\n http://www.audiosynth.com\n \n ==============================================================================\n */\n\n#ifndef _UGEN_ugen_BEQ_H_\n#define _UGEN_ugen_BEQ_H_\n\n\n#include \"../core/ugen_UGen.h\"\n#include \"../basics/ugen_MulAdd.h\"\n\n#define BEQ_COEFF_TYPE float\n#define BEQ_CALC_TYPE float\n\n/**\n Base class for the internal BEQ classes.\n \n Inherit from this using the control input for whatever use is appropriate for the base class (e.g., \n reciprocal of Q, S, or bandwidth.\n @ingroup UGenInternals */\nclass BEQBaseUGenInternal : public UGenInternal\n{\npublic:\n\tBEQBaseUGenInternal(UGen const& input, UGen const& freq, UGen const& control, UGen const& gain) throw();\n\tvoid processBlock(bool& shouldDelete, const unsigned int blockID, const int channel) throw();\n\t\n\tenum Inputs { Input, Freq, Control, Gain, NumInputs }; // subclass should define their own interpretation of \"Control\" whether rQ, rS or bw\n\t\n\tvirtual void calculateCoeffs(const float freq, const float control, const float gain) = 0;\n\t\t\n\tvoid initValue(const float value) throw();\n\t\nprotected:\n\tBEQ_COEFF_TYPE y1, y2, a0, a1, a2, b1, b2;\n\tfloat currentFreq, currentControl, currentGain;\t\n};\n\n/**\n Low pass filter internal. @ingroup UGenInternals\n */\nclass BLowPassUGenInternal : public BEQBaseUGenInternal\n{\npublic:\n\tBLowPassUGenInternal(UGen const& input, UGen const& freq, UGen const& rq) throw();\n\tUGenInternal* getChannel(const int channel) throw();\n\t\n\tenum ControlInput { ReciprocalQ = Control };\n\t\n\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n};\n\n#define BHiAndLowPass_Docs\t@param input\tThe input source to filter.\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t\t@param freq\t\tThe cut-off frequency.\t\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t\t@param rq\t\tThe reciprocal of Q (e.g., for a Q of 10 use 0.1)\n\n/**\n Low pass filter.\n \n 12dB/oct rolloff - 2nd order resonant Low Pass Filter.\n \n @ingroup AllUGens FilterUGens\n @see LPF, RLPF, SOS, BLowPass4, BHiPass, BHiPass4, BBandPass, BBandStop, BHiShelf, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BLowPass, (input, freq, rq), \n\t\t\t\t\t\t\t\t\t  (input, freq, rq, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t  (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f),\n\t\t\t\t\t\t\t\t\t  (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BHiAndLowPass_Docs MulAddArgs_Docs);\n\n/**\n Low pass filter.\n \n 24dB/oct rolloff - 4th order resonant Low Pass Filter.\n \n @ingroup AllUGens FilterUGens\n @see LPF, RLPF, SOS, BLowPass, BHiPass, BHiPass4, BBandPass, BBandStop, BHiShelf, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BLowPass4, (input, freq, rq), \n\t\t\t\t\t\t\t\t\t   (input, freq, rq, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t   (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f),\n\t\t\t\t\t\t\t\t\t   (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BHiAndLowPass_Docs MulAddArgs_Docs);\n\n/**\n High pass filter internal. @ingroup UGenInternals\n */\nclass BHiPassUGenInternal : public BEQBaseUGenInternal\n{\npublic:\n\tBHiPassUGenInternal(UGen const& input, UGen const& freq, UGen const& rq) throw();\n\tUGenInternal* getChannel(const int channel) throw();\n\t\n\tenum ControlInput { ReciprocalQ = Control };\n\t\n\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n};\n\n/**\n High pass filter.\n \n 12dB/oct rolloff - 2nd order resonant Low Pass Filter.\n \n @ingroup AllUGens FilterUGens\n @see HPF, RHPF, SOS, BLowPass, BLowPass4, BHiPass4, BBandPass, BBandStop, BHiShelf, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BHiPass, (input, freq, rq), \n\t\t\t\t\t\t\t\t\t (input, freq, rq, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f),\n\t\t\t\t\t\t\t\t\t (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BHiAndLowPass_Docs MulAddArgs_Docs);\n\n/**\n High pass filter.\n \n 24dB/oct rolloff - 4th order resonant Low Pass Filter.\n \n @ingroup AllUGens FilterUGens\n @see HPF, RHPF, SOS, BLowPass, BLowPass4, BHiPass, BBandPass, BBandStop, BHiShelf, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BHiPass4, (input, freq, rq), \n\t\t\t\t\t\t\t\t\t  (input, freq, rq, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t  (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f),\n\t\t\t\t\t\t\t\t\t  (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BHiAndLowPass_Docs MulAddArgs_Docs);\n\n/**\n Band pass filter internal. @ingroup UGenInternals\n */\nclass BBandPassUGenInternal : public BEQBaseUGenInternal\n{\npublic:\n\tBBandPassUGenInternal(UGen const& input, UGen const& freq, UGen const& bw) throw();\n\tUGenInternal* getChannel(const int channel) throw();\n\t\n\tenum ControlInput { BW = Control };\n\t\n\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n};\n\n#define BBandPassAndStop_Docs\t@param input\tThe input source to filter.\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t\t\t@param freq\t\tThe centre frequency.\t\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t\t\t@param bw\t\tThe bandwidth in octaves between -3dB frequencies.\n\n\n/**\n Band pass filter.\n\n @ingroup AllUGens FilterUGens\n @see SOS, BLowPass, BLowPass4, BHiPass, BHiPass4, BBandStop, BHiShelf, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BBandPass, (input, freq, bw), \n\t\t\t\t\t\t\t\t\t   (input, freq, bw, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t   (UGen const& input, UGen const& freq = 1200.f, UGen const& bw = 1.f),\n\t\t\t\t\t\t\t\t\t   (UGen const& input, UGen const& freq = 1200.f, UGen const& bw = 1.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BBandPassAndStop_Docs MulAddArgs_Docs);\n\n/**\n Band stop filter internal. @ingroup UGenInternals\n */\nclass BBandStopUGenInternal : public BEQBaseUGenInternal\n{\npublic:\n\tBBandStopUGenInternal(UGen const& input, UGen const& freq, UGen const& bw) throw();\n\tUGenInternal* getChannel(const int channel) throw();\n\t\n\tenum ControlInput { BW = Control };\n\t\n\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n};\n\n/**\n Band stop filter.\n \n @ingroup AllUGens FilterUGens\n @see SOS, BLowPass, BLowPass4, BHiPass, BHiPass4, BBandPass, BHiShelf, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BBandStop, (input, freq, bw), \n\t\t\t\t\t\t\t\t\t   (input, freq, bw, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t   (UGen const& input, UGen const& freq = 1200.f, UGen const& bw = 1.f),\n\t\t\t\t\t\t\t\t\t   (UGen const& input, UGen const& freq = 1200.f, UGen const& bw = 1.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BBandPassAndStop_Docs MulAddArgs_Docs);\n\n/**\n Peaking filter internal. @ingroup UGenInternals\n */\nclass BPeakEQUGenInternal : public BEQBaseUGenInternal\n{\npublic:\n\tBPeakEQUGenInternal(UGen const& input, UGen const& freq, UGen const& rq, UGen const& gain) throw();\n\tUGenInternal* getChannel(const int channel) throw();\n\t\n\tenum ControlInput { ReciprocalQ = Control };\n\t\n\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n};\n\n#define BPeapEQ_Docs\t@param input\tThe input source to filter.\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param freq\t\tThe centre frequency.\t\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param rq\t\tThe reciprocal of Q (e.g., for a Q of 10 use 0.1).\t\t\\\n\t\t\t\t\t\t@param gain\t\tBoost/cut the center frequency (in dBs).\n\n/**\n Peaking filter, parametric EQ.\n \n @ingroup AllUGens FilterUGens\n @see SOS, BLowPass, BLowPass4, BHiPass, BHiPass4, BBandPass, BBandStop, BHiShelf, BLowShelf\n*/\nDirectMulAddUGenDeclaration(BPeakEQ, (input, freq, rq, gain), \n\t\t\t\t\t\t\t\t\t (input, freq, rq, gain, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t\t\t (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f, UGen const& gain = 0.f),\n\t\t\t\t\t\t\t\t\t (UGen const& input, UGen const& freq = 1200.f, UGen const& rq = 1.f, UGen const& gain = 0.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BPeapEQ_Docs MulAddArgs_Docs);\n\n/**\n Low shelving filter internal. @ingroup UGenInternals\n */\nclass BLowShelfUGenInternal : public BEQBaseUGenInternal\n\t{\n\tpublic:\n\t\tBLowShelfUGenInternal(UGen const& input, UGen const& freq, UGen const& rs, UGen const& gain) throw();\n\t\tUGenInternal* getChannel(const int channel) throw();\n\t\t\n\t\tenum ControlInput { ReciprocalS = Control };\n\t\t\n\t\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n\t};\n\n#define BShelf_Docs\t\t@param input\tThe input source to filter.\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param freq\t\tThe centre frequency.\t\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param rs\t\tThe reciprocal of S.  Shell boost/cut slope.\t\t\t\\\n\t\t\t\t\t\t\t\t\t\tWhen S = 1, the shelf slope is as steep as it can be\t\\\n\t\t\t\t\t\t\t\t\t\tand remain monotonically increasing or decreasing gain\t\\\n\t\t\t\t\t\t\t\t\t\twith frequency.\t\t\t\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param gain\t\tBoost/cut beyond the center frequency (in dBs).\n\n/**\n Low shelving filter.\n \n @ingroup AllUGens FilterUGens\n @see SOS, BLowPass, BLowPass4, BHiPass, BHiPass4, BBandPass, BBandStop, BHiShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BLowShelf, \n\t\t\t\t\t\t\t(input, freq, rs, gain), \n\t\t\t\t\t\t\t(input, freq, rs, gain, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t(UGen const& input, UGen const& freq = 1200.f, UGen const& rs = 1.f, UGen const& gain = 0.f),\n\t\t\t\t\t\t\t(UGen const& input, UGen const& freq = 1200.f, UGen const& rs = 1.f, UGen const& gain = 0.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BShelf_Docs MulAddArgs_Docs);\n\n/**\n High shelving filter internal. @ingroup UGenInternals\n */\nclass BHiShelfUGenInternal : public BEQBaseUGenInternal\n\t{\n\tpublic:\n\t\tBHiShelfUGenInternal(UGen const& input, UGen const& freq, UGen const& rs, UGen const& gain) throw();\n\t\tUGenInternal* getChannel(const int channel) throw();\n\t\t\n\t\tenum ControlInput { ReciprocalS = Control };\n\t\t\n\t\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n\t};\n\n/**\n High shelving filter.\n \n @ingroup AllUGens FilterUGens\n @see SOS, BLowPass, BLowPass4, BHiPass, BHiPass4, BBandPass, BBandStop, BLowShelf, BPeakEQ\n */\nDirectMulAddUGenDeclaration(BHiShelf, \n\t\t\t\t\t\t\t(input, freq, rs, gain), \n\t\t\t\t\t\t\t(input, freq, rs, gain, MulAdd_ArgsCall), \n\t\t\t\t\t\t\t(UGen const& input, UGen const& freq = 1200.f, UGen const& rs = 1.f, UGen const& gain = 0.f),\n\t\t\t\t\t\t\t(UGen const& input, UGen const& freq = 1200.f, UGen const& rs = 1.f, UGen const& gain = 0.f, MulAdd_ArgsDeclare), \n\t\t\t\t\t\t\tCOMMON_UGEN_DOCS BShelf_Docs MulAddArgs_Docs);\n\n/**\n All pass filter internal. @ingroup UGenInternals\n */\nclass BAllPassUGenInternal : public BEQBaseUGenInternal\n\t{\n\tpublic:\n\t\tBAllPassUGenInternal(UGen const& input, UGen const& freq, UGen const& rq) throw();\n\t\tUGenInternal* getChannel(const int channel) throw();\n\t\t\n\t\tenum ControlInput { ReciprocalQ = Control };\n\t\t\n\t\tvoid calculateCoeffs(const float freq, const float control, const float gain);\n\t};\n\n#define BAllPass_Docs\t@param input\tThe input source to filter.\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param freq\t\tThe centre frequency.\t\t\t\t\t\t\t\t\t\\\n\t\t\t\t\t\t@param rq\t\tThe reciprocal of Q (e.g., for a Q of 10 use 0.1).\n\n\n/**\n All pass filter.\n \n @ingroup AllUGens FilterUGens\n @see AllpassL, AllpassN, SOS, BLowPass, BLowPass4, BHiPass, BHiPass4, BBandPass, BBandStop, BHiShelf, BLowShelf, BPeakEQ\n  */\nDirectMulAddUGenDeclaration(BAllPass, \n\t\t\t\t\t\t\t(input, freq, rq), \n\t\t\t\t\t\t\t(input, freq, rq, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/pierrekurza/deuxiemeAnnee/blob/fa25098e6c897e33442e7f15ad1a894562c5fa05/SE/TP/TP8/exercice.cpp", "id": "cpp-0-5c6d9d9b6a81b95bab286b750d19667b", "text": "#include <iostream>\n#define NB_BITS 8\nusing namespace std;\n\nvoid addition(int a1[NB_BITS], int a2[NB_BITS], int r[NB_BITS]);\nvoid subs(int a1[NB_BITS], int a2[NB_BITS], int r[NB_BITS]);\nvoid mult(int a1[NB_BITS], int a2[NB_BITS], int r[NB_BITS]);\n\nvoid print(int tabBits[NB_BITS])\n{\n    for (int i =0; i < NB_BITS;i++)\n    {\n        cout << tabBits[i];\n        if((i!=0) && ((i+1)%4 == 0))\n            cout << \" \";\n    }\n    cout << endl;\n}\n\nvoid complementA2(int tab[NB_BITS])\n{\n    int r, tmp;\n    for(int i = 0; i < NB_BITS; i++)\n        tab[i] = !tab[i];\n\n    for(int j = NB_BITS - 1, r = 1; j >=0; j--)\n    {\n        tmp = tab[j] +r;\n        tab[j] = tmp%2;\n        r = tmp/2;\n    }\n}\n\nvoid intToBin(int v, int tabBits[NB_BITS])\n{\n    bool negatif = v < 0;\n    if(negatif)\n        v = -v;\n    for(int i = NB_BITS -1; i >= 0; i--)\n    {\n        tabBits[i] = v%2;\n        v = v/2;\n    }\n    if(negatif)\n        complementA2(tabBits);\n\n}\n\nint toInt(int tab[NB_BITS])\n{\n    int res, tmp = 1;\n    if(tab[0] == 1)\n    {\n        complementA2(tab);\n        for(int i = NB_BITS - 1; i > 0; i--)\n        {\n            res += (tab[i] * tmp);\n            tmp *= 2;\n        }\n        res = -res;\n        return res;\n    }\n    for(int j = NB_BITS -1; j > 0; j--)\n    {\n        res += (tab[j] * tmp);\n        tmp *= 2;\n    }\n    return res;\n}\n\nvoid assign(int v[NB_BITS], int o[NB_BITS])\n{\n    for(int i = 0;i < NB_BITS; i++)\n        o[i] = v[i];\n}\n\nvoid abs(int tab1[NB_BITS], int tab2[NB_BITS])\n{\n    assign(tab1, tab2);\n    if(tab1[0] == 1)\n        complementA2(tab2);\n}\n\nbool gt_abs(int tab1[NB_BITS], int tab2[NB_BITS])\n{\n    for(int i = 1; i < NB_BITS; i++)\n    {\n        if(tab1[i] != tab2[i])\n            return (tab1[i] > tab2[i]);\n    }\n    return false;\n}\n\nbool lt_abs(int tab1[NB_BITS], int tab2[NB_BITS])\n{\n    return gt_abs(tab2, tab1);\n}\n\nbool eq_abs(int tab1[NB_BITS], int tab2[NB_BITS])\n{\n    return (!gt_abs(tab1, tab2) && (!lt_abs(tab1,tab2)) );\n}\n\nbool gt(int tab1[NB_BITS], int tab2[NB_BITS])\n{\n    if(tab1[0] != tab2[0])\n        return !tab1[0];è-\n    if(tab1[0])\n    {\n        abs(tab1, tab2);\n        return lt_abs(tab1, tab2);\n    }\n    return gt_abs(tab1, tab2);\n}\n\nvoid lShift(int tab[NB_BITS])\n{\n    for(int i = 1; i < NB_BITS -1 ; i++)\n        swap(tab[i],tab[i+1]);\n    for (int p = 0; p < NB_BITS; p++)\n    {\n        cout << tab[p];\n    }\n    cout << endl;\n}\n\nvoid rShift(int tab[NB_BITS])\n{\n    for(int i = 0; i < NB_BITS -2 ; i++)\n        swap(tab[i], tab[i-1]);\n    for (int p = 0; p < NB_BITS; p++)\n    {\n        cout << tab[p];\n    }\n    cout << endl;\n}\n\nvoid addition(int a1[NB_BITS], int a2[NB_BITS], int r[NB_BITS])\n{\n    int abs_a1[NB_BITS], abs_a2[NB_BITS];\n    if(a1[0] != a2[0])\n\n}\n\nint main()\n{\n   /* int nb;\n    cout << \"Entrez un nombre :\";\n    cin >> nb;*/\n    int tabExo1[NB_BITS] = {0,1,0,1,1,0,1,0};\n    int tabExo2[NB_BITS];\n    int tabExo3[NB_BITS] = {1,0,1,1,0,1,0,0};\n    int tabExo4[NB_BITS] = {0,1,0,1,1,0,1,0};\n\n    //print(tabExo1);\n    /*intToBin(nb,tabExo2);\n    print(tabExo2);*/\n    cout << \"Le tableau vaut : \" << toInt(tabExo3) << endl;\n    cout << \"On assign : \" ;\n    assign(tabExo4, tabExo2);\n    print(tabExo4);\n    cout << \"Après assign :\" << endl;\n    print(tabExo2);\n\n    lShift(tabExo1);\n    rShift(tabExo3);\n}\n", "text_preview": "#include <iostream> #define NB_BITS 8 using namespace std; void addition(int a1[NB_BITS], int a2[NB_BITS], int r[NB_BITS]); void subs(int a1[NB_BITS], int a2[NB_BITS], int r[NB_BITS]); void mult(int a1[NB_BITS], int a2[NB_BITS], int 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ishandutta2007/codeforces-j/blob/4aa224314a40fd0e83903e5ccfca2e2d7a892995/jacynkaa/normal/1685/B.cpp", "id": "cpp-0-3c23b52e937493e3fcaaffb7f509b6d2", "text": "#include <bits/stdc++.h>\n#include <chrono>\n#include <ext/pb_ds/assoc_container.hpp>\n#include <math.h>\nusing namespace __gnu_pbds;\nusing namespace std;\n#define endl \"\\n\"\n#define mp make_pair\n#define st first\n#define nd second\n#define pii pair<int, int>\n#define pb push_back\n#define _upgrade ios_base::sync_with_stdio(0), cout.setf(ios::fixed), cout.precision(10), cin.tie(0), cout.tie(0);\n#define rep(i, n) for (int i = 0; i < (n); ++i)\n#define fwd(i, a, b) for (int i = (a); i < (b); ++i)\n#define trav(a, x) for (auto &a : x)\n#define all(c) (c).begin(), (c).end()\n#define sz(X) (int)((X).size())\ntypedef long double ld;\ntypedef unsigned long long ull;\ntypedef long long ll;\ntypedef tree<int, null_type, less<int>, rb_tree_tag, tree_order_statistics_node_update> indexed_set; // find_by_order(x) <-returns x-th element   order_of_key(x) <- returns order of element x\n// mt19937_64 gen(chrono::steady_clock::now().time_since_epoch().count());uniform_int_distribution<int> distr(0, 1e9);auto my_rand = bind(distr, gen); // my_rand() zwraca teraz liczbe z przedzialu [a, b]\n#ifdef LOCAL\nostream &operator<<(ostream &out, string str) {\n   for (char c : str)\n      out << c;\n   return out;\n}\ntemplate <class L, class R> ostream &operator<<(ostream &out, pair<L, R> p) { return out << \"(\" << p.st << \", \" << p.nd << \")\"; }\ntemplate <class L, class R, class S> ostream &operator<<(ostream &out, tuple<L, R, S> p) {\n   auto &[a, b, c] = p;\n   return out << \"(\" << a << \", \" << b << \", \" << c << \")\";\n}\ntemplate <class T> auto operator<<(ostream &out, T a) -> decltype(a.begin(), out) {\n   out << '{';\n   for (auto it = a.begin(); it != a.end(); it = next(it))\n      out << (it != a.begin() ? \", \" : \"\") << *it;\n   return out << '}';\n}\nvoid dump() { cerr << \"\\n\"; }\ntemplate <class T, class... Ts> void dump(T a, Ts... x) {\n   cerr << a << \", \";\n   dump(x...);\n}\n#define debug(...) cerr << \"[\" #__VA_ARGS__ \"]: \", dump(__VA_ARGS__)\n#else\n#define debug(...) 42\n#endif\n\ntypedef vector<vector<int>> vii;\nvoid add(string &S, vii &X) {\n   debug(S);\n   if (sz(S) < 2)\n      return;\n   int t = sz(S) / 2;\n\n   if (sz(S) % 2 == 1)\n      X[0].push_back(t);\n   else {\n      if (S[0] == 'A')\n         X[1].push_back(t);\n      else\n         X[2].push_back(t);\n   }\n}\n\nbool ok(int t, int i, vector<int> Y, vii X, int marg) {\n   int j = i ^ 3;\n\n   debug(i, j);\n   sort(all(X[j]), greater<int>());\n   debug(X[j]);\n\n   rep(k, min(marg, sz(X[j]))) Y[i] += (X[j][k] - 1);\n   return t <= Y[0] + Y[i];\n}\n\nbool solve() {\n   int a, b, c, d;\n   cin >> a >> b >> c >> d;\n   string S;\n   cin >> S;\n   debug(S);\n   if (count(all(S), 'A') != a + c + d) {\n      debug(\"ZLE A\");\n      return false;\n   }\n\n   if (count(all(S), 'B') != b + c + d) {\n      debug(\"ZLE B\");\n      return false;\n   }\n\n   vii X(3);\n\n   string T;\n   for (char c : S) {\n      if (sz(T) && T.back() == c) {\n         add(T, X);\n         T = {c};\n      } else\n         T.push_back(c);\n   }\n   add(T, X);\n\n   debug(X);\n\n   vector<int> Y(3);\n   rep(i, 3) { Y[i] = accumulate(all(X[i]), 0); }\n\n   debug(Y);\n\n   int marg = accumulate(all(Y), 0) - c - d;\n\n   debug(marg);\n\n   if (marg < 0)\n      return false;\n   if (!ok(c, 1, Y, X, marg)) {\n      debug(\"WC\", c);\n      return false;\n   }\n   if (!ok(d, 2, Y, X, marg)) {\n      debug(\"WD\", c);\n\n      return false;\n   }\n\n   return true;\n}\n\nvoid sol() {\n   bool ok = solve();\n   cout << (ok ? \"YES\" : \"NO\") << endl;\n}\n\nint32_t main() {\n   _upgrade;\n   int Z;\n   cin >> Z;\n   while (Z--)\n      sol();\n}", "text_preview": "#include <bits/stdc++.h> #include <chrono> #include <ext/pb_ds/assoc_container.hpp> #include <math.h> using namespace __gnu_pbds; using namespace std; #define endl 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/chuck97/SIMUG/blob/5f02ada7828063c96455e2d7542b03b8b2dbc187/source/advection/fct_zalesak.cpp", "id": "cpp-0-1c9e3b4c5219b32d5939b40f9a2dd3ce", "text": "#include \"fct_zalesak.hpp\"\n\nusing namespace std;\nusing namespace INMOST;\n\nnamespace SIMUG\n{\n\nvoid FCT_Procedure(INMOST::Mesh* mesh,\n                   const std::vector<std::vector<std::vector<double>>>& M_C_minus_M_L,\n                   INMOST::Tag scal_tag,\n                   INMOST::Tag scal_low_tag,\n                   INMOST::Tag scal_high_tag,\n                   INMOST::Tag node_id_tag,\n                   double fct_cd)\n{\n    // calculate fluxes\n    std::vector<std::vector<double>> Fe_vector(M_C_minus_M_L.size());\n    int tr_num = 0;\n\n    for(auto trianit = mesh->BeginCell();\n             trianit != mesh->EndCell();\n             ++trianit)\n    {\n        if (trianit->GetStatus() != Element::Ghost)\n        {\n            INMOST::ElementArray<Node> local_nodes = trianit->getNodes();\n            \n            std::vector<double> scal_n_vec = \n            {\n                local_nodes[0]->Real(scal_tag),\n                local_nodes[1]->Real(scal_tag),\n                local_nodes[2]->Real(scal_tag)\n            };\n\n            std::vector<double> scal_H_vec = \n            {\n                local_nodes[0]->Real(scal_high_tag),\n                local_nodes[1]->Real(scal_high_tag),\n                local_nodes[2]->Real(scal_high_tag)\n            };\n\n            std::vector<double> vec = (fct_cd - 1.0)*scal_n_vec + scal_H_vec;\n\n            Fe_vector[tr_num] = M_C_minus_M_L[tr_num]*vec;\n\n            ++tr_num;\n        }\n    }\n    BARRIER\n\n    // make fluxes tags on triangles\n    INMOST::Tag Fe_trian_tag;\n    tr_num = 0;\n    Fe_trian_tag = mesh->CreateTag(\"Fe_trian\", DATA_REAL, CELL, NONE, 3);\n    mesh->SetFileOption(\"Tag:Fe_trian\", \"nosave\");\n    \n    for(auto trianit = mesh->BeginCell();\n             trianit != mesh->EndCell();\n             ++trianit)\n    {\n        if (trianit->GetStatus() != Element::Ghost)\n        {\n            trianit->RealArray(Fe_trian_tag)[0] = Fe_vector[tr_num][0];\n            trianit->RealArray(Fe_trian_tag)[1] = Fe_vector[tr_num][1];\n            trianit->RealArray(Fe_trian_tag)[2] = Fe_vector[tr_num][2];\n            ++tr_num;\n        }\n    }\n\n    // exchange data\n    mesh->ExchangeData(Fe_trian_tag, CELL, 0);\n    BARRIER\n\n    // make alpha tags on triangles\n    INMOST::Tag alpha_trian_tag;\n    tr_num = 0;\n    alpha_trian_tag = mesh->CreateTag(\"alpha_trian\", DATA_REAL, CELL, NONE, 1);\n\n    // calculate coefficients\n    CalculateAlpha(mesh, Fe_trian_tag, alpha_trian_tag, scal_tag, scal_low_tag, node_id_tag);\n\n    // initialize scal with scal_low\n    for(auto nodeit = mesh->BeginNode();\n             nodeit != mesh->EndNode();\n             ++nodeit)\n    {\n        if(nodeit->GetStatus() != Element::Ghost)\n        {\n            nodeit->Real(scal_tag) = nodeit->Real(scal_low_tag);\n        }\n    }\n    \n    // exchange data\n    mesh->ExchangeData(scal_tag, NODE, 0);\n    BARRIER\n\n    // update scalar value\n    for(auto trianit = mesh->BeginCell();\n             trianit != mesh->EndCell();\n             ++trianit)\n    {\n        INMOST::ElementArray<Node> local_nodes = trianit->getNodes();\n\n        for (int i = 0; i < 3; ++i)\n        {\n            if (local_nodes[i]->GetStatus() != Element::Ghost)\n            {\n                local_nodes[i].Real(scal_tag) += trianit->RealArray(Fe_trian_tag)[i]*\n                                                 trianit->Real(alpha_trian_tag);\n            }\n        }\n    }\n\n    mesh->ExchangeData(scal_tag, NODE, 0);\n    BARRIER\n\n    // Delete temporal Tags\n    mesh->DeleteTag(Fe_trian_tag, CELL);\n    mesh->DeleteTag(alpha_trian_tag, CELL);\n}\n\nvoid CalculateAlpha(INMOST::Mesh* mesh,\n                    INMOST::Tag Fe_trian_tag,\n                    INMOST::Tag alpha_trian_tag,\n                    INMOST::Tag scal_tag,\n                    INMOST::Tag scal_low_tag,\n                    INMOST::Tag node_id_tag)\n{\n    // calculate scal_node_str: 0 - min, 1 - max\n    INMOST::Tag scal_node_str_tag;\n    scal_node_str_tag = mesh->CreateTag(\"scal_node_str\", DATA_REAL, NODE, NONE, 2);\n    mesh->SetFileOption(\"Tag:scal_node_str\", \"nosave\");\n    \n    for(auto nodeit = mesh->BeginNode();\n             nodeit != mesh->EndNode();\n             ++nodeit)\n    {\n        if(nodeit->GetStatus() != Element::Ghost)\n        {\n            double scal_low = nodeit->Real(scal_low_tag);\n            double scal_n = nodeit->Real(scal_tag);\n            nodeit->RealArray(scal_node_str_tag)[0] = std::min(scal_low, scal_n);\n            nodeit->RealArray(scal_node_str_tag)[1] = std::max(scal_low, scal_n);\n        }\n    }\n    mesh->ExchangeData(scal_node_str_tag, NODE, 0);\n    BARRIER\n\n    // calculate scal_trian_str_str: 0 - min, 1 - max\n    INMOST::Tag scal_trian_str_str_tag;\n    scal_trian_str_str_tag = mesh->CreateTag(\"scal_trian_str_str_tag\", DATA_REAL, CELL, NONE, 2);\n    mesh->SetFileOption(\"Tag:scal_trian_str_str_tag\", \"nosave\");\n    \n    for(auto trianit = mesh->BeginCell();\n             trianit != mesh->EndCell();\n             ++trianit)\n    {\n        if(trianit->GetStatus() != Element::Ghost)\n        {\n            INMOST::ElementArray<Node> local_nodes = trianit->getNodes();\n            double min1 = local_nodes[0]->RealArray(scal_node_str_tag)[0];\n            double min2 = local_nodes[1]->RealArray(scal_node_str_tag)[0];\n            double min3 = local_nodes[2]->RealArray(scal_node_str_tag)[0];\n\n            double max1 = local_nodes[0]->RealArray(scal_node_str_tag)[1];\n            double max2 = local_nodes[1]->RealArray(scal_node_str_tag)[1];\n            double max3 = local_nodes[2]->RealArray(scal_node_str_tag)[1];\n\n            double min_trian = std::min(std::min(min1, min2), min3);\n            double max_trian = std::max(std::max(max1, max2), max3);\n\n            trianit->RealArray(scal_trian_str_str_tag)[0] = min_trian;\n            trianit->RealArray(scal_trian_str_str_tag)[1] = max_trian;\n        }\n    }\n    mesh->ExchangeData(scal_trian_str_str_tag, CELL, 0);\n    BARRIER\n\n    // calculate m_node_min_max: 0 - min, 1 - max\n    INMOST::Tag scal_node_min_max_tag;\n    scal_node_min_max_tag = mesh->CreateTag(\"m_node_min_max\", DATA_REAL, NODE, NONE, 2);\n    mesh->SetFileOption(\"Tag:m_node_min_max\", \"nosave\");\n    \n    for(auto nodeit = mesh->BeginNode();\n             nodeit != mesh->EndNode();\n             ++nodeit)\n    {\n        if(nodeit->GetStatus() != Element::Ghost)\n        {\n            INMOST::ElementArray<Cell> local_trians = nodeit->getCells();\n            double node_min = std::numeric_limits<double>::max();;\n            double node_max = std::numeric_limits<double>::min();;\n            for (size_t i = 0; i < local_trians.size(); ++i)\n            {\n                if (local_trians[i]->RealArray(scal_trian_str_str_tag)[0] < node_min)\n                {\n                    node_min = local_trians[i]->RealArray(scal_trian_str_str_tag)[0];\n                }\n\n                if (local_trians[i]->RealArray(scal_trian_str_str_tag)[1] > node_max)\n                {\n                    node_max = local_trians[i]->RealArray(scal_trian_str_str_tag)[1];\n                }\n            }\n            nodeit->RealArray(scal_node_min_max_tag)[0] = node_min;\n            nodeit->RealArray(scal_node_min_max_tag)[1] = node_max;\n        }\n    }\n    mesh->ExchangeData(scal_node_min_max_tag, NODE, 0);\n    BARRIER\n\n    // calculate q_node_min_max: 0 - min, 1 - max\n    INMOST::Tag q_node_min_max_tag;\n    q_node_min_max_tag = mesh->CreateTag(\"q_node_min_max\", DATA_REAL, NODE, NONE, 2);\n    mesh->SetFileOption(\"Tag:q_node_min_max_tag\", \"nosave\");\n    \n    for(auto nodeit = mesh->BeginNode();\n             nodeit != mesh->EndNode();\n             ++nodeit)\n    {\n        if(nodeit->GetStatus() != Element::Ghost)\n        {\n            double node_scal_min = nodeit->RealArray(scal_node_min_max_tag)[0];\n            double node_scal_max = nodeit->RealArray(scal_node_min_max_tag)[1];\n\n            double scal_low = nodeit->Real(scal_low_tag);\n\n            nodeit->RealArray(q_node_min_max_tag)[0] = node_scal_min - scal_low;\n            nodeit->RealArray(q_node_min_max_tag)[1] = node_scal_max - scal_low;\n        }\n    }\n    mesh->ExchangeData(q_node_min_max_tag, NODE, 0);\n    BARRIER\n\n    // calculate p_node_neg_pos: 0 - negative, 1 - positive\n    INMOST::Tag p_node_neg_pos_tag;\n    p_node_neg_pos_tag = mesh->CreateTag(\"p_node_neg_pos\", DATA_REAL, NODE, NONE, 2);\n    mesh->SetFileOption(\"Tag:p_node_neg_pos\", \"nosave\");\n    \n    for(auto nodeit = mesh->BeginNode();\n             nodeit != mesh->EndNode();\n             ++nodeit)\n    {\n        if(nodeit->GetStatus() != Element::Ghost)\n        {\n            double sum_neg = 0.0;\n            double sum_pos = 0.0;\n            INMOST::ElementArray<Cell> local_trians = nodeit->getCells();\n            for (size_t i = 0; i < local_trians.size(); ++i)\n            {\n                INMOST::ElementArray<Node> local_nodes = local_trians[i]->getNodes();\n                for (int j = 0; j < 3; ++j)\n                {\n                    if (local_nodes[j]->Integer(node_id_tag) == nodeit->Integer(node_id_tag))\n                    {\n                        sum_neg += std::min(local_trians[i]->RealArray(Fe_trian_tag)[j], 0.0);\n                        sum_pos += std::max(local_trians[i]->RealArray(Fe_trian_tag)[j], 0.0);\n                    }\n                }\n            }\n\n            nodeit->RealArray(p_node_neg_pos_tag)[0] = sum_neg;\n            nodeit->RealArray(p_node_neg_pos_tag)[1] = sum_pos;\n        }\n    }\n    mesh->ExchangeData(p_node_neg_pos_tag, NODE, 0);\n    BARRIER\n\n    // calculate R_node_neg_pos: 0 - negative, 1 - positive\n    INMOST::Tag R_node_neg_pos_tag;\n    R_node_neg_pos_tag = mesh->CreateTag(\"R_node_neg_pos\", DATA_REAL, NODE, NONE, 2);\n    mesh->SetFileOption(\"Tag:R_node_neg_pos\", \"nosave\");\n    \n    for(auto nodeit = mesh->BeginNode();\n             nodeit != mesh->EndNode();\n             ++nodeit)\n    {\n        if(nodeit->GetStatus() != Element::Ghost)\n        {\n            double P_neg = nodeit->RealArray(p_node_neg_pos_tag)[0];\n            double P_pos = nodeit->RealArray(p_node_neg_pos_tag)[1];\n\n            double Q_neg = nodeit->RealArray(q_node_min_max_tag)[0];\n            double Q_pos = nodeit->RealArray(q_node_min_max_tag)[1];\n\n            if (std::abs(P_neg) < REAL_MIN_ABS_VAL)\n            {\n                nodeit->RealArray(R_node_neg_pos_tag)[0] = 0.0;\n            }\n            else\n            {\n                nodeit->RealArray(R_node_neg_pos_tag)[0] = std::min(1.0, Q_neg/P_neg);\n            }\n\n            if (fabs(P_pos) < REAL_MIN_ABS_VAL)\n            {\n                nodeit->RealArray(R_node_neg_pos_tag)[1] = 0.0;\n            }\n            else\n            {\n                nodeit->RealArray(R_node_neg_pos_tag)[1] = std::min(1.0, Q_pos/P_pos);\n            }   \n        }\n    }\n\n    mesh->ExchangeData(R_node_neg_pos_tag, NODE, 0);\n    BARRIER\n\n    // calculate trian_alpha\n    for(auto trianit = mesh->BeginCell();\n             trianit != mesh->EndCell();\n             ++trianit)\n    {\n        if(trianit->GetStatus() != Element::Ghost)\n        {\n            INMOST::ElementArray<Node> local_nodes = trianit->getNodes();\n            std::vector<double> Fe_vec = \n            {\n                trianit->RealArray(Fe_trian_tag)[0],\n                trianit->RealArray(Fe_trian_tag)[1],\n                trianit->RealArray(Fe_trian_tag)[2]\n            };\n            \n            std::vector<double> R_vec = \n            {\n                (Fe_vec[0] < 0.0) ? local_nodes[0]->RealArray(R_node_neg_pos_tag)[0]: local_nodes[0]->RealArray(R_node_neg_pos_tag)[1],\n                (Fe_vec[1] < 0.0) ? local_nodes[1]->RealArray(R_node_neg_pos_tag)[0]: local_nodes[1]->RealArray(R_node_neg_pos_tag)[1],\n                (Fe_vec[2] < 0.0) ? local_nodes[2]->RealArray(R_node_neg_pos_tag)[0]: local_nodes[2]->RealArray(R_node_neg_pos_tag)[1]\n            };\n\n            trianit->Real(alpha_trian_tag) = std::min(std::min(R_vec[0], R_vec[1]), R_vec[2]); \n        }\n    }\n    mesh->ExchangeData(alpha_trian_tag, CELL, 0);\n    BARRIER\n\n    // delete all temporal tags\n    mesh->DeleteTag(scal_node_str_tag, NODE);\n    mesh->DeleteTag(scal_trian_str_str_tag, CELL);\n    mesh->DeleteTag(scal_node_min_max_tag, NODE);\n    mesh->DeleteTag(q_node_min_max_tag, NODE);\n    mesh->DeleteTag(p_node_neg_pos_tag, NODE);\n    mesh->DeleteTag(R_node_neg_pos_tag, NODE);\n    BARRIER\n}\n\n}", "text_preview": "#include \"fct_zalesak.hpp\" using namespace std; using namespace INMOST; namespace SIMUG { void FCT_Procedure(INMOST::Mesh* mesh, const std::vector<std::vector<std::vector<double>>>& M_C_minus_M_L, INMOST::Tag scal_tag, INMOST::Tag scal_low_", "input_sha256": "d1527e6e76f026780a6370d495fe14adf3e25d887297639948dc15f342d5d176", "text_sha256": "1c9e3b4c5219b32d5939b40f9a2dd3cee126a64691865894071b0771b6837836", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 52, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5758516182382023, "noise_severity": 0.021885887237604342, "missing_content_severity": 0.045777485442222494, "factual_density": 3.2261091343655246, "pretraining_utility": 0.45434010047646645, "math_density": 3.2661439966388683, "code_density": 3.8652609362158956, "reasoning_density": 0.9724617557404454, "education_level": 3.162701012412371, "creative_writing_quality": -0.3922212853163726}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008113815578591366, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9972349295613246, "secondary": 0.9894098197523703, "tertiary": 0.9978764099213351, "structured_records": 0.0019916374730662113, "procedural_steps": 0.030582841328170865, "worked_solution": 0.01036800008674214, "contextual_dialogue": 6.365620640011232e-06, "content_quality": 0.3843436766792765, "noise_severity": 0.9817617606353297, "missing_content_severity": 0.9618520954648145, "factual_density": 0.3550909453046034, "pretraining_utility": 0.38484790617334097, "math_density": 0.38845333053239006, "code_density": 0.8877174468465797, "reasoning_density": 0.18961520354962902, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/caoyucen/CSM/blob/955eb374a06917da7b510bbd972e3062ee2b0ed6/CIS279/Assignment/1/ComponentLabeling/array2d.hpp", "id": "cpp-0-7c494921d7c409bc3f1d65172ee29737", "text": "/**  Image Component Labeling\n *  Project 1\n *  < <NAME> >\n *  < 02/24/2019 >\n **  The program maintains a 2-D grid (array) of objects, where each object\n keeps track of two values, the image component label (to be assigned by\n DFS or BFS), and the order in which the pixel was discovered, which depends\n on the search strategy (DFS or BFS). Accordingly, each pixel is represented\n by an object that encapsulates a label (component label: 2, 3, 4, ...) and\n (order of discovery: 1, 2, 3, ...).\n *  . . .\n *  . . .\n **/\n\n#ifndef array2d_hpp\n#define array2d_hpp\n\n#include<iostream>\n#include<vector>\n\t\nclass Position2D {\npublic:\n\tint row;\n\tint col;\n\t\n\tPosition2D(int row = -1, int col = -1);\n\t\n};\n\ntemplate <class T>\nclass Array2D {\nprivate:\n\tstd::vector<std::vector<T> > array;\n\tint n_cols;\n\tint n_rows;\n\t\n\tvoid initArray(const int n_rows, const int n_cols, const T& value = T());\n\t//\tvoid deleteArray();\n\t\npublic:\n\tArray2D(const int n_rows, const int n_cols, const T& value);\n\tArray2D(Array2D<T>& array_2d);\n\tArray2D();\n\t\n\tvoid randomize(float density, const T& value); // move this outside\n\tbool inside(const Position2D& position);\n\t\n\tstd::vector<T>& operator[](const int idx);\n\tconst int get_nrows() const;\n\tconst int get_ncols() const;\n\t\n\t// Overrider << for string serialization\n\t// friend ostream& operator<<(ostream& ost, Array2D<T>& array_2d);\n\t\n\tvoid printImage();\n\t\n};\n\nPosition2D operator+(const Position2D& a, const Position2D& b);\n\nPosition2D::Position2D(int row, int col)\n:row(row), col(col) {}\n\nPosition2D operator+(const Position2D& a, const Position2D& b) {\n\tPosition2D sum(a.row + b.row, a.col + b.col);\n\treturn sum;\n}\n\ntemplate <class T>\nArray2D<T>::Array2D(const int n_rows, const int n_cols, const T& value)\n:n_rows(n_rows), n_cols(n_cols) {\n\tinitArray(n_rows, n_cols, value);\n}\n\ntemplate <class T>\nArray2D<T>::Array2D(Array2D<T>& array_2d) {\n\tn_rows = array_2d.n_rows;\n\tn_cols = array_2d.n_cols;\n\tinitArray(n_rows, n_cols, T());\n\tfor (int i = 0; i < n_rows + 2; i++) {\n\t\tfor (int j = 0; j < n_cols + 2; j++) {\n\t\t\tarray[i][j] = array_2d[i][j];\n\t\t}\n\t}\n}\n\ntemplate <class T>\nArray2D<T>::Array2D()\n:n_rows(0), n_cols(0) {}\n\ntemplate <class T>\nvoid Array2D<T>::randomize(float density, const T& value) {\n\tfor (int i = 1; i < n_rows + 1; i++) {\n\t\tfor (int j = 1; j < n_cols + 1; j++) {\n\t\t\tif (static_cast<float> (static_cast<float> (rand()) / static_cast<float> (RAND_MAX)) < density) {\n\t\t\t\tarray[i][j] = value;\n\t\t\t}\n\t\t}\n\t}\n}\n\ntemplate <class T>\nbool Array2D<T>::inside(const Position2D& position) {\n\treturn position.row < n_rows + 1 && position.row > 0 && position.col < n_cols + 1 && position.col > 0;\n}\n\ntemplate <class T>\nvoid Array2D<T>::initArray(const int n_rows, const int n_cols, const T& value) {\n\tarray = std::vector<std::vector<T> >(n_rows + 2, std::vector<T>(n_cols + 2, value));\n}\n\ntemplate <class T>\nstd::vector<T>& Array2D<T>::operator[](const int idx) {\n\treturn array[idx];\n}\n\ntemplate <class T>\nconst int Array2D<T>::get_nrows() const {\n\treturn n_rows;\n}\n\ntemplate <class T>\nconst int Array2D<T>::get_ncols() const {\n\treturn n_cols;\n}\n\ntemplate <class T>\nvoid Array2D<T>::printImage() {\n\tfor (int i = 1; i < n_rows + 1; i++) {\n\t\tfor (int j = 1; j < n_cols + 1; j++) {\n\t\t\tstd::cout << array[i][j] << \" \";\n\t\t}\n\t\tstd::cout << std::endl;\n\t}\n}\n\n#endif /* array2d_hpp */\n", "text_preview": "/** Image Component Labeling * Project 1 * < <NAME> > * < 02/24/2019 > ** The program maintains a 2-D grid (array) of objects, where each object keeps track of two values, the image component label (to be assigned by DFS or BFS), and the or", "input_sha256": "422aa24052d1dd113d3366666ff2ba1fbc05df243b5d348ee9f2980f21a8caad", "text_sha256": "7c494921d7c409bc3f1d65172ee29737e0ff7b8d40d1f67cc037ec79a2b40bf2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 55, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/mancadra/APS1/blob/53be0b1b826d4e26a66e282e67f4c3dedde066af/pozresniAlg.cpp", "id": "cpp-0-a9c712d68e3cb3b1f4171757c499c427", "text": "#include <iostream>\n#include <vector>\n#include <algorithm>\n#include <cstdio>\n#include <queue>\nusing namespace std;\n\ntypedef pair<int,int> PII;\ntypedef vector<pair<int,int>> VII;\ntypedef vector<vector<pair<int,int>>> VVP;\n\n\n// <NAME>\nint crpalke(int K, int T, vector<int> crpal) {\n\tcrpal.insert(crpal.begin(), 0);\n\tcrpal.insert(crpal.end(), K); // Konec in zac dodamo kot crpalke za bolj enostavno implementacijo\n\t\n\tint doseg = T, stT = 0;\n\tfor (int i = 0; i < crpal.size() - 1; i++) {\n\t\tif ((crpal[i + 1] - crpal[i]) > doseg) {\n\t\t\t//cout << i << endl;\n\t\t\tdoseg = T; // napolnimo\n\t\t\tstT++;\n\t\t}\n\t\tif ((crpal[i + 1] - crpal[i]) > doseg) return -1; // če rtudi s polnim tankom ne gre\n\t\tdoseg -= crpal[i + 1] - crpal[i];\n\t}\n\treturn stT;\n}\n\n// IZBIRA AKTIVNOSTI INTERAVAL SCHEDULING\nVII aktivnosti(VII a) {\n\tVII razpored;\n\tint konec=0;\n\twhile (1) {\n\t\tint j = -1;\n\t\tfor (int i = 0; i < a.size(); i++) {\n\t\t\tif (konec <= a[i].first) {\n\t\t\t\tif (j == -1 || a[i].second<a[j].second) j = i; // če je kater slučajno boljši se prej konča\n\t\t\t}\n\t\t}\n\t\tif (j == -1) break;\n\t\trazpored.push_back(a[j]);\n\t\tkonec = a[j].second;\n\t\ta.erase(a.begin() + j);\n\t}\n\treturn razpored;\n}\n// lahko še bolj učinkovito, če najprej aktivnosti razedlimo po njihovih koncih\nbool cmpSecond(pair<int,int> a, pair<int,int> b) {\n\treturn a.second < b.second;\n}\n\nVII aktivnosti2(VII a) {\n\tsort(a.begin(), a.end(), cmpSecond);\n\tVII razpored;\n\tint konec = 0;\n\tfor (auto [s,e] : a) {\n\t\tif (konec <= s) {\n\t\t\trazpored.push_back({s,e});\n\t\t\tkonec = e;\n\t\t}\n\t}\n\treturn razpored;\n}\n\n\n// PREDAVALNICE URNIK\nVVP predavalnice(VII predavanja) {\n\tsort(predavanja.begin(), predavanja.end());\n\tVVP urnik;\n\tfor (auto p : predavanja) {\n\t\tauto [s,e] = p;\n\t\tint x = -1;\n\t\tfor (int i = 0; i < urnik.size(); i++) { // sprehodimo se po učilnicah\n\t\t\tif (urnik[i].back().second<=s) { // pregledujemo kdaj se konča zadnje predavanje\n\t\t\t\tx = i;\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t\tif (x == -1) urnik.push_back({p}); // odpremo novo učilnico\n\t\telse urnik[x].push_back(p);\n\t}\n\treturn urnik;\n}\nVVP predavalniceHeap(VII predavanja) {\n    sort(predavanja.begin(), predavanja.end());\n    VVP urnik;\n    priority_queue<PII, VII, greater<PII>> pq;  // min-heap\n    pq.push({predavanja.back().second, -1});  // dummy\n    for (auto p : predavanja) {\n        auto [s,e] = p;\n        auto [konec, x] = pq.top();\n        if (konec<=s) {\n            pq.pop(); pq.push({e,x});\n            urnik[x].push_back(p);\n        } else {\n            pq.push({e, urnik.size()});\n            urnik.push_back({p});\n        }\n    }\n    return urnik;\n}\n\n\n\n// DATOTEKE NA TRAKU\n/*\ndi = (si, fi)   , si... velikost datoteke, fi...pogostost dostopa\nče želimo dostopati do datoteke oz. do podatka na x rabimo prevrteti celoten trak od začetk do tega mesta\n\nŽelimo torej najpogostejše zapisati bolj na začetek\nampak a je to dovolj, kaj če je ena ogromna datoteka enkrat več iskana kot ena majhna, potem bomo do tiste majhne potrebovali ogromno več kot če bi najprej njo in kasneje večje.\n\nUgotovimo da je požrešna rešitev da jih zložimo po vrsti od tistih z nižjim proti tistim z višjim razmerjem si/fj\n*/\nint score(vector<pair<int,int>> d) {  // ocenjujemo razpored\n\tint x = 0, sc=0;\n\tfor (auto [s,f] : d) {\n\t\tsc += x*f;\n\t\tx+=s;\n\t}\n\treturn sc;\n}\nbool cmpRatio(pair<int,int> a, pair<int,int> b) {\n\treturn a.first*b.second < b.first*a.second; // a.first/a.second < b.first/b.second ... racunska napaka?\n}\nint trak(vector<pair<int,int>> d) {\n\tsort(d.begin(), d.end(), cmpRatio);\n\treturn score(d);\n}\t\n\n\n\n\nint main() {\n\t// ČRPALKA\n\tint K = 950, T = 400;\n\tvector<int> crpal = {200, 375, 550, 750};\n\tcout << crpalke(K, T, crpal) << endl;\n\t\n\t// IZBIRA AKTIVNOSTI\n\tVII a = {{1,3}, {2,4}, {2,5}, {4,5}, {4,7}, {6,7}, {6,8}, {7,12}, {8,12}, {9,10}, {9,11}, {11,12}, {12,13}};\n\tVII r = aktivnosti(a);\n\tprintf(\"%d:\",(int)r.size());\n\tfor (auto [s,e] : r) printf(\" (%d,%d)\",s,e);\n\tprintf(\"\\n\");\n\tVII r2 = aktivnosti2(a);\n\tprintf(\"%d:\",(int)r2.size());\n\tfor (auto [s,e] : r2) printf(\" (%d,%d)\",s,e);\n\tprintf(\"\\n\");\n\t\n\t// PREDAVANJA URNIK\n\tVII predavanja = {{4,10}, {12,15}, {0,3}, {4,7}, {8,11}, {0,7}, {10,15}, {0,3}, {8,11}, {12,15}};\n\tVVP urnik = predavalnice(predavanja);\n\tfor (auto ucilnica : urnik) {\n\t\tfor (auto [s,e] : ucilnica) printf(\" (%d,%d)\",s,e);\n\t\tprintf(\"\\n\");\n\t}\n\tVVP urnik2 = predavalnice(predavanja);\n\tfor (auto ucilnica : urnik2) {\n\t\tfor (auto [s,e] : ucilnica) printf(\" (%d,%d)\",s,e);\n\t\tprintf(\"\\n\");\n\t}\n\t\n\t// DATOTEKE NA TRAKU \n\tVII d = {{60,5}, {27,3}, {1,20}, {32,4}};\n\tcout << \"score: \" << score(d) << endl;\n\tsort(d.begin(),d.end());\n\tdo {\n\t\tcout << score(d) << ' ';\n\t} while (next_permutation(d.begin(),d.end()));\n\tcout << trak(d) << endl;\n\t\n\t\n\treturn 0;\n}\n", "text_preview": "#include <iostream> #include <vector> #include <algorithm> #include <cstdio> #include <queue> using namespace std; typedef pair<int,int> PII; typedef vector<pair<int,int>> VII; typedef vector<vector<pair<int,int>>> VVP; // <NAME> int crpalk", "input_sha256": "868ef08566a21aadb3d43726f81b163722d2aa71f42074b04a1698110542beed", "text_sha256": "a9c712d68e3cb3b1f4171757c499c427db9a1df6e5800fe267862233ed1c84fe", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 56, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 2, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.4227063424080491, "noise_severity": 0.10205802752902302, "missing_content_severity": 0.018084368605887947, "factual_density": 3.2911183858098405, "pretraining_utility": 0.7060586202480221, "math_density": 3.263977716651075, "code_density": 3.688041482531014, "reasoning_density": 2.1318891901315165, 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0.00025865742760900954, "Health, Healthcare & Wellness": 0.00018809510776701926, "History & Geography": 0.00024825902963290493, "Home, Pets & Hobbies": 0.0001654473111543421, "Industry & Production": 0.00020293117427906457, "Language, Literature & Philosophy": 0.00024470166344060166, "Politics & Government": 9.447099230950351e-05, "Religion & Spirituality": 0.00014190569482092, "Science, Mathematics & Engineering": 0.08808088663908471, "Relationships & Community": 0.00010954668060539877, "Software Usage & Applications": 0.0005248115799866922, "Programming & Software Engineering": 0.9052115723685703, "Sports & Fitness": 0.0002467825149694899, "Vehicles & Transportation": 0.0006834964030942241, "Travel & Tourism": 0.00032398479409262887, "Other / Uncovered Subject": 0.0009386476547073833, "Unknown / Insufficient Content": 0.00021717917705564056}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005501448469365253, "Art & Design": 0.000596195834181513, "Law & Crime": 0.0006104718836798668, "Education & Careers": 0.001998840831507852, "Entertainment & Media": 0.00044240870136334895, "Fashion & Beauty": 0.0003074523468513989, "Business, Economics & Finance": 0.000746054763150236, "Food & Dining": 0.00028162941686290556, "Games & Gaming": 0.00047119363767044836, "Electronics & Hardware": 0.0005541374923676241, "Health, Healthcare & Wellness": 0.00042281897200085046, "History & Geography": 0.00043354153848594725, "Home, Pets & Hobbies": 0.00031664703154327627, "Industry & Production": 0.0003898459385939021, "Language, Literature & Philosophy": 0.000633246957454699, "Politics & Government": 0.00023596074802521792, "Religion & Spirituality": 0.0003622160963674821, "Science, Mathematics & Engineering": 0.3601358656754595, "Relationships & Community": 0.00020137341753791762, "Software Usage & Applications": 0.00042990480806338486, "Programming & Software Engineering": 0.14439372881265944, "Sports & Fitness": 0.00038929496957772317, "Vehicles & Transportation": 0.0020149742697053974, "Travel & Tourism": 0.0007196104781720293, "Other / Uncovered Subject": 0.0007924288278643764, "NONE": 0.4815700117039172}}, "probability_diagnostics": {"primary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10359, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10359, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:01.456087+00:00", "scoring_key_slot": "primary", "dataset_uid": "80fd82a9-6e0f-4020-a77c-3a0e5ffaf22a", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "2c522ffd4f38c7a458f5244021fcef5e977208b689cdb947f9df0bebeef7f680", "source_metadata": {"uuid": "80fd82a9-6e0f-4020-a77c-3a0e5ffaf22a", "repo_name": "mancadra/APS1", "relative_path": "pozresniAlg.cpp", "category": "ALGO", "algo_rel_score": 0.9997777342796326, "quality_score": 3.615612030029297, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 20, "repo_created_at": "2023-11-01T14:42:10.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "abc1c1502757a4a0a37154adfda5daef23519899", "size_bytes": 4674, "file_timestamp": 1706724080, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "53be0b1b826d4e26a66e282e67f4c3dedde066af"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 20, "repo_created_at": "2023-11-01T14:42:10.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 1, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "abc1c1502757a4a0a37154adfda5daef23519899", "size_bytes": 4674, "file_timestamp": 1706724080, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "53be0b1b826d4e26a66e282e67f4c3dedde066af", "url": "https://github.com/mancadra/APS1/blob/53be0b1b826d4e26a66e282e67f4c3dedde066af/pozresniAlg.cpp"}, "gpt4o_tokens": 1663, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.4227063424080491, "noise_severity": 0.10205802752902302, "missing_content_severity": 0.018084368605887947, "factual_density": 3.2911183858098405, "pretraining_utility": 0.7060586202480222, "math_density": 3.263977716651075, "code_density": 3.688041482531014, "reasoning_density": 2.1318891901315165, "education_level": 2.0029086388809993, "creative_writing_quality": -0.3528235993876752}, "score_applicability": {"education_level": 0.9988377908949965, "creative_writing_quality": 0.0011985963675466397}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/joppuyo/libjxl-0.6.1-mac/blob/2a63e27c0748fbb26f975f4e54a9cd49266bf28f/lib/jxl/simd_util-inl.h", "id": "cpp-0-45c56ec60ceddeaa14994129d076ceeb", "text": "// Copyright (c) the JPEG XL Project Authors. All rights reserved.\n//\n// Use of this source code is governed by a BSD-style\n// license that can be found in the LICENSE file.\n\n// Misc utilities for SIMD operations\n\n#if defined(LIB_JXL_SIMD_UTIL_INL_H_) == defined(HWY_TARGET_TOGGLE)\n#ifdef LIB_JXL_SIMD_UTIL_INL_H_\n#undef LIB_JXL_SIMD_UTIL_INL_H_\n#else\n#define LIB_JXL_SIMD_UTIL_INL_H_\n#endif\n\n#include <hwy/highway.h>\n\nHWY_BEFORE_NAMESPACE();\nnamespace jxl {\nnamespace HWY_NAMESPACE {\n\n#if HWY_CAP_GE512\nusing hwy::HWY_NAMESPACE::Half;\nusing hwy::HWY_NAMESPACE::Vec;\ntemplate <size_t i, class DF, class V>\nHWY_INLINE Vec<Half<Half<DF>>> Quarter(const DF df, V v) {\n  using HF = Half<DF>;\n  using HHF = Half<HF>;\n  auto half = i >= 2 ? UpperHalf(HF(), v) : LowerHalf(HF(), v);\n  return i & 1 ? UpperHalf(HHF(), half) : LowerHalf(HHF(), half);\n}\n\ntemplate <class DF, class V>\nHWY_INLINE Vec<DF> Concat4(const DF df, V v0, V v1, V v2, V v3) {\n  using HF = Half<DF>;\n  return Combine(DF(), Combine(HF(), v3, v2), Combine(HF(), v1, v0));\n}\n\n#endif\n\n// Stores v0[0], v1[0], v0[1], v1[1], ... to mem, in this order. Mem must be\n// aligned.\ntemplate <class DF, class V>\nvoid StoreInterleaved(const DF df, V v0, V v1, float* mem) {\n#if HWY_TARGET == HWY_SCALAR\n  Store(v0, df, mem);\n  Store(v1, df, mem + 1);\n#elif !HWY_CAP_GE256\n  Store(InterleaveLower(df, v0, v1), df, mem);\n  Store(InterleaveUpper(df, v0, v1), df, mem + Lanes(df));\n#elif !HWY_CAP_GE512\n  auto t0 = InterleaveLower(df, v0, v1);\n  auto t1 = InterleaveUpper(df, v0, v1);\n  Store(ConcatLowerLower(df, t1, t0), df, mem);\n  Store(ConcatUpperUpper(df, t1, t0), df, mem + Lanes(df));\n#else\n  auto t0 = InterleaveLower(df, v0, v1);\n  auto t1 = InterleaveUpper(df, v0, v1);\n  Store(Concat4(df, Quarter<0>(df, t0), Quarter<0>(df, t1), Quarter<1>(df, t0),\n                Quarter<1>(df, t1)),\n        df, mem);\n  Store(Concat4(df, Quarter<2>(df, t0), Quarter<2>(df, t1), Quarter<3>(df, t0),\n                Quarter<3>(df, t1)),\n        df, mem + Lanes(df));\n#endif\n}\n\n// Stores v0[0], v1[0], v2[0], v3[0], v0[1] ... to mem, in this order. Mem must\n// be aligned.\ntemplate <class DF, class V>\nvoid StoreInterleaved(const DF df, V v0, V v1, V v2, V v3, float* mem) {\n#if HWY_TARGET == HWY_SCALAR\n  Store(v0, df, mem);\n  Store(v1, df, mem + 1);\n  Store(v2, df, mem + 2);\n  Store(v3, df, mem + 3);\n#elif !HWY_CAP_GE256\n  auto t0 = InterleaveLower(df, v0, v2);\n  auto t1 = InterleaveLower(df, v1, v3);\n  auto t2 = InterleaveUpper(df, v0, v2);\n  auto t3 = InterleaveUpper(df, v1, v3);\n  Store(InterleaveLower(df, t0, t1), df, mem);\n  Store(InterleaveUpper(df, t0, t1), df, mem + Lanes(df));\n  Store(InterleaveLower(df, t2, t3), df, mem + 2 * Lanes(df));\n  Store(InterleaveUpper(df, t2, t3), df, mem + 3 * Lanes(df));\n#elif !HWY_CAP_GE512\n  auto t0 = InterleaveLower(df, v0, v2);\n  auto t1 = InterleaveLower(df, v1, v3);\n  auto t2 = InterleaveUpper(df, v0, v2);\n  auto t3 = InterleaveUpper(df, v1, v3);\n\n  auto m0 = InterleaveLower(df, t0, t1);\n  auto m1 = InterleaveUpper(df, t0, t1);\n  auto m2 = InterleaveLower(df, t2, t3);\n  auto m3 = InterleaveUpper(df, t2, t3);\n\n  Store(ConcatLowerLower(df, m1, m0), df, mem);\n  Store(ConcatLowerLower(df, m3, m2), df, mem + Lanes(df));\n  Store(ConcatUpperUpper(df, m1, m0), df, mem + 2 * Lanes(df));\n  Store(ConcatUpperUpper(df, m3, m2), df, mem + 3 * Lanes(df));\n#else\n  auto t0 = InterleaveLower(df, v0, v2);\n  auto t1 = InterleaveLower(df, v1, v3);\n  auto t2 = InterleaveUpper(df, v0, v2);\n  auto t3 = InterleaveUpper(df, v1, v3);\n\n  auto m0 = InterleaveLower(df, t0, t1);\n  auto m1 = InterleaveUpper(df, t0, t1);\n  auto m2 = InterleaveLower(df, t2, t3);\n  auto m3 = InterleaveUpper(df, t2, t3);\n\n  Store(Concat4(df, Quarter<0>(df, m0), Quarter<0>(df, m1), Quarter<0>(df, m2),\n                Quarter<0>(df, m3)),\n        df, mem);\n  Store(Concat4(df, Quarter<1>(df, m0), Quarter<1>(df, m1), Quarter<1>(df, m2),\n                Quarter<1>(df, m3)),\n        df, mem + Lanes(df));\n  Store(Concat4(df, Quarter<2>(df, m0), Quarter<2>(df, m1), Quarter<2>(df, m2),\n                Quarter<2>(df, m3)),\n        df, mem + 2 * Lanes(df));\n  Store(Concat4(df, Quarter<3>(df, m0), Quarter<3>(df, m1), Quarter<3>(df, m2),\n                Quarter<3>(df, m3)),\n        df, mem + 3 * Lanes(df));\n#endif\n}\n\n// Stores v0[0], v1[0], v2[0], v3[0], v4[0], v5[0], v6[0], v7[0], v0[1] ... to\n// mem, in this order. Mem must be aligned.\ntemplate <class DF, class V>\nvoid StoreInterleaved(const DF df, V v0, V v1, V v2, V v3, V v4, V v5, V v6,\n                      V v7, float* mem) {\n#if HWY_TARGET == HWY_SCALAR\n  Store(v0, df, mem);\n  Store(v1, df, mem + 1);\n  Store(v2, df, mem + 2);\n  Store(v3, df, mem + 3);\n  Store(v4, df, mem + 4);\n  Store(v5, df, mem + 5);\n  Store(v6, df, mem + 6);\n  Store(v7, df, mem + 7);\n#elif !HWY_CAP_GE256\n  auto t0 = InterleaveLower(df, v0, v4);\n  auto t1 = InterleaveLower(df, v1, v5);\n  auto t2 = InterleaveLower(df, v2, v6);\n  auto t3 = InterleaveLower(df, v3, v7);\n  auto t4 = InterleaveUpper(df, v0, v4);\n  auto t5 = InterleaveUpper(df, v1, v5);\n  auto t6 = InterleaveUpper(df, v2, v6);\n  auto t7 = InterleaveUpper(df, v3, v7);\n\n  auto w0 = InterleaveLower(df, t0, t2);\n  auto w1 = InterleaveLower(df, t1, t3);\n  auto w2 = InterleaveUpper(df, t0, t2);\n  auto w3 = InterleaveUpper(df, t1, t3);\n  auto w4 = InterleaveLower(df, t4, t6);\n  auto w5 = InterleaveLower(df, t5, t7);\n  auto w6 = InterleaveUpper(df, t4, t6);\n  auto w7 = InterleaveUpper(df, t5, t7);\n\n  Store(InterleaveLower(df, w0, w1), df, mem);\n  Store(InterleaveUpper(df, w0, w1), df, mem + Lanes(df));\n  Store(InterleaveLower(df, w2, w3), df, mem + 2 * Lanes(df));\n  Store(InterleaveUpper(df, w2, w3), df, mem + 3 * Lanes(df));\n  Store(InterleaveLower(df, w4, w5), df, mem + 4 * Lanes(df));\n  Store(InterleaveUpper(df, w4, w5), df, mem + 5 * Lanes(df));\n  Store(InterleaveLower(df, w6, w7), df, mem + 6 * Lanes(df));\n  Store(InterleaveUpper(df, w6, w7), df, mem + 7 * Lanes(df));\n#elif !HWY_CAP_GE512\n  auto t0 = InterleaveLower(df, v0, v4);\n  auto t1 = InterleaveLower(df, v1, v5);\n  auto t2 = InterleaveLower(df, v2, v6);\n  auto t3 = InterleaveLower(df, v3, v7);\n  auto t4 = InterleaveUpper(df, v0, v4);\n  auto t5 = InterleaveUpper(df, v1, v5);\n  auto t6 = InterleaveUpper(df, v2, v6);\n  auto t7 = InterleaveUpper(df, v3, v7);\n\n  auto w0 = InterleaveLower(df, t0, t2);\n  auto w1 = InterleaveLower(df, t1, t3);\n  auto w2 = InterleaveUpper(df, t0, t2);\n  auto w3 = InterleaveUpper(df, t1, t3);\n  auto w4 = InterleaveLower(df, t4, t6);\n  auto w5 = InterleaveLower(df, t5, t7);\n  auto w6 = InterleaveUpper(df, t4, t6);\n  auto w7 = InterleaveUpper(df, t5, t7);\n\n  auto m0 = InterleaveLower(df, w0, w1);\n  auto m1 = InterleaveUpper(df, w0, w1);\n  auto m2 = InterleaveLower(df, w2, w3);\n  auto m3 = InterleaveUpper(df, w2, w3);\n  auto m4 = InterleaveLower(df, w4, w5);\n  auto m5 = InterleaveUpper(df, w4, w5);\n  auto m6 = InterleaveLower(df, w6, w7);\n  auto m7 = InterleaveUpper(df, w6, w7);\n\n  Store(ConcatLowerLower(df, m1, m0), df, mem);\n  Store(ConcatLowerLower(df, m3, m2), df, mem + Lanes(df));\n  Store(ConcatLowerLower(df, m5, m4), df, mem + 2 * Lanes(df));\n  Store(ConcatLowerLower(df, m7, m6), df, mem + 3 * Lanes(df));\n  Store(ConcatUpperUpper(df, m1, m0), df, mem + 4 * Lanes(df));\n  Store(ConcatUpperUpper(df, m3, m2), df, mem + 5 * Lanes(df));\n  Store(ConcatUpperUpper(df, m5, m4), df, mem + 6 * Lanes(df));\n  Store(ConcatUpperUpper(df, m7, m6), df, mem + 7 * Lanes(df));\n#else\n  auto t0 = InterleaveLower(df, v0, v4);\n  auto t1 = InterleaveLower(df, v1, v5);\n  auto t2 = InterleaveLower(df, v2, v6);\n  auto t3 = InterleaveLower(df, v3, v7);\n  auto t4 = InterleaveUpper(df, v0, v4);\n  auto t5 = InterleaveUpper(df, v1, v5);\n  auto t6 = InterleaveUpper(df, v2, v6);\n  auto t7 = InterleaveUpper(df, v3, v7);\n\n  auto w0 = InterleaveLower(df, t0, t2);\n  auto w1 = InterleaveLower(df, t1, t3);\n  auto w2 = InterleaveUpper(df, t0, t2);\n  auto w3 = InterleaveUpper(df, t1, t3);\n  auto w4 = InterleaveLower(df, t4, t6);\n  auto w5 = InterleaveLower(df, t5, t7);\n  auto w6 = InterleaveUpper(df, t4, t6);\n  auto w7 = InterleaveUpper(df, t5, t7);\n\n  auto m0 = InterleaveLower(df, w0, w1);\n  auto m1 = InterleaveUpper(df, w0, w1);\n  auto m2 = InterleaveLower(df, w2, w3);\n  auto m3 = InterleaveUpper(df, w2, w3);\n  auto m4 = InterleaveLower(df, w4, w5);\n  auto m5 = InterleaveUpper(df, w4, w5);\n  auto m6 = InterleaveLower(df, w6, w7);\n  auto m7 = InterleaveUpper(df, w6, w7);\n\n  Store(Concat4(df, Quarter<0>(df, m0), Quarter<0>(df, m1), Quarter<0>(df, m2),\n                Quarter<0>(df, m3)),\n        df, mem);\n  Store(Concat4(df, Quarter<0>(df, m4), Quarter<0>(df, m5), Quarter<0>(df, m6),\n                Quarter<0>(df, m7)),\n        df, mem + Lanes(df));\n  Store(Concat4(df, Quarter<1>(df, m0), Quarter<1>(df, m1), Quarter<1>(df, m2),\n                Quarter<1>(df, m3)),\n        df, mem + 2 * Lanes(df));\n  Store(Concat4(df, Quarter<1>(df, m4), Quarter<1>(df, m5), Quarter<1>(df, m6),\n                Quarter<1>(df, m7)),\n        df, mem + 3 * Lanes(df));\n  Store(Concat4(df, Quarter<2>(df, m0), Quarter<2>(df, m1), Quarter<2>(df, m2),\n                Quarter<2>(df, m3)),\n        df, mem + 4 * Lanes(df));\n  Store(Concat4(df, Quarter<2>(df, m4), Quarter<2>(df, m5), Quarter<2>(df, m6),\n                Quarter<2>(df, m7)),\n        df, mem + 5 * Lanes(df));\n  Store(Concat4(df, Quarter<3>(df, m0), Quarter<3>(df, m1), Quarter<3>(df, m2),\n                Quarter<3>(df, m3)),\n        df, mem + 6 * Lanes(df));\n  Store(Concat4(df, Quarter<3>(df, m4), Quarter<3>(df, m5), Quarter<3>(df, m6),\n                Quarter<3>(df, m7)),\n        df, mem + 7 * Lanes(df));\n#endif\n}\n\n// NOLINTNEXTLINE(google-readability-namespace-comments)\n}  // namespace HWY_NAMESPACE\n}  // namespace jxl\nHWY_AFTER_NAMESPACE();\n\n#endif  // LIB_JXL_SIMD_UTIL_INL_H_\n", "text_preview": "// Copyright (c) the JPEG XL Project Authors. All rights reserved. // // Use of this source code is governed by a BSD-style // license that can be found in the LICENSE file. // Misc utilities for SIMD operations #if defined(LIB_JXL_SIMD_UTI", "input_sha256": "52d24a9cfad9306df271c238e66c66a5bae67e2e517acdb0b74368555921ca93", "text_sha256": "45c56ec60ceddeaa14994129d076ceeb1820c76c4edb2ff25a0ddee161481d42", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 57, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.3146184487792076, "noise_severity": 0.2853417382626312, "missing_content_severity": 0.01820903139062295, "factual_density": 3.093198844848925, "pretraining_utility": 0.2732445239809084, "math_density": 1.6671700280034498, "code_density": 3.89410848730341, "reasoning_density": 0.4222332176217351, "education_level": 3.318903643389484, "creative_writing_quality": -0.07354603092800065}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.5311 below 0.70", "secondary vs NONE margin 0.0976 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.4293325658665794, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9953091198147754, "secondary": 0.5107590646505014, "tertiary": 0.8569586645925507, "structured_records": 0.006591463754228787, "procedural_steps": 0.0037058934744980646, "worked_solution": 0.004010845633053922, "contextual_dialogue": 5.738325133315812e-06, "content_quality": 0.5478865634318246, "noise_severity": 0.762215218114474, "missing_content_severity": 0.9848258071744809, "factual_density": 0.5196200457106803, "pretraining_utility": 0.42578687228108814, "math_density": 0.0, "code_density": 0.9117570727528418, "reasoning_density": 0.6481389853152207, "education_level": 0.38825579968748886, "creative_writing_quality": 0.9991195721372021, "primary_topic": 0.9742186212644678, "secondary_topic": 0.8100108638019267}, "probability_margin": {"primary": 0.993093836468247, "secondary": 0.09755794925544858, "tertiary": 0.7380552681199897, "education_level": 0.07415124580993493, "creative_writing_quality": 0.9988746425670829, "primary_topic": 0.9578409059630205, "secondary_topic": 0.7202786934213647}, "raw_probabilities": {"primary": {"Academic Writing": 4.193513079776577e-05, "Content Listing": 2.4063619522911145e-05, "Creative Writing": 1.3126786805774304e-05, "Customer Support Page": 8.042416134389973e-06, "Q&A Forum": 1.122807495906412e-05, "Discussion Thread": 7.516873419811751e-06, "Comment Section": 1.511018629322306e-05, "Missing Content": 4.7710512572202844e-05, "Knowledge Article": 0.00010303367737809967, "Legal Notices": 0.002410736687579367, "News Article": 5.008566110773186e-06, "Organizational Announcement": 1.7789749358597633e-05, "Nonfiction Writing": 9.986770886388619e-06, "Organizational About Page": 1.3242250693819273e-05, "Personal About Page": 8.018124144626625e-06, "Personal Blog": 7.206865531322481e-06, "Product Page": 7.943641842810812e-06, "Spam / Ads": 9.005872018014913e-06, "Structured Data": 0.0002604464726491696, "Technical Writing": 0.9955045731558264, "Transcript / Interview": 1.4098238452656869e-05, "Tutorial / How-To Guide": 0.0001268183408308956, "User Reviews": 7.006964199070097e-06, "Other / Unknown": 0.0013263510219930091}, "secondary": {"Academic Writing": 0.0007270000805002601, "Content Listing": 0.0006164981812279227, "Creative Writing": 0.0005036524419811943, "Customer Support Page": 0.00032658877386994463, "Q&A Forum": 0.00035610116067164504, "Discussion Thread": 0.00032240310672872186, "Comment Section": 0.0004758482210769772, "Missing Content": 0.0006653822686150139, "Knowledge Article": 0.0010980886506335523, "Legal Notices": 0.5311441036233971, "News Article": 0.0005332864606286089, "Organizational Announcement": 0.0005584230935986893, "Nonfiction Writing": 0.0005040101674971516, "Organizational About Page": 0.0005686013075881844, "Personal About Page": 0.00036442871441045183, "Personal Blog": 0.0002916477331461232, "Product Page": 0.00041387854983978184, "Spam / Ads": 0.0003681234896258984, "Structured Data": 0.0010893365017636057, "Technical Writing": 0.02412730667704632, "Transcript / Interview": 0.000605916609735034, "Tutorial / How-To Guide": 0.0005053532398762468, "User Reviews": 0.0002478665785931623, "NONE": 0.43358615436794856}, "tertiary": {"Academic Writing": 0.0003392436401278069, "Content Listing": 0.0001986152744876475, "Creative Writing": 0.00024362844878678686, "Customer Support Page": 0.00013258582090902685, "Q&A Forum": 9.65572688156901e-05, "Discussion Thread": 9.296818160885986e-05, "Comment Section": 0.00015635685611819567, "Missing Content": 0.0002811540909710158, "Knowledge Article": 0.0006075233865303914, "Legal Notices": 0.12486345211453805, "News Article": 0.0001847904148343212, "Organizational Announcement": 0.000190761597446558, "Nonfiction Writing": 0.00016073115366180494, "Organizational About Page": 0.00019590439032108624, "Personal About Page": 0.0001273746704244335, "Personal Blog": 0.00010090801874540215, "Product Page": 0.00016551817586912343, "Spam / Ads": 0.00014023827034416504, "Structured Data": 0.0005643625807035689, "Technical Writing": 0.007697189391232607, "Transcript / Interview": 0.0002151255397757261, "Tutorial / How-To Guide": 0.00026312625251629805, "User Reviews": 6.316422670361864e-05, "NONE": 0.8629187202345278}, "structured_records": {"false": 0.9934085362457712, "true": 0.006591463754228787}, "procedural_steps": {"false": 0.996294106525502, "true": 0.0037058934744980646}, "worked_solution": {"false": 0.9959891543669461, "true": 0.004010845633053922}, "contextual_dialogue": {"false": 0.9999942616748667, "true": 5.738325133315812e-06}, "content_quality": {"-2": 0.0033886491343519194, "-1": 0.005372026982161345, "0": 0.0930488361839229, "1": 0.4696132013690549, "2": 0.428577286330509}, "noise_severity": {"0": 0.7233940249742681, "1": 0.27154920181752307, "2": 0.0025910848201782135, "3": 0.0012523867473704638, "4": 0.0012133016406600636}, "missing_content_severity": {"0": 0.9864793390501421, "1": 0.01017873085490338, "2": 0.0022785950298454954, "3": 0.0007802297844074046, "4": 0.0002831052807015914}, "factual_density": {"0": 0.006894772802047059, "1": 0.021140269400179407, "2": 0.17866369294262943, "3": 0.4584738698570899, "4": 0.33482739499805425}, "pretraining_utility": {"-2": 0.01729523795787852, "-1": 0.1733151387251358, "0": 0.40428314452851416, "1": 0.3290628189551418, "2": 0.07604365983332975}, "math_density": {"0": 0.44250369655839966, "1": 0.10092817010022787, "2": 0.07250449773988503, "3": 0.11502167998249803, "4": 0.26904195561898947}, "code_density": {"0": 0.00015054839165002713, "1": 0.00032024744137601995, "2": 0.007353163257629294, "3": 0.08962225029060311, "4": 0.9025537906187415}, "reasoning_density": {"0": 0.8430259205032303, "1": 0.032955127140176135, "2": 0.03729045224319766, "3": 0.03221681445841969, "4": 0.05451168565497615}, "education_level": {"none": 0.0012606552867235083, "0": 0.0011577048309201106, "1": 0.002780637398246111, "2": 0.08852591548156374, "3": 0.4902131664062407, "4": 0.41606192059630576}, "creative_writing_quality": {"not_applicable": 0.9992663101143351, "-2": 6.28594790427137e-05, "-1": 0.0001238473815257008, "0": 0.00039166754725216205, "1": 0.00011502459510019356, "2": 4.029088274413127e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 5.073359191254407e-05, "Art & Design": 0.00019944197896253136, "Law & Crime": 0.00011788761044200678, "Education & Careers": 4.926985134555287e-05, "Entertainment & Media": 5.328243017599635e-05, "Fashion & Beauty": 3.512578093777872e-05, "Business, Economics & Finance": 5.466153921510371e-05, "Food & Dining": 4.4591180436322534e-05, "Games & Gaming": 0.00010090618768484064, "Electronics & Hardware": 0.005615247220983518, "Health, Healthcare & Wellness": 4.8806120490923696e-05, "History & Geography": 6.13222621909955e-05, "Home, Pets & Hobbies": 3.230120471062744e-05, "Industry & Production": 4.9909138827339435e-05, "Language, Literature & Philosophy": 4.458952197408727e-05, "Politics & Government": 2.2316894974051453e-05, "Religion & Spirituality": 3.8548811987153375e-05, "Science, Mathematics & Engineering": 0.01736930679127548, "Relationships & Community": 2.4494015586400988e-05, "Software Usage & Applications": 0.00023921319855564304, "Programming & Software Engineering": 0.975210212754296, "Sports & Fitness": 4.7722518404977354e-05, "Vehicles & Transportation": 2.5105394353398014e-05, "Travel & Tourism": 5.46544240279539e-05, "Other / Uncovered Subject": 0.0003110270243001249, "Unknown / Insufficient Content": 9.932255194866415e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005861792411752331, "Art & Design": 0.004028049924803153, "Law & Crime": 0.008213539039671452, "Education & Careers": 0.0003657275682386477, "Entertainment & Media": 0.0007469638008492853, "Fashion & Beauty": 0.00023267342066606925, "Business, Economics & Finance": 0.0005932988163837514, "Food & Dining": 0.00021236029422794795, "Games & Gaming": 0.0005711644037080432, "Electronics & Hardware": 0.016113644209694775, "Health, Healthcare & Wellness": 0.0003141996054336096, "History & Geography": 0.0003015679011930465, "Home, Pets & Hobbies": 0.000186019656694334, "Industry & Production": 0.00019842379720502905, "Language, Literature & Philosophy": 0.00020038487646785428, "Politics & Government": 0.00022210906346261324, "Religion & Spirituality": 0.00036150463436473914, "Science, Mathematics & Engineering": 0.0970394448497186, "Relationships & Community": 0.0001505928909789567, "Software Usage & Applications": 0.0005248309260998034, "Programming & Software Engineering": 0.04988716567407726, "Sports & Fitness": 0.00023983787216532964, "Vehicles & Transportation": 0.00012278755401571082, "Travel & Tourism": 0.0003721024746302616, "Other / Uncovered Subject": 0.000897289232991129, "NONE": 0.8173181382710833}}, "probabilities": {"primary": {"Academic Writing": 4.1935130797765764e-05, "Content Listing": 2.4063619522911138e-05, "Creative Writing": 1.31267868057743e-05, "Customer Support Page": 8.042416134389971e-06, "Q&A Forum": 1.1228074959064119e-05, "Discussion Thread": 7.516873419811749e-06, "Comment Section": 1.5110186293223057e-05, "Missing Content": 4.771051257220283e-05, "Knowledge Article": 0.00010303367737809964, "Legal Notices": 0.0024107366875793664, "News Article": 5.008566110773185e-06, "Organizational Announcement": 1.778974935859763e-05, "Nonfiction Writing": 9.986770886388617e-06, "Organizational About Page": 1.324225069381927e-05, "Personal About Page": 8.018124144626623e-06, "Personal Blog": 7.206865531322479e-06, "Product Page": 7.94364184281081e-06, "Spam / Ads": 9.005872018014912e-06, "Structured Data": 0.00026044647264916954, "Technical Writing": 0.9955045731558262, "Transcript / Interview": 1.4098238452656866e-05, "Tutorial / How-To Guide": 0.00012681834083089557, "User Reviews": 7.006964199070096e-06, "Other / Unknown": 0.001326351021993009}, "secondary": {"Academic Writing": 0.00072700008050026, "Content Listing": 0.0006164981812279226, "Creative Writing": 0.0005036524419811942, "Customer Support Page": 0.0003265887738699446, "Q&A Forum": 0.000356101160671645, "Discussion Thread": 0.0003224031067287218, "Comment Section": 0.0004758482210769771, "Missing Content": 0.0006653822686150138, "Knowledge Article": 0.0010980886506335521, "Legal Notices": 0.531144103623397, "News Article": 0.0005332864606286088, "Organizational Announcement": 0.0005584230935986892, "Nonfiction Writing": 0.0005040101674971515, "Organizational About Page": 0.0005686013075881843, "Personal About Page": 0.0003644287144104518, "Personal Blog": 0.00029164773314612315, "Product Page": 0.00041387854983978173, "Spam / Ads": 0.0003681234896258983, "Structured 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0.00016551817586912346, "Spam / Ads": 0.00014023827034416507, "Structured Data": 0.000564362580703569, "Technical Writing": 0.007697189391232608, "Transcript / Interview": 0.00021512553977572613, "Tutorial / How-To Guide": 0.0002631262525162981, "User Reviews": 6.316422670361865e-05, "NONE": 0.8629187202345279}, "structured_records": {"false": 0.9934085362457712, "true": 0.006591463754228787}, "procedural_steps": {"false": 0.996294106525502, "true": 0.0037058934744980646}, "worked_solution": {"false": 0.9959891543669461, "true": 0.004010845633053922}, "contextual_dialogue": {"false": 0.9999942616748667, "true": 5.738325133315812e-06}, "content_quality": {"-2": 0.0033886491343519194, "-1": 0.005372026982161345, "0": 0.0930488361839229, "1": 0.4696132013690549, "2": 0.428577286330509}, "noise_severity": {"0": 0.7233940249742682, "1": 0.2715492018175231, "2": 0.002591084820178214, "3": 0.001252386747370464, "4": 0.0012133016406600638}, "missing_content_severity": {"0": 0.9864793390501422, "1": 0.010178730854903381, "2": 0.002278595029845496, "3": 0.0007802297844074047, "4": 0.00028310528070159144}, "factual_density": {"0": 0.006894772802047059, "1": 0.021140269400179407, "2": 0.17866369294262943, "3": 0.4584738698570899, "4": 0.33482739499805425}, "pretraining_utility": {"-2": 0.01729523795787852, "-1": 0.1733151387251358, "0": 0.40428314452851416, "1": 0.3290628189551418, "2": 0.07604365983332975}, "math_density": {"0": 0.44250369655839966, "1": 0.10092817010022787, "2": 0.07250449773988503, "3": 0.11502167998249803, "4": 0.26904195561898947}, "code_density": {"0": 0.00015054839165002713, "1": 0.00032024744137601995, "2": 0.007353163257629294, "3": 0.08962225029060311, "4": 0.9025537906187415}, "reasoning_density": {"0": 0.8430259205032303, "1": 0.032955127140176135, "2": 0.03729045224319766, "3": 0.03221681445841969, "4": 0.05451168565497615}, "education_level": {"none": 0.0012606552867235085, "0": 0.0011577048309201108, "1": 0.0027806373982461113, "2": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/cxx221016/CPP/blob/e67ff99bb346246166e36f00caf50c095e0b0e9f/CS2309/HuffmanCode.cpp", "id": "cpp-0-b3e570ddd56eb2d8a458a8abffabca59", "text": "#include<iostream>\n#include<string>\n#include<sstream>\n#include<vector>\n#include<unordered_map>\n#include<map>\n#include<queue>\n#include<stack>\nusing namespace std;\n\nnamespace std {\ntemplate<>\nclass hash<pair<int,int>> \n{\npublic:\n    size_t operator()(const pair<int,int> &p) const {\n        return hash<int>()(p.first)^hash<int>()(p.second);\n    }\n};\n};\n\ntemplate<class T>\nstruct HuffmanNode\n{\n    T val;\n    int weight;\n    int left;\n    int right;\n    int parent;\n    HuffmanNode(int _weight=0,int _left=0,int _right=0,int _parent=0):weight(_weight),left(_left),right(_right),parent(_parent){}\n    HuffmanNode(T _val,int _weight=0,int _left=0,int _right=0,int _parent=0):val(_val),weight(_weight),left(_left),right(_right),parent(_parent)\n    {\n\n    }\n    HuffmanNode(const HuffmanNode& other)\n    {\n        val=other.val;\n        weight=other.weight;\n        left=other.left;\n        right=other.right;\n        parent=other.parent;\n    }\n\n    friend ostream& operator<<(ostream& out,HuffmanNode<T>& Node)\n    {\n        out<<Node.val<<\" \"<<Node.weight<<\" \"<<Node.left<<\" \"<<Node.right<<\" \"<<Node.parent<<endl;\n        return out;\n    }\n\n    friend istream& operator>>(istream& in,HuffmanNode<T>& Node)\n    {\n        in>>Node.val>>Node.weight>>Node.left>>Node.right>>Node.parent;\n        return in;\n    }\n};\n\ntemplate<class T>\nclass HuffmanTree\n{\nprivate:\n    vector<HuffmanNode<T>> BBtree;\n    map<T,string> HFCode;\npublic:\n    HuffmanTree(){};\n    HuffmanTree(const HuffmanTree& other);\n    HuffmanTree(const map<T,int>& mp);\n    map<T,string> HuffmanCode();\n    friend ostream& operator<<(ostream& out,HuffmanTree<T>& tree)\n    {\n        for(auto& entry:tree.HFCode)\n        {\n            out<<endl<<entry.first<<\" \"<<entry.second;\n        }\n        return out;\n    }\n    friend istream& operator>>(istream& in,HuffmanTree<T>& tree)\n    {\n        T ch;\n        int n;\n        map<T,int> mp;\n        while(in)\n        {\n            in>>ch>>n;\n            mp[ch]=n;\n        }\n        tree=HuffmanTree<T> (mp);\n        return in;\n    }\n};\n\ntemplate<class T>\nHuffmanTree<T>::HuffmanTree(const HuffmanTree& other)\n{\n    BBtree=other.BBtree;\n}\n\n\ntemplate<class T>\nHuffmanTree<T>::HuffmanTree(const map<T,int>& mp)\n{\n    unordered_map<pair<int,int>,string> to_cmp;\n    auto cmp=[&](const pair<int,int>&a ,const pair<int,int>& b)\n    {\n        return a.first==b.first? to_cmp[a]>to_cmp[b] : a.first>b.first;\n    };\n    priority_queue<pair<int,int>,vector<pair<int,int>>,decltype(cmp)> que(cmp);\n    int n=mp.size();\n    int m=2*n;\n    BBtree.resize(m);\n    int index=n;\n    for(auto& entry:mp)\n    {\n        to_cmp[{entry.second,index}]=entry.first;\n        que.emplace(entry.second,index);\n        BBtree[index++]=HuffmanNode<T> (entry.first,entry.second,0,0,0);\n    }\n    index=n-1;\n    while(index)\n    {\n        string fir=to_cmp[que.top()];\n        int firstmin=que.top().second;que.pop();\n        string sec=to_cmp[que.top()];\n        int secondmin=que.top().second;que.pop();\n\n        BBtree[firstmin].parent=BBtree[secondmin].parent=index;\n        BBtree[index].weight=BBtree[firstmin].weight+BBtree[secondmin].weight;\n        BBtree[index].left=firstmin;\n        BBtree[index].right=secondmin;\n        to_cmp[{BBtree[index].weight,index}]=fir+sec;\n        que.emplace(BBtree[index].weight,index);\n        index--;\n    }\n}\n\n\ntemplate<class T>\nmap<T,string> HuffmanTree<T>::HuffmanCode()\n{\n    //unordered_map<T,string> HFCode;\n    int m=BBtree.size();\n    int n=m>>1;\n    if(n==0) return HFCode;\n    if(n==1) {HFCode[BBtree[1].val]=\"0\"; return HFCode;}\n    stack<pair<int,string>> stk;\n    stk.emplace(1,string());\n    while(!stk.empty())\n    {\n        int i=stk.top().first;\n        string str=stk.top().second;\n        stk.pop();\n        if(BBtree[i].right==0&&BBtree[i].left==0) { HFCode[BBtree[i].val]=str;}\n        if(BBtree[i].right!=0) stk.emplace(BBtree[i].right,str+\"1\");\n        if(BBtree[i].left!=0) stk.emplace(BBtree[i].left,str+\"0\");\n    }\n    return HFCode;\n}\n\n//定义main函数\nint main()\n{\n    /********** Begin **********/\n    //读取测试输入\n    char ch;\n    string input;\n    while((ch=cin.get())!=EOF) {input.push_back(ch);}\n    input.pop_back();\n    map<char,int> mp;\n\n    map<char,string> change;\n    for(auto& in:input)\n    {\n        mp[in]++;\n    }\n\n    HuffmanTree<char> tree(mp);\n    change=tree.HuffmanCode();\n    ostringstream os;\n    int res=0;\n    for(auto& entry: change)\n    {\n        res+=mp[entry.first]*entry.second.size();\n    }\n    os<<res;\n    os<<tree;\n    string output=os.str();\n    cout<<input.size()<<\":\"<<output.size()<<endl;;\n    cout<<output;\n\n    //请打印输出运算结果\n\n\n\t/********** End **********/\n    system(\"pause\");\n  \treturn 0;\n}\n", "text_preview": "#include<iostream> #include<string> #include<sstream> #include<vector> #include<unordered_map> #include<map> #include<queue> #include<stack> using namespace std; namespace std { template<> class hash<pair<int,int>> { public: size_t operator", "input_sha256": "d7de5b320216d4ee4dfbe6952d89c7528d8ccbbc0ba3470938882c48beea4236", "text_sha256": "b3e570ddd56eb2d8a458a8abffabca5974d46eaffa603b33ba78460fe283a13d", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 58, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.49630604309339377, "noise_severity": 0.0741944088717651, "missing_content_severity": 0.039599020224386154, "factual_density": 3.4720568666498393, "pretraining_utility": 0.6416275066649997, "math_density": 2.41796263432723, "code_density": 3.908263741484318, "reasoning_density": 1.0830781241043608, "education_level": 2.206257525770857, "creative_writing_quality": -0.38994874940705665}, 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0.9235531179035984, "reasoning_density": 0.09743489657969928, "education_level": 0.7434172916844958, "creative_writing_quality": 0.9984265225113225, "primary_topic": 0.9633131467990693, "secondary_topic": 0.6672886041344944}, "probability_margin": {"primary": 0.9935343956203871, "secondary": 0.976913864963115, "tertiary": 0.9826522034290086, "education_level": 0.5884611857900466, "creative_writing_quality": 0.9980307189195037, "primary_topic": 0.931502195563034, "secondary_topic": 0.4533104541361078}, "raw_probabilities": {"primary": {"Academic Writing": 5.363719875098183e-05, "Content Listing": 1.7349795586035134e-05, "Creative Writing": 1.4778545774179522e-05, "Customer Support Page": 6.458694883057467e-06, "Q&A Forum": 1.3417972775313149e-05, "Discussion Thread": 7.905046221769144e-06, "Comment Section": 1.0713841510141006e-05, "Missing Content": 2.8958314765168936e-05, "Knowledge Article": 0.00016567997736951297, "Legal Notices": 2.205451963840167e-05, "News Article": 4.532929976639116e-06, "Organizational Announcement": 9.19798443342995e-06, "Nonfiction Writing": 1.2203672006786641e-05, "Organizational About Page": 1.0337671998698882e-05, "Personal About Page": 1.1265356257355577e-05, "Personal Blog": 7.870368456624624e-06, "Product Page": 6.2035614824087426e-06, "Spam / Ads": 9.89146713026444e-06, "Structured Data": 0.00032501642355824134, "Technical Writing": 0.9957484880668237, "Transcript / Interview": 1.3681757383724325e-05, "Tutorial / How-To Guide": 0.002214092446436572, "User Reviews": 5.962459913794923e-06, "Other / Unknown": 0.00128030192686694}, "secondary": {"Academic Writing": 0.0001975763646071738, "Content Listing": 4.80963418571258e-05, "Creative Writing": 8.247125591451366e-05, "Customer Support Page": 3.9162730648744146e-05, "Q&A Forum": 3.4988831065487706e-05, "Discussion Thread": 2.3317101720357872e-05, "Comment Section": 4.76950725864243e-05, "Missing Content": 0.0002627914024298646, "Knowledge Article": 0.0005974018656169498, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/H00ong/2-2-Assignment/blob/1d28b1e730761ccdfd1b86fac74ee524f8ffc593/ClosetspairOld.cpp", "id": "cpp-0-a3c9a1f1cddbd2b77c1a383d98ffd33e", "text": "#include <iostream>\n#include <sstream>\n#include <vector>\n#include <limits>\n#include <algorithm>\n#include <stdio.h>\n#include <cmath>\n#include <stdlib.h>\n\nusing namespace std;\n\ntypedef long long L;\ndouble minDistance = numeric_limits<double>::max();\n\nvoid ClosestDistance(vector < pair<L, L>> &_pointsVec, int _left, int _right);\nvoid ThreeOrTwoPointsMinDistance(vector<pair<L, L>>& _pointsVec, int _left, int _right);\nvoid TwoPointsMinDistance(const pair<L, L>& a, const pair<L, L>& b);\nvoid ClosestBetweenDistance(vector<pair<L, L>>& _pointsVec, int _left, int _SLRightBound, int _SRLeftBound, int _right, float _currentMinDistance);\n\n\nvoid ClosestDistance(vector<pair<L, L>> &_pointsVec, int _left, int _right) \n{\n\tif (_right - _left <= 2) \n\t{\n\t\tThreeOrTwoPointsMinDistance(_pointsVec, _left, _right);\n\t}\n\telse \n\t{\n\t\tint rightBound = (_right - _left + 1) % 2 == 0 ? _left + (_right - _left + 1) / 2 - 1: _left + (_right - _left + 1) / 2;\n\n\t\tClosestDistance(_pointsVec, _left, rightBound);\n\t\tClosestDistance(_pointsVec, rightBound + 1, _right);\n\t\tClosestBetweenDistance(_pointsVec, _left, rightBound, rightBound + 1, _right, minDistance);\n\t}\n}\n\nvoid ThreeOrTwoPointsMinDistance(vector<pair<L, L>>& _pointsVec, int _left, int _right)\n{\n\tif (_right - _left <= 1)\n\t\tTwoPointsMinDistance(_pointsVec[_left], _pointsVec[_right]);\n\telse \n\t{\n\t\tTwoPointsMinDistance(_pointsVec[_left], _pointsVec[_left + 1]);\n\t\tTwoPointsMinDistance(_pointsVec[_left + 1], _pointsVec[_right]);\n\t\tTwoPointsMinDistance(_pointsVec[_left], _pointsVec[_right]);\n\t}\n}\n\nvoid TwoPointsMinDistance(const pair<L, L> &a, const pair<L , L> &b)\n{\n\tdouble distance = sqrt((a.first - b.first) * (a.first - b.first) + (a.second - b.second) * (a.second - b.second));\n\n\tif (distance < minDistance)\n\t\tminDistance = distance;\n}\n\nvoid ClosestBetweenDistance(vector<pair<L, L>>& _pointsVec, int _left, int _SLRightBound, int _SRLeftBound, int _right, float _currentMinDistance)\n{\n\tfloat _leftBoundaryVal = _pointsVec[_SLRightBound].first - _currentMinDistance;\n\tfloat _rightBoundaryVal = _pointsVec[_SRLeftBound].first + _currentMinDistance;\n\n\tint _leftBoundIndex = _SLRightBound;\n\tint _rightBoundIndex = _SRLeftBound;\n\n\twhile (_pointsVec[_leftBoundIndex].first >= _leftBoundaryVal && _leftBoundIndex > 0)\n\t\t_leftBoundIndex--;\n\twhile (_pointsVec[_rightBoundIndex].first <= _rightBoundaryVal && _rightBoundIndex < _right)\n\t\t_rightBoundIndex++;\n\n\tvector<pair<L, L>> SLySortingVec(_pointsVec.begin() + _leftBoundIndex, _pointsVec.begin() + _SLRightBound + 1);\n\tsort(SLySortingVec.begin(), SLySortingVec.end(), \n\t\t[](const pair<L, L>& a, const pair<L, L>& b) { return a.second < b.second;});\n\n\tvector<pair<L, L>> SRySortingVec(_pointsVec.begin() + _SRLeftBound, _pointsVec.begin() + _rightBoundIndex + 1);\n\tsort(SRySortingVec.begin(), SRySortingVec.end(),\n\t\t[](const pair<L, L>& a, const pair<L, L>& b) { return a.second < b.second;});\n\n\tfor (int i = 0; i < SLySortingVec.size(); i++) \n\t{\n\t\tfor (int j = 0; j < SRySortingVec.size(); j++) \n\t\t{\n\t\t\tif (abs(SRySortingVec[j].second - SLySortingVec[i].second) >= minDistance)\n\t\t\t\tbreak;\n\t\t\tTwoPointsMinDistance(SRySortingVec[j], SLySortingVec[i]);\n\t\t}\n\t}\n}\n\nint main() \n{\n\tint numberOfPoints;\n\n\tcin >> numberOfPoints;\n\tcin.ignore();\n\n\tvector<pair<L, L>> pointsVec;\n\n\tstring input;\n\t\n\tfor (int i = 0; i < numberOfPoints; i++) \n\t{\n\t\tgetline(cin, input);\n\t\tinput.erase(std::remove(input.begin(), input.end(), ','), input.end());\n\t\tstringstream ss(input);\n\t\t\n\t\tL x, y;\n\t\tss >> x >> y;\n\n\t\tpointsVec.push_back(pair<L, L>(x, y));\n;\t}\n\n\tsort(pointsVec.begin(), pointsVec.end(),\n\t\t[](const pair<L, L>& a, const pair<L, L>& b) { return a.first < b.first;});\n\n\tClosestDistance(pointsVec, 0, pointsVec.size() - 1);\n\n\tprintf(\"%.6f\", minDistance);\n}\n\n\n", "text_preview": "#include <iostream> #include <sstream> #include <vector> #include <limits> #include <algorithm> #include <stdio.h> #include <cmath> #include <stdlib.h> using namespace std; typedef long long L; double minDistance = numeric_limits<double>::m", "input_sha256": "19fc3a26b892aa62000bf6446cc41146922b89fc0e4e4c3713af46174f47097f", "text_sha256": "a3c9a1f1cddbd2b77c1a383d98ffd33e9fc09496c978a3696fa56fbd712c7b64", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 59, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/marvinguo/liangshi/blob/09711db820d2e0747e98f6542f521bd03d80fdb2/Client/src/hera/FitCurves.cpp", "id": "cpp-0-a252bf75e6f86fe8a788b950741dcae9", "text": "#include \"FitCurves.h\"\n\nbool isDoubleZero(double& d)\n{\n\t//if (denominator == 0.0f)\n\tif(d > -0.01 && d < 0.01)\n\t\treturn true;\n\treturn false;\n}\n\nconst int MAXPOINTS = 10000;\nvector<QVector2D> FitCurve(vector<QVector2D>& d, double error)\n{\n\tQVector2D tHat1, tHat2;\t/*  Unit tangent vectors at endpoints */\n\n\ttHat1 = ComputeLeftTangent(d, 0);\n\ttHat2 = ComputeRightTangent(d, d.size() - 1);\n\tvector<QVector2D> result;\n\tFitCubic(d, 0, d.size() - 1, tHat1, tHat2, error,result);\n\treturn result;\n}\n\nstatic void FitCubic(vector<QVector2D>& d, int first, int last, QVector2D tHat1, QVector2D tHat2, double error,vector<QVector2D>& result)\n{\n\tQVector2D* bezCurve; /*Control points of fitted Bezier curve*/\n\tdouble* u;\t\t/*  Parameter values for point  */\n\tdouble* uPrime;\t/*  Improved parameter values */\n\tdouble maxError;\t/*  Maximum fitting error\t */\n\tint splitPoint;\t/*  Point to split point set at\t */\n\tint nPts;\t\t/*  Number of points in subset  */\n\tdouble iterationError; /*Error below which you try iterating  */\n\tint maxIterations = 4; /*  Max times to try iterating  */\n\tQVector2D tHatCenter;   \t/* Unit tangent vector at splitPoint */\n\tint i;\n\n\titerationError = error * error;\n\tnPts = last - first + 1;\n\n\t/*  Use heuristic if region only has two points in it */\n\tif(nPts == 2)\n\t{\n\t\tdouble dist = (d[first]-d[last]).length() / 3.0;\n\n\t\tbezCurve = new QVector2D[4];\n\t\tbezCurve[0] = d[first];\n\t\tbezCurve[3] = d[last];\n\t\tQVector2D p1 = tHat1 * dist;\n\t\tbezCurve[1] = p1 + bezCurve[0];\n\t\tQVector2D p2 = tHat2 * dist;\n\t\tbezCurve[2] = p2 + bezCurve[3];\n\n\t\tresult.push_back(bezCurve[1]);\n\t\tresult.push_back(bezCurve[2]);\n\t\tresult.push_back(bezCurve[3]);\n\t\tdelete[] bezCurve;\n\t\treturn;\n\t}\n\n\t/*  Parameterize points, and attempt to fit curve */\n\tu = ChordLengthParameterize(d, first, last);\n\tbezCurve = GenerateBezier(d, first, last, u, tHat1, tHat2);\n\n\t/*  Find max deviation of points to fitted curve */\n\tmaxError = ComputeMaxError(d, first, last, bezCurve, u, splitPoint);\n\tif(maxError < error)\n\t{\n\t\tresult.push_back(bezCurve[1]);\n\t\tresult.push_back(bezCurve[2]);\n\t\tresult.push_back(bezCurve[3]);\n\t\tdelete[] u;\n\t\tdelete[] bezCurve;\n\t\treturn;\n\t}\n\n\t/*  If error not too large, try some reparameterization  */\n\t/*  and iteration */\n\tif(maxError < iterationError)\n\t{\n\t\tfor(i = 0; i < maxIterations; i++)\n\t\t{\n\t\t\tuPrime = Reparameterize(d, first, last, u, bezCurve);\n\t\t\tdelete[] bezCurve;\n\t\t\tbezCurve = GenerateBezier(d, first, last, uPrime, tHat1, tHat2);\n\t\t\tmaxError = ComputeMaxError(d, first, last, bezCurve, uPrime, splitPoint);\n\t\t\tif(maxError < error)\n\t\t\t{\n\t\t\t\tresult.push_back(bezCurve[1]);\n\t\t\t\tresult.push_back(bezCurve[2]);\n\t\t\t\tresult.push_back(bezCurve[3]);\n\t\t\t\tdelete[] bezCurve;\n\t\t\t\tdelete[] u;\n\t\t\t\tdelete[] uPrime;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tdelete[] u;\n\t\t\tu = uPrime;\n\t\t}\n\t}\n\n\tdelete[] bezCurve;\n\tdelete[] u;\n\t/* Fitting failed -- split at max error point and fit recursively */\n\ttHatCenter = ComputeCenterTangent(d, splitPoint);\n\tFitCubic(d, first, splitPoint, tHat1, tHatCenter, error,result);\n\ttHatCenter *= -1;\n\tFitCubic(d, splitPoint, last, tHatCenter, tHat2, error,result);\n}\n\nstatic QVector2D* GenerateBezier(vector<QVector2D>& d, int first, int last, double* uPrime, QVector2D& tHat1, QVector2D& tHat2)\n{\n\tint \ti;\n\tQVector2D A[MAXPOINTS* 2];/* Precomputed rhs for eqn\t*/\n\n\tint \tnPts;\t\t\t/* Number of pts in sub-curve */\n\tdouble \tC[2 * 2];\t\t\t/* Matrix C\t\t*/\n\tdouble \tX[2];\t\t\t/* Matrix X\t\t\t*/\n\tdouble \tdet_C0_C1,\t\t/* Determinants of matrices\t*/\n\t\tdet_C0_X,\n\t\tdet_X_C1;\n\tdouble \talpha_l,\t\t/* Alpha values, left and right\t*/\n\t\talpha_r;\n\tQVector2D \ttmp;\t\t\t/* Utility variable\t\t*/\n\tQVector2D* bezCurve = new QVector2D[4];\t/* RETURN bezier curve ctl pts\t*/\n\tnPts = last - first + 1;\n\n\t/* Compute the A's\t*/\n\tfor (i = 0; i < nPts; i++)\n\t{\n\t\tQVector2D\t\tv1, v2;\n\t\tv1 = tHat1;\n\t\tv2 = tHat2;\n\t\tv1 = v1 * (B1(uPrime[i]));\n\t\tv2 = v2 * (B2(uPrime[i]));\n\t\tA[i,0] = v1;\n\t\tA[i,1] = v2;\n\t}\n\n\t/* Create the C and X matrices\t*/\n\tC[0,0] = 0.0;\n\tC[0,1] = 0.0;\n\tC[1,0] = 0.0;\n\tC[1,1] = 0.0;\n\tX[0]    = 0.0;\n\tX[1]    = 0.0;\n\n\tfor (i = 0; i < nPts; i++)\n\t{\n\t\tC[0,0] +=  V2Dot(A[i,0], A[i,0]);\n\t\tC[0,1] += V2Dot(A[i,0], A[i,1]);\n\t\t/*\t\t\t\t\tC[1][0] += V2Dot(&A[i][0], &A[i][1]);*/\t\n\t\tC[1,0] = C[0,1];\n\t\tC[1,1] += V2Dot(A[i,1], A[i,1]);\n\t\tQVector2D add1 = d[first] * B0(uPrime[i]);\n\t\tQVector2D add2 = d[first] * B1(uPrime[i]);\n\t\tQVector2D add3 = d[last] * B2(uPrime[i]);\n\t\tQVector2D add4 = d[last] * B3(uPrime[i]);\n\t\ttmp = d[first + i] - (add1 + add2 + add3 + add4);\n\n\n\t\tX[0] += V2Dot(A[i,0], tmp);\n\t\tX[1] += V2Dot(A[i,1], tmp);\n\t}\n\n\t/* Compute the determinants of C and X\t*/\n\tdet_C0_C1 = C[0,0] * C[1,1] - C[1,0] * C[0,1];\n\tdet_C0_X  = C[0,0] * X[1]    - C[1,0] * X[0];\n\tdet_X_C1  = X[0]    * C[1,1] - X[1]    * C[0,1];\n\n\t/* Finally, derive alpha values\t*/\n\t//alpha_l = (det_C0_C1 == 0) ? 0.0 : det_X_C1 / det_C0_C1;\n\t//alpha_r = (det_C0_C1 == 0) ? 0.0 : det_C0_X / det_C0_C1;\n\t\n\talpha_l = isDoubleZero(det_C0_C1) ? 0.0 : det_X_C1 / det_C0_C1;\n\talpha_r = isDoubleZero (det_C0_C1) ? 0.0 : det_C0_X / det_C0_C1;\n\n\t/* If alpha negative, use the Wu/Barsky heuristic (see text) */\n\t/* (if alpha is 0, you get coincident control points that lead to\n\t* divide by zero in any subsequent NewtonRaphsonRootFind() call. */\n\tdouble segLength = (d[first] - d[last]).length();\n\tdouble epsilon = 1.0e-6 * segLength;\n\tif (alpha_l < epsilon || alpha_r < epsilon)\n\t{\n\t\t/* fall back on standard (probably inaccurate) formula, and subdivide further if needed. */\n\t\tdouble dist = segLength / 3.0;\n\t\tbezCurve[0] = d[first];\n\t\tbezCurve[3] = d[last];\n\t\tQVector2D p1 = tHat1 * dist;\n\t\tbezCurve[1] = p1 + bezCurve[0];\n\t\tQVector2D p2 = tHat2 * dist;\n\t\tbezCurve[2] = p2 + bezCurve[3];\n\t\treturn (bezCurve);\n\t}\n\n\t/*  First and last control points of the Bezier curve are */\n\t/*  positioned exactly at the first and last data points */\n\t/*  Control points 1 and 2 are positioned an alpha distance out */\n\t/*  on the tangent vectors, left and right, respectively */\n\tbezCurve[0] = d[first];\n\tbezCurve[3] = d[last];\n\tQVector2D p1= tHat1 * alpha_l;\n\tbezCurve[1] = p1 + bezCurve[0];\n\tQVector2D p2 = tHat2 * alpha_r;\n\tbezCurve[2] = p2 + bezCurve[3];\n\treturn (bezCurve);\n}\n\nstatic double* Reparameterize(vector<QVector2D>& d,int first,int last,double* u, QVector2D* bezCurve)\n{\n\tint \tnPts = last-first+1;\t\n\tint \ti;\n\tdouble*\tuPrime = new double[nPts];\t\t/*  New parameter values\t*/\n\n\tfor (i = first; i <= last; i++) {\n\t\tuPrime[i-first] = NewtonRaphsonRootFind(bezCurve, d[i], u[i-first]);\n\t}\n\treturn uPrime;\n}\n\nstatic double NewtonRaphsonRootFind(QVector2D* Q, QVector2D P, double u)\n{\n\tdouble \t\tnumerator, denominator;\n\tQVector2D Q1[3], Q2[2];\t/*  Q' and Q''\t\t\t*/\n\tQVector2D\t\tQ_u, Q1_u, Q2_u; /*u evaluated at Q, Q', & Q''\t*/\n\tdouble \t\tuPrime;\t\t/*  Improved u\t\t\t*/\n\tint \t\ti;\n\n\t/* Compute Q(u)\t*/\n\tQ_u = BezierII(3, Q, u);\n\n\t/* Generate control vertices for Q'\t*/\n\tfor (i = 0; i <= 2; i++)\n\t{\n\t\tQ1[i].setX((Q[i+1].x() - Q[i].x()) * 3.0);\n\t\tQ1[i].setY((Q[i+1].y() - Q[i].y()) * 3.0);\n\t}\n\n\t/* Generate control vertices for Q'' */\n\tfor (i = 0; i <= 1; i++) \n\t{\n\t\tQ2[i].setX((Q1[i+1].x() - Q1[i].x()) * 2.0);\n\t\tQ2[i].setY((Q1[i+1].y() - Q1[i].y()) * 2.0);\n\t}\n\n\t/* Compute Q'(u) and Q''(u)\t*/\n\tQ1_u = BezierII(2, Q1, u);\n\tQ2_u = BezierII(1, Q2, u);\n\n\t/* Compute f(u)/f'(u) */\n\tnumerator = (Q_u.x() - P.x()) * (Q1_u.x()) + (Q_u.y() - P.y()) * (Q1_u.y());\n\tdenominator = (Q1_u.x()) * (Q1_u.x()) + (Q1_u.y()) * (Q1_u.y()) +\n\t\t(Q_u.x() - P.x()) * (Q2_u.x()) + (Q_u.y() - P.y()) * (Q2_u.y());\n\n\t//if (denominator == 0.0f)\n\tif(isDoubleZero(denominator))\n\t\treturn u;\n\n\t/* u = u - f(u)/f'(u) */\n\tuPrime = u - (numerator/denominator);\n\treturn (uPrime);\n}\n\nstatic QVector2D BezierII(int degree, QVector2D* V, double t)\n{\n\tint \ti, j;\t\t\n\tQVector2D \tQ;\t        /* Point on curve at parameter t\t*/\n\tQVector2D* \tVtemp;\t\t/* Local copy of control points\t\t*/\n\n\t/* Copy array\t*/\n\tVtemp = new QVector2D[degree+1];\n\tfor (i = 0; i <= degree; i++) {\n\t\tVtemp[i] = V[i];\n\t}\n\n\t/* Triangle computation\t*/\n\tfor (i = 1; i <= degree; i++) {\t\n\t\tfor (j = 0; j <= degree-i; j++) \n\t\t{\n\t\t\tVtemp[j].setX((1.0 - t) * Vtemp[j].x() + t * Vtemp[j+1].x());\n\t\t\tVtemp[j].setY((1.0 - t) * Vtemp[j].y() + t * Vtemp[j+1].y());\n\t\t}\n\t}\n\n\tQ = Vtemp[0];\n\tdelete[] Vtemp;\n\treturn Q;\n}\n\nstatic double B0(double u)\n{\n\tdouble tmp = 1.0 - u;\n\treturn (tmp * tmp * tmp);\n}\n\n\nstatic double B1(double u)\n{\n\tdouble tmp = 1.0 - u;\n\treturn (3 * u * (tmp * tmp));\n}\n\nstatic double B2(double u)\n{\n\tdouble tmp = 1.0 - u;\n\treturn (3 * u * u * tmp);\n}\n\nstatic double B3(double u)\n{\n\treturn (u * u * u);\n}\n\nstatic QVector2D ComputeLeftTangent(vector<QVector2D>& d,int end)\n{\n\tQVector2D\ttHat1;\n\ttHat1 = d[end+1]- d[end];\n\ttHat1.normalize();\n\treturn tHat1;\n}\n\nstatic QVector2D ComputeRightTangent(vector<QVector2D>& d,int end)\n{\n\tQVector2D\ttHat2;\n\ttHat2 = d[end-1] - d[end];\n\ttHat2.normalize();\n\treturn tHat2;\n}\n\nstatic QVector2D ComputeCenterTangent(vector<QVector2D>& d,int center)\n{\n\tQVector2D\tV1, V2, tHatCenter;\n\n\tV1 = d[center-1] - d[center];\n\tV2 = d[center] - d[center+1];\n\ttHatCenter.setX((V1.x() + V2.x())/2.0);\n\ttHatCenter.setY((V1.y() + V2.y())/2.0);\n\ttHatCenter.normalize();\n\treturn tHatCenter;\n}\n\nstatic double* ChordLengthParameterize(vector<QVector2D>& d,int first,int last)\n{\n\tint\t\ti;\t\n\tdouble*\tu = new double[last-first+1];\t\t\t/*  Parameterization\t\t*/\n\n\tu[0] = 0.0;\n\tfor (i = first+1; i <= last; i++) {\n\t\tu[i-first] = u[i-first-1] + (d[i-1] - d[i]).length();\n\t}\n\n\tfor (i = first + 1; i <= last; i++) {\n\t\tu[i-first] = u[i-first] / u[last-first];\n\t}\n\n\treturn u;\n}\n\nstatic double ComputeMaxError(vector<QVector2D>& d, int first, int last, QVector2D* bezCurve, double* u, int& splitPoint)\n{\n\tint i;\n\tdouble\tmaxDist;\t\t/*  Maximum error\t\t*/\n\tdouble\tdist;\t\t/*  Current error\t\t*/\n\tQVector2D\tP;\t\t\t/*  Point on curve\t\t*/\n\tQVector2D\tv;\t\t\t/*  QVector2D from point to curve\t*/\n\n\tsplitPoint = (last - first + 1)/2;\n\tmaxDist = 0.0;\n\tfor (i = first + 1; i < last; i++) {\n\t\tP = BezierII(3, bezCurve, u[i-first]);\n\t\tv = P - d[i];\n\t\tdist = v.length() * v.length();\n\t\tif (dist >= maxDist) {\n\t\t\tmaxDist = dist;\n\t\t\tsplitPoint = i;\n\t\t}\n\t}\n\treturn maxDist;\n}\n\nstatic double V2Dot(QVector2D& a, QVector2D& b) \n{\n\treturn((a.x()*b.x())+(a.y()*b.y()));\n}", "text_preview": "#include \"FitCurves.h\" bool isDoubleZero(double& d) { //if (denominator == 0.0f) if(d > -0.01 && d < 0.01) return true; return false; } const int MAXPOINTS = 10000; vector<QVector2D> FitCurve(vector<QVector2D>& d, double error) { QVector2D ", "input_sha256": "72db340240b0bb3a2d0d65c6866e6cbcee67db8ae89090435cbed5542c62e467", "text_sha256": "a252bf75e6f86fe8a788b950741dcae9c284d105b51d787c271bceecae4b37d4", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", 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2.3632236190850542e-05, "Creative Writing": 3.7031908227645196e-05, "Customer Support Page": 1.4629239888277122e-05, "Q&A Forum": 1.243484737104033e-05, "Discussion Thread": 1.1766297298906034e-05, "Comment Section": 2.7912074130320788e-05, "Missing Content": 0.00014788274045726644, "Knowledge Article": 0.00024059085387529428, "Legal Notices": 7.982306328908555e-05, "News Article": 1.8767940522806583e-05, "Organizational Announcement": 1.6523672842881038e-05, "Nonfiction Writing": 2.0204375467044044e-05, "Organizational About Page": 3.187196876039101e-05, "Personal About Page": 1.6933686650334077e-05, "Personal Blog": 1.5662026035821458e-05, "Product Page": 1.3620276843571366e-05, "Spam / Ads": 2.0764015834497382e-05, "Structured Data": 0.00030991478305904984, "Technical Writing": 0.001478852132660556, "Transcript / Interview": 1.930425382055597e-05, "Tutorial / How-To Guide": 0.00047109625414775157, "User Reviews": 1.0230001955493378e-05, "NONE": 0.9968729836387058}, "structured_records": {"false": 0.9989309215069893, "true": 0.0010690784930107208}, "procedural_steps": {"false": 0.9721810707982492, "true": 0.027818929201750747}, "worked_solution": {"false": 0.9862193801390454, "true": 0.013780619860954574}, "contextual_dialogue": {"false": 0.9999923403983739, "true": 7.659601626070484e-06}, "content_quality": {"-2": 0.011934357465105678, "-1": 0.05768970818117288, "0": 0.27690756108485026, "1": 0.45806981001139874, "2": 0.1953985632574724}, "noise_severity": {"0": 0.9749488668763413, "1": 0.022894033084792754, "2": 0.0013373374461348973, "3": 0.0005018505681178289, "4": 0.0003179120246132491}, "missing_content_severity": {"0": 0.9737897883037576, "1": 0.017010134063333782, "2": 0.0067615646341646, "3": 0.001965070183593918, "4": 0.00047344281515004113}, "factual_density": {"0": 0.005870737022261553, "1": 0.013307440913304712, "2": 0.09513592555261179, "3": 0.37269286504008375, "4": 0.5129930314717382}, "pretraining_utility": {"-2": 0.009108474297993425, "-1": 0.06647706803925384, "0": 0.3311776089221969, "1": 0.45580676243426527, "2": 0.13743008630629067}, "math_density": {"0": 0.002925745569238795, "1": 0.005901387928766965, "2": 0.06959814001914513, "3": 0.36482332009847174, "4": 0.5567514063843774}, "code_density": {"0": 0.00035308223976975316, "1": 0.0006996050811769111, "2": 0.01845619343532437, "3": 0.11527940293970643, "4": 0.8652117163040226}, "reasoning_density": {"0": 0.3087096341817062, "1": 0.07720604478447606, "2": 0.09430838178636458, "3": 0.16226809205467096, "4": 0.35750784719278217}, "education_level": {"none": 0.001259359051493944, "0": 0.001408699286771931, "1": 0.006313520783861198, "2": 0.48540306118201376, "3": 0.39936163963666665, "4": 0.10625372005919251}, "creative_writing_quality": {"not_applicable": 0.9992538018950341, "-2": 9.117669448429528e-05, "-1": 0.00018860800468720185, "0": 0.0003450321237165062, "1": 8.225349105451209e-05, "2": 3.9127791023383405e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00017490544424854048, "Art & Design": 0.001732435326363547, "Law & Crime": 0.00022844572085142686, "Education & Careers": 0.00015875925181644824, "Entertainment & Media": 0.00019202031332308156, "Fashion & Beauty": 0.00015706984778306236, "Business, Economics & Finance": 0.0002055800732006781, "Food & Dining": 0.0001370954075478196, "Games & Gaming": 0.0104129973514767, "Electronics & Hardware": 0.0002887659262470436, "Health, Healthcare & Wellness": 0.00018199807049383037, "History & Geography": 0.0002430666359408213, "Home, Pets & Hobbies": 0.0001568765085935316, "Industry & Production": 0.00017628640694172592, "Language, Literature & Philosophy": 0.0001478795887057684, "Politics & Government": 9.0233545185827e-05, "Religion & Spirituality": 0.00014503801041011692, "Science, Mathematics & Engineering": 0.08138856398103428, "Relationships & Community": 7.68815363493959e-05, "Software Usage & Applications": 0.0004840194033131475, "Programming & Software Engineering": 0.9019540010246191, "Sports & Fitness": 0.00021981911493982546, "Vehicles & Transportation": 0.0001227724545373424, "Travel & Tourism": 0.00016344179096703743, "Other / Uncovered Subject": 0.0005298610804087663, "Unknown / Insufficient Content": 0.0002311861847011955}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0007906890479912293, "Art & Design": 0.015149700568129953, "Law & Crime": 0.0007389749302943696, "Education & Careers": 0.00046648964601794416, "Entertainment & Media": 0.0006941629811493881, "Fashion & Beauty": 0.0004516591142805556, "Business, Economics & Finance": 0.0006106831493499138, "Food & Dining": 0.0003477796137753441, "Games & Gaming": 0.009400977166570313, "Electronics & Hardware": 0.000738381072031887, "Health, Healthcare & Wellness": 0.0005392971945732801, "History & Geography": 0.0005018860532330557, "Home, Pets & Hobbies": 0.0003916090442746628, "Industry & Production": 0.0004493631183826359, "Language, Literature & Philosophy": 0.0002987270179052812, "Politics & Government": 0.0002771266366008344, "Religion & Spirituality": 0.0005090965539211118, "Science, Mathematics & Engineering": 0.4459440100500637, "Relationships & Community": 0.00023272456134424384, "Software Usage & Applications": 0.0006208607700076543, "Programming & Software Engineering": 0.14798652757060524, "Sports & Fitness": 0.000547024567077537, "Vehicles & Transportation": 0.0003615890917431416, "Travel & Tourism": 0.0005835526696223586, "Other / Uncovered Subject": 0.0010430150380007495, "NONE": 0.37032409277305356}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}}, "usage": {"input_tokens": 12579, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 12579, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:07.973493+00:00", "scoring_key_slot": "primary", "dataset_uid": "83a267fd-ed2f-4574-8330-3caed21dbb5d", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "6599dcaa33aefa2e072f4093719d03bff0b0a1a090fac5d44d9e1cc437d96042", "source_metadata": {"uuid": "83a267fd-ed2f-4574-8330-3caed21dbb5d", "repo_name": "marvinguo/liangshi", "relative_path": "Client/src/hera/FitCurves.cpp", "category": "ALGO", "algo_rel_score": 0.9994916915893555, "quality_score": 4.563323497772217, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2017-08-19T11:12:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 5, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "750c916f13444e9d7f2bf95d25bc93df421445f7", "size_bytes": 10094, "file_timestamp": 1503142030, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "09711db820d2e0747e98f6542f521bd03d80fdb2"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 2, "repo_created_at": "2017-08-19T11:12:28.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 5, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "750c916f13444e9d7f2bf95d25bc93df421445f7", "size_bytes": 10094, "file_timestamp": 1503142030, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "09711db820d2e0747e98f6542f521bd03d80fdb2", "url": "https://github.com/marvinguo/liangshi/blob/09711db820d2e0747e98f6542f521bd03d80fdb2/Client/src/hera/FitCurves.cpp"}, "gpt4o_tokens": 3759, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.7673085134149593, "noise_severity": 0.02834590777986903, "missing_content_severity": 0.0383222451430449, "factual_density": 3.3736300130257324, "pretraining_utility": 0.6459729184116059, "math_density": 3.466573253799982, "code_density": 3.8442970659870355, "reasoning_density": 2.1826584732923466, "education_level": 2.6034981812948206, "creative_writing_quality": -0.28203277273684735}, "score_applicability": {"education_level": 0.998740640948506, "creative_writing_quality": 0.0007461981049658988}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/mahbubul-haq/MySolvedProblems/blob/075426f02d5d120a332bd182f1c119ae68ba3213/leetcode/5.Longest%20Palindromic%20Substring%281%29.cpp", "id": "cpp-0-78377d74c21dacf2c39b8edf370d1bc4", "text": "#include <bits/stdc++.h>\nusing namespace std;\n\n#define dist2D(x1,y1,x2,y2)         ((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2))\n#define dist3D(x1,y1,z1,x2,y2,z2)   ((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2)+(z1-z2)*(z1-z2))\n#define EPS                         1e-12\n#define endl                        \"\\n\"\n#define FastIO                      ios_base::sync_with_stdio(0), cin.tie(0), cout.tie(0)\n#define FI                          freopen(\"in.txt\",\"r\",stdin)\n#define FO                          freopen(\"out.txt\",\"w\",stdout)\n#define fap(x)                      cout<<\"WTH: \"<<x<<endl\n#define ff                          first\n#define fof(i,x,y)                  for(int i=x;i<(int)y;i++)\n#define fob(i,x,y)                  for(int i=x;i>=(int)y;i--)\n#define INF                         1e18\n#define ld                          long double\n#define ll                          long long\n#define mem(x,y)                    memset(x,y,sizeof x)\n#define mp                          make_pair\n#define msi                         map<string,int>\n#define mii                         map<int,int>\n#define mis                         map<int,string>\n#define MOD                         998244353\n#define PI                          acos(-1.0)\n#define PQ                          priority_queue\n#define pb                          push_back\n#define pib                         pair<int,bool>\n#define pii                         pair<int,int>\n#define pll                         pair<ll,ll>\n#define sfi(x)                      scanf(\"%d\",&x)\n#define sfii(x,y)                   scanf(\"%d%d\",&x,&y)\n#define sfiii(x,y,z)                scanf(\"%d%d%d\",&x,&y,&z)\n#define siz(x)                      (int)x.size()\n#define sortv(v)                    sort(v.begin(),v.end())\n#define ss                          second\n#define timeTaken                   endd=clock();cout<<(double)(endd-beginn)/CLOCKS_PER_SEC*1000<<endl\n#define timeInit                    clock_t beginn=clock(),endd\n#define ull                         unsigned long long\n#define umsi                        unordered_map<string,int>\n#define umii                        unordered_map<int,int>\n#define umis                        unordered_map<int,string>\n#define vb                          vector<bool>\n#define vi                          vector<int>\n#define vvi                         vector<vi>\n#define vii                         vector<pii>\n#define vvii                        vector<vii>\n#define vll                         vector<ll>\n#define vpll                        vector<pll>\n\n/*\n#include <ext/pb_ds/assoc_container.hpp>\n#include <ext/pb_ds/tree_policy.hpp>\n#include <functional>\nusing namespace __gnu_pbds;\ntypedef tree<int,null_type,less<int>,rb_tree_tag,\n        tree_order_statistics_node_update> ordered_set;\nÂ\n//os.order_of_key(v): returns how many elements strictly less than v\n//os.find_by_order(k-1): returns kth smallest element's iterator\n*/\n\ntemplate<class T> class compare{\npublic:\n    bool operator()(pair<T,T> &x,pair<T,T> &y){\n        if(x.first==y.first){\n            return x.ss>y.ss;\n        }\n        return x.ff>y.ff;\n    }\n};\n\n//template<class T> ostream& operator<<(ostream &os,const pair<T,T> &a) { os<<a.ff<<\" \"<<a.ss; }\nll power(ll a,int b) {\n    ll po = 1;\n    while (b) {\n        if (b & 1)\n            po *= a;\n        a *= a;\n        b >>= 1;\n    }\n    return po;\n}\n\ntemplate<class T> pair<T,T> operator+(const pair<T,T> &a,const pair<T,T> &b){ return {a.ff+b.ff,a.ss+b.ss}; }\ntemplate<class T> pair<T,T> operator-(const pair<T,T> &a,const pair<T,T> &b){ return {a.ff-b.ff,a.ss-b.ss}; }\ntemplate<class T> pair<T,T> operator*(const pair<T,T> &a,const pair<T,T> &b){ return {a.ff*b.ff,a.ss*b.ss}; }\ntemplate<class T> pair<T,T> operator%(const pair<T,T> &a,const pair<T,T> &b){ return {a.ff%b.ff,a.ss%b.ss}; }\ntemplate<class T,class U> pair<T,T> operator+(const pair<T,T> &a,const U &b){ return { a.ff+b,a.ss+b}; }\ntemplate<class T,class U> pair<T,T> operator*(const pair<T,T> &a,const U &b){ return { a.ff*b,a.ss*b}; }\n\nint Set(int N,int pos) { return N=N|(1<<pos); }\nint reset(int N,int pos){ return N=N&~(1<<pos);}\nbool check(int N,int pos){  return (bool) (N&(1<<pos));}\n\n///=======================================template=======================================///\n\nclass Solution{\npublic:\n\n    pii longest(string &s,vi &len){\n        string str;\n        str+=\"$\";\n        for(int i=0;i<siz(s);i++){\n            str+=s[i];\n            str+=\"$\";\n        }\n        s=str;\n\n        int start=0,end=0,n=siz(str),maxlen=0,idx=0;\n        len.assign(n,0);\n\n        for(int i=0;i<n;){\n\n            while(start>0 and end<n-1 and str[start-1]==str[end+1]){\n                start--;\n                end++;\n            }\n            len[i]=end-start+1;\n\n            if(maxlen<len[i]){\n                maxlen=len[i];\n                idx=i;\n            }\n\n            if(end==n-1)\n                break;\n\n            int newCenter=end+(i&1?0:1);\n\n            for(int j=i+1;j<=end;j++){\n                len[j]=min(len[i-(j-i)],(end-j)*2+1);\n                if(j+len[i-(j-i)]/2==end) {\n                    newCenter = j;\n                    break;\n                }\n            }\n            i=newCenter;\n            start=i-len[i]/2;\n            end=i+len[i]/2;\n\n        }\n\n        return {maxlen,idx};\n\n    }\n\n    string longestPalindrome(string s){\n        vi Len;\n        pii lenidx=longest(s,Len);\n\n        string left,right;\n\n        lenidx.ff/=2;\n        int inc;\n\n        if(lenidx.ss&1)\n            right+=s[lenidx.ss],inc=2;\n        else inc=1;\n        \n        int i=inc;\n        \n        while(i<=lenidx.ff){\n            left+=s[lenidx.ss-i];\n            right+=s[lenidx.ss+i];\n            i+=2;\n        }\n        \n        reverse(left.begin(),left.end());\n        return (left+right);\n\n    }\n};\n", "text_preview": "#include <bits/stdc++.h> using namespace std; #define dist2D(x1,y1,x2,y2) ((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2)) #define dist3D(x1,y1,z1,x2,y2,z2) ((x1-x2)*(x1-x2)+(y1-y2)*(y1-y2)+(z1-z2)*(z1-z2)) #define EPS 1e-12 #define endl \"\\n\" #define Fast", "input_sha256": "0b4fee0e71d259e016d4e9743f494781a8c9f2ad2f66bcae4af6d4c1c1868f38", "text_sha256": "78377d74c21dacf2c39b8edf370d1bc43aabfbc0d351ea32c2d1d1584d00a415", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 62, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5734714442738699, "noise_severity": 0.6606147569364389, "missing_content_severity": 0.2714085852892006, "factual_density": 3.3576522674795464, "pretraining_utility": 0.43818825220787944, "math_density": 1.9867331497769478, "code_density": 3.890778448131591, "reasoning_density": 1.3769551831602376, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/nixie/rt/blob/ac967f363edcd7a6dddd86964db1118ce436852f/light.cpp", "id": "cpp-0-ff51cfcdd09b6e9a9755594bbf671233", "text": "#include <iostream>\n#include <string>\n#include \"Eigen/Dense\"\n#include \"light.h\"\n#include \"params.h\"\n#include \"formulae.h\"\n\nusing namespace Eigen;\nusing namespace std;\n\nextern Params params;\nextern Vector4d orbit_trap;\n\ndouble dfloor = 0.0;\n\n// surface parameters\nVector3d shad_col(0.0, 0.0, 0.0);  // shadow color\ndouble Camb = 0.25;\t\t   // ambient coefficient\nVector3d amb_col(1.0, 1.0, 1.0);   // ambient color\ndouble Cdiff = 0.4;\t\t   // diffuse coefficient\nVector3d diff_col(1.0, 1.0, 1.0);  // diffuse color\ndouble Cspec = 0.35;\t \t   // specular coefficient\nVector3d spec_col(1.0, 1.0, 1.0);  // specular color\ndouble shininess = 40.0;\t   // specular shininess\n\n// orbit traps colors\nVector3d YZ_col(1.0, 1.0, 1.0);\nVector3d XZ_col(0.34, 0.67, 0.0);\nVector3d XY_col(1.0, 0.67, 0.0);     \nVector3d R_col(0.1, 1.0, 0.94);\n\n// value normalization\ndouble constr(double minV, double maxV, double value){\n\treturn min(maxV,max(value,minV));\n}\n\n// vector normalization\nVector3d constr(double minV, double maxV, Vector3d vec){\n\tvec[0] = constr(minV,maxV,vec[0]);\n\tvec[1] = constr(minV,maxV,vec[1]);\n\tvec[2] = constr(minV,maxV,vec[2]);\n\treturn vec;\n}\n\n// vector(color) interpolation\nVector3d inter(Vector3d vecA, Vector3d vecB, double value){\n\treturn constr(0.0,1.0,(vecA+value*(vecB-vecA)));\n}\n\n// ambient occlusion approximation\ndouble ambOcclusion(Vector3d p, Vector3d n){\n\tdouble Cao = 0.7;\t// ambient occlusion coef.\n\tdouble ao = 0.0;\n\tdouble sum = 0.0;\n\tdouble w = 1.0;\t// weight\n\tdouble dt = distEstim(p);\n\tdouble d = 1.0 - (0.5*(double)rand()/RAND_MAX);\n\tfor(double i = 1.0; i < 6.0; i++){\n\t\tdouble D = (distEstim(p+n*0.1*d*i*i)-dt)/(0.1*d*i*i);\n\t\tw *= 0.6;\n\t\tao += w*constr(0.0, 1.0, 1.0-D);\n\t\tsum += w;\n\t}\n\treturn constr(0.0, 1.0, Cao*ao/sum);\n}\n\n// single shadow calculation\ndouble shadow(Vector3d p, Vector3d light){\n\tVector3d l = (p-light).normalized(); // light source vector\n\tdouble shad = 1.0;  // {1.0 = no shadow, 0.0 = hard shadow}\n\tdouble distance = 0.0;\n\tdouble dt;\n\tdouble dlight = length(p-(light+l*distance));\t// distance to light\n\tfor(int steps = 0; steps < params.max_steps; steps++){\n\t\tVector3d pos(light+l*distance);\n\t\tdt = distEstim(pos); // trace from light to surface\n\t\tshad = min(shad, 2.0*pow((dt/(dlight-distance)), 0.5));\n\t\tdistance += dt; \n\t\tif(dt < params.min_distance && \n\t\t\t\tdistance > dlight-params.min_distance){  // we hit something\n\t\t\treturn shad;\n\t\t}\n\t}\n\treturn 0.0;\n}\n\n// all shadows in the scene calculation\ndouble shadow_all(Vector3d p){\n\tdouble Cshad = 0.0;\n\tint n = sizeof(params.light) / sizeof(params.light[0]); // number of lights\n\tint lights = 0;  // number of active lights\n\tfor(int i = 0; i < n; i++){\n\t\tif(!params.light_en[i]){continue;} // inactive light\n\t\tCshad += shadow(p, params.light[i]);\n\t\tlights++;\n\t}\n\tif(params.torch_en){\n\t\tCshad += shadow(p, params.m_wld*params.camera_pos);\n\t\tlights++;\n\t}\n\treturn Cshad/max((int)1, lights);\n}\n\n// reflected color calculation\nVector3d reflect(Vector3d p, Vector3d col, int depth){\n\n\tVector3d n = normal(p);\t// surface normal\n\n\tVector3d v = (params.m_wld*params.camera_pos-p) // camera vector\n\t\t.normalized();\n\tVector3d r = (2*n*(n.dot(v))-v) // reflection vector\n\t\t.normalized();\n\tVector3d rc(params.bg_col);\t// reflected color\n\n\tdouble dt = 0.0;\n\tdouble distance = 10e-3 + params.min_distance;//3.0*params.eps;\n\n\tfor(int steps = 0; distance < params.far &&\n\t\t\tsteps < params.max_steps; steps++){\n\n\t\tVector3d pos(p+r*distance);\n\t\tdt = distEstim(pos);\t\t// trace to surface\n\t\tdistance += dt;\n\n\t\tif(dt < params.min_distance){\n\t\t\tdistance -= (params.min_distance-dt);\n\t\t\trc = computeColor(pos, dt, distance, depth-1);\n\t\t\tbreak;\n\t\t}\n\t}\n\n\treturn inter(col, rc, 0.8*(1.0-pow(max(0.0, r.dot(n)), 0.1)));\n}\n\n// Blinn-Phong shading model\nVector3d blinnPhong(Vector3d p, Vector3d n, Vector3d col){\n\tdouble Tdiff = 0.0;\t// total diffuse\n\tdouble Tspec = 0.0;\t// total specular\n\n\tVector3d v = \n\t\t(params.m_wld*params.camera_pos-p) // camera vector\n\t\t.normalized();\n\tdouble Tamb = max(0.0, v.dot(n));\n\n\tVector3d light;\n\tint lights = sizeof(params.light)/\t\t // number of lights\n\t\tsizeof(params.light[0]);\n\n\tif(params.torch_en){lights++;}\t\t\t// camera light is enabled\n\n\tfor(int i = 0; i < lights; i++){\n\t\t// select light\n\t\tif(i == lights-1 && params.torch_en){\n\t\t\tlight = params.m_wld*params.camera_pos;\n\t\t}else{\n\t\t\tif(!params.light_en[i]){continue;} // light inactive\n\t\t\tlight = params.light[i];\n\t\t}\n\n\t\tVector3d l = (light-p).normalized(); // light source vector\n\t\tdouble diff = l.dot(n);\n\t\tdouble spec = 0.0;\n\t\tdouble shad = 1.0;\n\n\t\tif(diff > 0.0){\n\t\t\tVector3d h = (l+v).normalized(); // halfway vector\n\t\t\tspec = h.dot(n);\n\t\t\t//Vector3d r = (2*n*(n.dot(l))-l)// light reflection vector\n\t\t\t//\t       .normalized();\n\t\t\t//spec = r.dot(v);\n\t\t\tif(spec > 0.0){\n\t\t\t\tspec = pow(spec, shininess);\n\t\t\t}else{\n\t\t\t\tspec = 0.0;\n\t\t\t}\n\t\t}else{\n\t\t\tdiff = 0.0;\n\t\t}\n\n\t\tif(params.shadow_en){shad = shadow(p,light);}\n\t\tTdiff += diff*shad;\n\t\tTspec += spec*shad;\n\t}\n\n\tVector3d bp = Vector3d(\n\t\t\tamb_col*Camb*Tamb\n\t\t\t+diff_col*constr(0.0,1.0,Tdiff)*Cdiff\n\t\t\t+spec_col*constr(0.0,1.0,Tspec)*Cspec\n\t\t\t);\n\n\tbp[0] *= col[0];\n\tbp[1] *= col[1];\n\tbp[2] *= col[2];\n\n\treturn bp;\n}\n\n// orbit trap coloring\nVector3d orbitTrap(){\n\torbit_trap[3] = sqrt(orbit_trap[3]);\n\tVector3d orbit_col;\n\tif(params.fractal_type==mengersponge){\n\t\torbit_col = Vector3d(\n\t\t\t\tYZ_col*0.2*orbit_trap[0] +\n\t\t\t\tXZ_col*0.1*orbit_trap[1] +\n\t\t\t\tXY_col*1.00*orbit_trap[2] +\n\t\t\t\tR_col*-0.04*orbit_trap[3]);\n\t}else{\n\n\t\torbit_col = Vector3d(\n\t\t\t\tYZ_col*1.0*orbit_trap[0] +\n\t\t\t\tXZ_col*0.1*orbit_trap[1] +\n\t\t\t\tXY_col*1.00*orbit_trap[2] +\n\t\t\t\tR_col*-0.07*orbit_trap[3]);\t\t\t\n\t}\n\treturn constr(0.0, 1.0, 3.0*orbit_col);\n}\n\n\n// get the color of a surface\nVector3d computeColor(Vector3d p, double dt, double distance, int depth){\n\n\torbit_trap = Vector4d(10.0, 10.0, 10.0, 10.0);\n\n\tVector3d col;\n\tVector3d n = normal(p);\n\n\t// color of the fractal\n\tif(params.orbit_en){\n\t\tcol = orbitTrap();\n\t}else{\n\t\tcol = diff_col;\n\t}\n\tif(dt == dfloor){ // floor hit\n\t\tcol = params.floor_col;\n\t}\n\n\t// ambient occlusion (normal sampling AO)\n\tif(params.ao_en){\n\t\tcol = inter(col, shad_col, ambOcclusion(p, n));\n\t}\n\n\t// shadows\n\tif(params.shadow_en && !params.phong_en){\n\t\tcol = inter(col, shad_col, 1.0 - shadow_all(p));\n\t}\n\n\t// Blinn-phong shading\n\tif(params.phong_en){\n\t\tcol = blinnPhong(p, n, col);\n\t}\n\n\t// reflection\n\tif(params.reflect_en){\n\t\tif(depth){\n\t\t\tcol = reflect(p, col, depth);\n\t\t}\n\t}\n\n\t// fog\n\tif(params.fog_en){\n\t\tcol = inter(col, params.bg_col,\n\t\t\t\t1.0-exp(-pow(params.fog_dist,4.0)*distance*distance));\n\t}\n\n\treturn col;\n}\n\n", "text_preview": "#include <iostream> #include <string> #include \"Eigen/Dense\" #include \"light.h\" #include \"params.h\" #include \"formulae.h\" using namespace Eigen; using namespace std; extern Params params; extern Vector4d orbit_trap; double dfloor = 0.0; // ", "input_sha256": "ce2d61f9c5e1e9496415a99b9bd04b53b426c13602847b81a6655ce485b9d6ee", "text_sha256": "ff51cfcdd09b6e9a9755594bbf67123375f4241d1e5a96de5a33e2c7cdcef06f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Joaobaf/Jogo-da-Memoria/blob/8258b932ad32b804722dca403fb3e26561682d1e/main.cpp", "id": "cpp-0-5e26b164e6188bd34c810cff87a5aa5a", "text": "#include <iostream>\n#include<windows.h>\n#include<time.h>\nusing namespace std;\n#define TAM 4\nint main(){\n\n//Autores: <NAME>, <NAME>, <NAME>, <NAME>\n\n    int matprin[TAM][TAM]={1,4,5,2,7,2,8,7,3,6,1,4,6,5,3,8};\n    int jogo[TAM][TAM]={}, gab[TAM][TAM];\n    int opcao, L1, L2, C1, C2, p=0, sm_gab=0,sm_jogo;\n    const char *cls, *pause;\n    srand(time(NULL));\n\n    opcao=rand()%3;/*Randomiza como será a matriz gabarito*/\n\n    switch(opcao){/*Seleciona um modo de jogo a partir do número aleatório*/\n        case 0:\n            for(int i=0;i<TAM;i++){ /*Seleciona a linha em que o número será escrito*/\n                for(int j=0;j<TAM;j++){/*Seleciona a coluna em que o número será escrito*/\n                    gab[i][j]=matprin[i][j];/*Escreve a matriz gabarito igual a principal*/\n                }\n            }\n            break;\n        case 1:\n            for(int i=0;i<TAM;i++){\n                for(int j=0;j<TAM;j++){\n                    gab[i][j]=matprin[j][i];/*Escreve a matriz gabarito igual a transposta da principal*/\n                }\n            }\n            break;\n        case 2:\n            for(int i=0;i<TAM;i++){\n                for(int j=0;j<TAM;j++){\n                    gab[i][j]= matprin[TAM-1-i][j];/*Escreve a matriz gabarito invertendo as linhas da principal*/\n                }\n            }\n            break;\n        case 3:\n            for(int i=0;i<TAM;i++){\n                for(int j=0;j<TAM;j++){\n                    gab[i][j]= matprin[i][TAM-1-j];/*Escreve a matriz gabarito invertendo as colunas da principal*/\n                }\n            }\n            break;\n    }\n\n    for(int i=0;i<TAM;i++){/*Seleciona a linha do número que vai ser somado*/\n        for(int j=0;j<TAM;j++){/*Seleciona a coluna do número que vai ser somado*/\n            sm_gab+=gab[i][j];/*Somatório dos valores da matriz gabarito*/\n        }\n    }\n\n    while(p<=TAM*6){/*Faz o jogo ciclar até p ser igual ao triplo do número de pares*/\n\n        for(int i=0;i<TAM;i++){\n            for(int j=0;j<TAM;j++){\n                cout<<jogo[i][j]<<\"\\t\";/*Escreve a matriz jogo*/\n            }\n            cout<<endl;\n        }\n        cout<<\"Jogadas realizadas: \"<<p;\n\n        int m=0;\n        while(m!=1){/*Cicla até m ser igual a 1*/\n            cout<<\"\\nDigite um valor entre 0 e 3 para a linha\";\n            cin>>L1;/*Dá o valor digitado a cima para linha*/\n            cout<<\"\\nDigite um valor entre 0 e 3 para a coluna\";\n            cin>>C1;/*Dá o valor digitado a cima para coluna*/\n            cout<<\"\\n\";\n            if(jogo[L1][C1]==0){/*Testa se a posição está livre*/\n                m=1;\n                jogo[L1][C1]=gab[L1][C1];/*Escreve em jogo o valor de gabarito na mesma posição*/\n                system(\"cls\");\n                for(int i=0;i<TAM;i++){\n                    for(int j=0;j<TAM;j++){\n                        cout<<jogo[i][j]<<\"\\t\";/*Reescreve a matriz jogo com o valor \"virado\"*/\n                    }\n                    cout<<endl;\n                }\n            }else{\n                m=0;\n                cout<<\"Essa combinacao ja foi usada!\";\n            }\n        }\n\n        int n=0;\n        while(n!=1){/*Cicla até n ser igual a 1*/\n            cout<<\"\\nDigite um valor entre 0 e 3 para a linha\";\n            cin>>L2;/*Dá o valor digitado a cima para linha*/\n            cout<<\"\\nDigite um valor entre 0 e 3 para a coluna\";\n            cin>>C2;/*Dá o valor digitado a cima para coluna*/\n            cout<<\"\\n\";\n            if(jogo[L2][C2]==0){/*Testa se a posição está livre*/\n                n=1;\n                jogo[L2][C2]=gab[L2][C2];/*Escreve em jogo o valor de gabarito na mesma posição*/\n                system(\"cls\");\n                for(int i=0;i<TAM;i++){\n                    for(int j=0;j<TAM;j++){\n                        cout<<jogo[i][j]<<\"\\t\";/*Reescreve a matriz jogo com o valor \"virado\"*/\n                    }\n                    cout<<endl;\n                }\n            }else{\n                cout<<\"Essa combinacao ja foi usada\"<<\"\\n\";\n                n=0;\n            }\n        }\n\n        if(jogo[L1][C1]==jogo[L2][C2]){/*Testa se os numeros são iguais*/\n            cout<<\"\\nJogada OK!!\";\n\n        }else{\n            cout<<\"\\nJogada NOK!!\";\n            jogo[L1][C1]={};/*\"Desvira\" o valor dessa posição*/\n            jogo[L2][C2]={};\n        }\n\n        Sleep(2000);\n        system(\"cls\");\n        p++;\n        sm_jogo=0;\n\n        for(int i=0;i<TAM;i++){\n            for(int j=0;j<TAM;j++){\n                sm_jogo+=jogo[i][j];/*Somatório dos valores da matriz jogo*/\n            }\n        }\n\n        if(sm_jogo==sm_gab){/*Testa se o somatório de jogo e gabarito são iguais*/\n            system(\"cls\");\n            cout<<\"PARABENS, VOCE VENCEU!!!\";\n            system(\"pause\");\n\n        }\n    }\n\n    if(sm_jogo==sm_gab&&p==TAM*6){/*Testa se o somatório de jogo e gabarito são iguais e se o jogo acabou*/\n        system(\"cls\");\n        cout<<\"FIM DE JOGO, VOCE VENCEU!!!\";\n        system(\"pause\");\n    }else{\n        system(\"cls\");\n        cout<<\"FIM DE JOGO, VOCE PERDEU!!!\";\n        system(\"pause\");\n        }\n\n    return 0;\n}\n", "text_preview": "#include <iostream> #include<windows.h> #include<time.h> using namespace std; #define TAM 4 int main(){ //Autores: <NAME>, <NAME>, <NAME>, <NAME> int matprin[TAM][TAM]={1,4,5,2,7,2,8,7,3,6,1,4,6,5,3,8}; int jogo[TAM][TAM]={}, gab[TAM][TAM];", "input_sha256": "96e80dd26831593db4729b9f68aab75ab4058eef9c80690cf4e9f872deb55564", "text_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/MinmusLin/Discrete_Mathematics_Course_Assignments/blob/062aae34b866bccb44aac2d1ec2db63831d87a5e/Assignment_4/assignment_4.cpp", "id": "cpp-0-4a4b6a4ba14b80e6e9aa0b4fa1d7116c", "text": "/****************************************************************\n * Project Name:  Assignment_4\n * File Name:     assignment_4.cpp\n * File Function: 最小生成树\n * Author:        <NAME> (林继申)\n * Update Date:   2023/12/22\n ****************************************************************/\n\n#define _CRT_SECURE_NO_WARNINGS\n\n#include <iostream>\n#include <limits>\n#include <conio.h>\n#include <vector>\n#include <algorithm>\n\n/* Macro definitions */\n#define MEMORY_ALLOCATION_ERROR -1\n#define INF INT_MAX\n\n/* Define Edge structure */\nstruct Edge {\n    int src;\n    int dst;\n    int weight;\n    bool operator<(const Edge& other) const { return this->weight < other.weight; }\n};\n\n/* Define MinimumSpanningTree class */\nclass MinimumSpanningTree {\nprivate:\n    int vertex;\n    int total;\n    int** graph;\n    std::vector<Edge> edges;\n    std::vector<int> parent;\n    std::vector<int> rank;\n    int findSet(int i);\n    void unionSets(int x, int y);\npublic:\n    MinimumSpanningTree(int V);\n    ~MinimumSpanningTree();\n    int getTotal(void) const;\n    void kruskalMST(void);\n    void setWeight(int src, int dst, int weight);\n};\n\n/*\n * Function Name:    findSet\n * Function:         Find in Union-Find\n * Input Parameters: int i\n * Notes:            Class external implementation of member functions\n */\nint MinimumSpanningTree::findSet(int i)\n{\n    if (parent[i] != i)\n        parent[i] = findSet(parent[i]);\n    return parent[i];\n}\n\n/*\n * Function Name:    unionSets\n * Function:         Union in Union-Find\n * Input Parameters: int x\n *                   int y\n * Notes:            Class external implementation of member functions\n */\nvoid MinimumSpanningTree::unionSets(int x, int y)\n{\n    int xroot = findSet(x), yroot = findSet(y);\n    if (rank[xroot] < rank[yroot])\n        parent[xroot] = yroot;\n    else if (rank[xroot] > rank[yroot])\n        parent[yroot] = xroot;\n    else {\n        parent[yroot] = xroot;\n        rank[xroot]++;\n    }\n}\n\n/*\n * Function Name:    MinimumSpanningTree\n * Function:         Constructed function\n * Input Parameters: int V\n * Notes:            Class external implementation of member functions\n */\nMinimumSpanningTree::MinimumSpanningTree(int V)\n{\n    vertex = V;\n    total = 0;\n    graph = new(std::nothrow) int* [vertex];\n    if (graph == NULL) {\n        std::cerr << \"Error: Memory allocation failed.\" << std::endl;\n        exit(MEMORY_ALLOCATION_ERROR);\n    }\n    for (int i = 0; i < vertex; i++) {\n        graph[i] = new(std::nothrow) int[vertex];\n        if (graph[i] == NULL) {\n            std::cerr << \"Error: Memory allocation failed.\" << std::endl;\n            exit(MEMORY_ALLOCATION_ERROR);\n        }\n    }\n    for (int i = 0; i < vertex; i++)\n        for (int j = 0; j < vertex; j++)\n            graph[i][j] = 0;\n}\n\n/*\n * Function Name:    ~MinimumSpanningTree\n * Function:         Destructor\n * Notes:            Class external implementation of member functions\n */\nMinimumSpanningTree::~MinimumSpanningTree()\n{\n    for (int i = 0; i < vertex; i++)\n        delete[] graph[i];\n    delete[] graph;\n}\n\n/*\n * Function Name:    getTotal\n * Function:         Get the total distance\n * Input Parameters: void\n * Return Value:     total distance\n */\nint MinimumSpanningTree::getTotal(void) const\n{\n    return total;\n}\n\n/*\n * Function Name:    kruskalMST\n * Function:         Generate minimum spanning tree using Kruskal algorithm\n * Input Parameters: void\n * Return Value:     void\n */\nvoid MinimumSpanningTree::kruskalMST(void)\n{\n    /* Initialize parent and rank arrays for Union-Find */\n    parent.resize(vertex);\n    rank.resize(vertex, 0);\n    for (int i = 0; i < vertex; ++i) {\n        parent[i] = i;\n    }\n\n    /* Sort all edges in ascending order of their weights */\n    std::sort(edges.begin(), edges.end());\n    std::vector<Edge> result; // Stores the edges in the MST\n    int count = 0; // Keeps track of the number of edges added to the MST\n\n    /* Iterate through all edges */\n    for (const auto& edge : edges) {\n        int x = findSet(edge.src); // Find the set of the source vertex\n        int y = findSet(edge.dst); // Find the set of the destination vertex\n        if (x != y) {\n            result.push_back(edge);\n            unionSets(x, y);\n            count++;\n            if (count == vertex - 1)\n                break;\n        }\n    }\n\n    /* Print the edges in the MST */\n    for (const auto& edge : result) {\n        std::cout << static_cast<char>(edge.src + 'A') << \" --(\" << edge.weight << \")-- \" << static_cast<char>(edge.dst + 'A') << std::endl;\n        total += edge.weight;\n    }\n}\n\n/*\n * Function Name:    setWeight\n * Function:         Set the weight of a specific edge\n * Input Parameters: int src\n *                   int dst\n *                   int weight\n * Return Value:     void\n */\nvoid MinimumSpanningTree::setWeight(int src, int dst, int weight)\n{\n    graph[src][dst] = weight;\n    graph[dst][src] = weight;\n    edges.push_back({ src, dst, weight });\n}\n\n/*\n * Function Name:    inputInteger\n * Function:         Input an integer\n * Input Parameters: int lowerLimit\n *                   int upperLimit\n *                   const char* prompt\n * Return Value:     an integer\n */\nint inputInteger(int lowerLimit, int upperLimit, const char* prompt)\n{\n    while (true) {\n        std::cout << \"请输入\" << prompt << \" [整数范围: \" << lowerLimit << \"~\" << upperLimit << \"]: \";\n        double tempInput;\n        std::cin >> tempInput;\n        if (std::cin.good() && tempInput == static_cast<int>(tempInput) && tempInput >= lowerLimit && tempInput <= upperLimit) {\n            std::cin.clear();\n            std::cin.ignore(std::numeric_limits<std::streamsize>::max(), '\\n');\n            return static_cast<int>(tempInput);\n        }\n        else {\n            std::cerr << std::endl << \">>> \" << prompt << \"输入不合法，请重新输入\" << prompt << \"！\" << std::endl << std::endl;\n            std::cin.clear();\n            std::cin.ignore(std::numeric_limits<std::streamsize>::max(), '\\n');\n        }\n    }\n}\n\n/*\n * Function Name:    inputLogicalValue\n * Function:         Input logical value\n * Input Parameters: char falseValue\n *                   char trueValue\n * Return Value:     true / false\n */\nbool inputLogicalValue(char falseValue = '0', char trueValue = '1')\n{\n    while (true) {\n        char optn = _getch();\n        if (optn == 0 || optn == -32)\n            optn = _getch();\n        else if (optn == falseValue || optn == trueValue) {\n            std::cout << optn << std::endl << std::endl;\n            return optn == falseValue ? false : true;\n        }\n    }\n}\n\n/*\n * Function Name:    main\n * Function:         Main function\n * Return Value:     0\n */\nint main()\n{\n    do {\n        /* System entry prompt */\n        system(\"cls\");\n        std::cout << \"+-------------------------+\" << std::endl;\n        std::cout << \"|       最小生成树        |\" << std::endl;\n        std::cout << \"|  Minimum Spanning Tree  |\" << std::endl;\n        std::cout << \"+-------------------------+\" << std::endl;\n\n        /* Establish road network */\n        std::cout << std::endl << \">>> 请创建路网\" << std::endl << std::endl;\n        int vertex = inputInteger(2, 'Z' - 'A' + 1, \"城市（路网节点）个数\");\n        MinimumSpanningTree MST(vertex);\n        std::cout << std::endl << \">>> 城市（路网节点）A\";\n        for (int i = 1; i < vertex; i++)\n            std::cout << \"、\" << static_cast<char>(i + 'A');\n        std::cout << \" 创建成功\" << std::endl;\n\n        /* Input the distance between any two cities */\n        std::cout << std::endl << \">>> 请输入任意两个城市（路网节点）之间的距离\" << std::endl;\n        std::cout << std::endl << \">>> 若两个城市（路网节点）之间没有路请输入 \" << SHRT_MAX << \" 表示输入上限\" << std::endl << std::endl;\n        for (int i = 0; i < vertex; i++)\n            for (int j = i + 1; j < vertex; j++) {\n                char tmp[64];\n                sprintf(tmp, \"城市（路网节点）%c 和 %c 之间的距离\", i + 'A', j + 'A');\n                MST.setWeight(i, j, inputInteger(1, SHRT_MAX, tmp));\n            }\n\n        /* Generate minimum spanning tree using Kruskal algorithm */\n        std::cout << std::endl << \">>> 建立 Kruskal 最小生成树:\" << std::endl << std::endl;\n        MST.kruskalMST();\n        std::cout << std::endl << \">>> Kruskal 最小生成树的总权重为 \" << MST.getTotal() << std::endl << std::endl;\n\n        /* Whether to exit the program */\n        std::cout << \"是否退出程序 [y/n]: \";\n    } while (!inputLogicalValue('n', 'y'));\n    return 0;\n}", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/etp19/CPSC230/blob/88682bbffe6b2dee47ebbba1bf7aae3af236538c/hw%20ch2/hw3ETP.cpp", "id": "cpp-0-3df2abc00c1d53891f87ed1c112e0978", "text": "/*******************************************************************************************************************************************\n*                      CPSC 230 TR Fall 2018               HW Ch03\n* <NAME>\n* <NAME>\n*\n* This game will ask the user to chose a weapon and it will play against a monster, the monster as well as the user will have a random \n* amount of points given to them when the game starts, if the user wins it the game will give them points\n* but at the same time the monster will also adquire points (making the game more challenging), on the other hand, if the user looses a battle \n* will loose points but the monster will not, the points of the weapons will be added every time for the 5 times the loop runs.\n*\n********************************************************************************************************************************************\n*/\n// Imports\n#include <iostream>\n#include <string>\n#include <iomanip>\n#include <cstdlib>\n#include <ctime>\n#include <chrono>\n#include <thread>\n\nusing namespace std;\nusing namespace this_thread;\nusing namespace chrono;\n\n// Constants\nenum WEAPONS_DAMAGE {\n\tSWORD = 5000,\n\tWAND = 6000,\n\tBATTLE = 7000,\n\tRING = 8000\n};\nint const TIMES_TO_RUN = 5;\nint main()\n{\t\n\n\t// Seed Random\n\tsrand(time(NULL));\n\t\t\n\t// Variables\n\tint min = 30000;\n\tint max = 50000;\n\tstring userName;\n\tstring userWeapon;\n\tchar playAgain = 'y';\n\tint weaponType, monsterPoints, initialUserPoints;\n\t\n\t//Input your name\n\tcout << \"Please enter your name:  \";\n\tcin >> userName;\n\tcout << \"\\n\";\n\t\n\twhile(playAgain == 'y'){\n\t// random points to user\n\tinitialUserPoints = (min + (rand() % (int)(max - min + 1)));\n\t// random points to monster\n\tmonsterPoints = (min + (rand() % (int)(max - min + 1)));\n\t// Display greetings and your points\n\tcout << \"Greetings Sr \" << userName << \"\\n\\n\";\n\n\n\tcout << \"You have \" << initialUserPoints << \"  Points\\n\";\n\n\t// Run this game five times or by choosing the menu number 5 you will exit back to the while loop.\n\tfor (int i = 1; i <= TIMES_TO_RUN; i++)\n\t{\n\t\tcout << \"\\nTry #  \" << i << endl;\n\t\t// Display menu\n\t\tcout << \"\\n ****** Choose a weapon from the following menu *******\" << endl;\n\t\tcout << \"1- Sword of Greyskull\" << endl;\n\t\tcout << \"2- Wand of Gandolf\" << endl;\n\t\tcout << \"3- Battle Axe of Grumpy\" << endl;\n\t\tcout << \"4- Ring of Gollum\" << endl;\n\t\tcout << \"5- To exit the weapons menu\" << endl;\t\n\t\t// User input\n\t\tcin >> weaponType;\n\n\t\t// Choose the weapon and add weapon damage points to the initial user points\n\t\tswitch (weaponType)\n\t\t{\n\t\tcase 1:\n\t\t\tuserWeapon = \"Sword of Greyskull\";\n\t\t\tinitialUserPoints += SWORD;\n\t\t\tbreak;\n\t\tcase 2:\n\t\t\tuserWeapon = \"Wand of Gandolf\";\n\t\t\tinitialUserPoints += WAND;\n\t\t\tbreak;\n\t\tcase 3:\n\t\t\tuserWeapon = \"Battle Axe of Grumpy\";\n\t\t\tinitialUserPoints += BATTLE;\n\t\t\tbreak;\n\t\tcase 4:\n\t\t\tuserWeapon = \"Ring of Gollum\";\n\t\t\tinitialUserPoints += RING;\n\t\t\tbreak;\n\t\tdefault:\n\t\t\tcout << \"No weapons were chosen, the game is ending ....\" << endl;\n\t\t\tsleep_until(system_clock::now() + seconds(2));\n\t\t\tbreak;\n\t\t}\n\t\t// If you choose a menu number equal or greater than 5 the for loop will exit\n\t\tif (weaponType >= 5) {\n\t\t\tbreak;\n\t\t}\n\t\telse\n\t\t{\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t// Keep record of points\n\t\t\tcout << \"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t************* Game Board ************\" << endl;\n\t\t\tcout << \"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t**  Monster points:  \" << monsterPoints << \"  **\" << endl;\n\t\t\tcout << \"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t**  User points:  \" << initialUserPoints << \"  **\" << endl;\n\t\t\tcout << \"You chose the weapon  \" << userWeapon << endl;\n\t\t\t// if user has more points he defeated the monster and it will have a 15% increase in points\n\t\t\t// Monster will have a 30 % increase.\n\t\t\tif (initialUserPoints > monsterPoints) {\n\t\t\t\tinitialUserPoints += (initialUserPoints * 0.15);\n\t\t\t\tmonsterPoints += (initialUserPoints * 0.30);\n\t\t\t\tcout << \"With the weapon  \" << userWeapon << \"  you have defeated the Monster\" << endl;\n\t\t\t}\n\t\t\t// If monster wins user points will be reduce by 15%\n\t\t\telse\n\t\t\t{\n\t\t\t\tinitialUserPoints -= (initialUserPoints * 0.15);\n\t\t\t\tcout << \"With the weapon  \" << userWeapon << \"  you have lost\" << endl;\n\t\t\t}\n\t\t}\n\t\t\n\t}\n\t// Exit the program or not.\n\tcout << \"Do You want to play again? 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/refracta/koreatech-assignment/blob/dd28b7ed37c60e9aeec3017d05255f6a8f639f57/RealTimeSystemNPractice/Assignment_Lab_%234/rr.cpp", "id": "cpp-0-d55b4d8d2143d687c2ebeda5b7b22f0d", "text": "#include <iostream>\n#include <algorithm>\n#include <iomanip>\n#include <queue>\n#include <cstring>\n\nusing namespace std;\n\n// 프로세스 정보를 담는 구조체\nstruct process {\n    int pid; // 프로세스 ID\n    int arrival_time; // 도착 시간\n    int burst_time; // 실행 시간(버스트 시간)\n    int start_time; // 시작 시간\n    int completion_time; // 완료 시간\n    int turnaround_time; // 반환 시간\n    int waiting_time; // 대기 시간\n    int response_time; // 응답 시간\n};\n\n// 도착 시간에 따라 프로세스를 정렬하는 함수\nbool compare1(process p1, process p2) {\n    return p1.arrival_time < p2.arrival_time;\n}\n\n// 프로세스 ID에 따라 프로세스를 정렬하는 함수\nbool compare2(process p1, process p2) {\n    return p1.pid < p2.pid;\n}\n\nint main() {\n    int n; // 프로세스의 수\n    int tq; // 타임 퀀텀\n    struct process p[100]; // 프로세스 배열\n    float avg_turnaround_time; // 평균 반환 시간\n    float avg_waiting_time; // 평균 대기 시간\n    float avg_response_time; // 평균 응답 시간\n    float cpu_utilisation; // CPU 사용률\n    int total_turnaround_time = 0; // 총 반환 시간\n    int total_waiting_time = 0; // 총 대기 시간\n    int total_response_time = 0; // 총 응답 시간\n    int total_idle_time = 0; // 총 유휴 시간\n    float throughput; // 처리량\n    int burst_remaining[100]; // 남은 실행 시간\n    int idx; // 큐 순회용 색인\n\n    // 프로세스 정보 입력 처리\n    cout << setprecision(2) << fixed;\n\n    cout << \"Enter the number of processes: \";\n    cin >> n;\n    cout << \"Enter time quantum: \";\n    cin >> tq;\n\n    for (int i = 0; i < n; i++) {\n        cout << \"Enter arrival time of process \" << i + 1 << \": \";\n        cin >> p[i].arrival_time;\n        cout << \"Enter burst time of process \" << i + 1 << \": \";\n        cin >> p[i].burst_time;\n        burst_remaining[i] = p[i].burst_time;\n        p[i].pid = i + 1;\n        cout << endl;\n    }\n\n    // 도착 시간에 따라 프로세스를 정렬\n    sort(p, p + n, compare1);\n\n    // 실행 시간 초기화\n    for (int i = 0; i < n; i++) {\n        burst_remaining[i] = p[i].burst_time;\n    }\n\n    // 프로세스 큐\n    queue<int> q;\n    // 현재 시간\n    int current_time = 0;\n    // 첫 번째 프로세스를 큐에 추가\n    q.push(0);\n    // 완료된 프로세스 수\n    int completed = 0;\n    // 프로세스의 상태 표시 배열\n    int mark[100];\n    memset(mark, 0, sizeof(mark));\n    mark[0] = 1; // 첫 번째 프로세스 표시 초기화\n\n    // 모든 프로세스가 완료될 때 까지\n    while (completed != n) {\n        // 큐의 첫번째 프로세스를 인출\n        idx = q.front();\n        q.pop();\n\n        // 프로세스가 처음 실행되는 경우 초기화\n        if (burst_remaining[idx] == p[idx].burst_time) {\n            p[idx].start_time = max(current_time, p[idx].arrival_time);\n            total_idle_time += p[idx].start_time - current_time;\n            current_time = p[idx].start_time;\n        }\n\n        // 타임 퀀텀보다 남은 시간이 많으면\n        if (burst_remaining[idx] - tq > 0) {\n            // 타임 퀀텀만큼 남은 시간 감소 후 현재 시간 갱신\n            burst_remaining[idx] -= tq;\n            current_time += tq;\n        } else {\n            // 타임 퀀텀보다 남은 시간이 적으면\n            // 남은 시간만큼 현재 시간 증가\n            current_time += burst_remaining[idx];\n            // 남은 시간을 0으로 설정\n            burst_remaining[idx] = 0;\n            // 완료된 프로세스 수 증가\n            completed++;\n\n            // 프로세스 완료 시간, 반환 시간, 대기 시간, 응답 시간을 계산\n            p[idx].completion_time = current_time;\n            p[idx].turnaround_time = p[idx].completion_time - p[idx].arrival_time;\n            p[idx].waiting_time = p[idx].turnaround_time - p[idx].burst_time;\n            p[idx].response_time = p[idx].start_time - p[idx].arrival_time;\n\n            // 총 시간들을 계산\n            total_turnaround_time += p[idx].turnaround_time;\n            total_waiting_time += p[idx].waiting_time;\n            total_response_time += p[idx].response_time;\n        }\n\n        // 다음에 실행할 프로세스를 큐에 추가\n        for (int i = 1; i < n; i++) {\n            if (burst_remaining[i] > 0 && p[i].arrival_time <= current_time && mark[i] == 0) {\n                q.push(i);\n                mark[i] = 1;\n            }\n        }\n\n        // 남은 시간이 있는 경우, 큐에 프로세스를 다시 추가\n        if (burst_remaining[idx] > 0) {\n            q.push(idx);\n        }\n\n        // 큐가 빈 경우, 다음 프로세스를 찾아 추가\n        if (q.empty()) {\n            for (int i = 1; i < n; i++) {\n                if (burst_remaining[i] > 0) {\n                    q.push(i);\n                    mark[i] = 1;\n                    break;\n                }\n            }\n        }\n    }\n\n    // 평균 시간과 CPU 사용률, 처리량을 계산\n    avg_turnaround_time = (float) total_turnaround_time / n;\n    avg_waiting_time = (float) total_waiting_time / n;\n    avg_response_time = (float) total_response_time / n;\n    cpu_utilisation = ((p[n - 1].completion_time - total_idle_time) / (float) p[n - 1].completion_time) * 100;\n    throughput = float(n) / (p[n - 1].completion_time - p[0].arrival_time);\n\n    sort(p, p + n, compare2);\n\n    cout << endl;\n    cout << \"#P\\t\" << \"AT\\t\" << \"BT\\t\" << \"ST\\t\" << \"CT\\t\" << \"TAT\\t\" << \"WT\\t\" << \"RT\\t\" << \"\\n\" << endl;\n\n    for (int i = 0; i < n; i++) {\n        cout << p[i].pid << \"\\t\" << p[i].arrival_time << \"\\t\" << p[i].burst_time << \"\\t\" << p[i].start_time << \"\\t\"\n             << p[i].completion_time << \"\\t\" << p[i].turnaround_time << \"\\t\" << p[i].waiting_time << \"\\t\"\n             << p[i].response_time << \"\\t\" << \"\\n\" << endl;\n    }\n    cout << \"Average Turnaround Time = \" << avg_turnaround_time << endl;\n    cout << \"Average Waiting Time = \" << avg_waiting_time << endl;\n    cout << \"Average Response Time = \" << avg_response_time << endl;\n    cout << \"CPU Utilization = \" << cpu_utilisation << \"%\" << endl;\n    cout << \"Throughput = \" << throughput << \" process/unit time\" << endl;\n}\n\n/*\n\nAT - 프로세스의 도착 시간\nBT - 프로세스의 실행 시간\nST - 프로세스의 시작 시간\nCT - 프로세스의 완료 시간\nTAT - 프로세스의 반환 시간\nWT - 프로세스의 대기 시간\nRT - 프로세스의 응답 시간\n\n사용된 공식들:\n\nTAT = CT - AT\nWT = TAT - BT\nRT = ST - AT\n\n*/", "text_preview": "#include <iostream> #include <algorithm> #include <iomanip> #include <queue> #include <cstring> using namespace std; // 프로세스 정보를 담는 구조체 struct process { int pid; // 프로세스 ID int arrival_time; // 도착 시간 int burst_time; // 실행 시간(버스트 시간) int sta", "input_sha256": "a67d947828fa660b249a7467cddad6049e721b47e7863b00aa832ed4cc5a2abd", "text_sha256": "d55b4d8d2143d687c2ebeda5b7b22f0d8875316757a6fc61428303ecabf37e86", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 65, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0449827196525452, "noise_severity": 0.02185635861353847, "missing_content_severity": 0.004583297404190374, "factual_density": 3.1287390431749795, "pretraining_utility": 0.7621751914240954, "math_density": 2.8494207450237807, "code_density": 3.684888093437448, "reasoning_density": 2.0017272690467993, "education_level": 2.032515785331643, "creative_writing_quality": -0.40771744603839694}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ompl/ompl/blob/a0b3c9b19da5d02aaa2a32a45d42a93de3ff7c10/src/ompl/geometric/planners/xxl/src/XXLPositionDecomposition.cpp", "id": "cpp-0-883f5bf7f285a386442d973201df9f46", "text": "/*********************************************************************\n * Software License Agreement (BSD License)\n *\n *  Copyright (c) 2015, Rice University\n *  All rights reserved.\n *\n *  Redistribution and use in source and binary forms, with or without\n *  modification, are permitted provided that the following conditions\n *  are met:\n *\n *   * Redistributions of source code must retain the above copyright\n *     notice, this list of conditions and the following disclaimer.\n *   * Redistributions in binary form must reproduce the above\n *     copyright notice, this list of conditions and the following\n *     disclaimer in the documentation and/or other materials provided\n *     with the distribution.\n *   * Neither the name of the Rice University nor the names of its\n *     contributors may be used to endorse or promote products derived\n *     from this software without specific prior written permission.\n *\n *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n *  \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n *  POSSIBILITY OF SUCH DAMAGE.\n *********************************************************************/\n\n/* Author: <NAME> */\n\n#include <stack>\n#include \"ompl/geometric/planners/xxl/XXLPositionDecomposition.h\"\n#include \"ompl/util/Exception.h\"\n\nompl::geometric::XXLPositionDecomposition::XXLPositionDecomposition(const base::RealVectorBounds &bounds,\n                                                                    const std::vector<int> &slices, bool diagonalEdges)\n  : bounds_(bounds), slices_(slices), diagonalEdges_(diagonalEdges)\n{\n    bounds_.check();\n\n    if (slices_.size() == 0)\n        throw ompl::Exception(\"There must be at least one dimension specified in slices\");\n    if (bounds_.low.size() != slices_.size())\n        throw ompl::Exception(\"Total slice count must be equal to the number of dimensions\");\n\n    numRegions_ = slices_[0];\n    for (size_t i = 1; i < slices_.size(); ++i)\n        numRegions_ *= slices_[i];\n\n    cellSizes_.resize(slices_.size());\n    for (size_t i = 0; i < slices_.size(); ++i)\n    {\n        double extent = fabs(bounds_.high[i] - bounds_.low[i]);\n        cellSizes_[i] = extent / slices_[i];\n    }\n}\n\nompl::geometric::XXLPositionDecomposition::~XXLPositionDecomposition()\n{\n}\n\nint ompl::geometric::XXLPositionDecomposition::getNumRegions() const\n{\n    return numRegions_;\n}\n\nint ompl::geometric::XXLPositionDecomposition::getDimension() const\n{\n    return (int)slices_.size();\n}\n\nint ompl::geometric::XXLPositionDecomposition::locateRegion(const base::State *s) const\n{\n    std::vector<double> coord;\n    project(s, coord);\n    return coordToRegion(coord);\n}\n\nint ompl::geometric::XXLPositionDecomposition::locateRegion(const std::vector<double> &coord) const\n{\n    return coordToRegion(coord);\n}\n\nvoid ompl::geometric::XXLPositionDecomposition::getNeighbors(int rid, std::vector<int> &neighbors) const\n{\n    if (diagonalEdges_)\n        getDiagonalNeighbors(rid, neighbors);\n    else\n        getNonDiagonalNeighbors(rid, neighbors);\n}\n\nvoid ompl::geometric::XXLPositionDecomposition::getNeighborhood(int rid, std::vector<int> &neighborhood) const\n{\n    getDiagonalNeighbors(rid, neighborhood);\n}\n\ndouble ompl::geometric::XXLPositionDecomposition::distanceHeuristic(int r1, int r2) const\n{\n    std::vector<int> c1, c2;\n    ridToGridCell(r1, c1);\n    ridToGridCell(r2, c2);\n\n    // manhattan distance for everything\n    double dist = 0.0;\n    for (size_t i = 0; i < 2; ++i)\n        dist += abs(c1[i] - c2[i]);\n\n    // should do Chebyshev for diagonal edges...\n\n    return dist;\n}\n\nvoid ompl::geometric::XXLPositionDecomposition::ridToGridCell(int rid, std::vector<int> &cell) const\n{\n    cell.resize(slices_.size());\n\n    int lowerDimensionalRegions = numRegions_ / slices_.back();\n    for (int i = slices_.size() - 1; i >= 0; --i)\n    {\n        cell[i] = rid / lowerDimensionalRegions;\n\n        rid %= lowerDimensionalRegions;\n        if (i > 0)\n            lowerDimensionalRegions /= slices_[i - 1];\n    }\n}\n\nint ompl::geometric::XXLPositionDecomposition::gridCellToRid(const std::vector<int> &cell) const\n{\n    int region = cell[0];\n    int lowerDimensionalRegions = slices_[0];\n    for (size_t i = 1; i < cell.size(); ++i)\n    {\n        region += (cell[i] * lowerDimensionalRegions);\n        lowerDimensionalRegions *= slices_[i - 1];\n    }\n    return region;\n}\n\nint ompl::geometric::XXLPositionDecomposition::coordToRegion(const std::vector<double> &coord) const\n{\n    return coordToRegion(&coord[0]);\n}\n\nint ompl::geometric::XXLPositionDecomposition::coordToRegion(const double *coord) const\n{\n    std::vector<int> cell(slices_.size());\n    for (size_t i = 0; i < slices_.size(); ++i)\n        cell[i] = (coord[i] - bounds_.low[i]) / cellSizes_[i];\n    return gridCellToRid(cell);\n}\n\nbool ompl::geometric::XXLPositionDecomposition::hasDiagonalEdges() const\n{\n    return diagonalEdges_;\n}\n\nvoid ompl::geometric::XXLPositionDecomposition::getNonDiagonalNeighbors(int rid, std::vector<int> &neighbors) const\n{\n    std::vector<int> cell;\n    ridToGridCell(rid, cell);\n    std::vector<int> workCell(cell.begin(), cell.end());\n\n    for (size_t i = 0; i < slices_.size(); ++i)\n    {\n        if (slices_[i] == 1)  // no neighbors in this dimension\n            continue;\n\n        workCell[i] -= 1;\n        if (workCell[i] >= 0 && workCell[i] < slices_[i])\n            neighbors.push_back(gridCellToRid(workCell));\n\n        if (slices_[i] > 2)\n        {\n            workCell[i] += 2;\n            if (workCell[i] >= 0 && workCell[i] < slices_[i])\n                neighbors.push_back(gridCellToRid(workCell));\n        }\n        workCell[i] = cell[i];\n    }\n}\n\nvoid ompl::geometric::XXLPositionDecomposition::getDiagonalNeighbors(int rid, std::vector<int> &neighbors) const\n{\n    std::vector<int> ridCell;\n    ridToGridCell(rid, ridCell);\n\n    std::stack<std::pair<int, std::vector<int>>> stack;\n    stack.push(std::make_pair(0, ridCell));\n\n    while (!stack.empty())\n    {\n        std::pair<int, std::vector<int>> entry = stack.top();\n        stack.pop();\n\n        if (entry.first == (int)slices_.size())\n        {\n            // make sure we don't add ourself as a neighbor\n            bool same = true;\n            for (size_t i = 0; i < entry.second.size() && same; ++i)\n                if (entry.second[i] != ridCell[i])\n                    same = false;\n\n            if (!same)\n                neighbors.push_back(gridCellToRid(entry.second));\n        }\n        else\n        {\n            int i = entry.first;\n\n            entry.second[i] -= 1;\n            if (entry.second[i] >= 0)  // don't go out of bounds\n                stack.push(std::make_pair(i + 1, entry.second));\n\n            entry.second[i] += 1;\n            stack.push(std::make_pair(i + 1, entry.second));\n\n            entry.second[i] += 1;\n            if (entry.second[i] < slices_[i])\n                stack.push(std::make_pair(i + 1, entry.second));\n        }\n    }\n}\n", "text_preview": "/********************************************************************* * Software License Agreement (BSD License) * * Copyright (c) 2015, Rice University * All rights reserved. * * Redistribution and use in source and binary forms, with or ", "input_sha256": "a03f5e0a451e72206be92b486e4c06da83dbf8a65e0943bf5225095a7cf4bc63", 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0.9455341049928776, "Sports & Fitness": 0.00011123584990707235, "Vehicles & Transportation": 8.999322296226931e-05, "Travel & Tourism": 0.0001353789228018531, "Other / Uncovered Subject": 0.0005353747965811432, "Unknown / Insufficient Content": 0.0001337432172748197}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0006685366009767927, "Art & Design": 0.0009264864274722548, "Law & Crime": 0.09111593404715003, "Education & Careers": 0.0006795928489810708, "Entertainment & Media": 0.000561165356346587, "Fashion & Beauty": 0.000409635135837454, "Business, Economics & Finance": 0.0011064031236310476, "Food & Dining": 0.0003334769027932034, "Games & Gaming": 0.0008647860384101436, "Electronics & Hardware": 0.000642970433461658, "Health, Healthcare & Wellness": 0.0005591343282026176, "History & Geography": 0.000549427041881745, "Home, Pets & Hobbies": 0.0003340428368851442, "Industry & Production": 0.0004026371818852152, "Language, Literature & Philosophy": 0.0003002977058207395, "Politics & Government": 0.0008879505607380521, "Religion & Spirituality": 0.0005468600805568391, "Science, Mathematics & Engineering": 0.2590272981859189, "Relationships & Community": 0.0001861677311770496, "Software Usage & Applications": 0.0006547892601337222, "Programming & Software Engineering": 0.07821085355099153, "Sports & Fitness": 0.00045523979067943373, "Vehicles & Transportation": 0.00032069701562828913, "Travel & Tourism": 0.0006454561701036563, "Other / Uncovered Subject": 0.0011885230762457865, "NONE": 0.5584216385680911}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10606, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10606, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:33:14.824643+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "2f4d4b19-4ced-49bc-aa05-d12629ac8bf1", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "421bbf7ba685d6c8d0e6e32319af59d0709606d7b25a2bceb5f6b7cd65730bff", "source_metadata": {"uuid": "2f4d4b19-4ced-49bc-aa05-d12629ac8bf1", "repo_name": "ompl/ompl", "relative_path": "src/ompl/geometric/planners/xxl/src/XXLPositionDecomposition.cpp", "category": "ALGO", "algo_rel_score": 0.9926347136497498, "quality_score": 5.149328708648682, "source_meta": {"github_metadata": {"branch": "main", "commit_count": 4948, "repo_created_at": "2014-05-20T14:22:59.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 1776, "forks": 636, "issues": 80, "pull_requests": 28}, "file_meta": {"content_id": "c0e07f579cfb3286e81fcb180d9945d65d600bbd", "size_bytes": 7574, "file_timestamp": 1750610722, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a0b3c9b19da5d02aaa2a32a45d42a93de3ff7c10"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 4948, "repo_created_at": "2014-05-20T14:22:59.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 1776, "forks": 636, "issues": 80, "pull_requests": 28}, "file_meta": {"content_id": "c0e07f579cfb3286e81fcb180d9945d65d600bbd", "size_bytes": 7574, "file_timestamp": 1750610722, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a0b3c9b19da5d02aaa2a32a45d42a93de3ff7c10", "url": "https://github.com/ompl/ompl/blob/a0b3c9b19da5d02aaa2a32a45d42a93de3ff7c10/src/ompl/geometric/planners/xxl/src/XXLPositionDecomposition.cpp"}, "gpt4o_tokens": 1906, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.9534986443097977, "noise_severity": 0.9141828404279381, "missing_content_severity": 0.03683821593846553, "factual_density": 3.3634483822456422, "pretraining_utility": 0.4328645678706189, "math_density": 2.6109388231118333, "code_density": 3.525952774716423, "reasoning_density": 1.345739661114333, "education_level": 2.7104026401082764, "creative_writing_quality": -0.20816046838285915}, "score_applicability": {"education_level": 0.9988781869218355, "creative_writing_quality": 0.0006656262459691592}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/tfoxkustudent/eecs560/blob/71a7fc0893624397b27e1159fbd8970becae2aa2/Fox_Lab3/hash.hpp", "id": "cpp-0-421e3dd9c4a45b158c8940e741f67692", "text": "#ifndef HASHTABLE_HPP\n#define HASHTABLE_HPP\n#include \"linkedList.hpp\"\n#include <string>\n#include <iostream>\n#include <list>\nusing namespace std;\n\nstruct Table\n{\n\tstring name;\n\tint rating;\n\tstring price;\n};\nclass HashTable\n{\n  public:\n\tint mod = 31;\n\tTable* QuadTable= new Table [mod];\n\tTable* DoubleTable = new Table [mod];\n\tint current_load = 0;\n \n\tvoid insert(string x, int y, string z)\n        {\n\tfor (int i = 0; i < x.length(); i++)\n\t{\n\t\tx[i] = tolower(x[i]);\n\t}\n\t\tint i;\n\t\tbool insertQuad = false;\n                bool insertDouble = false;\n\t\tfor (i = 0; i < mod; i++)\n\t\t{\n\t\t\tif (QuadTable[i].name == x)\n\t\t\t{\n\t\t\t\tinsertQuad = true;\n\t\t\t\tcout << \"Quadratic probing: \" << x << \" is duplicated, can't be added to the hash table.\" << endl;\n\t\t\t\t\n\t\t\t}\n\t\t}\n\t\tfor (i = 0; i < mod; i++)\n\t\t{\n\t\t\tif (DoubleTable[i].name == x)\n\t\t\t{\n\t\t\t\tinsertDouble = true;\n\t\t\t\tcout << \"Double hashing: \" << x << \" is duplicated, can't be added to the hash table.\" << endl;\n\t\t\t}\n\t\t}\n\t\tint hx = Hashfunction(x);\n\t\tint j = 0;\n\t\tint R = 29;\n\t\tint hplusx = 0;\n\t\tint hash_value = 0;\n\t\twhile (insertQuad == false){\n\t\t\tif(QuadTable[hx].name == \"\")\n\t\t\t{\n\t\t\t\tQuadTable[hx].name = x;\n\t\t\t\tQuadTable[hx].rating = y;\n\t\t\t\tQuadTable[hx].price = z;\n                \t\tcout << x << \" was inserted successfully at index \" << hx << \" using quadratic probing\" << endl;\n\t\t\t\tinsertQuad = true;\n\t\t\t}\t\n\t\t\telse if (j < 32)\n\t\t\t{\n\t\t\t\tj++;\n\t\t\t\thx = (Hashfunction(x) + j *j) % mod;\n\t\t\t}\n\t\t\telse if (j > 32)\n\t\t\t{\n\t\t\tcout << x << \" could not be inserted using quadratic probing\" << endl;\n\t\t\tinsertQuad = true;\n\t\t\t}\n\t\t\t}\n\t\thx = Hashfunction(x);\n\t\thplusx = R - (hx % R);\n\t\ti = 0;\n\t\twhile (insertDouble == false){\n\t\t\t\n\t\t\tif(DoubleTable[hx].name == \"\")\n\t\t\t{\n\t\t\t\tDoubleTable[hx].name = x;\n\t\t\t\tDoubleTable[hx].rating = y;\n\t\t\t\tDoubleTable[hx].price = z;\n\t\t\t\tcout << x << \" was inserted successfully at index \" << hx << \" using double hashing\" << endl;\n\t\t\t\tinsertDouble = true;\n\t\t\t}\n\t\t\telse if (i < 32)\n\t\t\t{\n\t\t\t\ti++;\n\t\t\t\thx = (hx + i*hplusx) % mod;\n\t\t\t}\n\t\t\telse if (i > 32)\n\t\t\t{\n\t\t\t\tcout << x << \"could not be inserted using double hashing\" << endl;\n\t\t\t\tinsertDouble = true;\n\t\t\t}\n\t\t}\n\t}\n\t\n\n\tvoid Delete(string x)\n\t{\n\t\tbool deleteQuad = false;\n\t\tbool deleteDouble = false;\n\t\tfor (int i = 0; i < mod; i++){\n\t\t\tif (QuadTable[i].name == x){\n\t\t\tdeleteQuad = true;\n\t\t\tQuadTable[i].name = \"\";\n\t\t\tQuadTable[i].rating = 0;\n\t\t\tQuadTable[i].price = \"\";\n\t\t\tcout << x << \" has been successfully deleted from Quadratic Probing Table\" << endl;\t\n\t\t\t}\n\t\t}\n\t\tif (deleteQuad == false)\n\t\t\tcout << x << \" has been failed to delete from Quadratic Probing Table\" << endl;\n\t\tfor (int i = 0; i < mod; i++){\n\t\t\tif (DoubleTable[i].name == x){\n\t\t\t\tdeleteDouble = true;\n\t\t\t\tDoubleTable[i].name = \"\";\n\t\t\t\tDoubleTable[i].rating = 0;\n\t\t\t\tDoubleTable[i].price = \"\";\n\t\t\t\tcout << x << \" has been successfully deleted from Double hashing table\" << endl;\n\t\t\t}\n\t\t}\n\t\tif (deleteDouble == false)\n\t\t\tcout << x << \" has been failed to delete from Double Hashing Table\" << endl;\n              \t\n\n\t\t}\n\tvoid Print()\n\t{\n\t\tcout << \"<OUTPUT FOR QUADRATIC PROBING>\" << endl << endl;\n\t\tfor(int i = 0; i < mod; i ++)\n\t\t{\n\t\t\tcout << \"INDEX[\" << i << \"]:   \";\n\t\t\tcout << QuadTable[i].name << \", \" << QuadTable[i].rating << \", \" << QuadTable[i].price;\n\t\t\tcout << endl;\n\t\t}\n\t\tcout << \"<OUTPUT FOR DOUBLE HASHING>\" << endl << endl;\n                for(int i = 0; i < mod; i ++)\n                {\n                        cout << \"INDEX[\" << i << \"]:   \";\n                        cout << DoubleTable[i].name << \", \" << DoubleTable[i].rating << \", \" << DoubleTable[i].price;\n                        cout << endl;\n                }\n\t}\n\tint Hashfunction(string inserted_key)\n\t{\n\t\tint hash_value = 0;\n\t\tfor( int i = 0; i < inserted_key.length(); i++) \n\t\t{\n\t\t\thash_value += static_cast<int>(inserted_key[i]);\n\t\t\t\n\t\t}\n\t\treturn hash_value % mod;\n\t}\n\t\n\tvoid FindByName(string x)\n\t{\n                bool findQuad = false;\n                bool findDouble = false;\n                for (int i = 0; i < mod; i++){\n                        if (QuadTable[i].name == x){\n                        findQuad = true;\n                        cout << x << \" has been found at location \" << i << \" in the  Quadratic Probing Table\" << endl;\n                        }\n                }\n                if (findQuad == false)\n                        cout << x << \" has not been found in the Quadratic Probing Table\" << endl;\n                for (int i = 0; i < mod; i++){\n                        if (DoubleTable[i].name == x){\n                                findDouble = true;\n                                cout << x << \" has been found at location \" << i << \" in the Double Hashing Table\" << endl;\n                        }\n                }\n                if (findDouble == false)\n                        cout << x << \" has not been found in the Double Hashing Table\" << endl;\n\t}\n\tvoid FindByRating(int rating)\n\t{\n\t\n\t\tlist<string> listofquadnames;\n\t\tlist<string> listofdoublenames;\n\t\tfor (int i = 0; i < mod; i++){\n\t\t\tstring x;\n\t\t\tif (QuadTable[i].rating == rating){\n\t\t\t\tx = QuadTable[i].name;\n\t\t\t\tlistofquadnames.push_back(x);\n\t\t\t}\n\t\t}\n\t\tif (!listofquadnames.empty()){\n\t\t\tcout << \"Quadratic probing: \";\n\t\t\tfor (string str : listofquadnames)\n\t\t\t\tcout << str << \",\";\n\t\t\tcout << endl;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tcout << \"Quadratic probing: No restaurants found by rating: \" << rating << endl;\n\t\t}\n\t\tfor (int i = 0; i < mod; i++){\n                        string x;\n                        if (DoubleTable[i].rating == rating){\n                                x = DoubleTable[i].name;\n                                listofdoublenames.push_back(x);\n                        }\n\t\t}\n                if (!listofdoublenames.empty()){\n                        cout << \"Double Hashing: \";\n                        for (string str : listofdoublenames)\n                                cout << str << \",\";\n                        cout << endl;\n                }\n                else\n                {\n                        cout << \"Double Hashing: No restaurants found by rating: \" << rating << endl;\n                }\n\t}\t\t\n\tvoid SearchByPrice(string price)\n\t{\n\t\tlist<string> listofquadnames;\n                list<string> listofdoublenames;\n                for (int i = 0; i < mod; i++){\n                        string x;\n                        if (QuadTable[i].price == price){\n                                x = QuadTable[i].name;\n                                listofquadnames.push_back(x);\n                        }\n\t\t}\n                if (!listofquadnames.empty()){\n                        cout << \"Quadratic probing: \";\n                        for (string str : listofquadnames)\n                                cout << str << \",\";\n                        cout << endl;\n                }\n                else\n                {\n                        cout << \"Quadratic probing: No restaurants found by price: \" << price << endl;\n                }\n                for (int i = 0; i < mod; i++){\n                        string x;\n                        if (DoubleTable[i].price == price){\n                                x = DoubleTable[i].name;\n                                listofdoublenames.push_back(x);\n                        }\n\t\t}\n                if (!listofdoublenames.empty()){\n                        cout << \"Double Hashing: \";\n                        for (string str : listofdoublenames)\n                                cout << str << \",\";\n                        cout << endl;\n                }\n                else\n                {\n                        cout << \"Double Hashing: No restaurants found by price: \" << price << endl;\n                }\n\t}\t\t\n           \t\n\tvoid Exit()\n\t{\n\t\tdelete[] QuadTable;\n\t\tdelete[] DoubleTable;\n\t\t\n\t\n\t}\n    \n};\n\n#endif\n", "text_preview": "#ifndef HASHTABLE_HPP #define HASHTABLE_HPP #include \"linkedList.hpp\" #include <string> #include <iostream> #include <list> using namespace std; struct Table { string name; int rating; string price; }; class HashTable { public: int mod = 31", "input_sha256": "2a9fa69f72d660c7f94315e48084bbef31ea3f69f51ed18e0a5e4c2a0361b46f", "text_sha256": "421e3dd9c4a45b158c8940e741f6769257ec90ed68be37b0edf068aba42afa16", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 67, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, 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0.7723123986174733, "education_level": 2.114974901127312, "creative_writing_quality": -0.5114503307520636}, "score_applicability": {"education_level": 0.9988698898918723, "creative_writing_quality": 0.000985751207247634}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ompl/ompl/blob/a0b3c9b19da5d02aaa2a32a45d42a93de3ff7c10/src/ompl/multilevel/datastructures/BundleSpaceSequenceImpl.h", "id": "cpp-0-b88a22d8299e131d243f9d5e51c762d0", "text": "/*********************************************************************\n * Software License Agreement (BSD License)\n *\n *  Copyright (c) 2019, University of Stuttgart\n *  All rights reserved.\n *\n *  Redistribution and use in source and binary forms, with or without\n *  modification, are permitted provided that the following conditions\n *  are met:\n *\n *   * Redistributions of source code must retain the above copyright\n *     notice, this list of conditions and the following disclaimer.\n *   * Redistributions in binary form must reproduce the above\n *     copyright notice, this list of conditions and the following\n *     disclaimer in the documentation and/or other materials provided\n *     with the distribution.\n *   * Neither the name of the University of Stuttgart nor the names\n *     of its contributors may be used to endorse or promote products\n *     derived from this software without specific prior written\n *     permission.\n *\n *  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS\n *  \"AS IS\" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT\n *  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS\n *  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE\n *  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,\n *  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,\n *  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;\n *  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER\n *  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT\n *  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN\n *  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE\n *  POSSIBILITY OF SUCH DAMAGE.\n *********************************************************************/\n\n/* Author: <NAME> */\n\n#include <ompl/multilevel/datastructures/PlannerDataVertexAnnotated.h>\n#include <ompl/base/goals/GoalSampleableRegion.h>\n#include <ompl/base/goals/GoalState.h>\n#include <ompl/base/goals/GoalStates.h>\n#include <ompl/util/Exception.h>\n#include <ompl/util/Time.h>\n#include <ompl/multilevel/datastructures/BundleSpaceGraph.h>\n#include <ompl/multilevel/datastructures/Projection.h>\n\ntemplate <class T>\nompl::multilevel::BundleSpaceSequence<T>::BundleSpaceSequence(ompl::base::SpaceInformationPtr si, std::string type)\n  : BaseT(si, type)\n{\n    declareBundleSpaces(true);\n}\n\ntemplate <class T>\nompl::multilevel::BundleSpaceSequence<T>::BundleSpaceSequence(std::vector<ompl::base::SpaceInformationPtr> &siVec,\n                                                              std::string type)\n  : BaseT(siVec, type)\n{\n    declareBundleSpaces(true);\n}\n\ntemplate <class T>\nompl::multilevel::BundleSpaceSequence<T>::BundleSpaceSequence(std::vector<ompl::base::SpaceInformationPtr> &siVec,\n                                                              std::vector<ompl::multilevel::ProjectionPtr> &projVec,\n                                                              std::string type)\n  : BaseT(siVec, type)\n{\n    assert(siVec.size() > 0);\n    assert((siVec.size() - 1) == projVec.size());\n    declareBundleSpaces(false);\n\n    // None projection (from last state to empty)\n    bundleSpaces_.front()->makeProjection();\n    for (unsigned int k = 1; k < bundleSpaces_.size(); k++)\n    {\n        BundleSpace *bk = bundleSpaces_.at(k);\n        bk->setProjection(projVec.at(k - 1));\n        // need to precompute the location helper functions to utilize\n        //\"copyToReals\" inside the projection function\n        bk->getBundle()->setup();\n    }\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::declareBundleSpaces(bool guessProjection)\n{\n    T::resetCounter();\n    for (unsigned int k = 0; k < siVec_.size(); k++)\n    {\n        T *parent = nullptr;\n        if (k > 0)\n            parent = bundleSpaces_.back();\n\n        T *ss = new T(siVec_.at(k), parent);\n        bundleSpaces_.push_back(ss);\n        static_cast<BundleSpace *>(bundleSpaces_.back())->setLevel(k);\n    }\n    stopAtLevel_ = bundleSpaces_.size();\n\n    if (guessProjection)\n    {\n        for (unsigned int k = 0; k < bundleSpaces_.size(); k++)\n        {\n            bundleSpaces_.at(k)->makeProjection();\n        }\n    }\n\n    OMPL_DEBUG(\"Created %d BundleSpace levels (%s).\", siVec_.size(), getName().c_str());\n}\n\ntemplate <class T>\nompl::multilevel::BundleSpaceSequence<T>::~BundleSpaceSequence()\n{\n    for (unsigned int k = 0; k < bundleSpaces_.size(); k++)\n    {\n        if (bundleSpaces_.at(k))\n        {\n            delete bundleSpaces_.at(k);\n        }\n    }\n    bundleSpaces_.clear();\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::setStopLevel(unsigned int level_)\n{\n    if (level_ > bundleSpaces_.size())\n    {\n        stopAtLevel_ = bundleSpaces_.size();\n    }\n    else\n    {\n        stopAtLevel_ = level_;\n    }\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::setFindSectionStrategy(FindSectionType type)\n{\n    for (unsigned int k = 0; k < bundleSpaces_.size(); k++)\n    {\n        BundleSpaceGraph *bsg = dynamic_cast<BundleSpaceGraph *>(bundleSpaces_.at(k));\n        if (bsg != nullptr)\n        {\n            bsg->setFindSectionStrategy(type);\n        }\n    }\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::setup()\n{\n    BaseT::setup();\n    for (unsigned int k = 0; k < stopAtLevel_; k++)\n    {\n        static_cast<BundleSpace *>(bundleSpaces_.at(k))->setup();\n    }\n    currentBundleSpaceLevel_ = 0;\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::clear()\n{\n    BaseT::clear();\n\n    for (unsigned int k = 0; k < bundleSpaces_.size(); k++)\n    {\n        static_cast<BundleSpace *>(bundleSpaces_.at(k))->clear();\n    }\n    currentBundleSpaceLevel_ = 0;\n\n    while (!priorityQueue_.empty())\n        priorityQueue_.pop();\n\n    foundKLevelSolution_ = false;\n}\n\ntemplate <class T>\nompl::base::PlannerStatus\nompl::multilevel::BundleSpaceSequence<T>::solve(const ompl::base::PlannerTerminationCondition &ptc)\n{\n    ompl::time::point t_start = ompl::time::now();\n\n    for (unsigned int k = currentBundleSpaceLevel_; k < stopAtLevel_; k++)\n    {\n        BundleSpace *kBundle = static_cast<BundleSpace *>(bundleSpaces_.at(k));\n\n        foundKLevelSolution_ = false;\n\n        if (priorityQueue_.size() <= currentBundleSpaceLevel_)\n            priorityQueue_.push(kBundle);\n\n        ompl::base::PlannerTerminationCondition ptcOrSolutionFound(\n            [this, &ptc, k] { return ptc || (foundKLevelSolution_ && k < bundleSpaces_.size() - 1); });\n\n        while (!ptcOrSolutionFound())\n        {\n            BundleSpace *jBundle = priorityQueue_.top();\n            priorityQueue_.pop();\n            jBundle->grow();\n\n            bool hasSolution = kBundle->hasSolution();\n            if (hasSolution)\n            {\n                ompl::base::PathPtr sol_k;\n                kBundle->getSolution(sol_k);\n                if (solutions_.size() < k + 1)\n                {\n                    solutions_.push_back(sol_k);\n                    double t_k_end = ompl::time::seconds(ompl::time::now() - t_start);\n                    OMPL_DEBUG(\"Found Solution on Level %d/%d after %f seconds.\", k + 1, stopAtLevel_, t_k_end);\n                    currentBundleSpaceLevel_ = k + 1;\n                    if (currentBundleSpaceLevel_ > (bundleSpaces_.size() - 1))\n                        currentBundleSpaceLevel_ = bundleSpaces_.size() - 1;\n                }\n                else\n                {\n                    solutions_.at(k) = sol_k;\n                }\n                foundKLevelSolution_ = true;\n\n                // add solution to pdef\n                ompl::base::PlannerSolution psol(sol_k);\n                std::string lvl_name = getName() + \" LvL\" + std::to_string(k);\n                psol.setPlannerName(lvl_name);\n\n                kBundle->getProblemDefinition()->clearSolutionPaths();\n                kBundle->getProblemDefinition()->addSolutionPath(psol);\n            }\n\n            bool isInfeasible = kBundle->isInfeasible();\n            if (isInfeasible)\n            {\n                double t_end = ompl::time::seconds(ompl::time::now() - t_start);\n                OMPL_DEBUG(\"Infeasibility detected after %f seconds (level %d).\", t_end, k);\n                return ompl::base::PlannerStatus::INFEASIBLE;\n            }\n            priorityQueue_.push(jBundle);\n        }\n\n        if (!foundKLevelSolution_)\n        {\n            OMPL_DEBUG(\"-- Planner failed finding solution on BundleSpace level %d\", k);\n            return ompl::base::PlannerStatus::TIMEOUT;\n        }\n    }\n\n    ompl::base::PathPtr sol;\n    ompl::base::PlannerSolution psol(sol);\n    static_cast<BundleSpace *>(bundleSpaces_.back())->getProblemDefinition()->getSolution(psol);\n    pdef_->addSolutionPath(psol);\n\n    return ompl::base::PlannerStatus::EXACT_SOLUTION;\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::setProblemDefinition(const ompl::base::ProblemDefinitionPtr &pdef)\n{\n    BaseT::setProblemDefinition(pdef);\n\n    if (siVec_.size() < 1)\n        return;\n\n    pdefVec_.clear();\n    pdefVec_.push_back(pdef);\n    bundleSpaces_.back()->setProblemDefinition(pdef);\n\n    if (siVec_.size() <= 1)\n        return;\n\n    assert(bundleSpaces_.size() == siVec_.size());\n\n    //#########################################################################\n    // Check if goal type is projectable\n    //#########################################################################\n    ompl::base::GoalType type = pdef_->getGoal()->getType();\n    if (!(type == ompl::base::GoalType::GOAL_STATE || type == ompl::base::GoalType::GOAL_STATES))\n    {\n        OMPL_ERROR(\"If you want to use other goal classes than \\\"GoalSampleableRegion\\\", you need to specify them \"\n                   \"manually for each SpaceInformationPtr in the hierarchy.\");\n        throw ompl::Exception(\"Multilevel framework does not support provided goal specs.\");\n    }\n\n    //#########################################################################\n    // Iterate through all levels and project from the last level down\n    //#########################################################################\n    ompl::base::GoalSampleableRegion *goalRegion =\n        static_cast<ompl::base::GoalSampleableRegion *>(pdef_->getGoal().get());\n    double epsilon = goalRegion->getThreshold();\n\n    base::OptimizationObjectivePtr obj = pdef->getOptimizationObjective();\n\n    for (unsigned int k = siVec_.size() - 1; k > 0; k--)\n    {\n        BundleSpace *parent = static_cast<BundleSpace *>(bundleSpaces_.at(k));\n        BundleSpace *child = static_cast<BundleSpace *>(bundleSpaces_.at(k - 1));\n        ompl::base::SpaceInformationPtr siChild = child->getBundle();\n\n        ompl::base::ProblemDefinitionPtr pdefParent = pdefVec_.back();\n\n        ompl::base::ProblemDefinitionPtr pdefChild = std::make_shared<base::ProblemDefinition>(siChild);\n\n        // Project Start State onto lower dimensional quotient space\n        const ompl::base::State *sInitParent = pdefParent->getStartState(0);\n        ompl::base::State *sInitChild = siChild->allocState();\n\n        parent->getProjection()->project(sInitParent, sInitChild);\n        parent->getProjection()->project(sInitParent, sInitChild);\n        pdefChild->addStartState(sInitChild);\n\n        // Now project goal state(s) down\n        if (type == ompl::base::GoalType::GOAL_STATE)\n        {\n            ompl::base::GoalState *goal = static_cast<ompl::base::GoalState *>(pdefParent->getGoal().get());\n\n            const ompl::base::State *sGoalParent = goal->getState();\n            ompl::base::State *sGoalChild = siChild->allocState();\n            parent->getProjection()->project(sGoalParent, sGoalChild);\n            pdefChild->setGoalState(sGoalChild, epsilon);\n        }\n        else if (type == ompl::base::GoalType::GOAL_STATES)\n        {\n            ompl::base::GoalStates *goal = static_cast<ompl::base::GoalStates *>(pdefParent->getGoal().get());\n            unsigned int N = goal->getStateCount();\n\n            ompl::base::GoalStatesPtr goalStates = std::make_shared<ompl::base::GoalStates>(siChild);\n            goalStates->setThreshold(epsilon);\n\n            for (unsigned int j = 0; j < N; j++)\n            {\n                const ompl::base::State *sGoalParent = goal->getState(j);\n                ompl::base::State *sGoalChild = siChild->allocState();\n                parent->getProjection()->project(sGoalParent, sGoalChild);\n                goalStates->addState(sGoalChild);\n            }\n            pdefChild->setGoal(goalStates);\n        }\n\n        child->setProblemDefinition(pdefChild);\n        pdefVec_.push_back(pdefChild);\n    }\n\n    std::reverse(pdefVec_.begin(), pdefVec_.end());\n}\n\ntemplate <class T>\nompl::base::State *ompl::multilevel::BundleSpaceSequence<T>::getTotalState(int baseLevel,\n                                                                           const base::State *baseState) const\n{\n    BundleSpace *Qprev = bundleSpaces_.at(baseLevel);\n    ompl::base::State *s_lift = Qprev->getBundle()->cloneState(baseState);\n\n    for (unsigned int m = baseLevel + 1; m < bundleSpaces_.size(); m++)\n    {\n        BundleSpace *Qm = bundleSpaces_.at(m);\n\n        if (Qm->getProjection()->getCoDimension() > 0)\n        {\n            base::State *s_Bundle = Qm->allocIdentityStateBundle();\n\n            Qm->getProjection()->lift(s_lift, s_Bundle);\n\n            Qprev->getBundle()->freeState(s_lift);\n\n            s_lift = Qm->getBundle()->cloneState(s_Bundle);\n\n            Qm->getBundle()->freeState(s_Bundle);\n\n            Qprev = Qm;\n        }\n    }\n    return s_lift;\n}\n\ntemplate <class T>\nvoid ompl::multilevel::BundleSpaceSequence<T>::getPlannerData(ompl::base::PlannerData &data) const\n{\n    unsigned int Nvertices = data.numVertices();\n    if (Nvertices > 0)\n    {\n        OMPL_ERROR(\"PlannerData has %d vertices.\", Nvertices);\n        throw ompl::Exception(\"cannot get planner data if plannerdata is already populated\");\n    }\n\n    unsigned int K = std::min(solutions_.size() + 1, bundleSpaces_.size());\n    K = std::min(K, stopAtLevel_);\n\n    BundleSpace *Qlast = this->bundleSpaces_.back();\n    for (unsigned int k = 0; k < K; k++)\n    {\n        BundleSpace *Qk = static_cast<BundleSpace *>(bundleSpaces_.at(k));\n        Qk->getPlannerData(data);\n\n        // lift all states into the last bundle space (original state space)\n        // Required for decouplePlannerData() function in PlannerData\n        for (unsigned int vidx = Nvertices; vidx < data.numVertices(); vidx++)\n        {\n            ompl::multilevel::PlannerDataVertexAnnotated &v =\n                static_cast<ompl::multilevel::PlannerDataVertexAnnotated &>(data.getVertex(vidx));\n            v.setLevel(k);\n            v.setMaxLevel(K);\n\n            base::State *s_lift = getTotalState(k, v.getBaseState());\n            v.setTotalState(s_lift, Qlast->getBundle());\n        }\n        Nvertices = data.numVertices();\n    }\n    OMPL_DEBUG(\"Multilevel Graph has %d/%d vertices/edges\", data.numVertices(), data.numEdges());\n}\n", "text_preview": "/********************************************************************* * Software License Agreement (BSD License) * * Copyright (c) 2019, University of Stuttgart * All rights reserved. * * Redistribution and use in source and binary forms, ", "input_sha256": "ed902e87861b5d17699e0cad345b0a34cb5d6f1faa01a6e3aa76cdc2e5057e3b", "text_sha256": "b88a22d8299e131d243f9d5e51c762d03f17687ffd1c564e56bebfbfdf048c79", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 70, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/kipr/opencv/blob/31450d613c0c091c6ad510cf2a42a25edbe01e62/modules/gpu/src/match_template.cpp", "id": "cpp-0-c4ddd6dffda88722a3565a6a21682c89", "text": "/*M///////////////////////////////////////////////////////////////////////////////////////\n//\n//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.\n//\n//  By downloading, copying, installing or using the software you agree to this license.\n//  If you do not agree to this license, do not download, install,\n//  copy or use the software.\n//\n//\n//                           License Agreement\n//                For Open Source Computer Vision Library\n//\n// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.\n// Copyright (C) 2009, <NAME> Inc., all rights reserved.\n// Third party copyrights are property of their respective owners.\n//\n// Redistribution and use in source and binary forms, with or without modification,\n// are permitted provided that the following conditions are met:\n//\n//   * Redistribution's of source code must retain the above copyright notice,\n//     this list of conditions and the following disclaimer.\n//\n//   * Redistribution's in binary form must reproduce the above copyright notice,\n//     this list of conditions and the following disclaimer in the documentation\n//     and/or other materials provided with the distribution.\n//\n//   * The name of the copyright holders may not be used to endorse or promote products\n//     derived from this software without specific prior written permission.\n//\n// This software is provided by the copyright holders and contributors \"as is\" and\n// any express or implied warranties, including, but not limited to, the implied\n// warranties of merchantability and fitness for a particular purpose are disclaimed.\n// In no event shall the Intel Corporation or contributors be liable for any direct,\n// indirect, incidental, special, exemplary, or consequential damages\n// (including, but not limited to, procurement of substitute goods or services;\n// loss of use, data, or profits; or business interruption) however caused\n// and on any theory of liability, whether in contract, strict liability,\n// or tort (including negligence or otherwise) arising in any way out of\n// the use of this software, even if advised of the possibility of such damage.\n//\n//M*/\n\n#include \"precomp.hpp\"\n\nusing namespace cv;\nusing namespace cv::gpu;\nusing namespace std;\n\n#if !defined (HAVE_CUDA) || defined (CUDA_DISABLER)\n\nvoid cv::gpu::matchTemplate(const GpuMat&, const GpuMat&, GpuMat&, int, Stream&) { throw_nogpu(); }\n\n#else\n\nnamespace cv { namespace gpu { namespace device\n{\n    namespace match_template\n    {\n        void matchTemplateNaive_CCORR_8U(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);\n        void matchTemplateNaive_CCORR_32F(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);\n\n        void matchTemplateNaive_SQDIFF_8U(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);\n        void matchTemplateNaive_SQDIFF_32F(const PtrStepSzb image, const PtrStepSzb templ, PtrStepSzf result, int cn, cudaStream_t stream);\n\n        void matchTemplatePrepared_SQDIFF_8U(int w, int h, const PtrStepSz<unsigned long long> image_sqsum, unsigned long long templ_sqsum, PtrStepSzf result,\n            int cn, cudaStream_t stream);\n\n        void matchTemplatePrepared_SQDIFF_NORMED_8U(int w, int h, const PtrStepSz<unsigned long long> image_sqsum, unsigned long long templ_sqsum, PtrStepSzf result,\n            int cn, cudaStream_t stream);\n\n        void matchTemplatePrepared_CCOFF_8U(int w, int h, const PtrStepSz<unsigned int> image_sum, unsigned int templ_sum, PtrStepSzf result, cudaStream_t stream);\n        void matchTemplatePrepared_CCOFF_8UC2(\n            int w, int h,\n            const PtrStepSz<unsigned int> image_sum_r,\n            const PtrStepSz<unsigned int> image_sum_g,\n            unsigned int templ_sum_r,\n            unsigned int templ_sum_g,\n            PtrStepSzf result, cudaStream_t stream);\n        void matchTemplatePrepared_CCOFF_8UC3(\n                int w, int h,\n                const PtrStepSz<unsigned int> image_sum_r,\n                const PtrStepSz<unsigned int> image_sum_g,\n                const PtrStepSz<unsigned int> image_sum_b,\n                unsigned int templ_sum_r,\n                unsigned int templ_sum_g,\n                unsigned int templ_sum_b,\n                PtrStepSzf result, cudaStream_t stream);\n        void matchTemplatePrepared_CCOFF_8UC4(\n                int w, int h,\n                const PtrStepSz<unsigned int> image_sum_r,\n                const PtrStepSz<unsigned int> image_sum_g,\n                const PtrStepSz<unsigned int> image_sum_b,\n                const PtrStepSz<unsigned int> image_sum_a,\n                unsigned int templ_sum_r,\n                unsigned int templ_sum_g,\n                unsigned int templ_sum_b,\n                unsigned int templ_sum_a,\n                PtrStepSzf result, cudaStream_t stream);\n\n\n        void matchTemplatePrepared_CCOFF_NORMED_8U(\n                int w, int h, const PtrStepSz<unsigned int> image_sum,\n                const PtrStepSz<unsigned long long> image_sqsum,\n                unsigned int templ_sum, unsigned long long templ_sqsum,\n                PtrStepSzf result, cudaStream_t stream);\n        void matchTemplatePrepared_CCOFF_NORMED_8UC2(\n                int w, int h,\n                const PtrStepSz<unsigned int> image_sum_r, const PtrStepSz<unsigned long long> image_sqsum_r,\n                const PtrStepSz<unsigned int> image_sum_g, const PtrStepSz<unsigned long long> image_sqsum_g,\n                unsigned int templ_sum_r, unsigned long long templ_sqsum_r,\n                unsigned int templ_sum_g, unsigned long long templ_sqsum_g,\n                PtrStepSzf result, cudaStream_t stream);\n        void matchTemplatePrepared_CCOFF_NORMED_8UC3(\n                int w, int h,\n                const PtrStepSz<unsigned int> image_sum_r, const PtrStepSz<unsigned long long> image_sqsum_r,\n                const PtrStepSz<unsigned int> image_sum_g, const PtrStepSz<unsigned long long> image_sqsum_g,\n                const PtrStepSz<unsigned int> image_sum_b, const PtrStepSz<unsigned long long> image_sqsum_b,\n                unsigned int templ_sum_r, unsigned long long templ_sqsum_r,\n                unsigned int templ_sum_g, unsigned long long templ_sqsum_g,\n                unsigned int templ_sum_b, unsigned long long templ_sqsum_b,\n                PtrStepSzf result, cudaStream_t stream);\n        void matchTemplatePrepared_CCOFF_NORMED_8UC4(\n                int w, int h,\n                const PtrStepSz<unsigned int> image_sum_r, const PtrStepSz<unsigned long long> image_sqsum_r,\n                const PtrStepSz<unsigned int> image_sum_g, const PtrStepSz<unsigned long long> image_sqsum_g,\n                const PtrStepSz<unsigned int> image_sum_b, const PtrStepSz<unsigned long long> image_sqsum_b,\n                const PtrStepSz<unsigned int> image_sum_a, const PtrStepSz<unsigned long long> image_sqsum_a,\n                unsigned int templ_sum_r, unsigned long long templ_sqsum_r,\n                unsigned int templ_sum_g, unsigned long long templ_sqsum_g,\n                unsigned int templ_sum_b, unsigned long long templ_sqsum_b,\n                unsigned int templ_sum_a, unsigned long long templ_sqsum_a,\n                PtrStepSzf result, cudaStream_t stream);\n\n        void normalize_8U(int w, int h, const PtrStepSz<unsigned long long> image_sqsum,\n                          unsigned long long templ_sqsum, PtrStepSzf result, int cn, cudaStream_t stream);\n\n        void extractFirstChannel_32F(const PtrStepSzb image, PtrStepSzf result, int cn, cudaStream_t stream);\n    }\n}}}\n\nusing namespace ::cv::gpu::device::match_template;\n\nnamespace\n{\n\n    // Evaluates optimal template's area threshold. If\n    // template's area is less  than the threshold, we use naive match\n    // template version, otherwise FFT-based (if available)\n    int getTemplateThreshold(int method, int depth)\n    {\n        switch (method)\n        {\n        case CV_TM_CCORR:\n            if (depth == CV_32F) return 250;\n            if (depth == CV_8U) return 300;\n            break;\n        case CV_TM_SQDIFF:\n            if (depth == CV_8U) return 300;\n            break;\n        }\n        CV_Error(CV_StsBadArg, \"getTemplateThreshold: unsupported match template mode\");\n        return 0;\n    }\n\n\n    void matchTemplate_CCORR_32F(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);\n        if (templ.size().area() < getTemplateThreshold(CV_TM_CCORR, CV_32F))\n        {\n            matchTemplateNaive_CCORR_32F(image, templ, result, image.channels(), StreamAccessor::getStream(stream));\n            return;\n        }\n\n        ConvolveBuf convolve_buf;\n        convolve_buf.user_block_size = buf.user_block_size;\n\n        if (image.channels() == 1)\n            convolve(image.reshape(1), templ.reshape(1), result, true, convolve_buf, stream);\n        else\n        {\n            GpuMat result_;\n            convolve(image.reshape(1), templ.reshape(1), result_, true, convolve_buf, stream);\n            extractFirstChannel_32F(result_, result, image.channels(), StreamAccessor::getStream(stream));\n        }\n    }\n\n\n    void matchTemplate_CCORR_8U(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        if (templ.size().area() < getTemplateThreshold(CV_TM_CCORR, CV_8U))\n        {\n            result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);\n            matchTemplateNaive_CCORR_8U(image, templ, result, image.channels(), StreamAccessor::getStream(stream));\n            return;\n        }\n\n        if (stream)\n        {\n            stream.enqueueConvert(image, buf.imagef, CV_32F);\n            stream.enqueueConvert(templ, buf.templf, CV_32F);\n        }\n        else\n        {\n            image.convertTo(buf.imagef, CV_32F);\n            templ.convertTo(buf.templf, CV_32F);\n        }\n        matchTemplate_CCORR_32F(buf.imagef, buf.templf, result, buf, stream);\n    }\n\n\n    void matchTemplate_CCORR_NORMED_8U(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        matchTemplate_CCORR_8U(image, templ, result, buf, stream);\n\n        buf.image_sqsums.resize(1);\n        sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);\n\n        unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];\n        normalize_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));\n    }\n\n\n    void matchTemplate_SQDIFF_32F(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        (void)buf;\n        result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);\n        matchTemplateNaive_SQDIFF_32F(image, templ, result, image.channels(), StreamAccessor::getStream(stream));\n    }\n\n\n    void matchTemplate_SQDIFF_8U(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        if (templ.size().area() < getTemplateThreshold(CV_TM_SQDIFF, CV_8U))\n        {\n            result.create(image.rows - templ.rows + 1, image.cols - templ.cols + 1, CV_32F);\n            matchTemplateNaive_SQDIFF_8U(image, templ, result, image.channels(), StreamAccessor::getStream(stream));\n            return;\n        }\n\n        buf.image_sqsums.resize(1);\n        sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);\n\n        unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];\n\n        matchTemplate_CCORR_8U(image, templ, result, buf, stream);\n        matchTemplatePrepared_SQDIFF_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));\n    }\n\n\n    void matchTemplate_SQDIFF_NORMED_8U(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        buf.image_sqsums.resize(1);\n        sqrIntegral(image.reshape(1), buf.image_sqsums[0], stream);\n\n        unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ.reshape(1))[0];\n\n        matchTemplate_CCORR_8U(image, templ, result, buf, stream);\n        matchTemplatePrepared_SQDIFF_NORMED_8U(templ.cols, templ.rows, buf.image_sqsums[0], templ_sqsum, result, image.channels(), StreamAccessor::getStream(stream));\n    }\n\n\n    void matchTemplate_CCOFF_8U(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        matchTemplate_CCORR_8U(image, templ, result, buf, stream);\n\n        if (image.channels() == 1)\n        {\n            buf.image_sums.resize(1);\n            integral(image, buf.image_sums[0], stream);\n\n            unsigned int templ_sum = (unsigned int)sum(templ)[0];\n            matchTemplatePrepared_CCOFF_8U(templ.cols, templ.rows, buf.image_sums[0], templ_sum, result, StreamAccessor::getStream(stream));\n        }\n        else\n        {\n            split(image, buf.images);\n            buf.image_sums.resize(buf.images.size());\n            for (int i = 0; i < image.channels(); ++i)\n                integral(buf.images[i], buf.image_sums[i], stream);\n\n            Scalar templ_sum = sum(templ);\n\n            switch (image.channels())\n            {\n            case 2:\n                matchTemplatePrepared_CCOFF_8UC2(\n                        templ.cols, templ.rows, buf.image_sums[0], buf.image_sums[1],\n                        (unsigned int)templ_sum[0], (unsigned int)templ_sum[1],\n                        result, StreamAccessor::getStream(stream));\n                break;\n            case 3:\n                matchTemplatePrepared_CCOFF_8UC3(\n                        templ.cols, templ.rows, buf.image_sums[0], buf.image_sums[1], buf.image_sums[2],\n                        (unsigned int)templ_sum[0], (unsigned int)templ_sum[1], (unsigned int)templ_sum[2],\n                        result, StreamAccessor::getStream(stream));\n                break;\n            case 4:\n                matchTemplatePrepared_CCOFF_8UC4(\n                        templ.cols, templ.rows, buf.image_sums[0], buf.image_sums[1], buf.image_sums[2], buf.image_sums[3],\n                        (unsigned int)templ_sum[0], (unsigned int)templ_sum[1], (unsigned int)templ_sum[2],\n                        (unsigned int)templ_sum[3], result, StreamAccessor::getStream(stream));\n                break;\n            default:\n                CV_Error(CV_StsBadArg, \"matchTemplate: unsupported number of channels\");\n            }\n        }\n    }\n\n\n    void matchTemplate_CCOFF_NORMED_8U(\n            const GpuMat& image, const GpuMat& templ, GpuMat& result, MatchTemplateBuf &buf, Stream& stream)\n    {\n        if (stream)\n        {\n            stream.enqueueConvert(image, buf.imagef, CV_32F);\n            stream.enqueueConvert(templ, buf.templf, CV_32F);\n        }\n        else\n        {\n            image.convertTo(buf.imagef, CV_32F);\n            templ.convertTo(buf.templf, CV_32F);\n        }\n\n        matchTemplate_CCORR_32F(buf.imagef, buf.templf, result, buf, stream);\n\n        if (image.channels() == 1)\n        {\n            buf.image_sums.resize(1);\n            integral(image, buf.image_sums[0], stream);\n            buf.image_sqsums.resize(1);\n            sqrIntegral(image, buf.image_sqsums[0], stream);\n\n            unsigned int templ_sum = (unsigned int)sum(templ)[0];\n            unsigned long long templ_sqsum = (unsigned long long)sqrSum(templ)[0];\n\n            matchTemplatePrepared_CCOFF_NORMED_8U(\n                    templ.cols, templ.rows, buf.image_sums[0], buf.image_sqsums[0],\n                    templ_sum, templ_sqsum, result, StreamAccessor::getStream(stream));\n        }\n        else\n        {\n            split(image, buf.images);\n            buf.image_sums.resize(buf.images.size());\n            buf.image_sqsums.resize(buf.images.size());\n            for (int i = 0; i < image.channels(); ++i)\n            {\n                integral(buf.images[i], buf.image_sums[i], stream);\n                sqrIntegral(buf.images[i], buf.image_sqsums[i], stream);\n            }\n\n            Scalar templ_sum = sum(templ);\n            Scalar templ_sqsum = sqrSum(templ);\n\n            switch (image.channels())\n            {\n            case 2:\n                matchTemplatePrepared_CCOFF_NORMED_8UC2(\n                        templ.cols, templ.rows,\n                        buf.image_sums[0], buf.image_sqsums[0],\n                        buf.image_sums[1], buf.image_sqsums[1],\n                        (unsigned int)templ_sum[0], (unsigned long long)templ_sqsum[0],\n                        (unsigned int)templ_sum[1], (unsigned long long)templ_sqsum[1],\n                        result, StreamAccessor::getStream(stream));\n                break;\n            case 3:\n                matchTemplatePrepared_CCOFF_NORMED_8UC3(\n                        templ.cols, templ.rows,\n                        buf.image_sums[0], buf.image_sqsums[0],\n                        buf.image_sums[1], buf.image_sqsums[1],\n                        buf.image_sums[2], buf.image_sqsums[2],\n                        (unsigned int)templ_sum[0], (unsigned long long)templ_sqsum[0],\n                        (unsigned int)templ_sum[1], (unsigned long long)templ_sqsum[1],\n                        (unsigned int)templ_sum[2], (unsigned long long)templ_sqsum[2],\n                        result, StreamAccessor::getStream(stream));\n                break;\n            case 4:\n                matchTemplatePrepared_CCOFF_NORMED_8UC4(\n                        templ.cols, templ.rows,\n                        buf.image_sums[0], buf.image_sqsums[0],\n                        buf.image_sums[1], buf.image_sqsums[1],\n                        buf.image_sums[2], buf.image_sqsums[2],\n                        buf.image_sums[3], buf.image_sqsums[3],\n                        (unsigned int)templ_sum[0], (unsigned long long)templ_sqsum[0],\n                        (unsigned int)templ_sum[1], (unsigned long long)templ_sqsum[1],\n                        (unsigned int)templ_sum[2], (unsigned long long)templ_sqsum[2],\n                        (unsigned int)templ_sum[3], (unsigned long long)templ_sqsum[3],\n                        result, StreamAccessor::getStream(stream));\n                break;\n            default:\n                CV_Error(CV_StsBadArg, \"matchTemplate: unsupported number of channels\");\n            }\n        }\n    }\n}\n\n\nvoid cv::gpu::matchTemplate(const GpuMat& image, const GpuMat& templ, GpuMat& result, int method, Stream& stream)\n{\n    MatchTemplateBuf buf;\n    matchTemplate(image, templ, result, method, buf, stream);\n}\n\n\nvoid cv::gpu::matchTemplate(\n        const GpuMat& image, const GpuMat& templ, GpuMat& result, int method,\n        MatchTemplateBuf &buf, Stream& stream)\n{\n    CV_Assert(image.type() == templ.type());\n    CV_Assert(image.cols >= templ.cols && image.rows >= templ.rows);\n\n    typedef void (*Caller)(const GpuMat&, const GpuMat&, GpuMat&, MatchTemplateBuf&, Stream& stream);\n\n    static const Caller callers8U[] = { ::matchTemplate_SQDIFF_8U, ::matchTemplate_SQDIFF_NORMED_8U,\n                                        ::matchTemplate_CCORR_8U, ::matchTemplate_CCORR_NORMED_8U,\n                                        ::matchTemplate_CCOFF_8U, ::matchTemplate_CCOFF_NORMED_8U };\n    static const Caller callers32F[] = { ::matchTemplate_SQDIFF_32F, 0,\n                                         ::matchTemplate_CCORR_32F, 0, 0, 0 };\n\n    const Caller* callers = 0;\n    switch (image.depth())\n    {\n        case CV_8U: callers = callers8U; break;\n        case CV_32F: callers = callers32F; break;\n        default: CV_Error(CV_StsBadArg, \"matchTemplate: unsupported data type\");\n    }\n\n    Caller caller = callers[method];\n    CV_Assert(caller);\n    caller(image, templ, result, buf, stream);\n}\n\n#endif\n", "text_preview": "/*M/////////////////////////////////////////////////////////////////////////////////////// // // IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. // // By downloading, copying, installing or using the software you agree to ", "input_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/jrl-umi3218/copra/blob/917b6600c99ec6ebaa35083503d298617a3a2c71/src/LMPC.cpp", "id": "cpp-0-3623b8150054392c87dbdc2e4b048609", "text": "/*\n * Copyright 2016-2019 CNRS-UM LIRMM, CNRS-AIST JRL\n */\n\n#include \"LMPC.h\"\n#include \"PreviewSystem.h\"\n#include \"constraints.h\"\n#include \"costFunctions.h\"\n#include <algorithm>\n#include <exception>\n#include <numeric>\n\nnamespace copra {\n\nLMPC::Constraints::Constraints()\n    : nrEqConstr(0)\n    , nrIneqConstr(0)\n{\n}\n\nvoid LMPC::Constraints::updateNr()\n{\n    nrEqConstr = 0;\n    nrIneqConstr = 0;\n\n    auto countConstr = [](auto& spc, int& nreq) {\n        for (auto& sp : spc) {\n            nreq += sp->nrConstr();\n        }\n    };\n\n    countConstr(spEqConstr, nrEqConstr);\n    countConstr(spIneqConstr, nrIneqConstr);\n}\n\nvoid LMPC::Constraints::clear()\n{\n    nrEqConstr = 0;\n    nrIneqConstr = 0;\n    spConstr.clear();\n    spEqConstr.clear();\n    spIneqConstr.clear();\n    spBoundConstr.clear();\n}\n\n/*************************************************************************************************\n *                                             LMPC                                              *\n *************************************************************************************************/\n\nLMPC::LMPC(SolverFlag sFlag)\n    : ps_(nullptr)\n    , sol_(solverFactory(sFlag))\n{\n}\n\nLMPC::LMPC(const std::shared_ptr<PreviewSystem>& ps, SolverFlag sFlag)\n    : sol_(solverFactory(sFlag))\n{\n    initializeController(ps);\n}\n\nvoid LMPC::selectQPSolver(SolverFlag flag)\n{\n    sol_ = solverFactory(flag);\n}\n\nvoid LMPC::useSolver(std::unique_ptr<SolverInterface>&& solver)\n{\n    sol_ = std::move(solver);\n}\n\nvoid LMPC::initializeController(const std::shared_ptr<PreviewSystem>& ps)\n{\n    ps_ = ps;\n    clearConstraintMatrices();\n    updateQPMatrixSize();\n}\n\nbool LMPC::solve()\n{\n    using namespace std::chrono;\n    auto sabTime = high_resolution_clock::now();\n\n    updateSystem();\n    // TODO: Need to be rewrite to minimize building time accross the solvers.\n    // It will be better to directly build all matrices in the solvers.\n    makeQPForm();\n    sol_->SI_problem(static_cast<int>(c_.size()), constraints_.nrEqConstr, constraints_.nrIneqConstr);\n\n    auto sTime = high_resolution_clock::now();\n    bool success = sol_->SI_solve(Q_, c_, Aeq_, beq_, Aineq_, bineq_, lb_, ub_);\n    solveTime_ = duration_cast<duration<double>>(high_resolution_clock::now() - sTime);\n\n    checkDeleteCostsAndConstraints();\n    if (success) {\n        updateResults();\n    }\n\n    solveAndBuildTime_ = duration_cast<duration<double>>(high_resolution_clock::now() - sabTime);\n    return success;\n}\n\nvoid LMPC::inform() const noexcept\n{\n    sol_->SI_inform();\n}\n\ndouble LMPC::solveTime() const noexcept\n{\n    return solveTime_.count();\n}\n\ndouble LMPC::solveAndBuildTime() const noexcept\n{\n    return solveAndBuildTime_.count();\n}\n\nvoid LMPC::addCost(const std::shared_ptr<CostFunction>& costFun)\n{\n    costFun->initializeCost(*ps_);\n    spCost_.emplace_back(costFun);\n}\n\nvoid LMPC::addConstraint(const std::shared_ptr<Constraint>& constr)\n{\n    constr->initializeConstraint(*ps_);\n    addConstraintByType(constr);\n}\n\nvoid LMPC::clearCosts() noexcept\n{\n    spCost_.clear();\n}\n\nvoid LMPC::clearConstraints() noexcept\n{\n    constraints_.clear();\n    clearConstraintMatrices();\n}\n\nvoid LMPC::removeCost(const std::shared_ptr<CostFunction>& costFun)\n{\n    auto rCost = std::find(spCost_.begin(), spCost_.end(), costFun);\n    if (rCost != spCost_.end()) {\n        spCost_.erase(rCost);\n    }\n}\n\nvoid LMPC::removeConstraint(const std::shared_ptr<Constraint>& constr)\n{\n    auto rConstr = std::find(constraints_.spConstr.begin(), constraints_.spConstr.end(), constr);\n    if (rConstr != constraints_.spConstr.end()) {\n        constraints_.spConstr.erase(rConstr);\n        switch (constr->constraintType()) {\n        case ConstraintFlag::Constraint:\n        case ConstraintFlag::EqualityConstraint:\n            constraints_.spEqConstr.erase(std::find(constraints_.spEqConstr.begin(), constraints_.spEqConstr.end(), constr));\n            break;\n        case ConstraintFlag::InequalityConstraint:\n            constraints_.spIneqConstr.erase(std::find(constraints_.spIneqConstr.begin(), constraints_.spIneqConstr.end(), constr));\n            break;\n        case ConstraintFlag::BoundConstraint:\n            constraints_.spBoundConstr.erase(std::find(constraints_.spBoundConstr.begin(), constraints_.spBoundConstr.end(), constr));\n            break;\n        }\n    }\n}\n\n/*\n *  Private methods\n */\n\nvoid LMPC::addConstraintByType(const std::shared_ptr<Constraint>& constr)\n{\n    switch (constr->constraintType()) {\n    case ConstraintFlag::EqualityConstraint: {\n        constraints_.nrEqConstr += constr->nrConstr();\n        // DownCasting to std::shared_ptr<EqIneqConstraint>\n        // This is a safe operation since we know that the object is a derived class of a EqIneqConstraint\n        constraints_.spEqConstr.emplace_back(std::static_pointer_cast<EqIneqConstraint>(constr));\n    } break;\n    case ConstraintFlag::InequalityConstraint: {\n        constraints_.nrIneqConstr += constr->nrConstr();\n        // DownCasting to std::shared_ptr<EqIneqConstraint>\n        // This is a safe operation since we know that the object is a derived class of a EqIneqConstraint\n        constraints_.spIneqConstr.emplace_back(std::static_pointer_cast<EqIneqConstraint>(constr));\n    } break;\n    case ConstraintFlag::BoundConstraint: {\n        // DownCasting to std::shared_ptr<ControlBoundConstraint>\n        // This is a safe operation since we know that the object is a ControlBoundConstraint\n        constraints_.spBoundConstr.emplace_back(std::static_pointer_cast<ControlBoundConstraint>(constr));\n    } break;\n    default:\n        return;\n    }\n    constraints_.spConstr.emplace_back(constr);\n}\n\nvoid LMPC::clearConstraintMatrices()\n{\n    assert(ps_ != nullptr);\n\n    Aineq_.resize(0, ps_->fullUDim);\n    Aeq_.resize(0, ps_->fullUDim);\n    bineq_.resize(0);\n    beq_.resize(0);\n    lb_.setConstant(ps_->fullUDim, -std::numeric_limits<double>::max());\n    ub_.setConstant(ps_->fullUDim, std::numeric_limits<double>::max());\n}\n\nvoid LMPC::updateQPMatrixSize()\n{\n    Q_.resize(ps_->fullUDim, ps_->fullUDim);\n    c_.resize(ps_->fullUDim);\n}\n\nvoid LMPC::updateConstraintMatrixSize()\n{\n    Aeq_.resize(constraints_.nrEqConstr, ps_->fullUDim);\n    beq_.resize(constraints_.nrEqConstr);\n    Aineq_.resize(constraints_.nrIneqConstr, ps_->fullUDim);\n    bineq_.resize(constraints_.nrIneqConstr);\n}\n\nvoid LMPC::updateSystem()\n{\n    // Reset the QP variables\n    Q_.setIdentity();\n    Q_ *= 1e-6; // Ensure that Q is positive definite if no cost functions are used\n    c_.setZero();\n\n    // Update the system\n    if (!ps_->isUpdated) {\n        ps_->updateSystem();\n    }\n\n    // Update the constraints\n    constraints_.updateNr();\n    updateConstraintMatrixSize();\n    for (auto& cstr : constraints_.spConstr) {\n        cstr->update(*ps_);\n    }\n\n    // Update the costs\n    for (auto& sp : spCost_) {\n        sp->update(*ps_);\n    }\n}\n\nvoid LMPC::makeQPForm()\n{\n    for (auto& cost : spCost_) {\n        Q_ += cost->Q();\n        c_ += cost->c();\n    }\n\n    int nrLines = 0;\n    // Get Equality constraints\n    for (auto& cstr : constraints_.spEqConstr) {\n        Aeq_.block(nrLines, 0, cstr->nrConstr(), ps_->fullUDim) = cstr->A();\n        beq_.segment(nrLines, cstr->nrConstr()) = cstr->b();\n        nrLines += cstr->nrConstr();\n    }\n\n    // Get Inequality constraints\n    nrLines = 0;\n    for (auto& cstr : constraints_.spIneqConstr) {\n        Aineq_.block(nrLines, 0, cstr->nrConstr(), ps_->fullUDim) = cstr->A();\n        bineq_.segment(nrLines, cstr->nrConstr()) = cstr->b();\n        nrLines += cstr->nrConstr();\n    }\n\n    // Get Bound constraints\n    nrLines = 0;\n    for (auto& cstr : constraints_.spBoundConstr) {\n        lb_.segment(nrLines, cstr->nrConstr()) = cstr->lower();\n        ub_.segment(nrLines, cstr->nrConstr()) = cstr->upper();\n        nrLines += cstr->nrConstr();\n    }\n}\n\nvoid LMPC::updateResults()\n{\n    control_ = sol_->SI_result();\n    trajectory_ = ps_->Phi * ps_->x0 + ps_->Psi * control_ + ps_->xi;\n}\n\nvoid LMPC::checkDeleteCostsAndConstraints()\n{\n    auto check = [](auto& sp, bool useWarn = false, int delLimit = 2) {\n        for (auto it = sp.begin(); it != sp.end();) {\n            if ((*it).use_count() <= delLimit) {\n                CONSTRAINT_DELETION_WARN(useWarn, \"%s%s%s\", \"A '\", (*it)->name().c_str(),\n                    \"' has been destroyed.\\nIt has been removed from the controller\\n\");\n                it = sp.erase(it);\n            } else {\n                ++it;\n            }\n        }\n    };\n\n    check(constraints_.spConstr, true);\n    check(constraints_.spEqConstr);\n    check(constraints_.spIneqConstr);\n    check(constraints_.spBoundConstr);\n    check(spCost_, true, 1);\n}\n\n} // namespace copra\n", "text_preview": "/* * Copyright 2016-2019 CNRS-UM LIRMM, CNRS-AIST JRL */ #include \"LMPC.h\" #include \"PreviewSystem.h\" #include \"constraints.h\" #include \"costFunctions.h\" #include <algorithm> #include <exception> #include <numeric> namespace copra { LMPC::C", "input_sha256": "46ac9f91d0a0ff930ca3a95479fdc0eff873bc5ecb8619b7fe327603843437d4", "text_sha256": "3623b8150054392c87dbdc2e4b04860983b8f93dde8b7f5ddad6871c3ab1d141", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 72, "input_policy": "text-and-optional-url-v1", "label_policy_version": 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{"github_metadata": {"branch": "master", "commit_count": 271, "repo_created_at": "2018-12-07T04:03:41.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 39, "forks": 10, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "ee22a5442be6437632d5d9e68331c83fe7e4d98a", "size_bytes": 8694, "file_timestamp": 1749048661, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "917b6600c99ec6ebaa35083503d298617a3a2c71"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 271, "repo_created_at": "2018-12-07T04:03:41.000Z", "is_fork": false, "is_org_owned": true, "forked_from": null, "stars": 39, "forks": 10, "issues": 0, "pull_requests": 1}, "file_meta": {"content_id": "ee22a5442be6437632d5d9e68331c83fe7e4d98a", "size_bytes": 8694, "file_timestamp": 1749048661, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "917b6600c99ec6ebaa35083503d298617a3a2c71", "url": "https://github.com/jrl-umi3218/copra/blob/917b6600c99ec6ebaa35083503d298617a3a2c71/src/LMPC.cpp"}, "gpt4o_tokens": 2216, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.9625059638685048, "noise_severity": 0.19396166837694048, "missing_content_severity": 0.03404337757158109, "factual_density": 3.4164798339791687, "pretraining_utility": 0.5285561486605814, "math_density": 2.1510531499845205, "code_density": 3.735250079647617, "reasoning_density": 0.5707313295327553, "education_level": 3.0581432494183654, "creative_writing_quality": -0.11039812221737769}, "score_applicability": {"education_level": 0.9987943085003669, "creative_writing_quality": 0.0012628148025123206}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ertugerdogan/Projects/blob/1805c883a2cba284911514b88220bf7706a9d66e/Algorithm/Algo1/estimator_on_household_power_consumption/main.cpp", "id": "cpp-0-a538d86ad9d93b05c18ed36b0bd57f74", "text": "#include <iostream>\n#include <fstream>\n#include <string>\n#include <vector>\n#include <map>\n#include \"math.h\"\n\nusing namespace std;\n\nbool any_bool = false;\nstring STARTING_DATE;\nstring STARTING_TIME;\nstring ENDING_DATE;\nstring ENDING_TIME;\n\nstruct Data{\n        float priority;\n\n        Data(float);\n        float getpriority();\n\n        Data(const Data &passenger1);\n};\n\n\nclass PriorityQueue\n{\npublic:\n\n    vector<Data> A;\n    vector<Data> A_max;\n    \n    int parent(int);\n    int Left(int) ;\n    int Right(int i);\n    void swap(Data&, Data&);\n    void minheap_down(int);\n    void heap_down(int);\n    void minheap_up(int);\n    void heap_up(int);\n    int size() ;\n    bool empty() ;\n    void push(Data );\n    void minpush(Data );\n    void pop();\n    void minpop();\n    Data top();\n    float top_pri();\n    double mean();\n    double stdev();\n    void heapSort();\n    float getMedian();\n    float getFirstq();\n    float getThirdq();\n    int partition(int,int);\n    void insertionSort();\n\n};\n\n\n\nvoid PriorityQueue::insertionSort()\n{\n   int i,  j;\n   double key;\n   for (i = 1; i < A.size(); i++)\n   {\nkey = A[i].priority;\nj = i - 1;\n\n        while (j >= 0 && A[j].priority > key)\n        {\n            A[j + 1].priority = A[j].priority;\n            j = j - 1;\n        }\n        A[j + 1].priority = key;\n}\n}\n\n\nvoid PriorityQueue::heapSort()\n{\n    int n = A.size();\n    n = n;\n    for (int i = n / 2 - 1; i >= 0; i--)\n    {heap_down(i);}\n \n    for (int i = n - 1; i >= 0; i--) {\n    swap(A[0], A[i]);\n    heap_down(0);\n    }\n    int a = 0;\n}\n\nfloat PriorityQueue::getMedian()\n{   \n    int size=A.size();\n    if(A.size()%2==0){\n        int n=(A.size())/2;\n        return (A[n-1].priority+A[n].priority)/2;\n    }\n    else{\n        int n=(A.size()-1)/2;\n        return (A[n].priority);\n    }\n}\n\n\nfloat PriorityQueue::getFirstq()\n{   \n    int size=A.size();\n    int n=size;\n    int x;\n    if(n%4==0){\n        x=(n)/4;\n        return (A[x-1].priority)*0.25+(A[x+1-1].priority)*0.75;\n    }\n    else if((n-1)%4==0){\n        x=(n-1)/4;\n        return (A[(n+3)/4-1].priority);\n    }\n    else if((n-2)%4==0){\n        x=(n+2)/4;\n        return (A[x].priority*0.25 + A[x-1].priority*0.75);\n    }\n    else if((n-3)%4==0){\n        x=(n-3)/4;\n        double some = (A[x+1-1].priority)*0.50+(A[x+2-1].priority)*0.50;\n        return (A[x+1-1].priority)*0.50+(A[x+2-1].priority)*0.50;\n    }\n}\n\n\nfloat PriorityQueue::getThirdq()\n{\n    int size=A.size();\n    int n=size;\n    int x;\n    if(n%4==0){\n        x=(n)/4;\n        return (A[3*x-1].priority)*0.75+(A[3*x+1-1].priority)*0.25;\n    }\n    else if((n-1)%4==0){\n        x=(n-1)/4;\n        return A[(3*n + 1)/4-1].priority;\n    }\n    else if((n-2)%4==0){\n        x=(3*n+2)/4;\n        return (A[x-1].priority*0.75 + A[x-2].priority*0.25);\n    }\n    else if((n-3)%4==0){\n        x=(n-3)/4;\n        return (A[3*x+2-1].priority)*0.50+(A[3*x+3-1].priority)*0.50;\n    }\n\n}\nData::Data(float priority){\n    this->priority=priority;\n}\n\nfloat Data::getpriority(){\n    return priority;\n};\n\nData::Data(const Data &data1){\n    priority=data1.priority;\n}\n\nint PriorityQueue::size() {\n        return A.size();\n    }\n\nbool PriorityQueue::empty() {\n        return size() == 0;\n    }\n\n\nvoid PriorityQueue::push(Data key)\n    {\n        A.push_back(key);\n        int index = size() - 1;\n        heap_up(index);\n    }\nvoid PriorityQueue::minpush(Data key)\n    {\n        A.push_back(key);\n        int index = size() - 1;\n        minheap_up(index);\n    }\n\nvoid PriorityQueue::pop(){       \n            A[0] = A.back();\n            A.pop_back();\n            heap_down(0);  \n    }\nvoid PriorityQueue::minpop(){       \n            A[0] = A.back();\n            A.pop_back();\n            minheap_down(0);  \n    }\n\nData  PriorityQueue::top()\n    {\n            return A[0];    \n    }\n\nfloat  PriorityQueue::top_pri()\n{\n        return A[0].priority;    \n}\n\nint PriorityQueue::parent(int i) {\n        return (i - 1) / 2;\n    } \n\nint PriorityQueue::Left(int i) {\n        return (2*i + 1);\n    }\n\nint PriorityQueue::Right(int i) {\n        return (2*i + 2);\n    }\n\n void PriorityQueue::swap(Data &a, Data &b){\n        Data temp=b;\n        b=a;\n        a=temp;\n    }\ndouble PriorityQueue::mean(){\n        int size=A.size();\n        double sum=0;\n        double mean = 0;\n        for(int i=0;i<size;i++){\n            sum+=A[i].priority;\n        }\n        mean=sum/size;\n        return mean;\n    }\n\ndouble PriorityQueue::stdev(){\n\n        int size=A.size();\n        double mean = PriorityQueue::mean();\n        double stdev = 0;\n\n        for(int i=0;i<size;i++){\n            stdev+=(A[i].priority-mean)*(A[i].priority-mean)/(size-1);\n        }\n        return sqrt(stdev);\n    } \n\n\n\n\n\nvoid PriorityQueue::heap_down(int i)\n    {\n        int left = Left(i);\n        int right = Right(i);\n        int largest = i;\n\n        if (left < size() && A[left].getpriority() > A[i].getpriority()) {\n            largest = left;\n        }\n        if (right < size() && A[right].getpriority() > A[largest].getpriority()) {\n            largest = right;\n        }\n\n        if (largest != i)\n        {\n            swap(A[i], A[largest]);\n            heap_down(largest);\n        }\n    }   \n\n\n\nvoid PriorityQueue::minheap_down(int i)\n    {\n        int left = Left(i);\n        int right = Right(i);\n        int smallest = i;\n\n        if (left < size() && A[left].getpriority() < A[i].getpriority()) {\n            smallest = left;\n        }\n        if (right < size() && A[right].getpriority() < A[smallest].getpriority()) {\n            smallest = right;\n        }\n\n        if (smallest != i)\n        {\n            swap(A[i], A[smallest]);\n            minheap_down(smallest);\n        }\n    }   \n\n\n  void PriorityQueue::minheap_up(int i)\n    {\n        if (i && A[parent(i)].getpriority() > A[i].getpriority())\n        {\n            swap(A[i], A[parent(i)]);\n            minheap_up(parent(i));\n        }\n    }       \n\n void PriorityQueue::heap_up(int i)\n    {\n        if (i && A[parent(i)].getpriority() < A[i].getpriority())\n        {\n            swap(A[i], A[parent(i)]);\n            heap_up(parent(i));\n        }\n    }    \n\n\nvector<int> read_statistics(){\n    \n    int statistics_num;\n    cin >> statistics_num;\n    \n\n    vector <int> statistic_avaliable;\n    for(int i = 0; i < 7; i++){\n        statistic_avaliable.push_back(0);\n    }\n\n    for(int i = 0; i < statistics_num; i++){\n        string statistic;\n        cin >> statistic;\n\n        if(statistic == \"mean\"){\n            statistic_avaliable[0] = 1;\n        }else if(statistic == \"std\"){\n            statistic_avaliable[1] = 1;\n        }\n        else if(statistic == \"min\"){\n            statistic_avaliable[2] = 1;\n        }\n        else if(statistic == \"firstq\"){\n            statistic_avaliable[3] = 1;\n        }\n        else if(statistic == \"median\"){\n            statistic_avaliable[4] = 1;\n        }\n        else if(statistic == \"thirdq\"){\n            statistic_avaliable[5] = 1;\n        }\n        else if(statistic == \"max\"){\n            statistic_avaliable[6] = 1;\n        }\n    }\n    return statistic_avaliable;\n}\n\nvoid print_func(PriorityQueue Pq, PriorityQueue &Pq_max, vector <int> statistic_avaliable){\n\n    cout << STARTING_DATE << \" ,\" << STARTING_TIME << \" ,\" << ENDING_DATE << \" ,\" << ENDING_TIME;\n\n    if(statistic_avaliable[0] == 1){\n        double mean = Pq.mean();\n        cout << \" ,\" << mean;        \n    }\n    if(statistic_avaliable[1] == 1){\n        double stdev = Pq.stdev();\n        cout << \" ,\" << stdev;    \n    }\n    if(statistic_avaliable[2] == 1){\n        Pq.minheap_down(0);\n        double min = Pq.top_pri();\n        cout << \" ,\" << min;   \n    }\n    if(statistic_avaliable[3] == 1){\n        int size = Pq.A.size();\n\n        //Pq.quickSort(0, size - 1);\n        Pq.insertionSort();\n\n        double firq = Pq.getFirstq();\n        cout << \" ,\" << firq; \n    }\n    if(statistic_avaliable[4] == 1){\n        int size = Pq.A.size();\n        //Pq.quickSort(0, size - 1);\n        Pq.insertionSort();\n\n\n        double median = Pq.getMedian();\n        cout << \" ,\" << median; \n    }\n    if(statistic_avaliable[5] == 1){\n        int size = Pq.A.size();\n        //Pq.quickSort(0, size - 1);\n        Pq.insertionSort();\n\n\n        double thirq = Pq.getThirdq();\n        cout << \" ,\" << thirq; \n    }\n    if(statistic_avaliable[6] == 1){\n        \n        Pq.heap_down(0);\n        double max = Pq_max.top_pri();\n        cout << \" ,\" << max;   \n    }\n    \n    return;\n}\n\nvoid add_value(string value, PriorityQueue &Pq, PriorityQueue &Pq_max, vector <int> statistic_avaliable, string line){\n    \n\n    int value_start_index = 0;\n    int value_end_index = line.find(',');\n    string date = line.substr(value_start_index, value_end_index);\n    value_start_index = value_end_index + 1;\n    value_end_index = line.find(',', value_end_index + 1);\n\n    string time = line.substr(value_start_index, value_end_index - value_start_index);\n    value_start_index = value_end_index + 1;\n    value_end_index = line.find(',', value_end_index + 1);\n\n    float gap = stof(line.substr(value_start_index, value_end_index - value_start_index));\n    value_start_index = value_end_index + 1;\n    value_end_index = line.find(',', value_end_index + 1);\n\n    float grp = stof(line.substr(value_start_index, value_end_index - value_start_index));\n    value_start_index = value_end_index + 1;\n    value_end_index = line.find(',', value_end_index + 1);\n\n    float v = stof(line.substr(value_start_index, value_end_index - value_start_index));\n    value_start_index = value_end_index + 1;\n    value_end_index = line.find(',', value_end_index + 1);\n\n    float gi = stof(line.substr(value_start_index, value_end_index - value_start_index));\n    \n    \n    if(value == \"gap\"){\n    Data temp = Data(gap);\n    Pq.minpush(temp);\n    }else if(value == \"grp\"){\n    Data temp = Data(grp);\n    Pq.minpush(temp);\n    }else if(value == \"v\"){\n    Data temp = Data(v);\n    Pq.minpush(temp);\n    }else if(value == \"gi\"){\n    Data temp = Data(gi);\n    Pq.minpush(temp);\n    }\n    if(statistic_avaliable[6] == 1){\n        if(value == \"gap\"){\n        Data temp = Data(gap);\n        Pq_max.push(temp);\n        }else if(value == \"grp\"){\n        Data temp = Data(grp);\n        Pq_max.push(temp);\n        }else if(value == \"v\"){\n        Data temp = Data(v);\n        Pq_max.push(temp);\n        }else if(value == \"gi\"){\n        Data temp = Data(gi);\n        Pq_max.push(temp);\n        }\n    }\n\n    if(!any_bool){\n        STARTING_DATE = date;\n        STARTING_TIME = time;\n        any_bool = true;\n    }\n    ENDING_DATE = date;\n    ENDING_TIME = time;\n    \n    \n}\n\nvoid read_values(vector <int> statistic_avaliable, PriorityQueue &Pq, PriorityQueue &Pq_max){\n    string line;\n    cin >> line;\n\n    int index = line.find(',');\n    string value = line.substr(0,index);\n    int number_of_lines = stoi(value);\n\n    string important_quantity = line.substr(line.find(',') + 1, line.size() - 1);\n    \n    for(int i = 0; i < number_of_lines; i++){\n        cin >> line;\n        if(line == \"print\"){\n            print_func(Pq, Pq_max, statistic_avaliable);\n        }else if(line == \"add\"){\n        }\n    }\n}\n\nvoid print_to_txt(string output_file_path, PriorityQueue Pq, PriorityQueue &Pq_max, vector <int> statistic_avaliable){\n\n    ofstream output_txt;\n    output_txt.open(output_file_path, ios::app);\n\n    for(int i = 0; i < 7; i++){\n    }\n    \n    output_txt << STARTING_DATE << \",\" << STARTING_TIME << \",\" << ENDING_DATE << \",\" << ENDING_TIME;\n    if(statistic_avaliable[0] == 1){\n        double mean = Pq.mean();\n        output_txt << \",\" << mean;\n    }\n    if(statistic_avaliable[1] == 1){\n        double stdev = Pq.stdev();\n        output_txt << \",\" << stdev;    \n    }\n    if(statistic_avaliable[2] == 1){\n        Pq.minheap_down(0);\n        double min = Pq.top_pri();\n       \n        output_txt << \",\" << min;   \n    }\n    if(statistic_avaliable[3] == 1){\n        int size = Pq.A.size();\n        //Pq.quickSort(0, size - 1);\n        Pq.insertionSort();\n\n\n        double firq = Pq.getFirstq();\n    \n        output_txt << \",\" << firq; \n    }\n    if(statistic_avaliable[4] == 1){\n        int size = Pq.A.size();\n        //Pq.quickSort(0, size - 1);\n        Pq.insertionSort();\n\n\n        double median = Pq.getMedian();\n       \n        output_txt << \",\" << median; \n    }\n    if(statistic_avaliable[5] == 1){\n        int size = Pq.A.size();\n        //Pq.quickSort(0, size - 1);\n        Pq.insertionSort();\n\n\n        double thirq = Pq.getThirdq();\n       \n        output_txt << \",\" << thirq; \n    }\n    if(statistic_avaliable[6] == 1){\n        \n        Pq.heap_down(0);\n        double max = Pq_max.top_pri();\n      \n        output_txt << \",\" << max;   \n    }\n    output_txt << endl;   \n    output_txt.close();\n}\n\nvoid read_txt_file(string file_name, PriorityQueue &Pq, PriorityQueue &Pq_max, string output_file_path){\n\n    ifstream txt_file(file_name);\n    string line;\n    getline(txt_file, line);\n    int statistics_num = stoi(line);\n\n\n    vector <int> statistic_avaliable;\n    for(int i = 0; i < 7; i++){\n        statistic_avaliable.push_back(0);\n    }\n    for(int i = 0; i < statistics_num; i++){\n        string statistic;\n        getline(txt_file, line);\n        statistic = line;\n     \n\n        if(statistic == \"mean\"){\n            statistic_avaliable[0] = 1;\n        }else if(statistic == \"std\"){\n            statistic_avaliable[1] = 1;\n        }\n        else if(statistic == \"min\"){\n            statistic_avaliable[2] = 1;\n        }\n        else if(statistic == \"firstq\"){\n            statistic_avaliable[3] = 1;\n        }\n        else if(statistic == \"median\"){\n            statistic_avaliable[4] = 1;\n        }\n        else if(statistic == \"thirdq\"){\n            statistic_avaliable[5] = 1;\n        }\n        else if(statistic == \"max\"){\n            statistic_avaliable[6] = 1;\n        }\n    }\n\n    getline(txt_file, line);\n    getline(txt_file, line);\n\n    int index = line.find(',');\n    string value = line.substr(0,index);\n    int number_of_lines = stoi(value);\n\n    string important_quantity = line.substr(line.find(',') + 1, line.size() - 1);\n    \n    for(int i = 0; i < number_of_lines; i++){\n        getline(txt_file, line);\n        if(line == \"print\"){\n            print_to_txt(output_file_path, Pq, Pq_max, statistic_avaliable);\n        }else if(line == \"add\"){\n            i++;\n            getline(txt_file, line);\n            add_value(important_quantity, Pq, Pq_max, statistic_avaliable, line);\n        }\n    }\n}\n\n\n\nint main(int argc, char **argv){\n \n    PriorityQueue Pq;\n    PriorityQueue Pq_max;\n    \n    string file_path = argv[1];\n\n    string output_file_path = \"output.txt\";\n    ofstream output_txt(output_file_path);\n    output_txt.close();\n    read_txt_file(file_path, Pq, Pq_max, output_file_path);\n    \n    return 0;\n}", "text_preview": "#include <iostream> #include <fstream> #include <string> #include <vector> #include <map> #include \"math.h\" using namespace std; bool any_bool = false; string STARTING_DATE; string STARTING_TIME; string ENDING_DATE; string ENDING_TIME; stru", "input_sha256": "764a84cbaeb401c6aef0dc67dd02f5c0b3959a0476c7839e2c22f15a007c3cf9", "text_sha256": "a538d86ad9d93b05c18ed36b0bd57f7410c0a521bbe63e9b479ddab5a6103435", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 73, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 3, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.05665649387465252, "noise_severity": 0.04790517245229768, "missing_content_severity": 0.04207421030189432, "factual_density": 3.0793071183946648, "pretraining_utility": 0.38519110082500874, "math_density": 2.808298630111033, "code_density": 3.9175000670082505, "reasoning_density": 1.0338034240657163, "education_level": 2.048899088384922, "creative_writing_quality": -0.6099412830720626}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5411 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010636480556213912, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9959811332898807, "secondary": 0.9786936309335627, "tertiary": 0.9897925671661335, "structured_records": 0.005413324778855091, "procedural_steps": 0.012732103120178584, "worked_solution": 0.009092763179179075, "contextual_dialogue": 8.468346708067662e-06, "content_quality": 0.4275108688862058, "noise_severity": 0.9600790229564186, "missing_content_severity": 0.9649381580817548, "factual_density": 0.456797412423012, "pretraining_utility": 0.44468920298082715, "math_density": 0.35716518188899626, "code_density": 0.9312500558402085, "reasoning_density": 0.13849714661190315, "education_level": 0.8279729127739893, "creative_writing_quality": 0.9987823819088684, "primary_topic": 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"Personal About Page": 1.5663194924421157e-05, "Personal Blog": 1.0584369521331107e-05, "Product Page": 7.692182293426319e-06, "Spam / Ads": 1.0234010097649407e-05, "Structured Data": 0.0006052670046804263, "Technical Writing": 0.996148586069469, "Transcript / Interview": 1.8448028804073805e-05, "Tutorial / How-To Guide": 0.0005249966980447836, "User Reviews": 7.0549273895864265e-06, "Other / Unknown": 0.0022764183829891986}, "secondary": {"Academic Writing": 0.0004971565496830887, "Content Listing": 6.285729378911219e-05, "Creative Writing": 0.00010720669037469551, "Customer Support Page": 3.558948813718663e-05, "Q&A Forum": 3.76110579235687e-05, "Discussion Thread": 2.7208877899892882e-05, "Comment Section": 5.443472880869305e-05, "Missing Content": 0.00017012208337502938, "Knowledge Article": 0.0005586483811557117, "Legal Notices": 0.00010675085271669888, "News Article": 3.914020670815254e-05, "Organizational Announcement": 3.815375562206359e-05, "Nonfiction Writing": 4.866517758608809e-05, "Organizational About Page": 5.047766424066092e-05, "Personal About Page": 4.353391295828104e-05, "Personal Blog": 4.203550616280985e-05, "Product Page": 2.5531189223807545e-05, "Spam / Ads": 3.7230723347169686e-05, "Structured Data": 0.0027410764023980214, "Technical Writing": 0.013207163436736594, "Transcript / Interview": 5.86645287327998e-05, "Tutorial / How-To Guide": 0.002404565556695963, "User Reviews": 2.4779624393142995e-05, "NONE": 0.9795813963113309}, "tertiary": {"Academic Writing": 0.0003978861865488748, "Content Listing": 5.226098935493471e-05, "Creative Writing": 7.822397809902887e-05, "Customer Support Page": 2.4393748200352428e-05, "Q&A Forum": 2.8609607845507235e-05, "Discussion Thread": 1.8725118122760733e-05, "Comment Section": 4.5914141680765625e-05, "Missing Content": 0.00022379284520494604, "Knowledge Article": 0.0004072794464455557, "Legal Notices": 0.00013840898687262251, "News Article": 3.0546409528407776e-05, "Organizational 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/BanBuDu0/hw/blob/3f12656ea12f3739ab9604faf0fc28d9190cd52b/cpp/solution/early_15/commit0409_1.cpp", "id": "cpp-0-9f231485d522f6e98e575f3354fe9c03", "text": "//\n// Created by syj on 2020/4/8.\n// scc的时候不生成子图，直接在子顶点上做\n// 暂时去掉了边上的weight\n// 结果变差了\n// 28.5289\n//\n#include <iostream>\n#include <list>\n#include <vector>\n#include <string>\n#include <fstream>\n#include <set>\n#include <ctime>\n#include <deque>\n#include <unordered_map>\n#include <algorithm>\n\n#pragma comment(linker, \"/STACK:10000000\")\n\nusing namespace std;\n\n\nbool cmp(vector<unsigned int> a, vector<unsigned int> b) {\n    if (a.size() == b.size()) {\n        for (int i = 0; i < a.size(); i++) {\n            if (a[i] == b[i]) {\n                continue;\n            } else {\n                return a[i] < b[i];\n            }\n        }\n    } else {\n        return a.size() < b.size();\n    }\n    return a.size() < b.size();\n}\n\nvoid ccc() {\n    cout << \"a\";\n}\n\nclass FindCycleSolution {\npublic:\n    FindCycleSolution() {\n        time = 0;\n    }\n\n    ~FindCycleSolution() = default;\n\n    void generate_graph(string &file_path) {\n        FILE *file = fopen(file_path.c_str(), \"r\");\n        unsigned int from, to, weight;\n        while (fscanf(file, \"%u,%u,%u\", &from, &to, &weight) != EOF) {\n            graph[from].push_back(to);\n        }\n    }\n\n    void scc() {\n        for (auto &it : graph) {\n            unsigned int vertex = it.first;\n            if (scc_visited.count(vertex) != 0) {\n                continue;\n            }\n            sccUtil(vertex);\n        }\n    }\n\n    void sccUtil(unsigned int vertex) {\n        scc_visited.insert(vertex);\n        visitedTime[vertex] = time;\n        lowTime[vertex] = time;\n        time++;\n        stack.push_front(vertex);\n        onStack.insert(vertex);\n\n        for (auto &adjacent : graph[vertex]) {\n            if (scc_visited.count(adjacent) == 0) {\n                sccUtil(adjacent);\n                if (lowTime[adjacent] < lowTime[vertex]) {\n                    lowTime[vertex] = lowTime[adjacent];\n                }\n            } else if (onStack.count(adjacent) != 0) {\n                if (visitedTime[adjacent] < lowTime[vertex]) {\n                    lowTime[vertex] = visitedTime[adjacent];\n                }\n            }\n        }\n\n        if (visitedTime[vertex] == lowTime[vertex]) {\n            set<unsigned int> stronglyConnectedComponent;\n            unsigned int v;\n            do {\n                v = stack.front();\n                stack.pop_front();\n                onStack.erase(v);\n                stronglyConnectedComponent.insert(v);\n            } while (vertex != v);\n            if (stronglyConnectedComponent.size() > 2) {\n                scc_vertexes.push_back(stronglyConnectedComponent);\n            }\n        }\n    }\n\n    void print_scc() {\n        for (auto &s:scc_vertexes) {\n            if (s.size() > 2) {\n                for (auto &v:s) {\n                    printf(\"%u \", v);\n                }\n                printf(\"\\n\");\n            }\n        }\n    }\n\n    void test_graph() {\n        for (auto &g : scc_vertexes) {\n            cout << \"graph-----------------------------------------\" << endl;\n            for (auto &it : g) {\n                cout << it << \" \";\n            }\n            cout << endl;\n        }\n    }\n\n    void findCyclesInScc() {\n        int i = 0;\n        for (auto &scc_vertex: scc_vertexes) {\n            int j = 0;\n            for (auto &vertex: scc_vertex) {\n                if (j < (scc_vertex.size() - 2)) {\n                    findAllSimpleCycles(vertex, vertex, 0, i);\n                }\n                ++j;\n            }\n            ++i;\n        }\n    }\n\n\n    void findAllSimpleCycles(unsigned int start, unsigned int current, int depth, int subGraph_index) {\n        pointStack.push_front(current);\n        visited.insert(current);\n\n        for (auto &adjacent : graph[current]) {\n            //when depth=6, there 7 points in the pointStack\n            //so just take care about the adjacent is start or not\n            if (adjacent == start) {\n                if (pointStack.size() > 2 && pointStack.size() < 8) {\n                    string temp;\n                    vector<unsigned int> cycle;\n                    for (auto it = pointStack.rbegin(); it != pointStack.rend(); ++it) {\n                        cycle.push_back(*it);\n                    }\n                    result.push_back(cycle);\n                }\n            } else if (visited.count(adjacent) == 0 && depth < 6 && adjacent > start ) {\n                findAllSimpleCycles(start, adjacent, depth + 1, subGraph_index);\n            }\n        }\n\n        visited.erase(current);\n        pointStack.pop_front();\n    }\n\n\n    void output(string &path) {\n        ofstream outfile(path, ios::trunc);\n        vector<vector<unsigned int>> all_res;\n        outfile << result.size() << endl;\n        sort(result.begin(), result.end(), cmp);\n        for (auto &cycle:result) {\n            for (auto &vertex: cycle) {\n                if (vertex == cycle.back()) {\n                    outfile << vertex;\n                } else {\n                    outfile << vertex << \",\";\n                }\n            }\n            outfile << \"\\n\";\n        }\n        outfile.close();\n    }\n\nprivate:\n    unordered_map<unsigned int, int> visitedTime;\n    unordered_map<unsigned int, int> lowTime;\n    set<unsigned int> onStack;\n    deque<unsigned int> stack;\n    set<unsigned int> scc_visited;\n    vector<set<unsigned int>> scc_vertexes;\n    int time;\n\n    set<unsigned int> visited;\n    deque<unsigned int> pointStack;\n    unordered_map<unsigned int, list < unsigned int>> graph;\n    vector<vector<unsigned int>> result;\n};\n\nint main() {\n//    clock_t start, finish;\n//    start = clock();\n//    string data_path = R\"(D:\\hw\\data\\test_data.txt)\";\n//    string linux_path = R\"(/home/syj/Documents/hw/data/test_data.txt)\";\n//    string huawei_path = R\"(/root/hw/data/test_data.txt)\";\n    string iPath = \"/data/test_data.txt\";\n    string oPath = \"/projects/student/result.txt\";\n    string o = \"result.txt\";\n\n    //生成数据\n    FindCycleSolution solution;\n    solution.generate_graph(iPath);\n//    finish = clock();\n//    printf(\"generate_graph: %f ms\\n\", ((double) (finish - start) / CLOCKS_PER_SEC) * 1000);\n//    start = clock();\n\n    //split scc\n    solution.scc();\n//    finish = clock();\n//    printf(\"splic scc: %f ms\\n\", ((double) (finish - start) / CLOCKS_PER_SEC) * 1000);\n//    start = clock();\n\n    //find cycle\n    solution.findCyclesInScc();\n//    finish = clock();\n//    printf(\"findCycles: %f ms\\n\", ((double) (finish - start) / CLOCKS_PER_SEC) * 1000);\n//    start = clock();\n\n    //output\n    solution.output(oPath);\n//    finish = clock();\n//    printf(\"output: %f ms\\n\", ((double) (finish - start) / CLOCKS_PER_SEC) * 1000);\n\n    return 0;\n}\n\n", "text_preview": "// // Created by syj on 2020/4/8. // scc的时候不生成子图，直接在子顶点上做 // 暂时去掉了边上的weight // 结果变差了 // 28.5289 // #include <iostream> #include <list> #include <vector> #include <string> #include <fstream> #include <set> #include <ctime> #include <deque> #", "input_sha256": "02751c568feaccdcab2ca794ffe1e111c8c7560e503508689af7373ebbcc7fbe", "text_sha256": "9f231485d522f6e98e575f3354fe9c031fa3690145797640959df6e48c031b2a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 68, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/chandanlal92/Grasp_Planner/blob/d051c851fd2178804d44f21f4ceb4fc12aab800a/my_pcl/src/example_voxelgrid_v1.cpp", "id": "cpp-0-4fa71601ab16be9b895cd5bccec7db0b", "text": "#include <ros/ros.h>\n// PCL specific includes\n#include <sensor_msgs/PointCloud2.h>\n#include <sensor_msgs/PointCloud.h>\n#include <pcl_conversions/pcl_conversions.h>\n#include <pcl/point_cloud.h>\n#include <pcl/point_types.h>\n#include <pcl/features/normal_3d.h>\n#include <pcl/filters/voxel_grid.h>\n#include <pcl/io/io.h>\n#include <pcl/io/pcd_io.h>\n#include <pcl/features/integral_image_normal.h>\n#include <pcl/visualization/cloud_viewer.h>\n#include \"geometry_msgs/Point.h\"\n#include <message_filters/sync_policies/approximate_time.h>\n#include <message_filters/subscriber.h>\n#include <pcl/octree/octree.h>\n#include <pcl/filters/voxel_grid.h>\n#include \"geometry_msgs/Pose.h\"\n#include \"geometry_msgs/Quaternion.h\"\n#include <boost/format.hpp>\n#include <boost/thread/thread.hpp>\n#include <pcl/common/common_headers.h>\n#include <pcl/visualization/pcl_visualizer.h>\n#include <pcl/console/parse.h>\n#include <pcl/visualization/point_cloud_color_handlers.h>\n#include <vector>\n\nusing namespace message_filters;\n\nros::Publisher pub;\nros::Publisher pub2;\nros::Publisher pub3;\nros::Publisher pub4;\nros::Publisher pub5;\n\nsensor_msgs::PointCloud2 output;\nsensor_msgs::PointCloud2 output2;\npcl::PointCloud<pcl::Normal>::Ptr normals (new pcl::PointCloud<pcl::Normal>);\npcl::PointCloud<pcl::PointXYZ>::Ptr temp_cloud(new pcl::PointCloud<pcl::PointXYZ>);\npcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);\npcl::PointCloud<pcl::PointXYZ>::Ptr cloud_pt ( new pcl::PointCloud<pcl::PointXYZ> );\npcl::PCLPointCloud2* cloud_p2 = new pcl::PCLPointCloud2; \npcl::PCLPointCloud2ConstPtr cloudPtr(cloud_p2);\ngeometry_msgs::Point Pointer;\npcl::PointXYZ searchPoint;\npcl::PointXYZ searchPoint1;\npcl::PointXYZ searchPoint2;\ngeometry_msgs::Quaternion Orientation;\nint count=0;\n\ndouble cx = 9.6762715580208510e+02;\ndouble cy = 5.5317956136661428e+02;\ndouble fx = 1.0628881742777685e+03;\ndouble fy = 1.0620718706590644e+03;\n\n//~ static float cx = 254.878f;\n//~ static float cy = 205.395f;\n//~ static float fx = 365.456f;\n//~ static float fy = 365.456f;\n//~ static float k1 = 0.0905474;\n//~ static float k2 = -0.26819;\n//~ static float k3 = 0.0950862;\n\nboost::mutex m;\n\n\nboost::shared_ptr<pcl::visualization::PCLVisualizer> normalsVis (\n    pcl::PointCloud<pcl::PointXYZ>::ConstPtr cloud, pcl::PointCloud<pcl::Normal>::ConstPtr normals, pcl::PointXYZ center,pcl::PointXYZ center1,pcl::PointXYZ center2, Eigen::Vector3f vector,Eigen::Vector3f vector1,Eigen::Vector3f vector2)\n{\n  // --------------------------------------------------------\n  // -----Open 3D viewer and add point cloud and normals-----\n  // --------------------------------------------------------\n      //~ m.lock();\n //~ pcl::visualization::PointCloudColorHandler<pcl::PCLPointCloud2> ColorHandler;\n   boost::shared_ptr<pcl::visualization::PCLVisualizer> viewer (new pcl::visualization::PCLVisualizer (\"3D Viewer\"));\n       \n   //~ viewer->spinOnce (10);\n        \n//~ cout<< (\"PointCloud with %d data points (%s)  and frame %s.\",\n            //~ cloud->width * cloud->height,\n            //~ pcl::getFieldsList (*cloud).c_str (),\n            //~ cloud->header.frame_id.c_str ());   \n  //~ color_handler.reset (new pcl::visualization::PointCloudColorHandlerCustom<pcl::PCLPointCloud2>(cloud, \"rgb\" ));            \n  viewer->setBackgroundColor (0, 0, 0);\n  viewer->addPointCloud<pcl::PointXYZ> (cloud ,\"sample cloud\");\n  viewer->setPointCloudRenderingProperties (pcl::visualization::PCL_VISUALIZER_POINT_SIZE, 1, \"sample cloud\");\n  //~ viewer->addPointCloudNormals<pcl::PointXYZ, pcl::Normal> (cloud, normals, 10, 0.05, \"normals\");\n  \n   pcl::PointXYZ x_axis (vector(0) + center.x, vector(1) + center.y, vector(2) + center.z);\n   pcl::PointXYZ x_axis1 (vector1(0) + center1.x, vector1(1) + center1.y, vector1(2) + center1.z);\n   pcl::PointXYZ x_axis2 (vector2(0) + center2.x, vector2(1) + center2.y, vector2(2) + center2.z);\n  pcl::PointXYZ y_axis (vector(0) + center.x, vector(1) + center.y, vector(2) + center.z);\n  pcl::PointXYZ y_axis1 (vector1(0) + center1.x, vector1(1) + center1.y, vector1(2) + center1.z);\n  pcl::PointXYZ y_axis2 (vector2(0) + center2.x, vector2(1) + center2.y, vector2(2) + center2.z);\n  pcl::PointXYZ z_axis (vector(0) + center.x, vector(1) + center.y, vector(2) + center.z);\n  pcl::PointXYZ z_axis1 (vector1(0) + center1.x, vector1(1) + center1.y, vector1(2) + center1.z);\n  pcl::PointXYZ z_axis2 (vector2(0) + center2.x, vector2(1) + center2.y, vector2(2) + center2.z);\n  viewer->addLine (center, x_axis, 1.0f, 0.0f, 0.0f, \"major eigen vector\"+1);\n  viewer->addLine (center1, x_axis1, 1.0f, 0.0f, 0.0f, \"major eigen vector1\"+2);\n  viewer->addLine (center2, x_axis2, 1.0f, 0.0f, 0.0f, \"major eigen vector2\"+3);\n  viewer->addLine (center, y_axis, 0.0f, 1.0f, 0.0f, \"middle eigen vector3\"+4);\n  viewer->addLine (center1, y_axis1, 0.0f, 1.0f, 0.0f, \"middle eigen vector4\"+5);\n  viewer->addLine (center2, y_axis2, 0.0f, 1.0f, 0.0f, \"middle eigen vector5\"+6);\n  viewer->addLine (center, z_axis, 0.0f, 0.0f, 1.0f, \"minor eigen vector6\"+7);\n  viewer->addLine (center1, z_axis1, 0.0f, 0.0f, 1.0f, \"minor eigen vector7\"+8);\n  viewer->addLine (center2, z_axis2, 0.0f, 0.0f, 1.0f, \"minor eigen vector8\"+9);\n   viewer->addCoordinateSystem (1.0);\n   //viewer->initCameraParameters ();\n     while(!viewer->wasStopped())\n  {\n    viewer->spinOnce (100);\n    boost::this_thread::sleep (boost::posix_time::microseconds (1000));\n  }\n   return(viewer);\n  //~ m.unlock();\n}\n\n\nvoid cloud_cb(const sensor_msgs::PointCloud2::ConstPtr& cloud_msg)\n    {\n\t\t  \n\t//~ m.lock();\n\t    //ros::NodeHandle nh2;\n        pcl::PointCloud<pcl::PointXYZ> cloud;\n        pcl::PointCloud<pcl::PointXYZ> cloud_partitioned;\n      \n      //  const std::string & cloudname = \"cloud\"; \n\n        pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_pt (new pcl::PointCloud<pcl::PointXYZ>);\n       // pcl::PCLPointCloud2* cloud_p2 = new pcl::PCLPointCloud2; \n      \n        //pcl::PointCloud<pcl::PointXYZ>::Ptr cloud_out (new pcl::PointCloud<pcl::PointXYZ>);\n        pcl_conversions::toPCL(*cloud_msg, *cloud_p2);\n       //cout<<*cloud_p2;\n\n        pcl::fromPCLPointCloud2(*cloud_p2, *cloud_pt);\n        std::vector<int> indices;\n        pcl::removeNaNFromPointCloud(*cloud_pt, *cloud_out, indices);\n        \n        //cout<<*cloud_pt;\n   //  sensor_msgs::PointCloud2 output;\n       // output= *cloud_msg;\n        //pcl::fromROSMsg(*cloud_msg, *cloud_pt);\n        //cout<<*cloud_pt;\n        //~ float resolution = 128.0f;\n        //~ pcl::octree::OctreePointCloudSearch<pcl::PointXYZ> octree (resolution);\n        pcl::KdTreeFLANN<pcl::PointXYZ> kdtree;\n        pcl::KdTreeFLANN<pcl::PointXYZ> kdtree1;\n        pcl::KdTreeFLANN<pcl::PointXYZ> kdtree2;\n\n        //~ octree.setInputCloud (cloud.makeShared());\n        kdtree.setInputCloud(cloud_out);\n         kdtree1.setInputCloud(cloud_out);\n           kdtree2.setInputCloud(cloud_out);\n        //kdtree.addPointsFromInputCloud ();\n\n        //pcl::PointXYZ center_point;\n        //center_point.x = 0 ;\n        //center_point.y = 0.4;\n        //center_point.z = -1.4;\n\n        float radius = 1;//search in sphere of 1 cm           \n        std::vector<int> radiusIdx;\n        std::vector<float> radiusSQDist;\n       \n          Eigen::Vector4f plane_parameters; \n                   float curvature; \n        \n          Eigen::Vector4f plane_parameters1; \n                   float curvature1; \n        \n          Eigen::Vector4f plane_parameters2; \n                   float curvature2; \n        \n     if (kdtree.radiusSearch (searchPoint, radius, radiusIdx, radiusSQDist) > 0)\n   {            \n             cout<<kdtree;\n             cout<<\"YES!\";\n     }                                     \n              //~ if (kdtree1.radiusSearch (searchPoint1, radius, radiusIdx, radiusSQDist) > 0)\n   //~ {\n             //~ cout<<\"YES!\";\n     //~ }       \n         //~ if (kdtree2.radiusSearch (searchPoint2, radius, radiusIdx, radiusSQDist) > 0)\n   //~ {\n             //~ cout<<\"YES!\";\n     //~ }      \n     computePointNormal(*cloud_out,radiusIdx,plane_parameters,curvature); \n     //~ computePointNormal(*cloud_out,radiusIdx,plane_parameters1,curvature1); \n     //~ computePointNormal(*cloud_out,radiusIdx,plane_parameters2,curvature2); \n          cout<<\"param: \"<<plane_parameters<<endl;\n         cout<<\"param: \"<<plane_parameters1<<endl;\n        cout<<\"param: \"<<plane_parameters2<<endl;\n         Orientation.x=(double)plane_parameters[0];\n         Orientation.y=(double)plane_parameters[1];\n         Orientation.z=(double)plane_parameters[2];\n         Orientation.w=(double)plane_parameters[3];\n         pub4.publish(Orientation);\n      Eigen::Quaternionf quat(plane_parameters);\n      Eigen::Vector3f position(searchPoint.x,searchPoint.y,searchPoint.z);\n      Eigen::Vector3f vector;\n      vector(0)=(double)plane_parameters[0];\n      vector(1)=(double)plane_parameters[1];\n      vector(2)=(double)plane_parameters[2];\n      //~ Eigen::Quaternionf quat1(plane_parameters1);\n      //~ Eigen::Vector3f position1(searchPoint1.x,searchPoint1.y,searchPoint1.z);\n      //~ Eigen::Vector3f vector1;\n      //~ vector1(0)=(double)plane_parameters1[0];\n      //~ vector1(1)=(double)plane_parameters1[1];\n      //~ vector1(2)=(double)plane_parameters1[2];\n      //~ Eigen::Quaternionf quat2(plane_parameters2);\n      //~ Eigen::Vector3f position2(searchPoint2.x,searchPoint2.y,searchPoint2.z);\n      //~ Eigen::Vector3f vector2;\n      //~ vector2(0)=(double)plane_parameters2[0];\n      //~ vector2(1)=(double)plane_parameters2[1];\n      //~ vector2(2)=(double)plane_parameters2[2];\n            \n        \n         //pub3.publish(*cloud_out);\n          // m.unlock();\n         \n         //cout<<searchPoint;\n         //cout<<radiusIdx[0];\n       pcl::toROSMsg(*cloud_out, output);\n          pub.publish(output);\n        //output.header.frame_id = \"odom\";\n        //pcl_pub.publish(output);\n      \n       //  m.lock();\n\n   \n     //~ m.unlock();\n\n  pcl::IntegralImageNormalEstimation<pcl::PointXYZ, pcl::Normal> ne;\n  \n       pcl::search::KdTree<pcl::PointXYZ>::Ptr tree (new pcl::search::KdTree<pcl::PointXYZ> ()); \n         ne.setSearchMethod (tree);\n\n  ne.setRadiusSearch (500);\n  ne.setKSearch(0);\n  //~ ne.setNormalEstimationMethod (ne.AVERAGE_3D_GRADIENT);\n  //~ ne.setMaxDepthChangeFactor(0.02f);\n  //~ ne.setNormalSmoothingSize(10.0f);\n  pcl::fromPCLPointCloud2(*cloud_p2,*temp_cloud);\n  ne.setInputCloud(temp_cloud);\n  ne.compute(*normals);\n\n\n      pcl::toROSMsg(*normals,output2);\n      pub3.publish(output2);  \n       \n //}\n   \n\n    // normalsVis(cloud_out,cloud_normals1);\n      pcl::PCLPointCloud2 cloud_filtered;\n  // Perform the actual filtering\n  pcl::VoxelGrid<pcl::PCLPointCloud2> sor;\n  sor.setInputCloud (cloudPtr);\n  sor.setLeafSize (0.1, 0.1, 0.1);\n  sor.filter (cloud_filtered);\n  \n\n  // Convert to ROS data type\n  sensor_msgs::PointCloud2 output5;\n  pcl_conversions::fromPCL(cloud_filtered, output5);\n\n  // Publish the data\n  pub5.publish (output5);\n  //~ normalsVis(*cloud_filtered,normals);\n  //~ normalsVis(temp_cloud,normals,searchPoint,searchPoint1,searchPoint2,vector,vector1,vector2);\n   //~ m.unlock();\n}\n\n\nvoid  normal_estimate_cb(const geometry_msgs::Point::ConstPtr& msg)\n{\n\n  searchPoint.z = (double)(msg->z)/1000.;\n  searchPoint.x =(double)(((msg->x) - cx) * searchPoint.z / fx);\n  searchPoint.y =(double)(((msg->y) - cy) * searchPoint.z / fy);\n  Pointer.x=searchPoint.x;\n  Pointer.y=searchPoint.y;\n  Pointer.z=searchPoint.z;\n   //cout<<searchPoint;\n   pub2.publish(Pointer);\n //~ count++;\n   //cout<<Pointer;\n  \n }\nvoid  normal_estimate_cb1(const geometry_msgs::Point::ConstPtr& msg)\n{\n\n  searchPoint1.z = (double)(msg->z)/1000.;\n  searchPoint1.x =(double)(((msg->x) - cx) * searchPoint1.z / fx);\n  searchPoint1.y =(double)(((msg->y) - cy) * searchPoint1.z / fy);\n  //~ Pointer1.x=searchPoint1.x;\n  //~ Pointer1.y=searchPoint1.y;\n  //~ Pointer1.z=searchPoint1.z;\n   //~ cout<<searchPoint1;\n   //~ pub2.publish(Pointer);\n //~ count++;\n   //cout<<Pointer;\n  \n }\nvoid  normal_estimate_cb2(const geometry_msgs::Point::ConstPtr& msg)\n{\n\n  searchPoint2.z = (double)(msg->z)/1000.;\n  searchPoint2.x =(double)(((msg->x) - cx) * searchPoint2.z / fx);\n  searchPoint2.y =(double)(((msg->y) - cy) * searchPoint2.z / fy);\n  //~ Pointer.x=searchPoint.x;\n  //~ Pointer.y=searchPoint.y;\n  //~ Pointer.z=searchPoint.z;\n   //~ cout<<searchPoint;\n   //~ pub2.publish(Pointer);\n //~ count++;\n   //cout<<Pointer;\n  \n }\n\n\n\n\n\n\nint main (int argc, char** argv)\n{\n  // Initialize ROS\n  ros::init (argc, argv, \"my_pcl\");\n  ros::NodeHandle nh;\n  geometry_msgs::Point search;\n\n  //ros::Publisher pub2;\n /* \n  message_filters::Subscriber<sensor_msgs::PointCloud2> sub_cloud(nh,\"/kinect2/qhd/points\", 1);\n  message_filters::Subscriber<geometry_msgs::Point> sub_point(nh,\"/sim_grasp/point\", 1);\n  sync_policies::ApproximateTime<sensor_msgs::PointCloud2, geometry_msgs::Point> MySyncPolicy;\n  Synchronizer<MySyncPolicy> sync(MySyncPolicy(10), sub_cloud, sub_point);\n  sync.registerCallback(boost::bind(&callback, _1, _2)*/\n  //~ m.lock();\n  pub = nh.advertise<sensor_msgs::PointCloud2> (\"output\", 1);\n  pub2 = nh.advertise<geometry_msgs::Point> (\"output2\", 1);\n  //~ pub6 = nh.advertise<geometry_msgs::Point> (\"output6\", 1);\n  //~ pub7 = nh.advertise<geometry_msgs::Point> (\"output7\", 1);\n   pub3 = nh.advertise< sensor_msgs::PointCloud2> (\"output3\", 1);\n      pub5 = nh.advertise< sensor_msgs::PointCloud2> (\"output5\", 1);\n\n  pub4 = nh.advertise<geometry_msgs::Quaternion> (\"output4\", 1);\n  // Create a ROS subscriber for the input point cloud\n ros::Subscriber pt = nh.subscribe<geometry_msgs::Point> (\"/sim_grasp/points_bat_box\", 1, normal_estimate_cb);\n //~ ros::Subscriber pt_1 = nh.subscribe<geometry_msgs::Point> (\"/sim_grasp/points_staple\", 1, normal_estimate_cb1);\n //~ ros::Subscriber pt_2 = nh.subscribe<geometry_msgs::Point> (\"/sim_grasp/points_test\", 1, normal_estimate_cb2);\n ros::Subscriber sub = nh.subscribe<sensor_msgs::PointCloud2> (\"/kinect2/qhd/points\", 1, cloud_cb);\n cout<<\"Called Cloud_cb\";\n  //ros::Subscriber sub2 = nh.subscribe<sensor_msgs::PointCloud2> (\"/output\", 1, normalsVis);\n//m.unlock();\n  //ros::Subscriber pub_2 = nh.subscribe<sensor_msgs::PointCloud2> (\"/kinect2/qhd/points\", 10, cloud_cb);\n    //~ m.unlock();\n    \n\n\n\n   ros::spin();\n}\n", "text_preview": "#include <ros/ros.h> // PCL specific includes #include <sensor_msgs/PointCloud2.h> #include <sensor_msgs/PointCloud.h> #include <pcl_conversions/pcl_conversions.h> #include <pcl/point_cloud.h> #include <pcl/point_types.h> #include <pcl/feat", "input_sha256": "56ebb1415f82f6ca640997c9060c3cef173a30991cd8b6eb548e13df418e2e06", "text_sha256": "4fa71601ab16be9b895cd5bccec7db0b33058944a9ea8c3cc1de4af6e20eeade", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 75, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.21508878184052022, "noise_severity": 0.5571240319959537, "missing_content_severity": 0.06929970370578314, "factual_density": 2.8567062099109273, "pretraining_utility": 0.22526074305095165, "math_density": 1.7635207443675172, "code_density": 3.904351257913033, "reasoning_density": 0.3224539138150456, "education_level": 2.93035956106643, "creative_writing_quality": -0.6276064292590519}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.00437452637661262, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9951536645708714, "secondary": 0.9867369632059887, "tertiary": 0.9913016863815844, "structured_records": 0.0017949372691709584, "procedural_steps": 0.0073579783371419604, "worked_solution": 0.005591441264173463, "contextual_dialogue": 7.828271468329278e-06, "content_quality": 0.4158864579925513, "noise_severity": 0.5357299733367051, "missing_content_severity": 0.9422502469118474, "factual_density": 0.36049992311036483, "pretraining_utility": 0.4794138348067819, "math_density": 0.30028671447158906, "code_density": 0.9202927149275276, "reasoning_density": 0.7312884051541286, "education_level": 0.5564121362072931, "creative_writing_quality": 0.9991737196269108, "primary_topic": 0.9633774977207819, "secondary_topic": 0.5237225921416154}, "probability_margin": {"primary": 0.9927414938921951, "secondary": 0.9811504945105124, "tertiary": 0.9881047016386941, "education_level": 0.4211442125768883, "creative_writing_quality": 0.9990697508018178, "primary_topic": 0.9341670637754865, "secondary_topic": 0.21372517113547568}, "raw_probabilities": {"primary": {"Academic Writing": 4.739741353637781e-05, "Content Listing": 3.1024544091763736e-05, "Creative Writing": 2.1555365703453752e-05, "Customer Support Page": 1.0321833026901784e-05, "Q&A Forum": 1.3765023097723612e-05, "Discussion Thread": 9.851566753379329e-06, "Comment Section": 1.4708940812659276e-05, "Missing Content": 3.687050124927236e-05, "Knowledge Article": 9.810618928628707e-05, "Legal Notices": 2.8582321407886327e-05, "News Article": 5.972774754044433e-06, "Organizational Announcement": 1.1399417122927484e-05, "Nonfiction Writing": 1.3240957166099361e-05, "Organizational About Page": 1.3284606667023412e-05, "Personal About Page": 1.3382754583065974e-05, "Personal Blog": 1.0105942819516138e-05, "Product Page": 8.581011647786105e-06, "Spam / Ads": 1.1668661469200288e-05, "Structured Data": 0.00016208036182706852, "Technical Writing": 0.9953555952137517, "Transcript / Interview": 1.552068448970096e-05, "Tutorial / How-To Guide": 0.0026141013215565524, "User Reviews": 7.745547272203873e-06, "Other / Unknown": 0.001445137045907296}, "secondary": {"Academic Writing": 7.321252202029544e-05, "Content Listing": 3.680194900958689e-05, "Creative Writing": 6.893613959412789e-05, "Customer Support Page": 2.68617114408472e-05, "Q&A Forum": 2.017043949968105e-05, "Discussion Thread": 1.6143839575733562e-05, "Comment Section": 4.173364118297503e-05, "Missing Content": 0.00016531645710052013, "Knowledge Article": 0.00030027797116378833, "Legal Notices": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/sagargoyal96/MPI-sparse-matrix-sorting/blob/60f59a79fc7b973a21f1515591d887109227edc0/code.cpp", "id": "cpp-0-468ef4ffe90717e74cb7aa38c39d9243", "text": "#include <mpi.h>\n#include <iostream>\n#include <vector>\n#include <utility>\n#include <fstream>\n#include <mpi.h>\n#include <string>\n#include <cstring>\n#include <sstream>\n#include <algorithm>\n#include <limits>\n#include <cmath>\n#include <stdio.h>\n#include <sys/stat.h>\n#include <float.h>\n#include <omp.h>\n#include <cstdlib>\n\nusing namespace std;\n\ntypedef struct node\n{\n\tint r;\n\tint c;\n\tint valint;\n\tfloat valfloat;\n}node;\n\n\nbool structrowcomparator(node a, node b)\n{\n\t// check equal to case\n\tif(a.r!=b.r)\n\t\treturn a.r<b.r;\n\telse \n\t\treturn a.c<b.c;\n}\n\nbool structcolcomparator(node a, node b)\n{\n\t// check equal to case\n\tif(a.c!=b.c)\n\t\treturn a.c<b.c;\n\telse \n\t\treturn a.r<b.r;\n}\n\nint intcomparator(const void* a, const void* b)\n{\n\t// check equal to case\n\tint temp= *(((int*)a) + 1) ;\n\tint temp1=*(((int*)b) + 1 ) ;\n\t// cout<<temp<<\"ddddddddd\"<<endl;\n\n\t// cout<<temp[0]<<\"    qwertyuio     \"<<temp[1] <<endl;\n\n\tif(temp<temp1) return -1 ;\n\tif(temp>temp1) return 1 ;\n\treturn -1 ;\n\n}\n\nint floatcomparator(const void* a, const void* b)\n{\n\t// check equal to case\n\tfloat temp= *(((float*)a) + 1) ;\n\tfloat temp1=*(((float*)b) + 1) ;\n\t// cout<<temp<<\"ddddddddd\"<<endl;\n\n\t// cout<<temp[0]<<\"    qwertyuio     \"<<temp[1] <<endl;\n\n\tif(temp<temp1) return -1 ;\n\tif(temp>temp1) return 1 ;\n\treturn -1 ;\n\n}\n\n// int colcomparator(const void* a, const void* b)\n// {\n// \t// check equal to case\n// \tfloat* temp=(float*)a;\n// \tfloat* temp1=(float*)b;\n// \t// cout<<temp<<\"ddddddddd\"<<endl;\n\n// \t// cout<<temp[0]<<\"    qwertyuio     \"<<temp[1] <<endl;\n\n// \treturn temp[1]-temp1[1];\n// \t// cout<<temp[0]<<endl;\n// \t// cout<<\"qwertyui\"<<endl;\n\n\n// \treturn 1;\n// \t// if(a.r!=b.r)\n// \t// \treturn a.r<b.r;\n// \t// else \n// \t// \treturn a.c<b.c;\n// }\n\n\nint main(int argc, char *argv[]) \n{\n\tint num_procs, pid, i=0; \n\tfloat* recv_data;\n\tint* recv_rc;\n\tint* recv_cr;\n\tint* rc_data;\n\tint* cr_data;\n\tlong fil_sz=0;\n\tint flag=0;\n\t// vector< vector<int> > col_inrows;\n\t// vector< vector<int> > row_incols;\n\n\tint* size_ofrows;\n\tint* size_ofcols;\n\tfloat* all_data;\n\tfloat* colrecv_data;\n\tfloat* colall_data;\n\tvector<node> all_nodes;\n\n\tMPI_Init(&argc,&argv);\n\tMPI_Comm_size(MPI_COMM_WORLD,&num_procs);\n\tMPI_Comm_rank(MPI_COMM_WORLD, &pid);\n\t// MPI_Get_processor_name(proc_name, &name_len);\n\n\tfloat* sorted_cols;\n\tfloat* sorted_rows;\n\t// printf(\"Hello World from processor %d out of %d, executing on %s\\n\",pid, num_procs, proc_name);\n\n\t// First Process - Input & Output / Distribute Data & Receive Results / Compute\n\tint num_entries=0;\n\tint* send_count=(int*)malloc(num_procs*sizeof(int));\n\tint * displ_arr=(int*)malloc(num_procs*sizeof(int));\n\tint* colsend_count=(int*)malloc(num_procs*sizeof(int));\n\tint * coldispl_arr=(int*)malloc(num_procs*sizeof(int));\n\t\n\n\tint rows=0, columns=0;\n\n\tif ( pid == 0 ) \n\t{\n\t\tsize_t vecLen;\n\t\tchar *vec=NULL; char *colI=NULL,*rowI=NULL;\n\n\t\tFILE *inputMat;\n\t\tinputMat = fopen(argv[1],\"rb\");\n\n\t\t// first make a struct and sort in row major form\n\n\t\tfseek (inputMat , 0 , SEEK_END);\n\t\tfil_sz = ftell (inputMat);\n\t\trewind (inputMat);\n\t\tnum_entries=fil_sz/16;\n\t\t\n\n\t\tfor (i = 0; i < num_entries; i++) \n\t\t{\n\t\t\t// getMatline(&vec,&rowI,&colI,&vecLen,inputMat);\n\n\t\t\tint* row_n=(int*) malloc(4);\n\t\t\tint* col_n=(int*) malloc(4);\n\t\t\tint* valint=(int*) malloc(4);\n\t\t\tfloat* valfloat=(float*) malloc(4);\n\n\t\t\tfread (row_n,4,1,inputMat);\n\t\t\tfread (col_n,4,1,inputMat);\n\t\t\tfread (valint,4,1,inputMat);\n\t\t\tfread (valfloat,4,1,inputMat);\n\n\t\t\t// assumed in row major form\n\t\t\tnode temp;\n\n\t\t\ttemp.valint = (valint[0]) ;\n\t\t\ttemp.valfloat = (valfloat[0]);\n\t\t\ttemp.c = col_n[0];\n\t\t\ttemp.r = row_n[0];\n\n\t\t\tif(temp.c>columns)\n\t\t\t\tcolumns=temp.c;\n\t\t\tif(temp.r>rows)\n\t\t\t\trows=temp.r;\n\n\t\t\t// if (row_n[0] > prevRowI)\n\t\t\t// \trowptr[j++] = i;  \n\t\t\t// prevRowI = row_n[0];\n\t\t\tall_nodes.push_back(temp);\n\n\t\t\tfree(row_n);\n\t\t\tfree(col_n);\n\t\t\tfree(valint);\n\t\t\tfree(valfloat);\n\t\t}\n\t\tfclose(inputMat);\n\n\n\t\trows++;\n\t\tcolumns++;\n\n\t\trows=(ceil((rows*1.0f)/num_procs))*num_procs;\n\t\tcolumns=(ceil((columns*1.0f)/num_procs))*num_procs;\n\n\n\t\t// cout<<rows<<endl<<columns<<endl<<endl<<endl<<endl;\n\n\t\tsort(all_nodes.begin(),all_nodes.end(), structrowcomparator);\n\t\t// for(int i=0;i<num_entries;i++)\n\t\t// {\n\t\t// \tcout<<all_nodes[i].r<<\"  \"<<all_nodes[i].c<<\"  \"<<all_nodes[i].valint<<\"  \"<<all_nodes[i].valfloat<<endl;\n\t\t// }\n\n\t\t\n\t\t// col_inrows.resize(rows);\n\t\t// row_incols.resize(columns);\n\n\t\tsize_ofrows=(int*)calloc(rows,sizeof(int));\n\t\tsize_ofcols=(int*)calloc(columns,sizeof(int));\n\t\t\n\t\t// for(int i=0;i<num_entries;i++)\n\t\t// {\n\t\t// \tcol_inrows[all_nodes[i].r].push_back(all_nodes[i].c);\n\t\t// \t// cout<<all_nodes[i].r<<\" \"<<all_nodes[i].c<<endl;\n\t\t// \trow_incols[all_nodes[i].c].push_back(all_nodes[i].r);\n\t\t// \t// cout<<all_nodes[i].c<<\" \"<<all_nodes[i].r<<endl;\n\t\t// }\n\t\t// exit(1);\n\n\t\t\n\n\n\t\tfor(int i=0;i<num_entries;i++)\n\t\t{\n\t\t\tsize_ofrows[all_nodes[i].r]++;\n\t\t\tsize_ofcols[all_nodes[i].c]++;\n\t\t\t// cout<<col_inrows[i].size()<<endl;\n\n\t\t}\n\t\t// exit(1);\n\n\t\t// cout<<endl<<endl;\n\t\t\n\n\t\t// for(int i=0;i<columns;i++)\n\t\t// {\n\t\t\t\n\t\t// \t// cout<<row_incols[i].size()<<endl;\n\t\t// }\n\n\t\tint row_tosend=ceil((rows*1.0f)/num_procs);\n\t\tint col_tosend=ceil((columns*1.0f)/num_procs);\n\n\t\t// cout<<\"before looper\"<<endl;\n\n\t\t// int row_tosend=ceil((rows*1.0f)/num_procs);\n\n\t\t// if(pid==0)\n\t\t// {\n\t\tall_data=(float*)malloc(2*sizeof(float)*num_entries);\n\t\trc_data=(int*)malloc(2*sizeof(int)*num_entries);\n\t\t// }\n\n\t\tfor(int i=0;i<num_procs;i++)\n\t\t{\n\t\t\tsend_count[i]=0;\n\t\t}\n\n\t\tfor(int i=0;i<rows;i++)\n\t\t{\n\t\t\tsend_count[i/row_tosend]+=size_ofrows[i]*2;\n\t\t}\n\n\t\tdispl_arr[0]=0;\n\n\t\tfor(int i=1;i<num_procs;i++)\n\t\t{\n\t\t\tdispl_arr[i]=displ_arr[i-1]+send_count[i-1];\n\t\t}\n\n\n\t\tfor(int i=0;i<num_procs;i++)\n\t\t{\n\t\t\tcolsend_count[i]=0;\n\t\t}\n\n\t\t// cout<<\"coltosend= \"<<col_tosend<<endl<<endl;\n\t\tfor(int i=0;i<columns;i++)\n\t\t{\n\t\t\tcolsend_count[i/col_tosend]+=size_ofcols[i]*2;\n\t\t}\n\n\t\tcoldispl_arr[0]=0;\n\n\t\tfor(int i=1;i<num_procs;i++)\n\t\t{\n\t\t\tcoldispl_arr[i]=coldispl_arr[i-1]+colsend_count[i-1];\n\t\t}\n\n\n\t}\n\n\t\t\n\tMPI_Bcast(send_count,num_procs,MPI_INT,0,MPI_COMM_WORLD);\n\tMPI_Bcast(colsend_count,num_procs,MPI_INT,0,MPI_COMM_WORLD);\n\n\tfor(int looper=0;looper<4;looper++)\n\t{\n\t\tif(pid==0)\n\t\t{\n\t\t\t\n\t\t\tfor(int i=0;i<2*num_entries;i+=2)\n\t\t\t{\n\t\t\t\tint ii = all_nodes[i/2].valint ;\n\t\t\t\tint* ptr = (int*) (all_data+i) ;\n\t\t\t\t*ptr = ii ;\n\n\t\t\t\tall_data[i+1]=all_nodes[i/2].valfloat;\n\t\t\t}\n\n\n\t\t\tfor(int i=0;i<2*num_entries;i+=2)\n\t\t\t{\n\t\t\t\trc_data[i]=all_nodes[i/2].r;\n\t\t\t\trc_data[i+1]=all_nodes[i/2].c;\n\t\t\t}\n\n\t\t\t\n\t\t\trecv_data=(float*)malloc(send_count[pid]*sizeof(float));\n\t\t\t// recv_data=(float*)malloc(10000000*sizeof(float));\n\t\t\trecv_rc=(int*)malloc(send_count[pid]*sizeof(int));\n\t\t\t// recv_rc=(int*)malloc(10000000*sizeof(int));\n\t\t\t// cout<<\"pid 0 \"<<endl;\n\t\t\t\n\t\t\t// cout<<\"after bcast for 0 \"<<pid<<endl;\n\t\t\tMPI_Scatterv(all_data,send_count,displ_arr,MPI_FLOAT,recv_data,send_count[pid],MPI_FLOAT,0,MPI_COMM_WORLD);\n\t\t\t// cout<<\"after scatter1 for 0 \"<<pid<<endl;\n\t\t\tMPI_Scatterv(rc_data,send_count,displ_arr,MPI_INT,recv_rc,send_count[pid],MPI_INT,0,MPI_COMM_WORLD);\n\t\t\t// cout<<\"after scatterv for 0 \"<<pid<<endl;\n\t\t\t// for(int i=0;i<send_count[pid];i++)\n\t\t\t// {\n\t\t\t// \tcout<<recv_data[i]<<\"   \";\n\t\t\t// }\n\t\t\t// cout<<endl;\n\n\n\t\t}\n\n\t\t// cout<<\"reached after pid=0\"<<pid<<endl;\n\n\n\t\tif(pid!=0)\n\t\t{\n\t\t\t// cout<<\"pid!=0  \"<<pid<<endl;\n\t\t\t// MPI_Bcast(send_count,num_procs,MPI_INT,0,MPI_COMM_WORLD);\n\t\t\t// cout<<\"after bcast \"<<pid<<endl;\n\t\t\trecv_data=(float*)malloc(send_count[pid]*sizeof(float));\n\t\t\t// recv_data=(float*)malloc(10000000*sizeof(float));\n\t\t\t// float* temp=(float*)malloc(1);\n\t\t\t// int* tempi=(int*)malloc(1);\n\t\t\tMPI_Scatterv(NULL,send_count,NULL,MPI_FLOAT,recv_data,send_count[pid],MPI_FLOAT,0,MPI_COMM_WORLD);\n\t\t\t// cout<<\"after scatterv1 \"<<pid<<endl;\n\t\t\trecv_rc=(int*)malloc(send_count[pid]*sizeof(int));\n\t\t\t// recv_rc=(int*)malloc(10000000*sizeof(int));\n\t\t\tMPI_Scatterv(NULL,send_count,NULL,MPI_INT,recv_rc,send_count[pid],MPI_INT,0,MPI_COMM_WORLD);\n\t\t\t// cout<<\"after scatterv2 \"<<pid<<endl;\n\n\t\t}\n\t\t// cout<<\"reached after pid!=0\"<<pid<<endl;\n\n\t\t// MPI_Barrier(MPI_COMM_WORLD);\n\n\t\t// cout<<\"reached after 1st barrier\"<<endl;\n\n\n\n\t\tvector<int> elem_count;\n\t\tint rownum=recv_rc[0];\n\t\tint row_count=0;\n\t\t// calculates num of elements\n\t\t// cout<<rownum<<endl;\n\t\tfor(int i=0;i<send_count[pid];i+=2)\n\t\t{\n\t\t\t// cout<<recv_rc[i]<<\"ee\"<<endl;\n\t\t\tif(recv_rc[i]!=rownum)\n\t\t\t{\n\t\t\t\telem_count.push_back(row_count);\n\t\t\t\t// cout<<row_count<<\"yuy\"<<endl;\n\t\t\t\trownum=recv_rc[i];\n\t\t\t\trow_count=1;\n\t\t\t\tif(i==send_count[pid]-1 || i==send_count[pid]-2)\n\t\t\t\t{\n\t\t\t\t\telem_count.push_back(row_count);\n\t\t\t\t}\n\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\trow_count++;\n\t\t\t\tif(i==send_count[pid]-1 || i==send_count[pid]-2)\n\t\t\t\t{\n\t\t\t\t\telem_count.push_back(row_count);\n\t\t\t\t\t// cout<<row_count<<\"yuy\"<<endl;\n\t\t\t\t}\t\n\t\t\t}\n\t\t}\n\n\n\t\tint num_rows=elem_count.size();\n\t\t// cout<<num_rows<<endl<<endl<<endl<<endl;\n\t\tint* elem_count_prefix=(int*)malloc(sizeof(int)*num_rows);\n\t\telem_count_prefix[0]=0;\n\t\tfor(int i=1;i<num_rows;i++)\n\t\t{\n\t\t\telem_count_prefix[i]=elem_count_prefix[i-1]+elem_count[i-1]*2;\n\t\t}\n\n\n\n\t\t#pragma omp parallel for\n\t\t\tfor(int i=0;i<num_rows;i++)\n\t\t\t{\n\t\t\t\t// cout<<\"temmmmmmm\"<<endl;\n\t\t\t\t// cout<<elem_count_prefix[i]<<\" \"<<pid<< endl;\n\t\t\t\t// cout<<elem_count[i]<<\" \"<<pid<<endl;\n\t\t\t\tqsort (recv_data + elem_count_prefix[i], elem_count[i], 2*sizeof(int), floatcomparator);\n\t\t\t}\n\n\t\tint num_objects=0;\n\t\tfor(int i=0;i<num_procs;i++)\n\t\t{\n\t\t\tnum_objects+=send_count[i];\n\t\t}\n\n\t\tif(pid==0)\n\t\t{\n\t\t\tsorted_rows=(float*)malloc(sizeof(float)*num_objects);\n\t\t}\n\t\t// sorted_rows=(float*)malloc(sizeof(float)*num_objects);\n\n\t\tMPI_Gatherv(recv_data, send_count[pid], MPI_FLOAT,\n\t\t\tsorted_rows, send_count, displ_arr,\n\t                MPI_FLOAT, 0, MPI_COMM_WORLD);\n\n\t\t// cout<<\"reached after 1st gatherv\"<<endl;\n\n\t\t// MPI_Barrier(MPI_COMM_WORLD);\n\t\t// cout<<\"reached after 2nd barrrier\"<<endl;\n\n\t\t// printing....................\n\t\t// if(pid==0)\n\t\t// {\n\t\t// \tfor(int i=0;i<num_objects;i+=2)\n\t\t// \t{\n\t\t\t\n\t\t// \t\tcout<<pid<<\": \"<<rc_data[i]<<\"  \"<<rc_data[i+1]<<\"->  \"<<sorted_rows[i]<<\"  \"<<sorted_rows[i+1]<< endl;\n\t\t// \t}\t\n\t\t// }\n\n\t\t// here all the data  is in the sorted by rows format in node 0.... now do for columns\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// --------------------------------------------------------------------------------------------------------------------------------\n\t\t// if(flag==1)\n\t\t// \texit(1);\n\t\t// else flag=1;\n\n\n\t\tif(pid==0)\n\t\t{\n\t\t\t\n\t\t\tfor(int i=0;i<2*num_entries;i+=2)\n\t\t\t{\n\t\t\t\tint hhval= *((int*)(sorted_rows+i));\n\t\t\t\tall_nodes[i/2].valint=hhval;\n\t\t\t\tall_nodes[i/2].valfloat=sorted_rows[i+1];\n\t\t\t}\n\t\t\tsort(all_nodes.begin(),all_nodes.end(), structcolcomparator);\n\t\t\t\n\n\t\t\tcolrecv_data=(float*)malloc(colsend_count[pid]*sizeof(float));\n\t\t\tcolall_data=(float*)malloc(2*sizeof(float)*num_entries);\n\n\t\t\tfor(int i=0;i<2*num_entries;i+=2)\n\t\t\t{\n\t\t\t\tint ii = all_nodes[i/2].valint ;\n\t\t\t\tint* ptr = (int*) (colall_data+i+1) ;\n\t\t\t\t*ptr = ii ;\n\n\t\t\t\tcolall_data[i]=all_nodes[i/2].valfloat;\n\t\t\t\t// colall_data[i+1]=all_nodes[i/2].valint;\n\t\t\t}\n\n\t\t\tcr_data=(int*)malloc(2*sizeof(int)*num_entries);\n\n\t\t\tfor(int i=0;i<2*num_entries;i+=2)\n\t\t\t{\n\t\t\t\tcr_data[i]=all_nodes[i/2].c;\n\t\t\t\tcr_data[i+1]=all_nodes[i/2].r;\n\t\t\t}\n\t\t\t\n\t\t\trecv_cr=(int*)malloc(colsend_count[pid]*sizeof(int));\n\t\t\t\n\t\t\tMPI_Scatterv(cr_data,colsend_count,coldispl_arr,MPI_INT,recv_cr,colsend_count[pid],MPI_INT,0,MPI_COMM_WORLD);\n\t\t\tMPI_Scatterv(colall_data,colsend_count,coldispl_arr,MPI_FLOAT,colrecv_data,colsend_count[pid],MPI_FLOAT,0,MPI_COMM_WORLD);\n\n\t\t}\n\n\t\t// cout<<\"reached after pid=0 column\"<<endl;\n\n\t\t\n\t\tif(pid!=0)\n\t\t{\n\t\t\t// MPI_Bcast(colsend_count,num_procs,MPI_INT,0,MPI_COMM_WORLD);\n\t\t\tcolrecv_data=(float*)malloc(colsend_count[pid]*sizeof(float));\n\n\n\t\t\t// float* temp33=(float*)malloc(1);\n\t\t\t// int* tempi33x`=(int*)malloc(1);\n\t\t\trecv_cr=(int*)malloc(colsend_count[pid]*sizeof(int));\n\t\t\tMPI_Scatterv(NULL,colsend_count,NULL,MPI_INT, recv_cr,colsend_count[pid],MPI_INT,0,MPI_COMM_WORLD);\n\t\t\tMPI_Scatterv(NULL,colsend_count,NULL,MPI_FLOAT,colrecv_data,colsend_count[pid],MPI_FLOAT,0,MPI_COMM_WORLD);\n\n\t\t}\n\n\t\t// cout<<\"reached after pid!=0 column\"<<endl;\n\n\t\t// MPI_Barrier(MPI_COMM_WORLD);\n\n\t\t// cout<<\"reached after 3rd barrier\"<<endl;\n\n\t\tvector<int> colelem_count;\n\t\tint colnum=recv_cr[0];\n\t\tint col_count=0;\n\t\t// calculates num of elements\n\t\t// cout<<rownum<<endl;\n\t\tfor(int i=0;i<colsend_count[pid];i+=2)\n\t\t{\n\t\t\t// cout<<recv_rc[i]<<\"ee\"<<endl;\n\t\t\tif(recv_cr[i]!=colnum)\n\t\t\t{\n\t\t\t\tcolelem_count.push_back(col_count);\n\t\t\t\t// cout<<row_count<<\"yuy\"<<endl;\n\t\t\t\tcolnum=recv_cr[i];\n\t\t\t\tcol_count=1;\n\t\t\t\tif(i==colsend_count[pid]-1 || i==colsend_count[pid]-2)\n\t\t\t\t{\n\t\t\t\t\tcolelem_count.push_back(col_count);\n\t\t\t\t\t// cout<<row_count<<endl;\n\t\t\t\t\t// cout<<row_count<<\"yuy\"<<endl;\n\t\t\t\t}\n\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tcol_count++;\n\t\t\t\tif(i==colsend_count[pid]-1 || i==colsend_count[pid]-2)\n\t\t\t\t{\n\t\t\t\t\tcolelem_count.push_back(col_count);\n\t\t\t\t\t// cout<<row_count<<\"yuy\"<<endl;\n\t\t\t\t}\t\n\t\t\t}\n\t\t}\n\n\n\n\t\tint num_cols=colelem_count.size();\n\t\t// cout<<num_rows<<endl<<endl<<endl<<endl;\n\t\tint* colelem_count_prefix=(int*)malloc(sizeof(int)*num_cols);\n\t\tcolelem_count_prefix[0]=0;\n\t\tfor(int i=1;i<num_cols;i++)\n\t\t{\n\t\t\tcolelem_count_prefix[i]=colelem_count_prefix[i-1]+colelem_count[i-1]*2;\n\t\t}\n\n\t\t#pragma omp parallel for\t\n\t\tfor(int i=0;i<num_cols;i++)\n\t\t{\n\t\t\tqsort (colrecv_data + colelem_count_prefix[i], colelem_count[i], 2*sizeof(int), intcomparator);\n\t\t}\n\t\t\n\n\t\t\n\n\t\t\n\n\t\tint colnum_objects=0;\n\t\tfor(int i=0;i<num_procs;i++)\n\t\t{\n\t\t\tcolnum_objects+=colsend_count[i];\n\t\t}\n\n\t\tif(pid==0)\n\t\t{\n\t\t\tsorted_cols=(float*)malloc(sizeof(float)*colnum_objects);\n\t\t}\n\t\t\n\n\t\tMPI_Gatherv(colrecv_data, colsend_count[pid], MPI_FLOAT,\n\t\t\tsorted_cols, colsend_count, coldispl_arr,\n\t                MPI_FLOAT, 0, MPI_COMM_WORLD);\n\n\t\t// cout<<\"reached after 2nd gatherv columns\"<<endl;\n\n\n\t\t// MPI_Barrier(MPI_COMM_WORLD);\n\t\t// cout<<\"reached after 4th barrier\"<<endl;\n\n\t\t// if(pid==0)\n\t\t// {\n\t\t// \tfor(int i=0;i<colnum_objects;i+=2)\n\t\t// \t{\n\t\t\t\n\t\t// \t\tint te=*((int*)(sorted_cols+i+1));\n\t\t// \t\tcout<<pid<<\": \"<<cr_data[i]<<\"  \"<<cr_data[i+1]<<\"->  \"<<sorted_cols[i]<<\"  \"<<te<< endl;\n\t\t// \t}\t\n\t\t// }\n\n\n\t\tif(pid==0)\n\t\t{\n\t\t\tfor(int i=0;i<2*num_entries;i+=2)\n\t\t\t{\n\t\t\t\tall_nodes[i/2].valfloat=sorted_cols[i];\n\t\t\t\tint te= *((int*)(sorted_cols+i+1));\n\t\t\t\tall_nodes[i/2].valint= te;\n\t\t\t}\n\t\t\tsort(all_nodes.begin(),all_nodes.end(), structrowcomparator);\t\n\t\t}\n\n\n\t\tfree(sorted_cols);\n\t\tcolelem_count.clear();\n\t\tfree(colelem_count_prefix);\n\t\tfree(sorted_rows);\n\t\tfree(colrecv_data);\n\t\tfree(colall_data);\n\t\tfree(cr_data);\n\t\tfree(recv_cr);\n\t\t// free(all_data);\n\t\t// free(rc_data);\n\t\tfree(recv_data);\n\t\tfree(recv_rc);\n\t\tfree(elem_count_prefix);\n\t\telem_count.clear();\n\n\t\t// cout<<\"a looper is finished\"<<endl;\n\n\t\t// exit(1);\n\n\t}\n\n\t// final writing\n\tif(pid==0)\n\t{\n\n\t\tFILE *file = fopen(argv[2],\"wb\") ;\n\t\tfor(int i=0 ; i<num_entries ; i++)\n\t\t{\n\t\t\t\n\t\t\tfwrite(&all_nodes[i].r,4,1,file) ;\n\t\t\tfwrite(&all_nodes[i].c,4,1,file) ;\n\t\t\tfwrite(&all_nodes[i].valint,4,1,file) ;\n\t\t\tfwrite(&all_nodes[i].valfloat,4,1,file) ;\n\t\t\t\n\t\t}\n\t\tfclose(file);\n\t}\n\n\t// cout<<\"file writing done\"<<endl;\n\t\n\n\nMPI_Finalize();\n\treturn 0;\n\t}\n\n\t\n", "text_preview": "#include <mpi.h> #include <iostream> #include <vector> #include <utility> #include <fstream> #include <mpi.h> #include <string> #include <cstring> #include <sstream> #include <algorithm> #include <limits> #include <cmath> #include <stdio.h>", "input_sha256": "7644a102bb74deb7e038730c706a4ff5bd4a0abaa5a5c026c03c841b9744b660", "text_sha256": "468ef4ffe90717e74cb7aa38c39d9243ff146e7ab86c65feae80b43906df1c81", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 74, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": -1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": -0.36685178877202257, "noise_severity": 0.79271083794916, "missing_content_severity": 0.10712428539281699, "factual_density": 3.10090010334027, "pretraining_utility": 0.33206995823090635, "math_density": 2.0630085324000964, "code_density": 3.903300241393833, "reasoning_density": 0.9619026002773923, "education_level": 2.7439662566201464, "creative_writing_quality": -0.8282030194122095}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.5006 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.008964025783990803, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9956689210937307, "secondary": 0.9839941482013852, "tertiary": 0.9933479142368539, "structured_records": 0.013562544819623474, "procedural_steps": 0.01925762440428162, "worked_solution": 0.011206527637347181, "contextual_dialogue": 8.621601837006198e-06, "content_quality": 0.3414740551729132, "noise_severity": 0.3394076350423668, "missing_content_severity": 0.9107297621726524, "factual_density": 0.42924710657786724, "pretraining_utility": 0.4349561107062144, "math_density": 0.0, "code_density": 0.9194168678281943, "reasoning_density": 0.19841449976883974, "education_level": 0.45720230968280884, "creative_writing_quality": 0.9984339040562062, "primary_topic": 0.9333175866795691, "secondary_topic": 0.4806451046914754}, "probability_margin": {"primary": 0.993941705239207, "secondary": 0.975882322128552, "tertiary": 0.9907902784534071, "education_level": 0.19936627834534082, "creative_writing_quality": 0.998169138239331, "primary_topic": 0.8742305894555419, "secondary_topic": 0.13561466207185469}, "raw_probabilities": {"primary": {"Academic Writing": 6.142092949695485e-05, "Content Listing": 2.722230355532844e-05, "Creative Writing": 3.1461642210861275e-05, "Customer Support Page": 8.955863250434195e-06, "Q&A Forum": 1.618104766176551e-05, "Discussion Thread": 1.2179502703840242e-05, "Comment Section": 2.135642718473499e-05, "Missing Content": 4.294331641919066e-05, "Knowledge Article": 0.00011801092786063335, "Legal Notices": 4.409443414463113e-05, "News Article": 6.6425301710204565e-06, "Organizational Announcement": 1.1852015573821595e-05, "Nonfiction Writing": 1.8601976620845985e-05, "Organizational About Page": 1.5731158625891172e-05, "Personal About Page": 1.754730041940425e-05, "Personal Blog": 1.0195516401021821e-05, "Product Page": 8.701434654625828e-06, "Spam / Ads": 1.5159009347306857e-05, "Structured Data": 0.0006122090017414113, "Technical Writing": 0.9958493827148253, "Transcript / Interview": 2.1921265294320694e-05, "Tutorial / How-To Guide": 0.0011116226063217311, "User Reviews": 8.92959989700301e-06, "Other / Unknown": 0.0019076774756183056}, "secondary": {"Academic Writing": 0.00013348590305038924, "Content Listing": 5.736874409094583e-05, "Creative Writing": 0.00010113643553800002, "Customer Support Page": 3.404935758759611e-05, "Q&A Forum": 3.337532674549433e-05, "Discussion Thread": 2.9387953105725767e-05, "Comment Section": 0.00022486104560160035, "Missing Content": 0.0003180035877204463, "Knowledge Article": 0.0004038334041589662, "Legal Notices": 0.00011667684587852176, "News Article": 4.043688778912658e-05, "Organizational Announcement": 3.712201701199604e-05, "Nonfiction Writing": 7.571176005776857e-05, "Organizational About Page": 4.4847296261973625e-05, "Personal About Page": 5.2134008119027856e-05, "Personal Blog": 3.763136104859339e-05, "Product Page": 2.5417913916862135e-05, "Spam / Ads": 4.0535074090422895e-05, "Structured Data": 0.002092148103708685, "Technical Writing": 0.008778736564442169, "Transcript / Interview": 5.476753817939094e-05, "Tutorial / How-To Guide": 0.002585834244419222, "User Reviews": 2.1439934482949124e-05, "NONE": 0.9846610586929941}, "tertiary": {"Academic Writing": 8.967386860577061e-05, "Content Listing": 3.682067018449698e-05, "Creative Writing": 7.557377321528985e-05, "Customer Support Page": 1.7783819886315485e-05, "Q&A Forum": 2.1476292888320865e-05, "Discussion Thread": 1.8251183802973762e-05, "Comment Section": 0.00013244056432914345, "Missing Content": 0.00035830264267308485, "Knowledge Article": 0.00024100767393024655, "Legal Notices": 0.0001346991230486396, "News Article": 2.791310085563972e-05, "Organizational Announcement": 2.3330399535827493e-05, "Nonfiction Writing": 5.6097370793302314e-05, "Organizational About Page": 3.8090444604858405e-05, "Personal About Page": 2.944919024731553e-05, "Personal Blog": 2.4689858964490953e-05, "Product Page": 1.743296541004284e-05, "Spam / Ads": 3.1377931564326744e-05, "Structured Data": 0.0006493364665509289, "Technical Writing": 0.002834806023577935, "Transcript / Interview": 3.1365006917452914e-05, "Tutorial / How-To Guide": 0.0014694919140572661, "User Reviews": 1.5505237371381436e-05, "NONE": 0.9936250844769849}, "structured_records": {"false": 0.9864374551803765, "true": 0.013562544819623474}, "procedural_steps": {"false": 0.9807423755957184, "true": 0.01925762440428162}, "worked_solution": {"false": 0.9887934723626528, "true": 0.011206527637347181}, "contextual_dialogue": {"false": 0.999991378398163, "true": 8.621601837006198e-06}, "content_quality": {"-2": 0.07854146128226333, "-1": 0.3888388462618988, "0": 0.38272723032038186, "1": 0.1207149442165092, "2": 0.029177517918946846}, "noise_severity": {"0": 0.45984327041461376, "1": 0.3236231602798005, "2": 0.1875489536318933, "3": 0.02194869228919628, "4": 0.007035923384495967}, "missing_content_severity": {"0": 0.9369583419097343, "1": 0.028244677770390532, "2": 0.026827580222069592, "3": 0.0066531532129355956, "4": 0.0013162468848701208}, "factual_density": {"0": 0.0170458911568501, "1": 0.045162486240160955, "2": 0.1505390384322724, "3": 0.3943507964473018, "4": 0.3929017877234147}, "pretraining_utility": {"-2": 0.01490007577689914, "-1": 0.14319120290701984, "0": 0.4206681337962916, "1": 0.33741986234785437, "2": 0.08382072517193503}, "math_density": {"0": 0.22784642591196066, "1": 0.18993287448439336, "2": 0.15544653347572507, "3": 0.14491407354743074, "4": 0.2818600925804902}, "code_density": {"0": 0.00019116565953927267, "1": 0.00036410447306891474, "2": 0.007685993526060835, "3": 0.07947079549668135, "4": 0.9122879408446496}, "reasoning_density": {"0": 0.6540183014256763, "1": 0.05583372510301259, "2": 0.0854907547348018, "3": 0.08354150924126102, "4": 0.12111570949524826}, "education_level": {"none": 0.000877994918499417, "0": 0.0009077922858995773, "1": 0.0023421884919211824, "2": 0.3483023130569999, "3": 0.5476685914023407, "4": 0.09990111984433929}, "creative_writing_quality": {"not_applicable": 0.9986949200468386, "-2": 0.0003345052320003599, "-1": 0.0005257818075075846, "0": 0.0003605619454706134, "1": 5.454082264038974e-05, "2": 2.969014554259149e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 8.018169853446188e-05, "Art & Design": 0.00012546331868687802, "Law & Crime": 0.00011120873372159705, "Education & Careers": 8.426334158429529e-05, "Entertainment & Media": 7.503211491295399e-05, "Fashion & Beauty": 6.393434955449446e-05, "Business, Economics & Finance": 8.771586654090097e-05, "Food & Dining": 6.879633372103431e-05, "Games & Gaming": 0.00011028070546565644, "Electronics & Hardware": 0.00017111424410936948, "Health, Healthcare & Wellness": 8.032363248048976e-05, "History & Geography": 9.417555038694682e-05, "Home, Pets & Hobbies": 8.747561195930461e-05, "Industry & Production": 8.284050656678076e-05, "Language, Literature & Philosophy": 7.18649857552817e-05, "Politics & Government": 4.1094096996608455e-05, "Religion & Spirituality": 5.96749488137813e-05, "Science, Mathematics & Engineering": 0.06165170542865914, "Relationships & Community": 3.9268189045874845e-05, "Software Usage & Applications": 0.00025245623488786515, "Programming & Software Engineering": 0.935882294884201, "Sports & Fitness": 8.867407205402663e-05, "Vehicles & Transportation": 4.6570730875496215e-05, "Travel & Tourism": 9.56468319320082e-05, "Other / Uncovered Subject": 0.0003229300082497747, "Unknown / Insufficient Content": 0.00012501358030378573}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005788154566606634, "Art & Design": 0.0005705962816409256, "Law & Crime": 0.0005629341367553846, "Education & Careers": 0.0004174201341194545, "Entertainment & Media": 0.00038976540323090947, "Fashion & Beauty": 0.00033585253363386695, "Business, Economics & Finance": 0.00043388375167094536, "Food & Dining": 0.0002763179838226649, "Games & Gaming": 0.00042235424515518923, "Electronics & Hardware": 0.0009585517291114107, "Health, Healthcare & Wellness": 0.00042350355159963244, "History & Geography": 0.00039936856161817215, "Home, Pets & Hobbies": 0.00040015143944364923, "Industry & Production": 0.0003757861589137869, "Language, Literature & Philosophy": 0.00023586959713040013, "Politics & Government": 0.00025631461191579516, "Religion & Spirituality": 0.00041783534186881817, "Science, Mathematics & Engineering": 0.5006202929725725, "Relationships & Community": 0.000181413733317407, "Software Usage & Applications": 0.0005332491101874236, "Programming & Software Engineering": 0.12424590546109872, "Sports & Fitness": 0.00037314527974333225, "Vehicles & Transportation": 0.00021182378004092428, "Travel & Tourism": 0.0006074828957375992, "Other / Uncovered Subject": 0.0007657349482926793, "NONE": 0.3650056309007178}}, "probabilities": {"primary": {"Academic Writing": 6.142092949695482e-05, "Content Listing": 2.7222303555328427e-05, "Creative Writing": 3.146164221086126e-05, "Customer Support Page": 8.955863250434192e-06, "Q&A Forum": 1.6181047661765505e-05, "Discussion Thread": 1.2179502703840237e-05, "Comment Section": 2.135642718473498e-05, "Missing Content": 4.294331641919064e-05, "Knowledge Article": 0.0001180109278606333, "Legal Notices": 4.409443414463111e-05, "News Article": 6.642530171020454e-06, "Organizational Announcement": 1.185201557382159e-05, "Nonfiction Writing": 1.860197662084598e-05, "Organizational About Page": 1.5731158625891165e-05, "Personal About Page": 1.7547300419404244e-05, "Personal Blog": 1.0195516401021816e-05, "Product Page": 8.701434654625824e-06, "Spam / Ads": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/JimEli/sorts/blob/b1513e1dfe5be95a4fcc037db1e5e7d62d4c0b92/concurrent_algorithms.h", "id": "cpp-0-f19e333d6734397c31bf120f86dbed38", "text": "#include <algorithm> // sort, make_heap, sort_heap, swap, distance, generate, inplace_merge, minmax\n#include <iostream>  // cout\n#include <cstring>   // memset\n#include <vector>    // used by counting sort\n#include <exception> // runtime exception\n\nusing namespace concurrency;\n\n/*************************************************************************\n * Implements a parallel sort algorithm.\n *************************************************************************/\n // Count the number of elements in each bucket\n // list: array pointer\n // sz: length of array\n // np: number of processors (threads)\n // range: range of buckets\n // bucket: number of items in each bucket (return value)\ntemplate <typename T>\nstatic void count(const T* list, size_t sz, unsigned np, const T* range, size_t* bucket)\n{\n  for (unsigned n = 0; n < np; n++)\n    bucket[n] = 0;\n\n  for (size_t i = 0; i < sz; i++)\n  {\n    unsigned n;\n\n    for (n = 0; n < np - 1; n++)\n    {\n      if (list[i] < range[n])\n      {\n        bucket[n]++;\n        break;\n      }\n    }\n\n    if (n == np - 1)\n      bucket[np - 1]++;\n  }\n}\n\n// Reorder array to separate buckets.\n// list: array pointer\n// sz: length of array\n// np: number of processors (threads)\n// range: range of buckets\n// map: reordering map\n// out: reordered array (return value)\ntemplate <typename T>\nstatic void reOrder(const T* list, size_t sz, unsigned np, const T* range, size_t* map, T* out)\n{\n  for (size_t i = 0; i < sz; i++)\n  {\n    unsigned n;\n\n    for (n = 0; n < np - 1; n++)\n    {\n      if (list[i] < range[n])\n      {\n        out[map[n]++] = list[i];\n        break;\n      }\n    }\n\n    if (n == np - 1)\n      out[map[np - 1]++] = list[i];\n  }\n}\n\n// Sort an array using std::sort, sz: length of array, list: array pointer\ntemplate <typename T> static void _sort(T* list, size_t sz) { std::sort(list, list + sz); }\n\n// Parallel sort\ntemplate <typename T>\nvoid pSort(T* begin, T* end)\n{\n  int sz = static_cast<int>(std::distance(begin, end));\n  unsigned np = 6;   // number of processors (threads).\n  unsigned sf = 100; // sampling factor (100 should be good enough for most cases).\n  unsigned nSample = np * sf;\n  unsigned nMap = np * np;\n  std::vector<T> range(np);\n  std::vector<T> sample(nSample);\n  std::vector<size_t> tmp(nMap, 0), map(nMap, 0), bucket(nMap, 0);\n  std::vector<std::thread> threads(np);\n\n  // Step 1: Pick np*sf random elements from the input array.\n  // We use these elements to split the array into even partitions.\n  for (unsigned i = 0; i < nSample; i++)\n    sample[i] = begin[rand() % sz];\n\n  std::sort(std::execution::par_unseq, &sample[0], &sample[0] + nSample);\n\n  for (unsigned i = 0; i < np - 1; i++)\n    range[i] = sample[(i + 1)*sf];\n\n  // Step 2: Count how many elements go into each bucket. This is done in parallel.\n  unsigned part = sz / np;\n\n  for (unsigned i = 0; i < np; i++)\n  {\n    size_t start = i * part;\n    size_t length = (i + 1 == np) ? sz - i * part : part;\n\n    threads[i] = std::thread(count<T>, begin, length, np, &range[0], &bucket[i*np]);\n  }\n\n  for (auto&& thread : threads)\n    thread.join();\n\n  // Step 3: Create a mapping scheme to separate partitions.\n  // Since the number of operations is small, we do this step in serial.\n  for (unsigned i = 0; i < nMap; i++)\n    tmp[i] = i ? tmp[i - 1] + bucket[((i - 1) % np)*np + (i - 1) / np] : 0;\n\n  for (unsigned i = 0; i < nMap; i++)\n    map[i] = tmp[(i%np)*np + i / np];\n\n  for (unsigned i = 0; i < nMap; i++)\n    tmp[i] = map[i];\n\n  // Step 4: Map the input array into np separate partitions.    \n  std::vector<T> sorted(sz);\n\n  for (unsigned i = 0; i < np; i++)\n  {\n    size_t start = i * part;\n    size_t length = (i + 1 == np) ? sz - i * part : part;\n\n    threads[i] = std::thread(reOrder<T>, begin, length, np, &range[0], &tmp[i*np], &sorted[0]);\n  }\n\n  for (auto&& thread : threads)\n    thread.join();\n\n  // Step 5: Sort each partition in a separate thread.    \n  for (unsigned i = 0; i < np; i++)\n  {\n    size_t start = map[i];\n    size_t length = (i + 1 == np) ? sz - map[i] : map[i + 1] - map[i];\n\n    threads[i] = std::thread(_sort<T>, &sorted[start], length);\n  }\n\n  for (auto&& thread : threads)\n    thread.join();\n\n  // Step 6: Copy the temporary container for the sorted array into the input array.    \n  std::memcpy(begin, &sorted[0], sz * sizeof(T));\n}\n\n#if (_MSC_VER && __cpp_lib_parallel_algorithm < 201603)\n/*************************************************************************\n Concurrent MSVC++ Parallel Patterns Library radix sort.\n *************************************************************************/\ntemplate <typename T>\nvoid pRadix(T* begin, T* end) { Concurrency::parallel_radixsort(begin, end); }\n\n/*************************************************************************\n * Implements a concurrent MSVC++ Parallel Patterns Library quicksort.\n *************************************************************************/\ntemplate <typename T>\nvoid ParallelQuickSortWithSTL(T* begin, T* end, int depthRemaining, long threshold)\n{\n  if (std::distance(begin, end) <= threshold)\n    std::sort(begin, end);\n\n  else\n  {\n    T* pivot = std::partition(begin, end, std::bind2nd(std::less<T>(), *begin));\n\n    if (depthRemaining > 0)\n    {\n      Concurrency::parallel_invoke(\n        [begin, end, pivot, depthRemaining, threshold] {\n        if (pivot != end)\n          ParallelQuickSortWithSTL(begin, pivot, depthRemaining - 1, threshold);\n      },\n        [&pivot, begin, end, depthRemaining, threshold] {\n        if (pivot == begin)\n          ++pivot;\n        ParallelQuickSortWithSTL(pivot, end, depthRemaining - 1, threshold);\n      });\n    }\n\n    else\n    {\n      T* pivot = std::partition(begin, end, std::bind2nd(std::less<T>(), *begin));\n\n      if (pivot != end)\n        std::sort(begin, pivot);\n\n      if (pivot == begin)\n        ++pivot;\n\n      std::sort(pivot, end);\n    }\n  }\n}\n/*\ntemplate <typename T>\nvoid SequentialQuickSort(T* begin, T* end, long threshold)\n{\n  if (std::distance(begin, end) <= threshold)\n    insertionSort(begin, end);\n\n  else\n  {\n    T* pivot = std::partition(begin + 1, end, std::bind2nd(std::less<int>(), *begin));\n\n    //std::iter_swap(begin, pivot - 1);\n    std::swap(*begin, *(pivot - 1));\n\n    SequentialQuickSort(begin, pivot - 1, threshold);\n    SequentialQuickSort(pivot, end, threshold);\n  }\n}\n\ntemplate <typename T>\nvoid ParallelQuickSort(T* begin, T* end, long threshold, int depthRemaining)\n{\n  if (std::distance(begin, end) <= threshold)\n    insertionSort(begin, end);\n\n  else\n  {\n    T* pivot = std::partition(begin + 1, end, std::bind2nd(std::less<int>(), *begin));\n\n    std::swap(*begin, *(pivot - 1));\n\n    if (depthRemaining > 0)\n    {\n      Concurrency::parallel_invoke([begin, end, pivot, depthRemaining, threshold]\n      {\n        ParallelQuickSort(begin, pivot - 1, depthRemaining - 1, threshold);\n      },\n        [&pivot, begin, end, depthRemaining, threshold] {\n        ParallelQuickSort(pivot, end, depthRemaining - 1, threshold);\n      });\n    }\n\n    else\n    {\n      SequentialQuickSort(begin, pivot - 1, threshold);\n      SequentialQuickSort(pivot, end, threshold);\n    }\n  }\n}\n*/\n\ntemplate <typename T>\nvoid pSort(T* begin, T* end)\n{\n  const int maxTasks = Concurrency::CurrentScheduler::Get()->GetNumberOfVirtualProcessors();\n  //ParallelQuickSort(begin, end, (int)LogN(float(maxTasks), 2.0f) + 4, 256);\n  ParallelQuickSortWithSTL(begin, end, (int)LogN((float)maxTasks) + 4, 256);\n}\n\n// To generate logarithms for base 2. Log base b of a == natural log (a) / natural log (b).\ninline float LogN(float value) { return logf(value) / logf(2.0f); }\n#endif\n\n#if (__cpp_lib_parallel_algorithm >= 201603)\n/*************************************************************************\n * C++ STL <algorithm> library sort function with C++17 parallel option.\n * Defaults to sequential version if C++17 features not available.\n *************************************************************************/\ntemplate <typename T>\n// Use parallel algorithm.\nvoid pSort(T* begin, T* end) { std::sort(std::execution::par_unseq, begin, end); }\n#endif\n\n/*************************************************************************\n * C++ AMP radix sort function. If not available, defaults to std::sort.\n*************************************************************************/\nvoid arr_fill(concurrency::array_view<unsigned>& dest, concurrency::array_view<unsigned>& src, unsigned val)\n{\n  parallel_for_each(dest.extent, [dest, val, src](concurrency::index<1> idx) restrict(amp)\n  {\n    dest[idx] = ((unsigned)idx[0] < src.get_extent().size()) ? src[idx] : val;\n  });\n}\n\nunsigned get_bits(unsigned x, unsigned numbits, unsigned bitoffset) restrict(amp) { return (x >> bitoffset) & ~(~0 << numbits); }\nunsigned pow2(unsigned x) restrict(amp, cpu) { return (((unsigned)1) << x); }\n\nunsigned tile_sum(unsigned x, concurrency::tiled_index<256> tidx) restrict(amp)\n{\n  unsigned l_id = tidx.local[0];\n  tile_static unsigned l_sums[256][2];\n\n  l_sums[l_id][0] = x;\n  tidx.barrier.wait();\n\n  for (unsigned i = 0; i < 8; i++)\n  {\n    if (l_id < pow2(7 - i))\n    {\n      unsigned w = (i + 1) % 2;\n      unsigned r = i % 2;\n\n      l_sums[l_id][w] = l_sums[l_id * 2][r] + l_sums[l_id * 2 + 1][r];\n    }\n    tidx.barrier.wait();\n  }\n  return l_sums[0][0];\n\n}\n\nunsigned tile_prefix_sum(unsigned x, concurrency::tiled_index<256> tidx, unsigned& last_val) restrict(amp)\n{\n  unsigned l_id = tidx.local[0];\n  tile_static unsigned l_prefix_sums[256][2];\n\n  l_prefix_sums[l_id][0] = x;\n  tidx.barrier.wait();\n\n  for (unsigned i = 0; i < 8; i++)\n  {\n    unsigned pow2i = pow2(i);\n\n    unsigned w = (i + 1) % 2;\n    unsigned r = i % 2;\n\n    l_prefix_sums[l_id][w] = (l_id >= pow2i) ? (l_prefix_sums[l_id][r] + l_prefix_sums[l_id - pow2i][r]) : l_prefix_sums[l_id][r];\n\n    tidx.barrier.wait();\n  }\n  last_val = l_prefix_sums[255][0];\n\n  unsigned retval = (l_id == 0) ? 0 : l_prefix_sums[l_id - 1][0];\n  return retval;\n}\n\nunsigned tile_prefix_sum(unsigned x, concurrency::tiled_index<256> tidx) restrict(amp)\n{\n  unsigned ll = 0;\n  return tile_prefix_sum(x, tidx, ll);\n}\n\nvoid calc_interm_sums(unsigned bitoffset, concurrency::array<unsigned>& interm_arr, concurrency::array<unsigned>& interm_sums, concurrency::array<unsigned>& interm_prefix_sums, unsigned num_tiles)\n{\n  auto ext = extent<1>(num_tiles * 256).tile<256>();\n\n  parallel_for_each(ext, [=, &interm_sums, &interm_arr](tiled_index<256> tidx) restrict(amp)\n  {\n    unsigned inbound = ((unsigned)tidx.global[0] < interm_arr.get_extent().size());\n    unsigned num = (inbound) ? get_bits(interm_arr[tidx.global[0]], 2, bitoffset) : get_bits(0xffffffff, 2, bitoffset);\n\n    for (unsigned i = 0; i < 4; i++)\n    {\n      unsigned to_sum = (num == i);\n      unsigned sum = tile_sum(to_sum, tidx);\n\n      if (tidx.local[0] == 0)\n        interm_sums[i*num_tiles + tidx.tile[0]] = sum;\n    }\n  });\n\n  unsigned numiter = (num_tiles / 64) + ((num_tiles % 64 == 0) ? 0 : 1);\n  ext = extent<1>(256).tile<256>();\n\n  parallel_for_each(ext, [=, &interm_prefix_sums, &interm_sums](tiled_index<256> tidx) restrict(amp)\n  {\n    unsigned last_val0 = 0;\n    unsigned last_val1 = 0;\n\n    for (unsigned i = 0; i < numiter; i++)\n    {\n      unsigned g_id = tidx.local[0] + i * 256;\n      unsigned num = (g_id < (num_tiles * 4)) ? interm_sums[g_id] : 0;\n      unsigned scan = tile_prefix_sum(num, tidx, last_val0);\n      if (g_id < (num_tiles * 4)) interm_prefix_sums[g_id] = scan + last_val1;\n\n      last_val1 += last_val0;\n    }\n  });\n}\n\nvoid sort_step(unsigned bitoffset, concurrency::array<unsigned>& src, concurrency::array<unsigned>& dest, concurrency::array<unsigned>& interm_prefix_sums, unsigned num_tiles)\n{\n  auto ext = extent<1>(num_tiles * 256).tile<256>();\n\n  parallel_for_each(ext, [=, &interm_prefix_sums, &src, &dest](tiled_index<256> tidx) restrict(amp)\n  {\n    unsigned inbounds = ((unsigned)tidx.global[0] < src.get_extent().size());\n    unsigned element = (inbounds) ? src[tidx.global[0]] : 0xffffffff;\n    unsigned num = get_bits(element, 2, bitoffset);\n    for (unsigned i = 0; i < 4; i++)\n    {\n      unsigned scan = tile_prefix_sum((num == i), tidx) + interm_prefix_sums[i*num_tiles + tidx.tile[0]];\n      if (num == i && inbounds) dest[scan] = element;\n    }\n  });\n}\n\nvoid amp_radix_sort(concurrency::array<unsigned>& arr)\n{\n  unsigned size = arr.get_extent().size();\n  const unsigned num_tiles = (size / 256) + ((size % 256 == 0) ? 0 : 1);\n  array<unsigned> interm_arr(size);\n  array<unsigned> interm_sums(num_tiles * 4);\n  array<unsigned> interm_prefix_sums(num_tiles * 4);\n\n  for (unsigned i = 0; i < 16; i++)\n  {\n    array<unsigned>& src = (i % 2 == 0) ? arr : interm_arr;\n    array<unsigned>& dest = (i % 2 == 0) ? interm_arr : arr;\n    unsigned bitoffset = i * 2;\n\n    calc_interm_sums(bitoffset, src, interm_sums, interm_prefix_sums, num_tiles);\n    sort_step(bitoffset, src, dest, interm_prefix_sums, num_tiles);\n  }\n}\n\ntemplate <typename T>\nvoid aRadix(T* begin, T* end)\n{\n  accelerator default_device;\n  if (default_device == accelerator(accelerator::direct3d_ref))\n  {\n    std::sort(begin, end);\n    return;\n  }\n  {\n    unsigned size = std::distance(begin, end);\n    array<unsigned, 1> av(size, begin);\n\n    amp_radix_sort(av);\n    av.accelerator_view.wait();\n    // Synchronise.\n    copy(av, begin);\n    av.accelerator_view.wait();\n  }\n}\n\n#endif\n", "text_preview": "#include <algorithm> // sort, make_heap, sort_heap, swap, distance, generate, inplace_merge, minmax #include <iostream> // cout #include <cstring> // memset #include <vector> // used by counting sort #include <exception> // runtime exceptio", "input_sha256": "57ad839609a184894ab9dd188788c00f6a7846aca74ce274c7f6315e7fa0cc62", "text_sha256": "f19e333d6734397c31bf120f86dbed3863997a898d4649c136bd5f629667bc45", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 76, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7655718384166228, "noise_severity": 0.091335856044818, "missing_content_severity": 0.08752333150397122, "factual_density": 3.3260406868737387, "pretraining_utility": 0.7193776795121755, "math_density": 2.782197548923974, "code_density": 3.7871024929493835, "reasoning_density": 1.1999527086485038, "education_level": 2.8368488245402363, "creative_writing_quality": -0.25636214500090193}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.00851860217094269, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9936192274067153, "secondary": 0.9455245715403061, "tertiary": 0.954413547718681, "structured_records": 0.0012130730385265269, "procedural_steps": 0.24276794837572707, "worked_solution": 0.013358315121475992, "contextual_dialogue": 4.934103631791611e-06, "content_quality": 0.4873405472208179, "noise_severity": 0.9238867866293183, "missing_content_severity": 0.9270638904133572, "factual_density": 0.4383672390614487, "pretraining_utility": 0.5316082366468704, "math_density": 0.0, "code_density": 0.8225854107911529, "reasoning_density": 3.9409459579919925e-05, "education_level": 0.46566292587183095, "creative_writing_quality": 0.9992257825535531, "primary_topic": 0.9431704384047126, "secondary_topic": 0.6063880095985676}, "probability_margin": {"primary": 0.9899367252808821, "secondary": 0.9197996012051954, "tertiary": 0.9291315904366427, "education_level": 0.25828629118324675, "creative_writing_quality": 0.9990750501019178, "primary_topic": 0.8941134860942551, "secondary_topic": 0.3521701835848429}, "raw_probabilities": {"primary": {"Academic Writing": 7.877490457217449e-05, "Content Listing": 3.2887878087454004e-05, "Creative Writing": 2.0191439386835662e-05, "Customer Support Page": 1.3854245211669834e-05, "Q&A Forum": 1.692075480947325e-05, "Discussion Thread": 1.0241881251280426e-05, "Comment Section": 1.844890265261488e-05, "Missing Content": 3.9484765978027076e-05, "Knowledge Article": 0.0002557362652176526, "Legal Notices": 3.947774383178269e-05, "News Article": 6.460212807574886e-06, "Organizational Announcement": 1.4486179354461382e-05, "Nonfiction Writing": 1.7048571337602998e-05, "Organizational About Page": 1.4328788675962856e-05, "Personal About Page": 1.6437902488189607e-05, "Personal Blog": 1.2442752848386387e-05, "Product Page": 1.0878657421016556e-05, "Spam / Ads": 1.4607191091737532e-05, "Structured Data": 0.00021564973211759793, "Technical Writing": 0.9938850929314355, "Transcript / Interview": 2.214884072617556e-05, "Tutorial / How-To Guide": 0.003948367650553424, "User Reviews": 1.0479987007994267e-05, "Other / Unknown": 0.0012855518211353444}, "secondary": {"Academic Writing": 0.0006441547321291089, "Content Listing": 0.00018313389288530013, "Creative Writing": 0.00016891184520234566, "Customer Support Page": 0.0001237758724548193, "Q&A Forum": 9.390092220959052e-05, "Discussion Thread": 7.50011552561601e-05, "Comment Section": 0.00028683236219995855, "Missing Content": 0.0005032480696510952, "Knowledge Article": 0.0033767102598523404, "Legal Notices": 0.00024361017241791348, "News Article": 8.743664101537811e-05, "Organizational Announcement": 0.0001071524997872803, "Nonfiction Writing": 0.00014198480038744163, "Organizational About Page": 0.00011528815391937395, "Personal About Page": 0.00011711825315725049, "Personal Blog": 9.498410592214635e-05, "Product Page": 7.414631357356894e-05, "Spam / Ads": 0.00011013989127480228, "Structured Data": 0.0009449157224784365, "Technical Writing": 0.016529983963849776, "Transcript / Interview": 0.00012730221186172064, "Tutorial / How-To Guide": 0.027994779854264573, "User Reviews": 6.110724478969751e-05, "NONE": 0.94779438105946}, "tertiary": {"Academic Writing": 0.0004805560187411768, "Content Listing": 0.00012885727578058895, "Creative Writing": 0.0001261894878487827, "Customer Support Page": 7.060190109832592e-05, "Q&A Forum": 6.5533331777265e-05, "Discussion Thread": 5.1695441340393815e-05, "Comment Section": 0.0001994526632412258, "Missing Content": 0.0005364728429169571, "Knowledge Article": 0.002793050579968591, "Legal Notices": 0.0006154393852693148, "News Article": 6.677145757591626e-05, "Organizational Announcement": 7.168294967591002e-05, "Nonfiction Writing": 0.00011103827800354464, "Organizational About Page": 9.110240743720065e-05, "Personal About Page": 7.848748926690677e-05, "Personal Blog": 6.391338192097931e-05, "Product Page": 5.427862447465275e-05, "Spam / Ads": 8.175020239540436e-05, "Structured Data": 0.0007312026772986495, "Technical Writing": 0.009945067304724466, "Transcript / Interview": 9.78552454995607e-05, "Tutorial / How-To Guide": 0.02718139279375995, "User Reviews": 4.462502958151614e-05, "NONE": 0.9563129832304027}, "structured_records": {"false": 0.9987869269614734, "true": 0.0012130730385265269}, "procedural_steps": {"false": 0.757232051624273, "true": 0.24276794837572707}, "worked_solution": {"false": 0.986641684878524, "true": 0.013358315121475992}, "contextual_dialogue": {"false": 0.9999950658963682, "true": 4.934103631791611e-06}, "content_quality": {"-2": 0.011654469425604509, "-1": 0.05981904989984234, "0": 0.2702082443826997, "1": 0.4679366454160329, "2": 0.19038159087582063}, "noise_severity": {"0": 0.9234957112468256, "1": 0.0655476626720688, "2": 0.00792462915020246, "3": 0.002189052651267917, "4": 0.0008429442796351307}, "missing_content_severity": {"0": 0.9370758491395818, "1": 0.04330916518775306, "2": 0.015265652011482738, "3": 0.0037144723514778346, "4": 0.0006348613097048012}, "factual_density": {"0": 0.005832314590582573, "1": 0.018062568762162706, "2": 0.11577133366462214, "3": 0.3648996811481989, "4": 0.4954341018344337}, "pretraining_utility": {"-2": 0.00821781379815813, "-1": 0.053823577693292435, "0": 0.28904562147473084, "1": 0.5081890892658532, "2": 0.14072389776796548}, "math_density": {"0": 0.059916210727341525, "1": 0.12161049869905988, "2": 0.1917719183021437, "3": 0.22976227546519296, "4": 0.39693909680626194}, "code_density": {"0": 0.0003286814530364966, "1": 0.0007006376121193627, "2": 0.02210964475969261, "3": 0.16526157888272727, "4": 0.8115994572924243}, "reasoning_density": {"0": 0.5131403475575972, "1": 0.14735194411904712, "2": 0.09867583588031166, "3": 0.10807839700334293, "4": 0.13275347543970115}, "education_level": {"none": 0.0008820799575227729, "0": 0.0011189411788396426, "1": 0.003354895363959668, "2": 0.2964328137099457, "3": 0.5547191048931924, "4": 0.14349216489653976}, "creative_writing_quality": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/idkmanweb/Simple-Survival-Game-C/blob/b1612766a194c58b1e98902665fb38d5fba40f6c/antelope.cpp", "id": "cpp-0-704321a3ba925a6b15dc5bdfc96060d4", "text": "#include <iostream>\n#include <conio.h>\n#include <vector>\n#include \"antelope.h\"\n#include \"fox.h\"\nusing namespace std;\n\nstruct sort_key\n{\n\tinline bool operator() (Entity* entity1, Entity* entity2)\n\t{\n\t\treturn (entity1->getInitiative() > entity2->getInitiative());\n\t}\n};\n\nAntelope::Antelope() {\n\tsymbol = 'A';\n\tstrength = 4;\n\tinitiative = 4;\n\tx = 1;\n\ty = 1;\n}\n\nAntelope::Antelope(int xCoord, int yCoord) {\n\tsymbol = 'A';\n\tstrength = 4;\n\tinitiative = 4;\n\tx = xCoord;\n\ty = yCoord;\n}\n\nvoid Antelope::action(vector<Entity*>& entities, World* world, Logger &log, int width, int height) {\n\tint direction = rand() % 4;\n\tif (direction == 0 && x < width-1) {\n\t\tx+=2;\n\t}\n\telse if (direction == 1 && x > 2) {\n\t\tx-=2;\n\t}\n\telse if (direction == 2 && y > 2) {\n\t\ty-=2;\n\t}\n\telse if (direction == 3 && y < height-1) {\n\t\ty+=2;\n\t}\n\n\tif (world->getEntity(x, y) != ' ') {\n\t\tfor (int j = 0; j < entities.size(); j++) {\n\t\t\tif (x == entities[j]->getX() && y == entities[j]->getY()) {\n\t\t\t\tif (this != entities[j] && typeid(*this) == typeid(*entities[j])) {\n\t\t\t\t\tif (direction == 0) {\n\t\t\t\t\t\tx--;\n\t\t\t\t\t}\n\t\t\t\t\telse if (direction == 1) {\n\t\t\t\t\t\tx++;\n\t\t\t\t\t}\n\t\t\t\t\telse if (direction == 2) {\n\t\t\t\t\t\ty++;\n\t\t\t\t\t}\n\t\t\t\t\telse if (direction == 3) {\n\t\t\t\t\t\ty--;\n\t\t\t\t\t}\n\n\t\t\t\t\tif (entities[j]->getX() < width && world->getEntity(entities[j]->getX() + 1, entities[j]->getY()) == ' ') {\n\t\t\t\t\t\tentities[j]->makeChild(entities, entities[j]->getX() + 1, entities[j]->getY());\n\t\t\t\t\t\tworld->addEntity(entities[j]->getName(), entities[j]->getX() + 1, entities[j]->getY());\n\t\t\t\t\t\tlog.addBirth(entities[j]);\n\t\t\t\t\t\tsort(entities.begin(), entities.end(), sort_key());\n\t\t\t\t\t}\n\t\t\t\t\telse if (entities[j]->getX() > 1 && world->getEntity(entities[j]->getX() - 1, entities[j]->getY()) == ' ') {\n\t\t\t\t\t\tentities[j]->makeChild(entities, entities[j]->getX() - 1, entities[j]->getY());\n\t\t\t\t\t\tworld->addEntity(entities[j]->getName(), entities[j]->getX() - 1, entities[j]->getY());\n\t\t\t\t\t\tlog.addBirth(entities[j]);\n\t\t\t\t\t\tsort(entities.begin(), entities.end(), sort_key());\n\t\t\t\t\t}\n\t\t\t\t\telse if (entities[j]->getY() > 1 && world->getEntity(entities[j]->getX(), entities[j]->getY() - 1) == ' ') {\n\t\t\t\t\t\tentities[j]->makeChild(entities, entities[j]->getX(), entities[j]->getY() - 1);\n\t\t\t\t\t\tworld->addEntity(entities[j]->getName(), entities[j]->getX(), entities[j]->getY() - 1);\n\t\t\t\t\t\tlog.addBirth(entities[j]);\n\t\t\t\t\t\tsort(entities.begin(), entities.end(), sort_key());\n\t\t\t\t\t}\n\t\t\t\t\telse if (entities[j]->getY() < height && world->getEntity(entities[j]->getX(), entities[j]->getY() + 1) == ' ') {\n\t\t\t\t\t\tentities[j]->makeChild(entities, entities[j]->getX(), entities[j]->getY() + 1);\n\t\t\t\t\t\tworld->addEntity(entities[j]->getName(), entities[j]->getX(), entities[j]->getY() + 1);\n\t\t\t\t\t\tlog.addBirth(entities[j]);\n\t\t\t\t\t\tsort(entities.begin(), entities.end(), sort_key());\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if (entities[j]->collision(this) == 1) {\n\t\t\t\t\tif (this != entities[j]) {\n\t\t\t\t\t\tlog.addKill(entities[j], this);\n\t\t\t\t\t}\n\n\t\t\t\t}\n\t\t\t\telse if (entities[j]->collision(this) == 0) {\n\t\t\t\t\tif (dynamic_cast<Animal*>(entities[j]) == nullptr) {\n\t\t\t\t\t\tif (this != entities[j]) {\n\t\t\t\t\t\t\tlog.addFood(this, entities[j]);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tif (this != entities[j]) {\n\t\t\t\t\t\t\tlog.addKill(this, entities[j]);\n\t\t\t\t\t\t}\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse if (entities[j]->collision(this) == 2) {\n\t\t\t\t\tif (direction == 0) {\n\t\t\t\t\t\tx--;\n\t\t\t\t\t}\n\t\t\t\t\telse if (direction == 1) {\n\t\t\t\t\t\tx++;\n\t\t\t\t\t}\n\t\t\t\t\telse if (direction == 2) {\n\t\t\t\t\t\ty++;\n\t\t\t\t\t}\n\t\t\t\t\telse if (direction == 3) {\n\t\t\t\t\t\ty--;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\telse  if (entities[j]->collision(this) == 3) {\n\t\t\t\t\tif (entities[j]->getX() < width && world->getEntity(entities[j]->getX() + 1, entities[j]->getY()) != ' ') {\n\t\t\t\t\t\tentities[j]->setCoords(entities[j]->getX() + 1, entities[j]->getY());\n\t\t\t\t\t}\n\t\t\t\t\telse if (entities[j]->getX() > 1 && world->getEntity(entities[j]->getX() - 1, entities[j]->getY()) != ' ') {\n\t\t\t\t\t\tentities[j]->setCoords(entities[j]->getX() - 1, entities[j]->getY());\n\t\t\t\t\t}\n\t\t\t\t\telse if (entities[j]->getY() > 1 && world->getEntity(entities[j]->getX(), entities[j]->getY() - 1) != ' ') {\n\t\t\t\t\t\tentities[j]->setCoords(entities[j]->getX(), entities[j]->getY() - 1);\n\t\t\t\t\t}\n\t\t\t\t\telse if (entities[j]->getY() < height && world->getEntity(entities[j]->getX(), entities[j]->getY() + 1) != ' ') {\n\t\t\t\t\t\tentities[j]->setCoords(entities[j]->getX(), entities[j]->getY() + 1);\n\t\t\t\t\t}\n\t\t\t\t\telse {\n\t\t\t\t\t\tentities[j]->setCoords(0, 0);\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t\tbreak;\n\t\t\t}\n\t\t}\n\t}\n\telse {\n\t\tif (world->getEntity(x + 1, y) == 'B') {\n\t\t\tfor (int j = 0; j < entities.size(); j++) {\n\t\t\t\tif (x + 1 == entities[j]->getX() && y == entities[j]->getY()) {\n\t\t\t\t\tentities[j]->collision(this);\n\t\t\t\t\tlog.addKill(entities[j], this);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\telse if (world->getEntity(x - 1, y) == 'B') {\n\t\t\tfor (int j = 0; j < entities.size(); j++) {\n\t\t\t\tif (x - 1 == entities[j]->getX() && y == entities[j]->getY()) {\n\t\t\t\t\tentities[j]->collision(this);\n\t\t\t\t\tlog.addKill(entities[j], this);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\telse if (world->getEntity(x, y + 1) == 'B') {\n\t\t\tfor (int j = 0; j < entities.size(); j++) {\n\t\t\t\tif (x == entities[j]->getX() && y + 1 == entities[j]->getY()) {\n\t\t\t\t\tentities[j]->collision(this);\n\t\t\t\t\tlog.addKill(entities[j], this);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\telse if (world->getEntity(x, y - 1) == 'B') {\n\t\t\tfor (int j = 0; j < entities.size(); j++) {\n\t\t\t\tif (x == entities[j]->getX() && y - 1 == entities[j]->getY()) {\n\t\t\t\t\tentities[j]->collision(this);\n\t\t\t\t\tlog.addKill(entities[j], this);\n\t\t\t\t\tbreak;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n}\n\nint Antelope::collision(Entity* attacker) {\n\tif (attacker != this) {\n\t\tif (strength > attacker->getStrength()) {\n\t\t\tif (dynamic_cast<Fox*>(attacker) == nullptr) {\n\t\t\t\tattacker->setCoords(0, 0);\n\t\t\t\treturn 1;\n\t\t\t}\n\t\t\telse 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/memset0/OI-Code/blob/237e66d21520651a87764c385345e250f73b245c/LJOJ/4973%20%E5%AE%8C%E7%BE%8E%E5%8C%B9%E9%85%8D.cpp", "id": "cpp-0-8f2aff3bd1febe518109f3f851b237c1", "text": "// ==============================\n//  author: memset0\n//  website: https://memset0.cn\n// ==============================\n#include <bits/stdc++.h>\n#define ll long long\nusing namespace std;\n\nint read() {\n    int x = 0; bool m = 0; char c = getchar();\n    while (!isdigit(c) && c != '-') c = getchar();\n    if (c == '-') m = 1, c = getchar();\n    while (isdigit(c)) x = x * 10 + c - '0', c = getchar();\n    if (m) return -x; else return x;\n}\n\nconst int maxn = 35, maxm = 8010, mod = 1e6 + 3;\n\nint n, m, u, v, tu, tv, ln, rn, cnt;\nint f[1 << 24], g[1 << 24];\n\nint tot = 2, hed[maxn], nxt[maxm], to[maxm];\n\nvoid dfs1(int s, int t) {\n\tif (~f[s]) return;\n\tcnt++;\n\tf[s] = 0;\n\tfor (int u = 1; u <= ln; u++) {\n\t\tint tu = 1 << (u - 1);\n\t\tif ((s & tu) == 0) {\n\t\t\tfor (int i = hed[u], v = to[i]; i; i = nxt[i], v = to[i]) {\n\t\t\t\tint tv = 1 << (v - 1);\n\t\t\t\tif ((s & tv) == 0) {\n\t\t\t\t\tif (v <= ln) {\n\t\t\t\t\t\tdfs(s | tu | tv);\n\t\t\t\t\t\tf[s] = (f[s] + f[s | tu | tv]) % mod;\n\t\t\t\t\t\t// printf(\"%d <- %d\\n\", s, s | tu | tv);\n\t\t\t\t\t} else {\n\t\t\t\t\t\tdfs()\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t}\n}\n\nvoid dfs2(int s) {\n\tif (~g[s]) return;\n\tg[s] = 0;\n\tfor (int u = 25; u <= rn; u++) {\n\t\tint tu = 1 << (u - 1);\n\t\tif ((s & tu) == 0) {\n\t\t\tfor (int i = hed[u], v = to[i]; i; i = nxt[i], v = to[i]) {\n\t\t\t\tint tv = 1 << (v - 1);\n\t\t\t\tif ((s & tv) == 0 && tv > 24) {\n\t\t\t\t\tdfs(s | tu | tv);\n\t\t\t\t\tg[s] = (g[s] + g[s | tu | tv]) % mod;\n\t\t\t\t\t// printf(\"%d <- %d\\n\", s, s | tu | tv);\n\t\t\t\t}\n\t\t\t}\n\t\t\tbreak;\n\t\t}\n\t}\n}\n\nint main() {\n\t// freopen(\"INPUT\", \"r\", stdin);\n\tmemset(f, -1, sizeof(f));\n\n\tn = read(), m = read();\n\tfor (int i = 1; i <= m; i++) {\n\t\tu = read(), v = read();\n\t\tif (u > v) swap(u, v);\n\t\tnxt[tot] = hed[u], to[tot] = v, hed[u] = tot++;\n\t\t// nxt[tot] = hed[v], to[tot] = u, hed[v] = tot++;\n\t}\n\t\n\n\t// ln = 2, rn = 2;\n\t// f[(1 << ln) - 1] = 1;\n\t// g[(1 << rn) - 1] = 1;\n\t// dfs2(0);\n\t// dfs1(0);\n\n\t// if (n <= 24) {\n\t// \tln = n;\n\t// \tf[(1 << ln) - 1] = 1;\n\t// \tdfs1(0);\n\t// \tprintf(\"%d\\n\", f[0]);\n\t// } else {\n\t// \tln = 24, rn = n - 24;\n\t// \tf[(1 << ln) - 1] = 1;\n\t// \tg[(1 << rn) - 1] = 1;\n\t// \tdfs1(0), dfs2(0);\n\t// \tfor (int i = 1; i < (1 << ln); i++) {\n\t// \t\tfor (int j = 1; j < (1 << rn); j++) {\n\t// \t\t\tdfs3(i, j);\n\t// \t\t}\n\t// \t}\n\t// \treturn 0;\n\t// }\n\n\t// for (int i = 0; i < (1 << ln); i++)\n\t// \tprintf(\"%d %d\\n\", i, f[i]);\n\n\treturn 0;\n}\n\n// // ==============================\n// //  author: memset0\n// //  website: https://memset0.cn\n// // ==============================\n// #include <bits/stdc++.h>\n// #define ll long long\n// using namespace std;\n\n// int read() {\n//     int x = 0; bool m = 0; char c = getchar();\n//     while (!isdigit(c) && c != '-') c = getchar();\n//     if (c == '-') m = 1, c = getchar();\n//     while (isdigit(c)) x = x * 10 + c - '0', c = getchar();\n//     if (m) return -x; else return x;\n// }\n\n// const int maxn = 35, maxm = 8010, mod = 1e6 + 3;\n\n// int n, m, u, v, tu, tv, ln, rn, cnt;\n// int f[1 << 24], g[1 << 24];\n\n// int tot = 2, hed[maxn], nxt[maxm], to[maxm];\n\n// void dfs1(int s, int t) {\n// \tif (~f[s]) return;\n// \tcnt++;\n// \tf[s] = 0;\n// \tfor (int u = 1; u <= ln; u++) {\n// \t\tint tu = 1 << (u - 1);\n// \t\tif ((s & tu) == 0) {\n// \t\t\tfor (int i = hed[u], v = to[i]; i; i = nxt[i], v = to[i]) {\n// \t\t\t\tint tv = 1 << (v - 1);\n// \t\t\t\tif ((s & tv) == 0) {\n// \t\t\t\t\tif (v <= ln) {\n// \t\t\t\t\t\tdfs(s | tu | tv);\n// \t\t\t\t\t\tf[s] = (f[s] + f[s | tu | tv]) % mod;\n// \t\t\t\t\t\t// printf(\"%d <- %d\\n\", s, s | tu | tv);\n// \t\t\t\t\t} else {\n// \t\t\t\t\t\tdfs()\n// \t\t\t\t\t}\n// \t\t\t\t}\n// \t\t\t}\n// \t\t\tbreak;\n// \t\t}\n// \t}\n// }\n\n// void dfs2(int s) {\n// \tif (~g[s]) return;\n// \tg[s] = 0;\n// \tfor (int u = 25; u <= rn; u++) {\n// \t\tint tu = 1 << (u - 1);\n// \t\tif ((s & tu) == 0) {\n// \t\t\tfor (int i = hed[u], v = to[i]; i; i = nxt[i], v = to[i]) {\n// \t\t\t\tint tv = 1 << (v - 1);\n// \t\t\t\tif ((s & tv) == 0 && tv > 24) {\n// \t\t\t\t\tdfs(s | tu | tv);\n// \t\t\t\t\tg[s] = (g[s] + g[s | tu | tv]) % mod;\n// \t\t\t\t\t// printf(\"%d <- %d\\n\", s, s | tu | tv);\n// \t\t\t\t}\n// \t\t\t}\n// \t\t\tbreak;\n// \t\t}\n// \t}\n// }\n\n// int main() {\n// \t// freopen(\"INPUT\", \"r\", stdin);\n// \tmemset(f, -1, sizeof(f));\n\n// \tn = read(), m = read();\n// \tfor (int i = 1; i <= m; i++) {\n// \t\tu = read(), v = read();\n// \t\tif (u > v) swap(u, v);\n// \t\tnxt[tot] = hed[u], to[tot] = v, hed[u] = tot++;\n// \t\t// nxt[tot] = hed[v], to[tot] = u, hed[v] = tot++;\n// \t}\n\t\n\n// \t// ln = 2, rn = 2;\n// \t// f[(1 << ln) - 1] = 1;\n// \t// g[(1 << rn) - 1] = 1;\n// \t// dfs2(0);\n// \t// dfs1(0);\n\n// \t// if (n <= 24) {\n// \t// \tln = n;\n// \t// \tf[(1 << ln) - 1] = 1;\n// \t// \tdfs1(0);\n// \t// \tprintf(\"%d\\n\", f[0]);\n// \t// } else {\n// \t// \tln = 24, rn = n - 24;\n// \t// \tf[(1 << ln) - 1] = 1;\n// \t// \tg[(1 << rn) - 1] = 1;\n// \t// \tdfs1(0), dfs2(0);\n// \t// \tfor (int i = 1; i < (1 << ln); i++) {\n// \t// \t\tfor (int j = 1; j < (1 << rn); j++) {\n// \t// \t\t\tdfs3(i, j);\n// \t// \t\t}\n// \t// \t}\n// \t// \treturn 0;\n// \t// }\n\n// \t// for (int i = 0; i < (1 << ln); i++)\n// \t// \tprintf(\"%d %d\\n\", i, f[i]);\n\n// \treturn 0;\n// }\n\n\n\n// // // ==============================\n// // //  author: memset0\n// // //  website: https://memset0.cn\n// // // ==============================\n// // #include <bits/stdc++.h>\n// // #define ll long long\n// // using namespace std;\n\n// // int read() {\n// //     int x = 0; bool m = 0; char c = getchar();\n// //     while (!isdigit(c) && c != '-') c = getchar();\n// //     if (c == '-') m = 1, c = getchar();\n// //     while (isdigit(c)) x = x * 10 + c - '0', c = getchar();\n// //     if (m) return -x; else return x;\n// // }\n\n// // const int maxn = 35, maxm = 8010, mod = 1e6 + 3;\n\n// // int n, m, u, v, tu, tv, ln, rn;\n// // int f[1 << 24];\n\n// // int tot = 2, hed[maxn], nxt[maxm], to[maxm];\n\n// // int main() {\n// // \t// freopen(\"INPUT\", \"r\", stdin);\n\t\n// // \tn = read(), m = read();\n// // \tfor (int i = 1; i <= m; i++) {\n// // \t\tu = read(), v = read();\n// // \t\tif (u > v) swap(u, v);\n// // \t\tnxt[tot] = hed[u], to[tot] = v, hed[u] = tot++;\n// // \t\t// nxt[tot] = hed[v], to[tot] = u, hed[v] = tot++;\n// // \t}\n\t\n// // \tif (n > 24) {\n// // \t\tputs(\"0\");\n// // \t\treturn 0;\n// // \t}\n// // \tln = min(24, n);\n// // \tf[0] = 1;\n// // \t// for (int t = 0; t <= ((1 << ln) - 1); t++) {\n// // \t// \tfor (int u = 1; u <= ln; u++)\n// // \t// \t\tif (!(t & (1 << (u - 1))))\n// // \t// \t\t\tfor (int i = hed[u], v = to[i]; i; i = nxt[i], v = to[i])\n// // \t// \t\t\t\tif (!(t & (1 << (v - 1)))) {\n// // \t// \t\t\t\t\tf[t | (1 << (u - 1)) | (1 << (v - 1))] += f[t];\n// // \t// \t\t\t\t\tprintf(\"%d(%d) -> %d(%d) : %d %d\\n\", t, f[t], t | (1 << (u - 1)) | (1 << (v - 1)), f[t | (1 << (u - 1)) | (1 << (v - 1))], u, v);\n// // \t// \t\t\t\t}\n// // \t// }\n// // \t// for (int u = 1; u <= ln; u++)\n// // \t// \t\tfor (int i = hed[u], v = to[i]; i; i = nxt[i], v = to[i])\n// // \t// \t\t\tfor (int t = 0; t <= ((1 << ln) - 1); t++) {\n// // \t// \t\t\t\ttu = 1 << (u - 1), tv = 1 << (v - 1);\n// // \t// \t\t\t\tif ((t & tu) == 0 && (t & tv) == 0)\n// // \t// \t\t\t\t\tf[t | tu | tv] = (f[t] + f[t | tu | tv]) % mod;\n// // \t// \t\t\t}\n// // \tcout << f[(1 << ln) - 1] << endl;\n\n// // \treturn 0;\n// // }\n", "text_preview": "// ============================== // author: memset0 // website: https://memset0.cn // ============================== #include <bits/stdc++.h> #define ll long long using namespace std; int read() { int x = 0; bool m = 0; char c = getchar();", "input_sha256": "d366662cb0e70391500c7d632ff0da7a99ec01bf2a1d606defea649a1f77f7be", "text_sha256": "8f2aff3bd1febe518109f3f851b237c1d70e369a64cb2de2a15f8e0ce1c7a0e2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 77, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": 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Ads": 0.0009801730449564329, "Structured Data": 0.0005390831089009136, "Technical Writing": 0.00793814525341496, "Transcript / Interview": 0.0001405122038034499, "Tutorial / How-To Guide": 0.0019125502493647839, "User Reviews": 7.750025317906857e-05, "NONE": 0.9703219368861066}, "structured_records": {"false": 0.99879963092116, "true": 0.0012003690788399807}, "procedural_steps": {"false": 0.9977048807042314, "true": 0.0022951192957686}, "worked_solution": {"false": 0.9938363001258459, "true": 0.006163699874154055}, "contextual_dialogue": {"false": 0.9999924005199946, "true": 7.599480005368789e-06}, "content_quality": {"-2": 0.09019016479943473, "-1": 0.3945548198426688, "0": 0.3435033424433849, "1": 0.1312538261670543, "2": 0.04049784674745726}, "noise_severity": {"0": 0.26366087937192867, "1": 0.2255331456123551, "2": 0.31373512376683194, "3": 0.1588749967520396, "4": 0.03819585449684475}, "missing_content_severity": {"0": 0.7721679816335953, "1": 0.12933371637034374, "2": 0.07535699747966754, "3": 0.020432003910804908, "4": 0.0027093006055885385}, "factual_density": {"0": 0.028800077749262958, "1": 0.10869979097021745, "2": 0.23363876446134516, "3": 0.42519109074249484, "4": 0.2036702760766796}, "pretraining_utility": {"-2": 0.08805395454941461, "-1": 0.36248361462315254, "0": 0.34687792766392667, "1": 0.15632478528622043, "2": 0.046259717877285784}, "math_density": {"0": 0.034184089730924815, "1": 0.06341495998239959, "2": 0.10314872979608714, "3": 0.3178647170785996, "4": 0.48138750341198894}, "code_density": {"0": 0.00025108238986660094, "1": 0.0006017842443676472, "2": 0.021110054581544812, "3": 0.12925447048652125, "4": 0.8487826082976997}, "reasoning_density": {"0": 0.5457464314456085, "1": 0.046744024237320075, "2": 0.09521804055505491, "3": 0.11439117553317128, "4": 0.19790032822884526}, "education_level": {"none": 0.003413956795510638, "0": 0.001987654949117347, "1": 0.03638419238249452, "2": 0.6697801331865421, "3": 0.2031961696621591, "4": 0.08523789302417636}, "creative_writing_quality": {"not_applicable": 0.9986286216307969, "-2": 0.00028789492644139535, "-1": 0.0004313104260059495, "0": 0.000498829742715674, "1": 0.00010878960174101821, "2": 4.45536722990097e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00011754444369746441, "Art & Design": 0.0001588011942756045, "Law & Crime": 0.00015531278338618294, "Education & Careers": 0.0001879375306481136, "Entertainment & Media": 0.00012478816353729072, "Fashion & Beauty": 8.442264983400503e-05, "Business, Economics & Finance": 0.0001328476471984062, "Food & Dining": 8.95587106193219e-05, "Games & Gaming": 0.001190794360821631, "Electronics & Hardware": 0.00013917488408869783, "Health, Healthcare & Wellness": 0.00010460338772368575, "History & Geography": 0.0001528534155536497, "Home, Pets & Hobbies": 8.379403159571722e-05, "Industry & Production": 8.859500123128467e-05, "Language, Literature & Philosophy": 0.00011892451963660622, "Politics & Government": 5.242746445417897e-05, "Religion & Spirituality": 9.57917529016963e-05, "Science, Mathematics & Engineering": 0.04748070487127166, "Relationships & Community": 5.883042998422317e-05, "Software Usage & Applications": 0.00024168714144052442, "Programming & Software Engineering": 0.9475922186530272, "Sports & Fitness": 0.0001453007756855967, "Vehicles & Transportation": 5.9766734292163935e-05, "Travel & Tourism": 0.00013119278290129295, "Other / Uncovered Subject": 0.0006449155893719607, "Unknown / Insufficient Content": 0.0005672110808219046}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005155399174567675, "Art & Design": 0.0004768112590826245, "Law & Crime": 0.0005435686759278069, "Education & Careers": 0.000713285464912695, "Entertainment & Media": 0.0004949799022797419, "Fashion & Beauty": 0.0002917983135348744, "Business, Economics & Finance": 0.00044905038238346787, "Food & Dining": 0.00023731282816644487, "Games & Gaming": 0.0017587573559987167, "Electronics & Hardware": 0.00039958493112348146, "Health, Healthcare & Wellness": 0.000342818254824579, "History & Geography": 0.0003293301563356432, "Home, Pets & Hobbies": 0.00024308005481638365, "Industry & Production": 0.00024476505045228295, "Language, Literature & Philosophy": 0.00021513868144010115, "Politics & Government": 0.00020901416307120665, "Religion & Spirituality": 0.00035291759054977115, "Science, Mathematics & Engineering": 0.28037241712122685, "Relationships & Community": 0.0001772478716936459, "Software Usage & Applications": 0.0004358555297951201, "Programming & Software Engineering": 0.10009846319912373, "Sports & Fitness": 0.0003394967507712323, "Vehicles & Transportation": 0.00016271873666916651, "Travel & Tourism": 0.000457909291051871, "Other / Uncovered Subject": 0.0010311860402973277, "NONE": 0.6091069524770145}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 11772, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 11772, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, "scored_at_utc": "2026-10-25T18:34:10.841003+00:00", "scoring_key_slot": "assistant_1", "dataset_uid": "4359a86c-9bab-4743-9c39-e79b54da0840", "dataset_revision": "62077439f2c81c79b71c084d1057ee1d042369a3", "dataset_config": "default", "content_variant": "content", "source_record_sha256": "9f59a2336eb60bcd2cf635bda8c72f3965f108ac2fce20444048a03ac9cbd945", "source_metadata": {"uuid": "4359a86c-9bab-4743-9c39-e79b54da0840", "repo_name": "memset0/OI-Code", "relative_path": "LJOJ/4973 完美匹配.cpp", "category": "ALGO", "algo_rel_score": 0.9999523162841797, "quality_score": 4.107203960418701, "source_meta": {"github_metadata": {"branch": "master", "commit_count": 294, "repo_created_at": "2018-07-31T14:56:49.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 14, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4756fadaa4dc1d4f63c62c8fbc02f1acf47fc618", "size_bytes": 6908, "file_timestamp": 1546691908, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "237e66d21520651a87764c385345e250f73b245c"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 294, "repo_created_at": "2018-07-31T14:56:49.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 14, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "4756fadaa4dc1d4f63c62c8fbc02f1acf47fc618", "size_bytes": 6908, "file_timestamp": 1546691908, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "237e66d21520651a87764c385345e250f73b245c", "url": "https://github.com/memset0/OI-Code/blob/237e66d21520651a87764c385345e250f73b245c/LJOJ/4973%20%E5%AE%8C%E7%BE%8E%E5%8C%B9%E9%85%8D.cpp"}, "gpt4o_tokens": 2869, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": -0.3626856297795695, "noise_severity": 1.4824118013895167, "missing_content_severity": 0.3521809254844477, "factual_density": 2.666231696427111, "pretraining_utility": -0.28974730268118976, "math_density": 3.1488565844583283, "code_density": 3.82571573805782, "reasoning_density": 1.3719549448623247, "education_level": 2.3344542658434464, "creative_writing_quality": -0.5900656964714687}, "score_applicability": {"education_level": 0.9965860432044894, "creative_writing_quality": 0.0013713783692030467}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/WiktorKociuba/AOC_2023/blob/fea90b3db8b129df07d409a036440e0aee6d5e25/Day_3/gearc2.cpp", "id": "cpp-0-d43dd1f599edc99c88333aa645f89d9d", "text": "#include <bits/stdc++.h>\nusing namespace std;\n\nchar silnik[142][142];\nvector<int> cor;\n\nbool czy_dwa(int i, int j){\n    int ile_liczb = 0;\n    //cout << 1 << \" \";\n    if(isdigit(silnik[i][j+1])){\n        ile_liczb++;\n    }\n    if(isdigit(silnik[i][j-1])){\n        ile_liczb++;\n    }\n    if(isdigit(silnik[i+1][j-1]) && !isdigit(silnik[i+1][j]) && isdigit(silnik[i+1][j+1])){\n        ile_liczb += 2;\n    }\n    else{\n        if(isdigit(silnik[i+1][j-1])){\n            ile_liczb++;\n        }\n        else if(isdigit(silnik[i+1][j])){\n            ile_liczb++;\n        }\n        else if(isdigit(silnik[i+1][j+1])){\n            ile_liczb++;\n        }\n    }\n    if(isdigit(silnik[i-1][j-1]) && !isdigit(silnik[i-1][j]) && isdigit(silnik[i-1][j+1])){\n        ile_liczb += 2;\n    }\n    else{\n        if(isdigit(silnik[i-1][j-1])){\n            ile_liczb++;\n        }\n        else if(isdigit(silnik[i-1][j])){\n            ile_liczb++;\n        }\n        else if(isdigit(silnik[i-1][j+1])){\n            ile_liczb++;\n        }\n    }\n    if(ile_liczb == 2){\n        return true;\n    }\n    return false;\n\n}\n\nvoid liczba(int i, int j){\n    if(isdigit(silnik[i][j+1])){\n        cor.push_back(i);\n        cor.push_back(j+1);\n    }\n    if(isdigit(silnik[i][j-1])){\n        cor.push_back(i);\n        cor.push_back(j-1);\n    }\n    if(isdigit(silnik[i-1][j-1]) && isdigit(silnik[i-1][j+1]) && silnik[i-1][j] == '.'){\n        cor.push_back(i-1);\n        cor.push_back(j-1);\n        cor.push_back(i-1);\n        cor.push_back(j+1);\n    }\n    else if(isdigit(silnik[i-1][j])){\n        cor.push_back(i-1);\n        cor.push_back(j);\n    }\n    else if(isdigit(silnik[i-1][j-1])){\n        cor.push_back(i-1);\n        cor.push_back(j-1);\n    }\n    else if(isdigit(silnik[i-1][j+1])){\n        cor.push_back(i-1);\n        cor.push_back(j+1);\n    }\n    if(isdigit(silnik[i+1][j-1]) && isdigit(silnik[i+1][j+1]) && silnik[i+1][j] == '.'){\n        cor.push_back(i+1);\n        cor.push_back(j-1);\n        cor.push_back(i+1);\n        cor.push_back(j+1);\n    }\n    else if(isdigit(silnik[i+1][j-1])){\n        cor.push_back(i+1);\n        cor.push_back(j-1);\n    }\n    else if(isdigit(silnik[i+1][j+1])){\n        cor.push_back(i+1);\n        cor.push_back(j+1);\n    }\n    else if(isdigit(silnik[i+1][j])){\n        cor.push_back(i+1);\n        cor.push_back(j);\n    }\n}\n\nint main(){\n    \n    for (int i = 0; i < 142; i++){\n        for (int j = 0; j < 142; j++){\n            cin >> silnik[i][j];\n        }\n    }\n\n    int pierwsza = 0, druga = 0, trzecia = 0,p = 0, d = 0;\n    long long wynik = 0;\n    for (int i = 1; i < 142; i++){\n        for (int j = 1; j < 142; j++){\n            if(silnik[i][j] == '*'){\n                cor.clear();\n                if(czy_dwa(i,j)){\n                        liczba(i,j);\n                        if(isdigit(silnik[cor[0]][cor[1]-1]) && isdigit(silnik[cor[0]][cor[1]+1])){\n                            pierwsza = (silnik[cor[0]][cor[1]-1]-48)*100;\n                            druga = (silnik[cor[0]][cor[1]]-48)*10;\n                            trzecia = silnik[cor[0]][cor[1]+1]-48;\n                            p = pierwsza+druga+trzecia;\n                        }\n                        else if(isdigit(silnik[cor[0]][cor[1]-1]) && isdigit(silnik[cor[0]][cor[1]-2])){\n                            pierwsza = (silnik[cor[0]][cor[1]-2]-48)*100;\n                            druga = (silnik[cor[0]][cor[1]-1]-48)*10;\n                            trzecia = silnik[cor[0]][cor[1]]-48;\n                            p = pierwsza+druga+trzecia;\n                        }\n                        else if(isdigit(silnik[cor[0]][cor[1]-1])){\n                            pierwsza = (silnik[cor[0]][cor[1]-1]-48)*10;\n                            druga = silnik[cor[0]][cor[1]]-48;\n                            p = pierwsza+druga;\n                        }\n                        else if(isdigit(silnik[cor[0]][cor[1]+1]) && isdigit(silnik[cor[0]][cor[1]+2])){\n                            pierwsza = (silnik[cor[0]][cor[1]]-48)*100;\n                            druga = (silnik[cor[0]][cor[1]+1]-48)*10;\n                            trzecia = silnik[cor[0]][cor[1]+2]-48;\n                            p = pierwsza+druga+trzecia;\n                        }\n                        else if(isdigit(silnik[cor[0]][cor[1]+1])){\n                            pierwsza = (silnik[cor[0]][cor[1]]-48)*10;\n                            druga = silnik[cor[0]][cor[1]+1]-48;\n                            p = pierwsza+druga;\n                        }\n                        else{\n                            p = silnik[cor[0]][cor[1]]-48;\n                        }\n                        if(isdigit(silnik[cor[2]][cor[3]-1]) && isdigit(silnik[cor[2]][cor[3]+1])){\n                            pierwsza = (silnik[cor[2]][cor[3]-1]-48)*100;\n                            druga = (silnik[cor[2]][cor[3]]-48)*10;\n                            trzecia = silnik[cor[2]][cor[3]+1]-48;\n                            d = pierwsza+druga+trzecia;\n                        }\n                        else if(isdigit(silnik[cor[2]][cor[3]-1]) && isdigit(silnik[cor[2]][cor[3]-2])){\n                            pierwsza = (silnik[cor[2]][cor[3]-2]-48)*100;\n                            druga = (silnik[cor[2]][cor[3]-1]-48)*10;\n                            trzecia = silnik[cor[2]][cor[3]]-48;\n                            d = pierwsza+druga+trzecia;\n                        }\n                        else if(isdigit(silnik[cor[2]][cor[3]-1])){\n                            pierwsza = (silnik[cor[2]][cor[3]-1]-48)*10;\n                            druga = silnik[cor[2]][cor[3]]-48;\n                            d = pierwsza+druga;\n                        }\n                        else if(isdigit(silnik[cor[2]][cor[3]+1]) && isdigit(silnik[cor[2]][cor[3]+2])){\n                            pierwsza = (silnik[cor[2]][cor[3]]-48)*100;\n                            druga = (silnik[cor[2]][cor[3]+1]-48)*10;\n                            trzecia = silnik[cor[2]][cor[3]+2]-48;\n                            d = pierwsza+druga+trzecia;\n                        }\n                        else if(isdigit(silnik[cor[2]][cor[3]+1])){\n                            pierwsza = (silnik[cor[2]][cor[3]]-48)*10;\n                            druga = silnik[cor[2]][cor[3]+1]-48;\n                            d = pierwsza+druga;\n                        }\n                        else{\n                            d = silnik[cor[2]][cor[3]]-48;\n                        }\n                        wynik += p*d;\n                }\n            }\n        }    \n    }\n    cout << wynik;\n}", "text_preview": "#include <bits/stdc++.h> using namespace std; char silnik[142][142]; vector<int> cor; bool czy_dwa(int i, int j){ int ile_liczb = 0; //cout << 1 << \" \"; if(isdigit(silnik[i][j+1])){ ile_liczb++; } if(isdigit(silnik[i][j-1])){ ile_liczb++; }", "input_sha256": "e01d1ac02ce20fbe9a9827e7c4e96132a19ba12a4bee5b7551d1997659e7b2f5", "text_sha256": "d43dd1f599edc99c88333aa645f89d9d63e6accacb57d1afc3a5b5866a36ed3e", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 79, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.35739983663692665, "noise_severity": 0.05891453103526437, "missing_content_severity": 0.006937486310796116, "factual_density": 2.576579493109331, "pretraining_utility": 0.17483028192347913, "math_density": 2.416769116833882, "code_density": 3.90930189395768, "reasoning_density": 1.792471516560104, "education_level": 1.8191547984777516, "creative_writing_quality": -0.48927065831491806}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0141216119947839, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9924787613559463, "secondary": 0.981927876786972, "tertiary": 0.9966634719119639, "structured_records": 0.0010810198965734356, "procedural_steps": 0.0014787411872751776, "worked_solution": 0.022950238411677246, "contextual_dialogue": 1.0603092126457574e-05, "content_quality": 0.29371737626890637, "noise_severity": 0.950904557470613, "missing_content_severity": 0.9942187614076698, "factual_density": 0.20306774209401657, "pretraining_utility": 0.49336897549161207, "math_density": 0.0, "code_density": 0.9244182449647331, "reasoning_density": 0.0, "education_level": 0.6806599816816172, "creative_writing_quality": 0.9981946930797191, "primary_topic": 0.9689666368616249, "secondary_topic": 0.796517978138822}, "probability_margin": {"primary": 0.9883390657196973, "secondary": 0.9696320106634078, "tertiary": 0.9947680113725347, "education_level": 0.5138157278528878, "creative_writing_quality": 0.997802717967998, "primary_topic": 0.954672924244992, "secondary_topic": 0.6975423064710746}, "raw_probabilities": {"primary": {"Academic Writing": 9.48950021983817e-05, "Content Listing": 3.65238919303943e-05, "Creative Writing": 5.9067223263123364e-05, "Customer Support Page": 1.2120685360368824e-05, "Q&A Forum": 2.4665221548051974e-05, "Discussion Thread": 1.8050960846842623e-05, "Comment Section": 3.0048504919776136e-05, "Missing Content": 6.572808581389332e-05, "Knowledge Article": 9.874329707177626e-05, "Legal Notices": 4.468650698206783e-05, "News Article": 9.200920715643097e-06, "Organizational Announcement": 1.5701301349513093e-05, "Nonfiction Writing": 3.356692377007152e-05, "Organizational About Page": 1.8419759309498738e-05, "Personal About Page": 2.675186935535768e-05, "Personal Blog": 1.710873356377457e-05, "Product Page": 1.0580281666900106e-05, "Spam / Ads": 2.1518528664475118e-05, "Structured Data": 0.0004712978854567143, "Technical Writing": 0.9927921462994485, "Transcript / Interview": 2.7505023857565224e-05, "Tutorial / How-To Guide": 0.0016069402429503906, "User Reviews": 1.1652270205512592e-05, "Other / Unknown": 0.004453080579751182}, "secondary": {"Academic Writing": 0.00014665671783649118, "Content Listing": 5.453519795547694e-05, "Creative Writing": 8.566852620533861e-05, "Customer Support Page": 3.1430926431435265e-05, "Q&A Forum": 3.027650664660846e-05, "Discussion Thread": 2.5156157505146435e-05, "Comment Section": 4.457753533196571e-05, "Missing Content": 0.00011724858370947279, "Knowledge Article": 0.00014645685042694994, "Legal Notices": 4.8151589967319274e-05, "News Article": 3.422914633582437e-05, "Organizational Announcement": 3.350038390253933e-05, "Nonfiction Writing": 3.232727863595378e-05, "Organizational About Page": 3.86576213548284e-05, "Personal About Page": 4.232108001197427e-05, "Personal Blog": 3.0197279374559073e-05, "Product Page": 1.917551219354307e-05, "Spam / Ads": 3.745012152225913e-05, "Structured Data": 0.0007074475015186924, "Technical Writing": 0.013048871257440428, "Transcript / Interview": 3.9167560275972324e-05, "Tutorial / How-To Guide": 0.002507957738536191, "User Reviews": 1.765700603279042e-05, "NONE": 0.9826808819208482}, "tertiary": {"Academic Writing": 4.3691875494150496e-05, "Content Listing": 1.6631555132788863e-05, "Creative Writing": 2.9986603483032515e-05, "Customer Support Page": 9.77791886061091e-06, "Q&A Forum": 9.1234536834445e-06, "Discussion Thread": 8.387031697342741e-06, "Comment Section": 1.5664451185054515e-05, "Missing Content": 7.441163905229211e-05, "Knowledge Article": 5.4588471233490644e-05, "Legal Notices": 3.398206030759699e-05, "News Article": 1.2243065285964541e-05, "Organizational Announcement": 1.1409484676273486e-05, "Nonfiction Writing": 1.4855831555651707e-05, "Organizational About Page": 1.8652277646947297e-05, "Personal About Page": 1.678873108029377e-05, "Personal Blog": 1.36246984941563e-05, "Product Page": 7.261552367367203e-06, "Spam / Ads": 1.3191255860460864e-05, "Structured Data": 0.00022726626027813002, "Technical Writing": 0.0020344825430974075, "Transcript / Interview": 1.3236761133829081e-05, "Tutorial / How-To Guide": 0.0005112101396035652, "User Reviews": 7.0384231580057104e-06, "NONE": 0.9968024939156321}, "structured_records": {"false": 0.9989189801034266, "true": 0.0010810198965734356}, "procedural_steps": {"false": 0.9985212588127248, "true": 0.0014787411872751776}, "worked_solution": {"false": 0.9770497615883228, "true": 0.022950238411677246}, "contextual_dialogue": {"false": 0.9999893969078736, "true": 1.0603092126457574e-05}, "content_quality": {"-2": 0.03753729973601413, "-1": 0.1355284357970928, "0": 0.36140088511796487, "1": 0.36306388679180845, "2": 0.10246949255711961}, "noise_severity": {"0": 0.9461271935791007, "1": 0.05057417191306112, "2": 0.0021025437606703415, "3": 0.0006490913878088538, "4": 0.0005469993593590002}, "missing_content_severity": {"0": 0.9957008738262936, "1": 0.0024705754619453336, "2": 0.0012106571044051963, "3": 0.00042597778938264136, "4": 0.00019191581797311616}, "factual_density": {"0": 0.08304821047232792, "1": 0.1097417355023539, "2": 0.22124150576723298, "3": 0.3195194469598296, "4": 0.26644910129825555}, "pretraining_utility": {"-2": 0.017927963429941126, "-1": 0.18070754688341092, "0": 0.4695894978867709, "1": 0.2721562279329823, "2": 0.05961876386689504}, "math_density": {"0": 0.1764914079072807, "1": 0.13441581549239195, "2": 0.11197579831679226, "3": 0.2500662084262348, "4": 0.32705076985730036}, "code_density": {"0": 0.000244213401203901, "1": 0.00046595367327998945, "2": 0.0127880540236059, "3": 0.06274728337045282, "4": 0.9237544955314574}, "reasoning_density": {"0": 0.4353811772736416, "1": 0.03233739810548286, "2": 0.11252307746267931, "3": 0.14394542510352248, "4": 0.2758129220546737}, "education_level": {"none": 0.002134596032709631, "0": 0.0030265411874400685, "1": 0.22006759021512656, "2": 0.7338833180680143, "3": 0.03611440647595705, "4": 0.004773548020752501}, "creative_writing_quality": {"not_applicable": 0.9984955775664326, "-2": 0.000229779589355147, "-1": 0.0004488165445653113, "0": 0.0006928595984345228, "1": 9.362743360452067e-05, "2": 3.933926760791237e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00020402338757938485, "Art & Design": 0.00011492481623675965, "Law & Crime": 7.69084530429792e-05, "Education & Careers": 9.85041861721925e-05, "Entertainment & Media": 0.00019266733696557536, "Fashion & Beauty": 6.489175804016567e-05, "Business, Economics & Finance": 9.801989395579361e-05, "Food & Dining": 5.862546087492703e-05, "Games & Gaming": 0.010645912424348923, "Electronics & Hardware": 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0.000534781839622204, "Education & Careers": 0.0003749642675359972, "Entertainment & Media": 0.0009215143041866042, "Fashion & Beauty": 0.0002539541368494661, "Business, Economics & Finance": 0.0004799205969084645, "Food & Dining": 0.00021894866671262527, "Games & Gaming": 0.0328477216519233, "Electronics & Hardware": 0.000412336033118853, "Health, Healthcare & Wellness": 0.0002488890754767881, "History & Geography": 0.0003275359465285662, "Home, Pets & Hobbies": 0.00031610076628132684, "Industry & Production": 0.0002091834497824895, "Language, Literature & Philosophy": 0.00039792412009186784, "Politics & Government": 0.00022308357711341858, "Religion & Spirituality": 0.0003054492379579343, "Science, Mathematics & Engineering": 0.10680190327779257, "Relationships & Community": 0.00015242687391632033, "Software Usage & Applications": 0.00042745922777968345, "Programming & Software Engineering": 0.046028768830346574, "Sports & Fitness": 0.0003634652636777338, "Vehicles & Transportation": 0.00026945506929992234, "Travel & Tourism": 0.0003994714553452178, "Other / Uncovered Subject": 0.0018108050399944144, "NONE": 0.8043442097488672}}, "probabilities": {"primary": {"Academic Writing": 9.489500219838173e-05, "Content Listing": 3.6523891930394304e-05, "Creative Writing": 5.906722326312338e-05, "Customer Support Page": 1.2120685360368827e-05, "Q&A Forum": 2.466522154805198e-05, "Discussion Thread": 1.8050960846842627e-05, "Comment Section": 3.0048504919776143e-05, "Missing Content": 6.572808581389334e-05, "Knowledge Article": 9.874329707177629e-05, "Legal Notices": 4.468650698206784e-05, "News Article": 9.200920715643099e-06, "Organizational Announcement": 1.5701301349513097e-05, "Nonfiction Writing": 3.356692377007153e-05, "Organizational About Page": 1.841975930949874e-05, "Personal About Page": 2.6751869355357686e-05, "Personal Blog": 1.7108733563774574e-05, "Product Page": 1.0580281666900108e-05, "Spam / Ads": 2.151852866447512e-05, "Structured Data": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/58191554/CSC4005-Parallel-Computing-2023Fall/blob/2ad2d44ede7e4e22a5fb3b54342ed0ee2e0abbbe/project3/src/quicksort/mpi.cpp", "id": "cpp-0-dc9d2e0b11fc0a3f0c6d07c5728597ea", "text": "//\n// Created by <NAME>.\n// Email: <EMAIL>\n//\n// Parallel Quick Sort with MPI\n//\n\n\n#include <iostream>\n#include <vector>\n#include <mpi.h>\n#include <queue>\n#include <utility>\n#include \"../utils.hpp\"\n\n#define MASTER 0\n#define TAG_GATHER 0\n\nint partition(std::vector<int> &vec, int low, int high) {\n    int pivot = vec[high];\n    int i = low - 1;\n\n    for (int j = low; j < high; j++) {\n        if (vec[j] <= pivot) {\n            i++;\n            std::swap(vec[i], vec[j]);\n        }\n    }\n\n    std::swap(vec[i + 1], vec[high]);\n    return i + 1;\n}\n\nvoid quickSort(std::vector<int> &vec, int low, int high) {\n    if (low < high) {\n        int pivotIndex = partition(vec, low, high);\n        quickSort(vec, low, pivotIndex - 1);\n        quickSort(vec, pivotIndex + 1, high);\n    }\n}\n\nint main(int argc, char** argv) {\n    // Verify input argument format\n    if (argc != 2) {\n        throw std::invalid_argument(\n            \"Invalid argument, should be: ./executable vector_size\\n\"\n            );\n    }\n    // Start the MPI\n    MPI_Init(&argc, &argv);\n    // How many processes are running\n    int numtasks;\n    MPI_Comm_size(MPI_COMM_WORLD, &numtasks);\n    // What's my rank?\n    int taskid;\n    MPI_Comm_rank(MPI_COMM_WORLD, &taskid);\n    // Which node am I running on?\n    int len;\n    char hostname[MPI_MAX_PROCESSOR_NAME];\n    MPI_Get_processor_name(hostname, &len);\n    MPI_Status status;\n\n    const int size = atoi(argv[1]);\n\n    const int seed = 4005;\n\n    std::vector<int> vec = createRandomVec(size, seed);\n    std::vector<int> vec_clone = vec;\n\n    auto start_time = std::chrono::high_resolution_clock::now();\n    \n    // divide task\n    int num_per_task = size / numtasks;\n    int left_num = size % numtasks;\n    std::vector<int> cuts(numtasks + 1, 0);\n    int divided_left_num = 0;\n    for (int i = 0; i < numtasks; ++i) {\n        if (divided_left_num < left_num) {\n            cuts[i+1] = cuts[i] + num_per_task + 1;\n            divided_left_num++;\n        } else cuts[i+1] = cuts[i] + num_per_task;\n    }\n\n    if (taskid == MASTER) {\n        // malloc pointer array for answer storing\n        int ** rows = (int**)malloc(numtasks*sizeof(int*));\n        for(int i = 0; i < numtasks; ++i){\n            rows[i] = (int*)malloc((cuts[i+1]-cuts[i])*sizeof(int));\n        }\n\n        quickSort(vec, cuts[taskid], cuts[taskid+1]-1);\n        auto row = rows[MASTER];\n        int row_size = cuts[MASTER+1]-cuts[MASTER];\n        for(int i = 0; i < row_size; ++i){\n            row[i] = vec[i];\n        }\n\n        for (int tid = MASTER + 1; tid < numtasks; ++tid) {\n            int length = cuts[tid+1]-cuts[tid];\n            MPI_Recv(rows[tid], length, MPI_INT, tid, TAG_GATHER, MPI_COMM_WORLD, &status);\n        }\n\n\n        // TODO Merge answers\n        typedef std::pair<int, int> pi; \n        std::priority_queue<pi , std::vector<pi>, std::greater<pi> >compare_q;  \n        \n        // initialize the queue\n        int indcies[numtasks];\n        for(int i = 0; i < numtasks; i++){\n            auto p0 = std::make_pair(rows[i][0], i);\n            compare_q.push(p0);\n            indcies[i] = 1;\n        }\n        \n        int index = 0;\n        while(!compare_q.empty()){\n            auto smallest = compare_q.top();\n            // add the smallest element into the vector\n            vec[index] = smallest.first;\n            index++;\n            compare_q.pop();\n            int row_num = smallest.second;\n            // Check if there are element to add?\n            if(indcies[row_num] <cuts[row_num+1]-cuts[row_num]){\n                compare_q.push(std::make_pair(rows[row_num][indcies[row_num]], row_num));\n                indcies[row_num]+=1;\n            }\n        }\n\n\n\n        auto end_time = std::chrono::high_resolution_clock::now();\n        auto elapsed_time = std::chrono::duration_cast<std::chrono::milliseconds>(\n            end_time - start_time);\n        \n        std::cout << \"Quick Sort Complete!\" << std::endl;\n        std::cout << \"Execution Time: \" << elapsed_time.count() << \" milliseconds\"\n                << std::endl;\n        \n        checkSortResult(vec_clone, vec);\n    }else{\n        quickSort(vec, cuts[taskid], cuts[taskid+1]-1);\n        int slave_size = cuts[taskid+1]-cuts[taskid];\n        MPI_Send(&vec[cuts[taskid]], slave_size, MPI_INT, MASTER, TAG_GATHER, MPI_COMM_WORLD);\n    }\n\n    MPI_Finalize();\n    return 0;\n}", "text_preview": "// // Created by <NAME>. // Email: <EMAIL> // // Parallel Quick Sort with MPI // 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/dune-project/dune-grid-howto/blob/38ccfd017adc164467ed8c4d6934019526f8bbba/traversal.cc", "id": "cpp-0-db251513b1f6d1ced939dbab751d8e28", "text": "// -*- tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 2 -*-\n// vi: set et ts=4 sw=2 sts=2:\n#include <config.h>           // file generated by CMake\n\n// C/C++ includes\n#include <array>\n#include <iostream>           // for standard I/O\n\n// Dune includes\n#include <dune/grid/yaspgrid.hh> // load yaspgrid definition\n#include <dune/common/parallel/mpihelper.hh> // include mpi helper class\n\n// example for a generic algorithm that traverses\n// the entities of a given mesh in various ways\ntemplate<class G>                                      /*@\\label{tc:tra0}@*/\nvoid traversal (G& grid)\n{\n  // first we extract the dimensions of the grid\n  const int dim = G::dimension;                        /*@\\label{tc:dim}@*/\n\n  // type used for coordinates in the grid\n  // such a type is exported by every grid implementation\n  typedef typename G::ctype ct;                        /*@\\label{tc:ct}@*/\n\n  // Leaf Traversal\n  std::cout << \"*** Traverse codim 0 leaves\" << std::endl;\n\n  // type of the GridView used for traversal\n  // every grid exports a LeafGridView and a LevelGridView\n  typedef typename G :: LeafGridView LeafGridView;     /*@\\label{tc:lfgv}@*/\n\n  // get the instance of the LeafGridView\n  LeafGridView leafView = grid.leafGridView();         /*@\\label{tc:lfv}@*/\n\n  // Get the iterator type\n  // Note the use of the typename and template keywords\n  typedef typename LeafGridView::template Codim<0>::Iterator ElementLeafIterator; /*@\\label{tc:ittype}@*/\n\n  // iterate through all entities of codim 0 at the leaves\n  int count = 0;\n  for (ElementLeafIterator it = leafView.template begin<0>();      /*@\\label{tc:forel}@*/\n       it!=leafView.template end<0>(); ++it)\n  {                                                    /*@\\label{tc:forel0}@*/\n    Dune::GeometryType gt = it->type();       /*@\\label{tc:reftype}@*/\n    std::cout << \"visiting leaf \" << gt                /*@\\label{tc:print}@*/\n              << \" with first vertex at \" << it->geometry().corner(0)\n              << std::endl;\n    count++;                                           /*@\\label{tc:count}@*/\n  }                                                    /*@\\label{tc:forel1}@*/\n\n  std::cout << \"there are/is \" << count << \" leaf element(s)\" << std::endl;\n\n  // Leafwise traversal of codim dim\n  std::cout << std::endl;\n  std::cout << \"*** Traverse codim \" << dim << \" leaves\" << std::endl;\n\n  // Get the iterator type\n  // Note the use of the typename and template keywords\n  typedef typename LeafGridView :: template Codim<dim>\n  :: Iterator VertexLeafIterator;                  /*@\\label{tc:vertit}@*/\n\n  // iterate through all entities of codim 0 on the given level\n  count = 0;\n  for (VertexLeafIterator it = leafView.template begin<dim>(); /*@\\label{tc:forve}@*/\n       it!=leafView.template end<dim>(); ++it)\n  {\n    Dune::GeometryType gt = it->type();\n    std::cout << \"visiting \" << gt\n              << \" at \" << it->geometry().corner(0)\n              << std::endl;\n    count++;\n  }\n  std::cout << \"there are/is \" << count << \" leaf vertices(s)\"\n            << std::endl;\n\n  // Levelwise traversal of codim 0\n  std::cout << std::endl;\n  std::cout << \"*** Traverse codim 0 level-wise\" << std::endl;\n\n  // type of the GridView used for traversal\n  // every grid exports a LeafGridView and a LevelGridView\n  typedef typename G :: LevelGridView LevelGridView;   /*@\\label{tc:level0}@*/\n\n  // Get the iterator type\n  // Note the use of the typename and template keywords\n  typedef typename LevelGridView :: template Codim<0>\n  :: Iterator ElementLevelIterator;\n\n  // iterate through all entities of codim 0 on the given level\n  for (int level=0; level<=grid.maxLevel(); level++)\n  {\n    // get the instance of the LeafGridView\n    LevelGridView levelView = grid.levelGridView(level);\n\n    count = 0;\n    for (ElementLevelIterator it = levelView.template begin<0>();\n         it!=levelView.template end<0>(); ++it)\n    {\n      Dune::GeometryType gt = it->type();\n      std::cout << \"visiting \" << gt\n                << \" with first vertex at \" << it->geometry().corner(0)\n                << std::endl;\n      count++;\n    }\n    std::cout << \"there are/is \" << count << \" element(s) on level \"\n              << level << std::endl;\n    std::cout << std::endl;\n  }                                                    /*@\\label{tc:level1}@*/\n}                                                      /*@\\label{tc:tra1}@*/\n\n\nint main(int argc, char **argv)\n{\n  // initialize MPI, finalize is done automatically on exit\n  Dune::MPIHelper::instance(argc,argv);\n\n  // start try/catch block to get error messages from dune\n  try {\n    // make a grid\n    const int dim=2;\n    typedef Dune::YaspGrid<dim> GridType;\n    std::array<int,dim> N;\n    std::fill(N.begin(), N.end(), 1);\n    Dune::FieldVector<GridType::ctype,dim> H(1.0);\n    GridType grid(H,N);\n\n    // refine all elements once using the standard refinement rule\n    grid.globalRefine(1);                                /*@\\label{tc:refine}@*/\n\n    // traverse the grid and print some info\n    traversal(grid);                                     /*@\\label{tc:call}@*/\n  }\n  catch (std::exception & e) {\n    std::cout << \"ERROR: \" << e.what() << std::endl;\n    return 1;\n  }\n  catch (...) {\n    std::cout << \"Unknown ERROR\" << std::endl;\n    return 1;\n  }\n\n  // done\n  return 0;\n}\n", "text_preview": "// -*- tab-width: 4; indent-tabs-mode: nil; c-basic-offset: 2 -*- // vi: set et ts=4 sw=2 sts=2: #include <config.h> // file generated by CMake // C/C++ includes #include <array> 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Thinklab-SJTU/ML4CO-Kit/blob/61ec74d0a9e059795b5427433cdd1a0517ba3af8/ml4co_kit/algorithm/tsp/decoder/c_tsp_mcts/tsp.h", "id": "cpp-0-ec1ffc6b667b71a2c2014a88780fa1e6", "text": "#ifndef TSP_H\n#define TSP_H\n\n#include <iostream>\n#include <stdlib.h>\n#include <string.h>\n#include <time.h>\n#include <string.h>\n#include <math.h>\n#include <stdbool.h>\n\n#define NULL_1             -1 \n#define INF                1000000000\n#define MAGNITY_RATE       10000\n#define MAX_CITY_NUM       10000 \n#define MAX_CANDIDATE_NUM  1000 \n#define RANDOM_SEED        489663920 \n\n\n// -------------------- VARIABLE -------------------- // \n\n// input parameters\nint city_num;\nint start_city;\nint max_depth;\ndouble *coord_x;\ndouble *coord_y;\nint **distance;\ndouble **edge_heatmap;\n\n// hyper parameters \ndouble alpha = 1;\ndouble beta = 10;\ndouble param_h = 10;\nfloat time_limit = 1.0;\n\n// city_info\nstruct Node\n{\n    int pre_city;\n    int next_city;\n};\nstruct Node *all_node;\nstruct Node *best_all_node;   \nint *candidate_num; \nint **candidate;\nbool *if_city_selected;\n\n// mcts & 2opt\ndouble begin_time;\nint best_distance;\nint promising_city_num;\nint total_simulation_times;\ndouble avg_weight;\nint pair_city_num;\nint temp_pair_num;\ndouble **weight;\nint **chosen_times;\nint *promising_city;\nint *probabilistic;\nint *city_sequence;\nint *temp_city_sequence;\nint *gain;\nint *real_gain;\nint max_iterations_2opt;\nint version_2opt;\n\n// solution\nint *solution;  \n\n\n// -------------------- UTILS FUNCTION --------------------- // \n// RANDOM\nextern int get_random_num(int range);\n// CALCULATE DISTANCE\nextern double calculate_double_distance(int city_1,int city_2);\nextern int calculate_int_distance(int city_1, int city_2);\nextern void calculate_all_pair_distance();\nextern int get_distance(int city_1,int city_2);\nextern int get_solution_total_distance();\nextern double get_current_solution_double_distance();\n// CONVERSION\nextern void convert_solution_to_all_node();\nextern bool convert_all_node_to_solution();\n// CHECK\nextern bool check_solution_feasible();\nextern bool check_if_two_city_same_or_adjacent(int city_1, int city_2);\n// SELECT & candidate \nextern int get_best_unselected_city(int cur_city);\nextern void identify_candidate_set();\n// STORE & RESTORE\nextern void store_best_solution();\nextern void restore_best_solution();\n// REVERSE\nextern void reverse_sub_path(int city_1,int city_2);\n\n\n// --------------------- INIT FUNCTION --------------------- //\n// Allocate Memory\nextern void allocate_memory(int city_num);\n// Release Memory\nextern void release_memory(int city_num);\n//Estimate the potential of each edge by upper bound confidence function\nextern double temp_get_potential(int city_1, int city_2);\n// Indentify the promising cities as candidates which are possible to connect to cur_city\nextern void temp_identify_promising_city(); \n// Set the probability (stored in probabilistic[]) of selecting each candidate city \n// (proportion to the potential of the corresponding edge)\nextern bool temp_get_probabilistic(int cur_city);\n// probabilistically choose a city, controled by the values stored in probabilistic[] \nextern int temp_probabilistic_get_city_to_connect();\n// The whole process of choosing a city (a_{i+1} in the paper) to connect cur_city (b_i in the paper)\nextern int temp_choose_city_to_connect(int cur_city);\n// Generate Initial Solution\nextern bool generate_initial_solution();\n\n\n// --------------------- 2OPT FUNCTION --------------------- //\n// Evaluate the delta after applying a 2-opt move (delta >0 indicates an improving solution)\nextern int get_2opt_delta(int city_1, int city_2);\n// Apply a chosen 2-opt move\nextern void apply_2opt_move(int city_1,int city_2);\nextern bool improve_by_2opt_move();\n// Iteratively apply an improving 2-opt move until no improvement is possible\nextern void local_search_by_2opt_move();\n\n\n// ----------------- MARKOV DECISION PROGRESS ---------------- //\nextern int mdp();\n\n\n// -------------------------- MCTS -------------------------- //\n// READ DATA\nextern void read_heatmap(float *heatmap);\nextern void read_nodes_coords(float *nodes_coords);\n// INIT\nextern void mcts_init();\n// GET POTENTIAL\nextern double get_avg_weight(int cur_city);\ndouble get_potential(int city_1, int city_2);\nvoid identify_promising_city(int cur_city, int begin_city);\nbool get_probabilistic(int cur_city);\nextern int probabilistic_get_city_to_connect();\n// SIMULATION\n// the whole process of choosing a city (a_{i+1} in the paper) to connect cur_city (b_i in the paper)\nextern int choose_city_to_connect(int cur_city, int begin_city);\n// generate an action starting form begin_city (corresponding to a_1 in the paper), return the delta value\nextern int get_simulated_action_delta(int begin_city);\n// if the delta of an action is greater than zero, use the information of this action \n//(stored in city_sequence[]) to update the parameters by back propagation\nextern void back_propagation(int before_simulation_distance, int action_delta);\n//Execute the best action stored in city_sequence[] with depth pair_city_num\nextern bool execute_best_action();\n// MCTS \nextern void mcts();\n\n\n#endif  // TSP_H", "text_preview": "#ifndef TSP_H #define TSP_H #include <iostream> #include <stdlib.h> #include <string.h> #include <time.h> #include <string.h> #include <math.h> #include <stdbool.h> #define NULL_1 -1 #define INF 1000000000 #define MAGNITY_RATE 10000 #define", "input_sha256": "014bd733f3dee6c128bb3bc68dbda24f66c0a1e657698b6bf6bc760c88ef34a9", "text_sha256": "ec1ffc6b667b71a2c2014a88780fa1e613fcc8b01fcd3ab43dd659db83b9d39f", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 82, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/ishandutta2007/codeforces-b/blob/49a4647a33acd52420d957553bb190e95e1efb14/bluediamond/normal/616/F.cpp", "id": "cpp-0-16d4d93d239387c4c96b85e8f7fe236d", "text": "#include <bits/stdc++.h>\n\nusing namespace std;\n\n#define int long long\n\nstruct T {\n  int id;\n  int c1;\n  int c2;\n};\n\nbool operator < (T a, T b) {\n  if (a.c1 != b.c1) {\n    return a.c1 < b.c1;\n  } else {\n    return a.c2 < b.c2;\n  }\n}\n\n\nconst int N = 700000 + 7 + 777;\nint n;\nint p[N];\nint c[N];\nint f[N];\nint lcp[N];\nint coef[N];\nint s[N];\n\nvector<T> so(vector<T> a) {\n  int n = (int) a.size();\n  vector<T> b(n);\n  int mc = 0;\n  for (auto &it : a) {\n    mc = max(mc, it.c1);\n    mc = max(mc, it.c2);\n  }\n  for (int i = 0; i <= mc; i++) {\n    f[i] = 0;\n  }\n  for (auto &it : a) {\n    f[it.c2]++;\n  }\n  for (int i = 1; i <= mc; i++) {\n    f[i] += f[i - 1];\n  }\n  for (int i = 0; i < n; i++) {\n    b[--f[a[i].c2]] = a[i];\n  }\n  for (int i = 0; i <= mc; i++) {\n    f[i] = 0;\n  }\n  for (auto &it : b) {\n    f[it.c1]++;\n  }\n  for (int i = 1; i <= mc; i++) {\n    f[i] += f[i - 1];\n  }\n  for (int i = n - 1; i >= 0; i--) {\n    a[--f[b[i].c1]] = b[i];\n  }\n  return a;\n}\n\nint init[N], kek[N], lo[N], hi[N], pre[N], l2[N], le[N];\nstring strs[N];\n\n\nint t[4 * N];\n\nvoid build(int v, int tl, int tr) {\n  if (tl == tr) {\n    t[v] = lcp[tl];\n  } else {\n    int tm = (tl + tr) / 2;\n    build(2 * v, tl, tm);\n    build(2 * v + 1, tm + 1, tr);\n    t[v] = min(t[2 * v], t[2 * v + 1]);\n  }\n}\n\nint get(int v, int tl, int tr, int l, int r) {\n  if (tr < l || r < tl) return (int) 1e9;\n  if (l <= tl && tr <= r) return t[v];\n  int tm = (tl + tr) / 2;\n  return min(get(2 * v, tl, tm, l, r), get(2 * v + 1, tm + 1, tr, l, r));\n}\n\nint get(int l, int r) {\n  return get(1, 0, n, l, r);\n}\n\nsigned main() {\n  ios::sync_with_stdio(0); cin.tie(0);\n\n  int te = (int) 1e5 + 1;\n  int cnt, sz = 0;\n  cin >> cnt;\n  for (int i = 1; i <= cnt; i++) {\n    cin >> strs[i];\n    lo[i] = sz;\n    hi[i] = sz + (int) strs[i].size() - 1;\n    for (int j = lo[i]; j <= hi[i]; j++) {\n      l2[j] = hi[i] - j + 1;\n    }\n    for (auto &ch : strs[i]) {\n      s[sz++] = ch + (int) 1e5 + 3;\n    }\n    s[sz++] = --te;\n  }\n  assert(sz < N);\n  for (int i = 1; i <= cnt; i++) {\n    cin >> init[i];\n    for (int j = lo[i]; j <= hi[i]; j++) {\n      kek[j] = init[i];\n    }\n  }\n  n = sz;\n  for (int i = 0; i < n; i++) {\n    f[s[i]]++;\n  }\n  for (int x = 1; x < N; x++) {\n    f[x] += f[x - 1];\n  }\n  for (int i = 0; i < n; i++) {\n    p[--f[s[i]]] = i;\n    c[i] = s[i];\n  }\n  for (int l = 1; l <= n; l *= 2) {\n    vector<T> vec;\n    for (int i = 0; i < n; i++) {\n      vec.push_back({i, c[i], c[(i + l) % n]});\n    }\n    vec = so(vec);\n    for (int i = 0; i < n; i++) {\n      p[i] = vec[i].id;\n    }\n    c[vec[0].id] = 0;\n    int cur = 0;\n    for (int i = 1; i < n; i++) {\n      if (vec[i - 1].c1 != vec[i].c1 || vec[i - 1].c2 != vec[i].c2) {\n        cur++;\n      }\n      c[vec[i].id] = cur;\n    }\n  }\n  int k = 0;\n  for (int i = 0; i < n - 1; i++) {\n    int pi = c[i];\n    int j = p[pi - 1];\n    while (s[i + k] == s[j + k]) {\n      k++;\n    }\n    lcp[pi] = k;\n    k = max(0LL, k - 1);\n  }\n  for (int i = 0; i < n; i++) {\n    le[i] = l2[p[i]];\n    coef[i] = kek[p[i]];\n    pre[i] = coef[i];\n    if (i) {\n      pre[i] += pre[i - 1];\n    }\n  }\n  int sol = 0;\n  for (int l = 1; l < n; l++) {\n    if (le[l] > lcp[l] && le[l] > lcp[l + 1]) {\n      sol = max(sol, le[l] * (pre[l] - pre[l - 1]));\n    }\n  }\n  build(1, 0, n);\n  vector<int> stk;\n  for (int i = 2; i <= n; i++) {\n    while (!stk.empty() && lcp[i] < lcp[stk.back()]) {\n      stk.pop_back();\n    }\n    if (!stk.empty()) {\n      int l = stk.back() + 1, r = i - 1;\n      if (l > r) continue;\n      bool ok = (2 <= l && l < n && l <= r && r < n);\n      if (!ok) continue;\n      int mn = get(l, r);\n      if (mn > lcp[l - 1] && mn > lcp[r + 1]) {\n        mn = min(mn, le[l - 1]);\n        if (mn > lcp[l - 1] && mn > lcp[r + 1]) {\n          sol = max(sol, mn * (pre[r] - pre[l - 2]));\n        }\n      }\n    }\n    stk.push_back(i);\n  }\n  stk.clear();\n\n  for (int i = n; i >= 0; i--) {\n    while (!stk.empty() && lcp[i] < lcp[stk.back()]) {\n      stk.pop_back();\n    }\n    if (!stk.empty()) {\n      int l = i + 1, r = stk.back() - 1;\n      bool ok = (2 <= l && l < n && l <= r && r < n);\n      if (!ok) continue;\n      int mn = get(l, r);\n      if (mn > lcp[l - 1] && mn > lcp[r + 1]) {\n        mn = min(mn, le[l - 1]);\n        if (mn > lcp[l - 1] && mn > lcp[r + 1]) {\n          sol = max(sol, mn * (pre[r] - pre[l - 2]));\n        }\n      }\n    }\n    stk.push_back(i);\n  }\n  cout << sol << \"\\n\";\n}", "text_preview": "#include <bits/stdc++.h> using namespace std; #define int long long struct T { 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Chandiharh-university/opps-and-dsa-ignite72/blob/e20195da639a0730a309da4d4647af0c6d204eb0/binarytree_Avl.cpp", "id": "cpp-0-794920dda06ca726786f2b28a4ee36a2", "text": "#include <iostream>\n#include <algorithm> // For std::max\n#include <queue>     // For level-order insertion\n#include <functional> // For std::function\nusing namespace std;\n\n// Definition of a Node in the Binary Tree/AVL Tree\nstruct Node {\n    int data;          // Data stored in the node\n    Node* left;       // Pointer to the left child\n    Node* right;      // Pointer to the right child\n    int height;       // Height of the node (for AVL balancing)\n\n    // Constructor to create a new node\n    Node(int value) {\n        data = value;\n        left = nullptr;\n        right = nullptr;\n        height = 1; // Height is initially 1 (leaf node)\n    }\n};\n\n// Class to represent a Binary Tree\nclass BinaryTree {\nprivate:\n    Node* root; // Root node of the tree\n\n    // Helper function for in-order traversal\n    void inOrder(Node* node) {\n        if (node == nullptr)\n            return;\n        inOrder(node->left);\n        cout << node->data << \" \";\n        inOrder(node->right);\n    }\n\n    // Function to insert a new node in the Binary Tree (level-order insertion)\n    Node* insertBinary(Node* node, int value) {\n        if (!node)\n            return new Node(value);\n\n        queue<Node*> q;\n        q.push(node);\n\n        while (!q.empty()) {\n            Node* temp = q.front();\n            q.pop();\n\n            if (!temp->left) {\n                temp->left = new Node(value);\n                break;\n            } else {\n                q.push(temp->left);\n            }\n\n            if (!temp->right) {\n                temp->right = new Node(value);\n                break;\n            } else {\n                q.push(temp->right);\n            }\n        }\n        return node;\n    }\n\n    // Helper function to get the height of a node\n    int getHeight(Node* node) {\n        return node ? node->height : 0;\n    }\n\n    // Helper function to calculate balance factor\n    int getBalanceFactor(Node* node) {\n        return node ? getHeight(node->left) - getHeight(node->right) : 0;\n    }\n\n    // Right rotation for AVL balancing\n    Node* rightRotate(Node* y) {\n        Node* x = y->left;\n        Node* T2 = x->right;\n\n        // Perform rotation\n        x->right = y;\n        y->left = T2;\n\n        // Update heights\n        y->height = std::max(getHeight(y->left), getHeight(y->right)) + 1;\n        x->height = std::max(getHeight(x->left), getHeight(x->right)) + 1;\n\n        // Return new root\n        return x;\n    }\n\n    // Left rotation for AVL balancing\n    Node* leftRotate(Node* x) {\n        Node* y = x->right;\n        Node* T2 = y->left;\n\n        // Perform rotation\n        y->left = x;\n        x->right = T2;\n\n        // Update heights\n        x->height = std::max(getHeight(x->left), getHeight(x->right)) + 1;\n        y->height = std::max(getHeight(y->left), getHeight(y->right)) + 1;\n\n        // Return new root\n        return y;\n    }\n\n    // Helper function to insert a node in AVL tree\n    Node* insertAVL(Node* node, int value) {\n        if (!node)\n            return new Node(value);\n\n        // Perform normal BST insertion\n        if (value < node->data)\n            node->left = insertAVL(node->left, value);\n        else if (value > node->data)\n            node->right = insertAVL(node->right, value);\n        else\n            return node; // Duplicate values not allowed\n\n        // Update height of the current node\n        node->height = 1 + std::max(getHeight(node->left), getHeight(node->right));\n\n        // Check balance factor and perform rotations if unbalanced\n        int balance = getBalanceFactor(node);\n\n        // Left Left Case\n        if (balance > 1 && value < node->left->data)\n            return rightRotate(node);\n\n        // Right Right Case\n        if (balance < -1 && value > node->right->data)\n            return leftRotate(node);\n\n        // Left Right Case\n        if (balance > 1 && value > node->left->data) {\n            node->left = leftRotate(node->left);\n            return rightRotate(node);\n        }\n\n        // Right Left Case\n        if (balance < -1 && value < node->right->data) {\n            node->right = rightRotate(node->right);\n            return leftRotate(node);\n        }\n\n        return node; // Return the (unchanged) node pointer\n    }\n\npublic:\n    // Constructor to initialize the Binary Tree\n    BinaryTree() {\n        root = nullptr;\n    }\n\n    // Function to insert a new node into the Binary Tree\n    void insertBinaryTree(int value) {\n        root = insertBinary(root, value);\n    }\n\n    // Function to convert Binary Tree to AVL Tree\n    void convertToAVL() {\n        Node* newRoot = nullptr;\n        queue<int> values;\n\n        // Helper to collect all values from the binary tree\n        auto collectValues = [&](Node* node, auto& collect) -> void {\n            if (!node) return;\n            collect(node->left, collect);\n            values.push(node->data);\n            collect(node->right, collect);\n        };\n\n        collectValues(root, collectValues);\n\n        // Insert collected values into a new AVL tree\n        while (!values.empty()) {\n            newRoot = insertAVL(newRoot, values.front());\n            values.pop();\n        }\n\n        root = newRoot;\n    }\n\n    // Function to perform in-order traversal\n    void inOrderTraversal() {\n        inOrder(root);\n        cout << endl;\n    }\n\n    // Function to print Binary Tree (pre-order traversal for simplicity)\n    void printBinaryTree() {\n        cout << \"Binary Tree (Pre-order Traversal): \";\n        inOrderTraversal();\n    }\n\n    // Function to print AVL Tree (in-order for sorted view)\n    void printAVLTree() {\n        cout << \"AVL Tree (In-order Traversal): \";\n        inOrderTraversal();\n    }\n};\n\n// Main function to demonstrate the Binary Tree and AVL Tree\nint main() {\n    BinaryTree tree;\n\n    // Inserting nodes into the Binary Tree\n    tree.insertBinaryTree(10);\n    tree.insertBinaryTree(20);\n    tree.insertBinaryTree(30);\n    tree.insertBinaryTree(40);\n    tree.insertBinaryTree(50);\n    tree.insertBinaryTree(25);\n\n    // Print Binary Tree\n    tree.printBinaryTree();\n\n    // Convert to AVL Tree\n    tree.convertToAVL();\n\n    // Print AVL Tree\n    tree.printAVLTree();\n\n    return 0;\n}\n", "text_preview": "#include <iostream> #include <algorithm> // For std::max #include <queue> // For level-order insertion #include <functional> // For std::function using namespace std; // Definition of a Node in the Binary Tree/AVL Tree struct Node { int 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/LamaShawahny/BinaryTreeB/blob/27d955c9eed8dfa11c843cb5e80f5632ed206f04/sources/BinaryTree.hpp", "id": "cpp-0-310713bf3e0a8cc59899dec02b4440c2", "text": "#pragma once\n#include <iostream>\n#include <sstream>\n#include <stack>\n#include\"Node.hpp\"\n\nnamespace ariel {\n    template<typename T>\n    class BinaryTree {\n    private:\n        Node<T> * root;\n     void bulidTree(Node<T>* src, Node<T>* dist) {\n        \n         if(!(dist->right == nullptr)){\n            src->right = new Node<T>(dist->right->value);\n            bulidTree(src->right, dist->right);\n        }\n\n        if(!(dist->left == nullptr)){\n            src->left = new Node<T>(dist->left->value);\n            bulidTree(src->left, dist->left);\n        }\n    }\n\n    Node<T>* Search_For_Node(Node<T> *node ,T key) {\n        if(!node) {return nullptr;}\n        if(node->value == key) {return node;}\n        Node<T>* exist = Search_For_Node(node->left,key);\n        if(exist!=nullptr) {return exist;}\n        return Search_For_Node(node->right,key);\n        \n    }\n\n    public:        \n    BinaryTree():root(nullptr) {\n    }\n\n    BinaryTree(BinaryTree& tree) {\n\n         this->root = new Node<T>(tree.root->value);\n    }\n    ~BinaryTree(){}\n\n    BinaryTree(BinaryTree &&tree)  noexcept {\n        this->root = tree.root;\n    }\n\n\n    BinaryTree<T> &add_root(T value) {\n        if(root != nullptr){\n            this->root->value = value;\n            return *this;     \n        }\n        this->root= new Node<T>(value);\n        return *this;       \n    }\n\n    BinaryTree<T> &add_left(const T &parent, const T &child) {\n       \n        if(Search_For_Node (this->root,parent)!=nullptr){\n             Node<T>* node = Search_For_Node (this->root,parent);\n             if(node->left== nullptr) {\n            node->left = new Node<T>(child);\n        }\n        node->left->value=child;\n        node->left->parent = node;\n        return *this;\n        }\n           throw (\" SOMETHING WENT WRONG \");\n    }\n    \n    BinaryTree<T> &add_right(const T &parent, const T &child) {\n        if(Search_For_Node (this->root,parent)!=nullptr){\n             Node<T>* node = Search_For_Node (this->root,parent);\n             if(node->right== nullptr) {\n            node->right = new Node<T>(child);\n        }\n        node->right->value=child;\n        node->right->parent = node;\n        return *this;\n        }\n           throw (\" SOMETHING WENT WRONG \");\n    }\n\n    BinaryTree& operator=(BinaryTree<T>&& tree) noexcept {\n        root  = tree.root;\n        return *this;\n    }\n    \n    BinaryTree &operator=(const BinaryTree<T> &tree) {\n        if(tree.root==nullptr||this == &tree){\n            return *this;\n        }\n        \n        if(tree.root!= nullptr) {\n            root = new Node<T>{tree.root->value};\n            bulidTree(root, tree.root);\n            return *this;\n        }\n        return *this;\n    }\n\n \n        \n         // preorder: \n        class preorder {\n        private:\n            std::stack<Node<T>*> stkN;\n            Node<T>  *PreorderN;\n        public:\n            preorder(Node<T>  *node = nullptr):PreorderN(node) {\n                stkN.push(node);\n            };\n           \n            T &operator*(){\n                return PreorderN->value;\n            }\n\n            preorder &operator++() {\n                this->stkN.pop();\n                if (!(this->PreorderN->right == nullptr)) {\n                    this->stkN.push(this->PreorderN->right);\n                }\n                if (!(this->PreorderN->left == nullptr)) {\n                    this->stkN.push(this->PreorderN->left);\n                }\n                if (this->stkN.empty()) {\n                    this->PreorderN = nullptr;\n                } \n                else {\n                    this->PreorderN = this->stkN.top();\n                }\n\n                return *this;\n            }\n\n            preorder operator++(int) {\n                const preorder x = *this;\n                ++(*this);\n                return x;\n            }\n\n            bool operator==(const preorder &eq) const {\n                return eq.PreorderN==this->PreorderN;\n            }\n\n            bool operator!=(const preorder &eq) const {\n                return !(eq.PreorderN==this->PreorderN);\n            }\n\n            T *operator->() {\n                return &(PreorderN->value);\n            }\n            \n            friend std::ostream &operator<<(std::ostream &os, const preorder &node) { return os; }\n        };\n        // inorder\n        class inorder {\n        private:\n            Node<T>  *InorderN;\n            std::stack<Node<T>*> stkN;\n        public:\n            inorder(Node<T>  *node = nullptr){\n                this->InorderN = node;\n                while(!(this->InorderN== nullptr)){\n                    this->stkN.push(this->InorderN);\n                    this->InorderN = this->InorderN->left;\n                }\n                if(!(this->stkN.empty())){\n                    this->InorderN = this->stkN.top();\n                }else{\n                     this->InorderN= nullptr;\n                }\n            };\n            T &operator*() {\n                return InorderN->value;\n            }\n\n            inorder &operator++() {\n        \n                this->InorderN = this->stkN.top();\n                this->stkN.pop();\n                if (this->InorderN->right != nullptr) {\n                    this->stkN.push(this->InorderN->right);\n                    this->InorderN = this->InorderN->right->left;\n                    while (this->InorderN != nullptr) {\n                        this->stkN.push(this->InorderN);\n                        this->InorderN = this->InorderN->left;\n                    }\n                }\n                if(!(this->stkN.empty())){\n                    this->InorderN = this->stkN.top();\n                    return *this;\n                }\n                this->InorderN = nullptr;\n                return *this;\n            }\n\n            inorder operator++(int) {\n                inorder x = *this;\n                ++(*this);\n                return x;\n            }\n\n            bool operator==(const inorder &eq) {\n                return eq.InorderN==this->InorderN;\n            }\n\n            bool operator!=(const inorder &eq) const {\n                return !(eq.InorderN==this->InorderN);\n            }\n\n            T *operator->(){\n                return &(InorderN->value);\n            }\n            \n            friend std::ostream &operator<<(std::ostream &os, const inorder &node) { return os; }\n        };\n        // postorder\n        class postorder {\n       \n        private:\n            Node<T>  *PostorderN;\n            std::stack<Node<T>*> stkN;\n        public:\n            postorder( Node <T> *node = nullptr){\n                std::stack<Node<T> *> temp;\n                temp.push(node);\n                if(node!= nullptr) {\n                    while (!temp.empty()) {\n                        Node <T>*node = temp.top();\n                        temp.pop();\n                        stkN.push(node);\n                        if (node->left) {\n                            temp.push(node->left);\n                        }\n                        if (node->right) {\n                            temp.push(node->right);\n                        }\n                    }\n                    this->PostorderN = stkN.top();\n                    \n                }else\n                {\n                    this->PostorderN= nullptr;\n                }\n            }\n            \n            postorder( std::stack<Node<T>*> stkN){\n                PostorderN = stkN.top();\n                stkN = stkN;\n            }\n            \n            T &operator*(){\n                return PostorderN->value;\n            }\n    \n            postorder &operator++(){\n                if(!stkN.empty()) {\n                    this->PostorderN= nullptr;\n                    this->stkN.pop();\n                }\n                if(!stkN.empty()) {\n                    this->PostorderN=this->stkN.top();\n                }\n                return *this;\n            }\n            postorder operator++(int) {\n                postorder x=*this;\n                ++*(this);\n                return x;\n            }\n           \n            bool operator==(const postorder &eq){\n                return eq.PostorderN==this->PostorderN;\n            }\n\n            bool operator!=(const postorder &eq) const {\n                return !(eq.PostorderN==this->PostorderN);\n            }\n\n            T *operator->()  {\n                return &(PostorderN->value);\n            }\n\n            friend std::ostream &operator<<(std::ostream &os, const postorder &node) { return os; }\n        };\n\n    \n\n\n\n        preorder begin_preorder(){\n            return preorder(root);\n        }\n        inorder begin_inorder(){\n            return inorder(root);\n        }\n        postorder begin_postorder(){\n            return postorder(root);\n        }\n        inorder begin(){\n            return begin_inorder();\n        };\n\n\n\n         preorder end_preorder(){\n            return  preorder();\n        }\n        inorder end_inorder(){\n            return inorder();\n        }\n        \n        postorder end_postorder(){\n            return postorder();\n        }    \n        \n        inorder end(){\n            return end_inorder();\n        };\n\n\n        friend std::ostream &operator<<(std::ostream &outs, const BinaryTree<T>& binaryTree){\n        outs<<\"tree\";\n        return outs;\n    };\n        \n        \n    };\n}\n", "text_preview": "#pragma once #include <iostream> #include <sstream> #include <stack> #include\"Node.hpp\" namespace ariel { template<typename T> class BinaryTree { private: Node<T> * root; void bulidTree(Node<T>* src, Node<T>* dist) { if(!(dist->right == nul", "input_sha256": "a2815127f4121b8750b60b292ed8789f399616f1d23e9cce8dafddc10ef96d49", "text_sha256": "310713bf3e0a8cc59899dec02b4440c24a508fd9c2d08cc772aca20d341d88c2", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 85, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/uvbs/klib/blob/a59837857689d0e60d3df6d2ebd12c3160efa794/master/core/third/algorithms/bstree/bstree/bstree.h", "id": "cpp-0-5a785ccba91e8445a53399108c2eec55", "text": "/*******************************************************************************\n*\n* FileName : bstree.h\n* Comment  : binary search tree\n* Version  : 1.0\n* Author   : <EMAIL>\n* Date     : 2007-9-23 12:46\n*\n*******************************************************************************/\n\n#ifndef _BSTREE_H_INCLUDED\n#define _BSTREE_H_INCLUDED\n\n#include <iostream>\n#include \"../../binary_tree/binary_tree/binary_tree.h\"\n\ntemplate<typename T>\nclass Bstree : public Binary_tree<T> {\npublic:\n\tPBtn find_min(PBtn pTn=0) const;\n\tPBtn find_max(PBtn pTn=0) const;\n\n\tPBtn find(const T& elem) const;\n\tvirtual void insert(const T& elem);\n\tvirtual void erase(const T& elem);\n\tvirtual void print(std::ostream& output) const;\n\nprotected:\n\tPBtn delete_min(PBtn pTn);\n\tvirtual void print(std::ostream& output, const PBtn pTn) const;\n};\n\ntemplate<typename T>\ninline typename Bstree<T>::PBtn Bstree<T>::find_min(\n\tPBtn pTn=0) const\n{\n\tif (0 == pTn)\n\t\tpTn = hb_root;\n\tif (0 == pTn)\n\t\treturn 0;\n\twhile (pTn->lchild != 0)\n\t\tpTn=pTn->lchild;\n\treturn pTn;\n}\n\ntemplate<typename T>\ninline typename Bstree<T>::PBtn Bstree<T>::find_max(\n\tPBtn pTn=0) const\n{\n\tif (0 == pTn)\n\t\tpTn = hb_root;\n\tif (0 == pTn)\n\t\treturn 0;\n\twhile (pTn->rchild != 0)\n\t\tpTn=pTn->rchild;\n\treturn pTn;\n}\n\ntemplate<typename T>\ntypename Bstree<T>::PBtn Bstree<T>::find(const T& elem) const\n{\n\tPBtn pTn=hb_root;\n\twhile (pTn != 0) {\n\t\tif (elem == pTn->element)\n\t\t\treturn pTn;\n\t\tif (elem < pTn->element)\n\t\t\tpTn=pTn->lchild;\n\t\telse\n\t\t\tpTn=pTn->rchild;\n\t}\n\treturn 0;\t//can not find\n}\n\ntemplate<typename T>\nvoid Bstree<T>::insert(const T& elem)\n{\n\tif (0 == hb_root) {\n\t\thb_root=new Bintree_node<T>(elem,0,0);\n\t\t++hb_size;\n\t\treturn;\n\t}\n\n\tPBtn pTn=hb_root;\n\twhile (true) {\n\t\tif (elem == pTn->element) {\n\t\t\treturn;\n\t\t}\n\t\tif (elem < pTn->element) {\n\t\t\tif (0 == pTn->lchild) {\n\t\t\t\tpTn->lchild=new Bintree_node<T>(elem,0,0);\n\t\t\t\t++hb_size;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tpTn=pTn->lchild;\n\t\t}//if\n\t\telse {\n\t\t\tif (0 == pTn->rchild) {\n\t\t\t\tpTn->rchild=new Bintree_node<T>(elem,0,0);\n\t\t\t\t++hb_size;\n\t\t\t\treturn;\n\t\t\t}\n\t\t\tpTn=pTn->rchild;\n\t\t}//else\n\t}//while\n}\n\ntemplate<typename T>\nvoid Bstree<T>::erase(const T &elem)\n{\n\tPBtn pTn=hb_root;\n\tPBtn parent=pTn;\n\n\twhile (pTn != 0) {\n\t\tif (elem == pTn->element) {\n\t\t\tif (pTn->lchild!=0 && pTn->rchild!=0) {  //two children\n\t\t\t\tPBtn ptm=delete_min(pTn->rchild);\n\t\t\t\t// the right child of pTn don't has left subtree\n\t\t\t\tif (ptm == pTn->rchild)\n\t\t\t\t\tpTn->rchild = ptm->rchild;\n\t\t\t\tpTn->element = ptm->element;\n\t\t\t\tdelete ptm;\n\t\t\t\t--hb_size;\n\t\t\t}\n\t\t\telse {\t//one or zero children\n\t\t\t\tPBtn pReplace = pTn->lchild!=0 ? pTn->lchild : pTn->rchild;\n\t\t\t\tif (parent == pTn) {\t//delete the root node\n\t\t\t\t\thb_root=pReplace;\n\t\t\t\t}\n\t\t\t\telse {\n\t\t\t\t\tif (parent->lchild == pTn)\n\t\t\t\t\t\tparent->lchild = pReplace;\n\t\t\t\t\telse\n\t\t\t\t\t\tparent->rchild = pReplace;\n\t\t\t\t}\n\t\t\t\tdelete pTn;\n\t\t\t\t--hb_size;\n\t\t\t}//else\n\t\t\tbreak;\n\t\t}//end of if(elem == pTn->element)\n\n\t\tparent = pTn;\n\t\tif (elem < pTn->element)\n\t\t\tpTn = pTn->lchild;\n\t\telse  //elem > pTn->element\n\t\t\tpTn = pTn->rchild;\n\n\t}//end of while\n\tif (0 == pTn) {\n\t\tstd::cerr<<\"error, the delete element not exist!\\n\";\n\t}\n}\n\ntemplate<typename T>\ninline typename Bstree<T>::PBtn Bstree<T>::delete_min(PBtn pTn)\n{\n\tassert(pTn != 0);\n\n\tPBtn parent=pTn;\n\twhile 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/llnoor/CPP/blob/a8021cbdf92bcf1ec7c8a0b784687b117e5bb165/Containers%20in%20C%2B%2B%20STL%20%28Standard%20Template%20Library%29/src/tree/tree.h", "id": "cpp-0-ebb7dd4309d77c424b02fcee7319efc2", "text": "#ifndef CONTAINERS_SRC_TREE_TREE_H_\n#define CONTAINERS_SRC_TREE_TREE_H_\n\n#include <iostream>\n#include <stdexcept>\n#include <utility>\n#include <vector>\n\nnamespace  {\n\ntemplate <typename TTree>\nclass TreeConstIterator;\n\ntemplate <typename TTree>\nclass TreeIterator;\n\ntemplate <typename TKey>\nclass TreeNode;\n\ntemplate <typename TKey>\nclass Tree {\n public:\n  using tree_type = Tree<TKey>;\n  using node_type = TreeNode<TKey>;\n\n  using key_type = TKey;\n  using reference = TKey &;\n  using const_reference = const TKey &;\n  using size_type = size_t;\n\n  using iterator = TreeIterator<tree_type>;\n  using const_iterator = TreeConstIterator<tree_type>;\n\n  Tree() : header_(nullptr) {}\n\n  ~Tree() {\n    ClearTree(header_);\n    num_nodes_ = 0;\n  }\n\n  void ClearTree(node_type *node) {\n    if (node != nullptr) {\n      ClearTree(node->left);\n      ClearTree(node->right);\n      delete node;\n      node = nullptr;\n    }\n  }\n\n  Tree(const Tree &second) {\n    if (this->header_ != second.header_) {\n      ClearTree(this->header_);\n\n      if (second.header_ != nullptr) {\n        this->header_ = new node_type(second.header_);\n        CopyWithoutBalancing(second.header_, this->header_);\n      }\n\n      this->num_nodes_ = second.num_nodes_;\n    }\n  }\n\n  Tree(Tree &&second) { MoveConstructor(std::move(second)); }\n\n  Tree &operator=(Tree &&second) {\n    MoveConstructor(std::move(second));\n    return *this;\n  }\n\n  void MoveConstructor(Tree &&second) {\n    std::swap(header_, second.header_);\n    std::swap(num_nodes_, second.num_nodes_);\n  }\n\n  void swap(Tree &second) {\n    std::swap(header_, second.header_);\n    std::swap(num_nodes_, second.num_nodes_);\n  }\n\n  void clean(node_type *node) {\n    node->left = nullptr;\n    node->right = nullptr;\n    node->red = true;\n    node->parent = nullptr;\n  }\n\n  void merge(tree_type &second) {\n    if (second.header_ != nullptr && this->header_ != nullptr) {\n      node_type *thisNode = nullptr;\n      node_type *secondNode = nullptr;\n      for (const_iterator iterator_second_tree = second.begin();\n           iterator_second_tree != second.end();) {\n        secondNode = (iterator_second_tree++).node_;\n        thisNode = second.DeleteNode(secondNode);\n        clean(thisNode);\n        if (!InsertNode(thisNode)) second.InsertNode(thisNode);\n      }\n    }\n  }\n\n  void clear() {\n    if (header_ != nullptr) {\n      ClearTree(header_);\n      header_ = nullptr;\n      num_nodes_ = 0;\n    }\n  }\n\n  virtual bool contains(const TKey &key) { return (FindNode(key) != nullptr); }\n\n  std::pair<iterator, bool> insert(const TKey &key) {\n    node_type *node = nullptr;\n    bool temp_bool = false;\n\n    if (!IsMultiset()) node = FindNode(key);\n\n    if (node == nullptr) {\n      node = new node_type(key);\n      temp_bool = InsertNode(node);\n    }\n    return std::pair<iterator, bool>(node, temp_bool);\n  }\n\n  iterator find(const TKey &key) { return iterator(FindNode(key)); }\n\n  template <typename... Args>\n  std::vector<std::pair<iterator, bool>> insert_many(Args &&...args) {\n    std::initializer_list<key_type> items = {args...};\n    std::vector<std::pair<iterator, bool>> temp_vector;\n\n    for (const TKey &item : items) {\n      std::pair<node_type *, bool> temp_insert = tree_type::InsertKey(item);\n      temp_vector.push_back(std::pair<iterator, bool>(\n          iterator(temp_insert.first), temp_insert.second));\n    }\n    return temp_vector;\n  }\n\n  const_iterator begin() const { return iterator(MinNode(header_)); }\n  const_iterator rbegin() const { return iterator(MaxNode(header_)); }\n  const_iterator end() const { return iterator(nullptr); }\n\n  iterator begin() { return iterator(MinNode(header_)); }\n  iterator rbegin() { return iterator(MaxNode(header_)); }\n  iterator end() { return iterator(nullptr); }\n\n  node_type *root() { return header_; }\n  bool empty() { return (header_ == nullptr); }\n  size_type size() const { return num_nodes_; }\n  size_type max_size() {\n    std::allocator<key_type> size;\n    return size.max_size();\n  }\n\n  void erase(TKey key) {\n    node_type *node = FindNode(key);\n    if (node != nullptr) {\n      node = DeleteNode(node);\n      delete node;\n    }\n  }\n\n  void erase(iterator it) {\n    node_type *node = it.node_;\n    if (node != nullptr) {\n      node = DeleteNode(node);\n      delete node;\n    }\n  }\n\n  virtual bool IsMultiset() const { return false; }\n\n  virtual int CompareNode(node_type *x, node_type *y) {\n    int i = -1;\n    if (x->key < y->key)\n      i = -1;\n    else if (x->key > y->key)\n      i = 1;\n    else\n      i = 0;\n    return i;\n  }\n\n  virtual int CompareNode(TKey key, node_type *y) {\n    int i = -1;\n    if (key < y->key)\n      i = -1;\n    else if (key > y->key)\n      i = 1;\n    else\n      i = 0;\n    return i;\n  }\n\n  node_type *FindNode(TKey key) {\n    node_type *node = header_;\n    if (node == nullptr) return nullptr;\n\n    while (node != nullptr && 0 != CompareNode(key, node)) {\n      if (CompareNode(key, node) > 0)\n        node = node->right;\n      else if (CompareNode(key, node) < 0)\n        node = node->left;\n    }\n    return node;\n  }\n\n  // Проверка возможности поворота\n  int Rotation() {\n    int flag = 0;\n    node_type *p_parent_temp = new node_type();\n    node_type *uncleNode = Uncle(p_parent_temp);\n    node_type *grandparent = Grandpa(p_parent_temp);\n    node_type *parent_temp = new node_type();\n    node_type *left_temp = new node_type();\n    node_type *left_right_temp = new node_type();\n    node_type *right_temp = new node_type();\n    p_parent_temp->left = parent_temp;\n    parent_temp->parent = p_parent_temp;\n    parent_temp->left = left_temp;\n    parent_temp->right = right_temp;\n    left_temp->parent = parent_temp;\n    left_temp->right = left_right_temp;\n    left_right_temp->parent = left_temp;\n    left_right_temp->red = true;\n    right_temp->parent = parent_temp;\n    left_temp->red = false;\n    LowerNeighbor(right_temp);\n    ClearTree(p_parent_temp);\n    if (grandparent == nullptr && uncleNode == nullptr) flag = 1;\n    return flag;\n  }\n\n  // Функция для добавления новый ноды без балансировки\n  // Если ключ ноды меньше ключа рассмаотриваемого узла то добавляем как левый\n  // сын Если больше то как правый сын Если равны, то не добавляем (для set)\n  bool InsertNode(node_type *node) {\n    if (header_ == nullptr) {\n      header_ = node;\n    } else {\n      node_type *parentNode = header_;\n      node_type *currentNode = header_;\n      while (currentNode != nullptr) {\n        if (CompareNode(node, parentNode) < 0)\n          currentNode = parentNode->left;\n        else\n          currentNode = parentNode->right;\n        if (0 == CompareNode(node, parentNode) && !IsMultiset()) return false;\n        if (currentNode != nullptr) parentNode = currentNode;\n      }\n      node->red = (node == header_) ? false : true;\n\n      if (CompareNode(node, parentNode) < 0)\n        parentNode->left = node;\n      else\n        parentNode->right = node;\n      node->parent = parentNode;\n\n      TreeBalancing(node);\n    }\n    num_nodes_ += 1;\n    return true;\n  }\n\n  static node_type *MaxNode(node_type *node) {\n    if (node == nullptr) return nullptr;\n    while (node->right != nullptr) node = node->right;\n    return node;\n  }\n\n  static node_type *MinNode(node_type *node) {\n    if (node == nullptr) return nullptr;\n    while (node->left != nullptr) node = node->left;\n    return node;\n  }\n\n private:\n  std::pair<node_type *, bool> InsertKey(const TKey &key) {\n    node_type *node = nullptr;\n    bool temp_bool = false;\n\n    if (!IsMultiset()) node = FindNode(key);\n\n    if (node == nullptr) {\n      node = new node_type(key);\n      temp_bool = InsertNode(node);\n    }\n    return std::pair<node_type *, bool>(node, temp_bool);\n  }\n\n  node_type *Grandpa(node_type *node) {\n    if (node->parent != nullptr && node->parent->parent != nullptr)\n      return node->parent->parent;\n    return nullptr;\n  }\n\n  node_type *Uncle(node_type *node) {\n    node_type *grandparent = Grandpa(node);\n    if (grandparent == nullptr)\n      return nullptr;\n    else if (grandparent->left == node->parent)\n      return grandparent->right;\n    else\n      return grandparent->left;\n  }\n\n  node_type *Neighbour(node_type *node) {\n    if (node == node->parent->left)\n      return node->parent->right;\n    else\n      return node->parent->left;\n  }\n\n  void ReplaceChildsOfParent(node_type *x, node_type *y) {\n    if (x->parent != nullptr) {\n      if (x == x->parent->left)\n        x->parent->left = y;\n      else if (x == x->parent->right)\n        x->parent->right = y;\n    }\n  }\n\n  void ReplaceNode(node_type *x, node_type *y) {\n    ReplaceChildsOfParent(x, y);\n    ReplaceChildsOfParent(y, x);\n\n    std::swap(x->left, y->left);\n    std::swap(x->right, y->right);\n    std::swap(x->parent, y->parent);\n    std::swap(x->red, y->red);\n\n    if (x->left != nullptr) x->left->parent = x;\n    if (x->right != nullptr) x->right->parent = x;\n    if (y->left != nullptr) y->left->parent = y;\n    if (y->right != nullptr) y->right->parent = y;\n  }\n\n  void CopyWithoutBalancing(node_type *from, node_type *to) {\n    if (from->right != nullptr) {\n      node_type *node = new node_type(from->right);\n      to->right = node;\n      node->parent = to;\n      CopyWithoutBalancing(from->right, node);\n    }\n\n    if (from->left != nullptr) {\n      node_type *node = new node_type(from->left);\n      to->left = node;\n      node->parent = to;\n      CopyWithoutBalancing(from->left, node);\n    }\n  }\n\n  // Надо удалить значение-ключ\n  // Это значение хранится в узле\n  // Этот узел отправляем в эту функцию чтобы выдернуть из дерева и уничтожить\n  // Функцию надо сводить к удалению узла который имеет только одного сына или\n  // вообще не имеет Известно что у узла слева находятся значения меньше\n  // значения узла Поэтому делаем замену (узел <-> максимальный узел среди левой\n  // ветки этого узла) У этого узла нет правого сына.\n  node_type *DeleteNode(node_type *node) {\n    if (node == nullptr) return nullptr;\n    node_type *replaceableNode = node;\n    if (replaceableNode->left != nullptr) {\n      replaceableNode = MaxNode(replaceableNode->left);\n    } else if (replaceableNode->right != nullptr) {\n      replaceableNode = MinNode(replaceableNode->right);\n    }\n    if (replaceableNode != node) {\n      ReplaceNode(replaceableNode, node);\n      if (header_ == node) header_ = replaceableNode;\n      replaceableNode = node;\n    }\n\n    // Если заменяемый узел черный, нельзя просто удалить узел, так как черная\n    // высота меняется Поэтому\n    if (replaceableNode->red == false) {\n      node_type *child;\n      if (replaceableNode->left != nullptr)\n        child = replaceableNode->left;\n      else\n        child = replaceableNode->right;\n\n      // Если есть один ребенок (а может быть только один ребенок, так как мы\n      // спустились до самого нижнего узла) Тогда этого ребенка поднимаем на\n      // место удаляемого узла и меняем цвет на черный\n      if (child != nullptr) {\n        child->parent = replaceableNode->parent;\n\n        if (replaceableNode->parent->left == replaceableNode) {\n          replaceableNode->parent->left = child;\n        } else if (replaceableNode->parent->right == replaceableNode) {\n          replaceableNode->parent->right = child;\n        }\n\n        child->red = false;\n      } else {  //если нет детей\n        DeleteNodeWithoutChild(replaceableNode);\n      }\n    }  // else: Если заменяемый узел красный, тогда можно просто удалить узел,\n    // так как черная высота не меняется\n\n    // Родителю сообщаем, что уходим\n    if (replaceableNode->parent != nullptr) {\n      if (replaceableNode->parent->left == replaceableNode) {\n        replaceableNode->parent->left = nullptr;\n      } else if (replaceableNode->parent->right == replaceableNode) {\n        replaceableNode->parent->right = nullptr;\n      }\n    } else\n      header_ =\n          nullptr;  // Если нет родителя, то сообщаем дереву, что оно пусто\n\n    num_nodes_ -= 1;\n\n    return replaceableNode;\n  }\n\n  // Удаляем узла без детей\n  // Если узел левый, тогда черного соседа поднимаем на место родителя\n  void DeleteNodeWithoutChild(node_type *node) {\n    if (node->parent != nullptr) {\n      node_type *neighbor = Neighbour(node);\n      if (neighbor->red == true && neighbor != nullptr) {\n        node->parent->red = true;\n        neighbor->red = false;\n        if (node == node->parent->left)\n          RotateLeft(node->parent);\n        else\n          RotateRight(node->parent);\n      }\n      ChangeNeighborToRed(node);\n    }\n  }\n\n  // Если родитель черный и сосед черный, тогда поменяем цвет соседа на красный\n  void ChangeNeighborToRed(node_type *node) {\n    node_type *neighbor = Neighbour(node);\n    if ((node->parent->red == false) && (neighbor->red == false) &&\n        (neighbor->left == nullptr || neighbor->left->red == false) &&\n        (neighbor->right == nullptr || neighbor->right->red == false)) {\n      neighbor->red = true;\n      DeleteNodeWithoutChild(node->parent);\n    } else {\n      ReplaceParentNeighborColor(node);\n    }\n  }\n\n  // Если родитель красный и сосед черный, тогда меняем их цвета\n  void ReplaceParentNeighborColor(node_type *node) {\n    node_type *neighbor = Neighbour(node);\n    if ((node->parent->red == true) && (neighbor->red == false) &&\n        (neighbor->left == nullptr || neighbor->left->red == false) &&\n        (neighbor->right == nullptr || neighbor->right->red == false)) {\n      neighbor->red = true;\n      node->parent->red = false;\n    } else {\n      LowerNeighbor(node);\n      RaiseNeighbor(node);\n    }\n  }\n\n  // Если сосед черный и сын соседа черный\n  // тогда соседа делаем красным и меняем с сыном\n  void LowerNeighbor(node_type *node) {\n    node_type *neighbor = Neighbour(node);\n    if (neighbor->red == false) {\n      if ((node == node->parent->left) &&\n          (neighbor->right == nullptr || neighbor->right->red == false) &&\n          (neighbor->left != nullptr && neighbor->left->red == true)) {\n        neighbor->red = true;\n        neighbor->left->red = false;\n        RotateRight(neighbor);\n      } else if ((node == node->parent->right) &&\n                 (neighbor->left == nullptr || neighbor->left->red == false) &&\n                 (neighbor->right != nullptr && neighbor->right->red == true)) {\n        neighbor->red = true;\n        neighbor->right->red = false;\n        RotateLeft(neighbor);\n      }\n    }\n  }\n\n  // Если отец и сосед одного цвета\n  // тогда отца делаем черным, сына соседа делаем черным\n  // поднимаем соседа на место отца\n  void RaiseNeighbor(node_type *node) {\n    node_type *neighbor = Neighbour(node);\n    neighbor->red = node->parent->red;\n    node->parent->red = false;\n    if (node == node->parent->left) {\n      neighbor->right->red = false;\n      RotateLeft(node->parent);\n    } else {\n      neighbor->left->red = false;\n      RotateRight(node->parent);\n    }\n  }\n\n  // Балансировка:\n  // Перекрашиваем отца в черный, дядя тоже должен быть черным, чтобы черная\n  // высота у них совпадала Если дядя красный, тогда ...\n  void TreeBalancing(node_type *node) {\n    if (node->parent == nullptr)\n      node->red = false;\n    else if (node->parent->red == false)\n      return;\n    else\n      RedUncle(node);\n  }\n\n  // Балансировка: Дядя то красный оказывается\n  // Если дядя красный, тогда дядю перекрасим в черный.\n  // Поскольку дядя черный, дед должен быть красным (если только не корень)\n  // Поскольку дед красный, проверим отца деда, он должен быть черным\n  // ->TreeBalancing Если дяди не существует тогда ...\n  void RedUncle(node_type *node) {\n    node_type *uncleNode = Uncle(node);\n    node_type *grandparent;\n    if ((uncleNode != nullptr) && (uncleNode->red == true)) {\n      node->parent->red = false;\n      uncleNode->red = false;\n      grandparent = Grandpa(node);\n      grandparent->red = true;\n      TreeBalancing(grandparent);\n    } else {\n      Rotate(node);\n    }\n  }\n\n  // Балансировка\n  // Повороты\n  void Rotate(node_type *node) {\n    node_type *grandparent = Grandpa(node);\n\n    // Если узел правый и отец левый, тогда правый поворот для отца\n    // Если узел левый и отец правый, тогда левый поворот для отца\n    if ((node == node->parent->right) && (node->parent == grandparent->left)) {\n      RotateLeft(node->parent);\n      node = node->left;\n    } else if ((node == node->parent->left) &&\n               (node->parent == grandparent->right)) {\n      RotateRight(node->parent);\n      node = node->right;\n    }\n\n    // Если узел левый и отец левый, тогда правый поворот для деда\n    // Если узел правый и отец правый, тогда левый поворот для деда\n    node->parent->red = false;\n    grandparent->red = true;\n    if ((node == node->parent->left) && (node->parent == grandparent->left)) {\n      RotateRight(grandparent);\n    } else {\n      RotateLeft(grandparent);\n    }\n  }\n\n  // Левый поворот\n  // Правый сын узла поднимается на место узла\n  // Переназначаем родителя, правого, левого сына\n  void RotateLeft(node_type *node) {\n    node_type *temp = node->right;\n    temp->parent = node->parent;\n    if (node->parent != nullptr) {\n      if (node->parent->left == node)\n        node->parent->left = temp;\n      else\n        node->parent->right = temp;\n    }\n    node->right = temp->left;\n    if (temp->left != nullptr) temp->left->parent = node;\n    node->parent = temp;\n    temp->left = node;\n    if (temp->parent == nullptr) header_ = temp;\n  }\n\n  // Правый поворот\n  // Левый сын узла поднимается на место узла\n  void RotateRight(node_type *node) {\n    node_type *temp = node->left;\n    temp->parent = node->parent;\n    if (node->parent != nullptr) {\n      if (node->parent->left == node)\n        node->parent->left = temp;\n      else\n        node->parent->right = temp;\n    }\n    node->left = temp->right;\n    if (temp->right != nullptr) temp->right->parent = node;\n    node->parent = temp;\n    temp->right = node;\n    if (temp->parent == nullptr) header_ = temp;\n  }\n\n  TreeNode<TKey> *header_ = nullptr;\n  size_type num_nodes_ = 0;\n};\n\ntemplate <typename TTree>\nclass TreeConstIterator {\n public:\n  using tree_type = TTree;\n  using node_type = typename TTree::node_type;\n  using key_type = typename TTree::key_type;\n  using const_reference = const key_type &;\n\n  template <typename TKey>\n  friend class Tree;\n\n  TreeConstIterator(node_type *node) : node_(node) {}\n\n  bool operator==(const TreeConstIterator &second) const {\n    return node_ == second.node_;\n  }\n\n  bool operator!=(const TreeConstIterator &second) const {\n    return node_ != second.node_;\n  }\n\n  TreeConstIterator operator++(int) {\n    TreeConstIterator temp(*this);\n    ++(*this);\n    return temp;\n  }\n\n  TreeConstIterator &operator++() {\n    if (node_ != nullptr) {\n      if (node_->right != nullptr) {\n        node_ = tree_type::MinNode(node_->right);\n      } else {\n        while (node_->parent != nullptr && node_ == node_->parent->right) {\n          node_ = node_->parent;\n        }\n        node_ = node_->parent;\n      }\n    }\n    return *this;\n  }\n\n  TreeConstIterator operator--(int) {\n    TreeConstIterator temp(*this);\n    --(*this);\n    return temp;\n  }\n\n  TreeConstIterator &operator--() {\n    if (node_ != nullptr) {\n      if (node_->left != nullptr) {\n        node_ = tree_type::MaxNode(node_->left);\n      } else {\n        while (node_->parent != nullptr && node_ == node_->parent->left) {\n          node_ = node_->parent;\n        }\n        node_ = node_->parent;\n      }\n    }\n    return *this;\n  }\n\n  const_reference operator*() const {\n    if (node_ != nullptr)\n      return node_->key;\n    else {\n      static key_type empty_key = key_type{};\n      return empty_key;\n    }\n  }\n\n protected:\n  node_type *node_;\n};\n\ntemplate <typename TTree>\nclass TreeIterator : public TreeConstIterator<TTree> {\n public:\n  using tree_type = TreeConstIterator<TTree>;\n  using node_type = typename TTree::node_type;\n  using key_type = typename TTree::key_type;\n  using reference = key_type &;\n\n  template <typename TKey>\n  friend class Tree;\n\n  TreeIterator(node_type *node) : tree_type(node) {}\n\n  reference operator*() const {\n    return const_cast<reference>(tree_type::operator*());\n  }\n\n  TreeIterator &operator++() {\n    tree_type::operator++();\n    return *this;\n  }\n\n  TreeIterator &operator--() {\n    tree_type::operator--();\n    return *this;\n  }\n\n  TreeIterator operator++(int) {\n    tree_type::operator++();\n    return *this;\n  }\n\n  TreeIterator operator--(int) {\n    tree_type::operator--();\n    return *this;\n  }\n\n  bool operator==(const TreeIterator &second) const {\n    return tree_type::operator==(second);\n  }\n  bool operator!=(const TreeIterator &second) const {\n    return tree_type::operator!=(second);\n  }\n};\n\ntemplate <typename TKey>\nclass TreeNode {\n public:\n  using node_type = TreeNode<TKey>;\n\n  node_type *left, *right, *parent;\n  bool red;\n  TKey key;\n\n  TreeNode()\n      : left(nullptr), right(nullptr), parent(nullptr), red(false), key(0) {}\n\n  TreeNode(TKey key)\n      : left(nullptr), right(nullptr), parent(nullptr), red(false), key(key) {}\n\n  TreeNode(TreeNode *node)\n      : left(nullptr),\n        right(nullptr),\n        parent(nullptr),\n        red(node->red),\n        key(node->key) {}\n};\n\n}  // namespace \n\n#endif /* CONTAINERS_SRC_TREE_TREE_H_ */\n", "text_preview": "#ifndef CONTAINERS_SRC_TREE_TREE_H_ #define CONTAINERS_SRC_TREE_TREE_H_ #include <iostream> #include <stdexcept> #include <utility> #include <vector> namespace { template <typename TTree> class 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/MuhGhifari/KULIAH/blob/1ae0816dec1afc33a136301201618e4abccd93ac/Semester%202/Algoritma%20Pemrograman%20%26%20Struktur%20Data/Teori/Remedian%20UTS/main.cpp", "id": "cpp-0-d03854e63f53d042fefd3a6833bea0a0", "text": "#include <iostream>\n#include <fstream>\n#include <vector>\n#include <sstream>\n#include <string>\n#include <iomanip>\n\nusing namespace std;\n\nstruct NilaiMahasiswa {\n  string nama;\n  char npm[9];\n  int nilaiAbsen;\n  int nilaiTugas;\n  int nilaiPraktikum;\n  int nilaiUTS;\n  int nilaiUAS;\n};\n\nvoid printTabel(const vector<vector<string>>& tabel, ofstream& output);\n\nint main() {\n  ifstream input(\"input.csv\");\n\n  int i = 0, n = 0;\n  float total = 0, rata;\n  string str;\n\n  vector<string> header;\n  vector<vector<string>> tabelHasil;\n\n  while (getline(input, str)) {\n    ++i;\n    vector<string> baris;\n    stringstream ss(str);\n    string value;\n\n    while (getline(ss, value, ';')) {\n      baris.push_back(value);\n    }\n\n    if (i == 1) {\n      header.push_back(\"No\");\n      for (auto& kolom : baris) {\n        header.push_back(kolom);\n      }\n      tabelHasil.push_back(header);\n      continue;\n    }\n\n    NilaiMahasiswa nilaiMahasiswa;\n    size_t npmLen = sizeof(nilaiMahasiswa.npm)/sizeof(char);\n\n    nilaiMahasiswa.nama = baris[0];\n    strncpy(nilaiMahasiswa.npm, baris[1].c_str(), npmLen);\n    nilaiMahasiswa.nilaiAbsen = stoi(baris[2]);\n    nilaiMahasiswa.nilaiTugas = stoi(baris[3]);\n    nilaiMahasiswa.nilaiPraktikum = stoi(baris[4]);\n    nilaiMahasiswa.nilaiUTS = stoi(baris[5]);\n    nilaiMahasiswa.nilaiUAS = stoi(baris[6]);\n\n    vector<string> barisNilaiMahasiswa;\n\n    char huruf;\n    int nilaiAkhir = (0.1 * nilaiMahasiswa.nilaiAbsen) + (0.3 * nilaiMahasiswa.nilaiTugas) + (0.1 * nilaiMahasiswa.nilaiPraktikum) + (0.2 * nilaiMahasiswa.nilaiUTS) + (0.3 * nilaiMahasiswa.nilaiUAS);\n\n    if (nilaiAkhir < 40) {\n      huruf = 'E';\n    } else if (nilaiAkhir <= 55) {\n      huruf = 'D';\n    } else if (nilaiAkhir <= 66) {\n      huruf = 'C';\n    } else if (nilaiAkhir <= 74) {\n      huruf = 'B';\n    } else if (nilaiAkhir <= 100) {\n      huruf = 'A';\n    }\n\n    barisNilaiMahasiswa.push_back(to_string(i - 1));\n    barisNilaiMahasiswa.push_back(baris[0]);\n    barisNilaiMahasiswa.push_back(baris[1]);\n    barisNilaiMahasiswa.push_back(baris[2]);\n    barisNilaiMahasiswa.push_back(baris[3]);\n    barisNilaiMahasiswa.push_back(baris[4]);\n    barisNilaiMahasiswa.push_back(baris[5]);\n    barisNilaiMahasiswa.push_back(baris[6]);\n    barisNilaiMahasiswa.push_back(to_string(nilaiAkhir));\n    string temp(1, huruf);\n    barisNilaiMahasiswa.push_back(temp);\n\n    tabelHasil.push_back(barisNilaiMahasiswa);\n  }\n\n  ofstream fileOutput;\n  fileOutput.open(\"output.txt\");\n\n  printTabel(tabelHasil, fileOutput);\n}\n\nvoid printTabel(const vector<vector<string>>& tabel, ofstream& output) {\n  vector<size_t> lebarKolom(tabel[0].size(), 0);\n\n  for (const auto& baris : tabel) {\n    for (size_t i = 0; i < baris.size(); ++i) {\n      lebarKolom[i] = max(lebarKolom[i], baris[i].size());\n    }\n  }\n\n  for (size_t i = 0; i < lebarKolom.size(); ++i) {\n    output << \"+\" << string(lebarKolom[i] + 2, '-');\n  }\n  output << \"+\" << endl;\n\n  auto indeksTabel = tabel.begin();\n\n  for (const auto& baris : tabel) {\n    for (size_t i = 0; i < baris.size(); ++i) {\n      output << \"| \" << setw(lebarKolom[i] + 1) << left << baris[i];\n    }\n    output << \"|\" << endl;\n\n    if (indeksTabel == tabel.begin()) {\n      for (size_t i = 0; i < lebarKolom.size(); ++i) {\n        output << \"+\" << string(lebarKolom[i] + 2, '-');\n      }\n      output << \"+\" << endl;\n    }\n\n    indeksTabel++;\n  }\n\n  for (size_t i = 0; i < lebarKolom.size(); ++i) {\n    output << \"+\"<< string(lebarKolom[i] + 2, '-');\n  }\n  output << \"+\";\n}", "text_preview": "#include <iostream> #include <fstream> #include <vector> #include <sstream> #include <string> #include <iomanip> using namespace std; struct NilaiMahasiswa { string nama; char npm[9]; int nilaiAbsen; int nilaiTugas; int nilaiPraktikum; int ", "input_sha256": "078f8d7070cf5faf8af38a8fa3d6e258264f3500639f37eb8d23cfc334402495", "text_sha256": "d03854e63f53d042fefd3a6833bea0a07d39c0adae10e6ba806e875eab29174b", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 88, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/kivvix/miMaS/blob/ad3894522e64f21827ba3b8f8d1a48c3dc9216e3/code/include/miMaS/fft.h", "id": "cpp-0-1494cb4de27d22a5381a1ea1e5cfcd95", "text": "#ifndef _FTTRP_H_\n#define _FTTRP_H_\n\n#include <complex>\n\n#include <fftw3.h>\n\n#include \"array_view.h\"\n\nnamespace fft {\n\nenum complex_part { re=0, im=1 };\n\n/**\n  class: spectrum\n  brief: la classe `spectrum` est un *wrapper* pour un signal réel 1D depuis `fftw`\n\n  La classe contient seulement le tableau des coefficients de la FFT. On initialise le spectre en\n   fonction de la taille des données en entrée, puis on appelle la méthode `spectrum::fft()` sur le\n   signal dont on veut la transformée. On peut utiliser la structure `spectrum` comme un tableau\n   (héritage de la structure `tools::array_view<T>` avec le type `T=fftw_complex` qui représente un\n   complexe dans `fftw`. L'accès à ce complexe s'effectue par `spectre[i]` on accède à la partie\n   réelle ou imaginaire à l'aide de l'énumération `complex_part`, `spectre[i][fft::re]` ou\n   `spectre[i][fft:im]` (avec `fft::re=0` et `fft::im=1`).\n  La gestion de la mémoire (allocation et libération) est transparente pour l'utilisateur, tout\n   s'effectue dans le constructeur et destructeur.\n\n  TODO: il semblerait en voyant le code de Lukas que std::complex<double> et fftw_complex se castent\n   facilement ensemble, cela peut simplifier l'écriture des manipulations des coefficients de\n   Fourier (pour Poisson et Lawson), mais demande un calcul d'exponentiel au lieu de 4 fonctions\n   trigonométriques (donc à tester).\n\n  function: spectrum::spectrum ( std::size_t n )\n  brief: Constructeur de la classe `spectrum` permettant de créer un\n  spectre de taille `n` (`n` est aussi la taille des données en entrée)\n**/\n\nstruct spectrum\n  : public tools::array_view<fftw_complex>\n{\n  spectrum ( std::size_t n )\n    : tools::array_view<fftw_complex>( (fftw_complex*)fftw_malloc(sizeof(fftw_complex)*n) , n )\n  {}\n\n  ~spectrum ()\n  { fftw_free((fftw_complex*)this->data()); }\n\n  template < typename Iterator >\n  void\n  fft ( Iterator it )\n  {\n    fftw_plan p = fftw_plan_dft_r2c_1d(this->size(),it,(fftw_complex*)this->front(),FFTW_ESTIMATE);\n    fftw_execute(p); fftw_destroy_plan(p);\n  }\n\n  template < typename Iterator >\n  void\n  ifft ( Iterator it )\n  {\n    fftw_plan pI = fftw_plan_dft_c2r_1d(this->size(),(fftw_complex*)this->front(),it,FFTW_ESTIMATE);\n    fftw_execute(pI); fftw_destroy_plan(pI);\n    for ( std::size_t i=0 ; i<this->size() ; ++i,++it )\n      { *it /= this->size(); }\n  }\n};\n\nstruct spectrum_\n  : public tools::array_view< std::complex<double> >\n{\n  spectrum_ ( std::size_t n )\n    : tools::array_view<std::complex<double>>( new std::complex<double>[n] , n )\n  {}\n\n  ~spectrum_ ()\n  { delete[] this->data(); }\n\n  template < typename Iterator >\n  void\n  fft ( Iterator it )\n  {\n    fftw_plan p = fftw_plan_dft_r2c_1d(this->size(),&(*it),(fftw_complex*)(&(this->operator[](0))),FFTW_ESTIMATE);\n    fftw_execute(p); fftw_destroy_plan(p);\n  }\n\n  template < typename Iterator >\n  void\n  ifft ( Iterator it )\n  {\n    fftw_plan pI = fftw_plan_dft_c2r_1d(this->size(),(fftw_complex*)(&(this->operator[](0))),&(*it),FFTW_ESTIMATE);\n    fftw_execute(pI); fftw_destroy_plan(pI);\n    for ( std::size_t i=0 ; i<this->size() ; ++i,++it )\n      { *it /= this->size(); }\n  }\n};\n\ntemplate < typename InputIt , typename OutputIt >\nvoid\nfft ( InputIt first , InputIt last , OutputIt d_it )\n{\n  std::size_t size = std::distance(first,last);\n  fftw_plan p = fftw_plan_dft_r2c_1d( size , &(*first) , (fftw_complex*)(&(*d_it)) , FFTW_ESTIMATE);\n  fftw_execute(p); fftw_destroy_plan(p);\n}\n\ntemplate < typename InputIt , typename OutputIt >\nvoid\nifft ( InputIt first , InputIt last , OutputIt d_it )\n{\n  std::size_t size = std::distance(first,last);\n  fftw_plan pI = fftw_plan_dft_c2r_1d( size , (fftw_complex*)(&(*first)) , &(*d_it) , FFTW_ESTIMATE);\n  fftw_execute(pI); fftw_destroy_plan(pI);\n  for ( std::size_t i=0 ; i<size ; ++i,++d_it )\n    { *d_it /= size; }\n}\n\n} // namespace fft\n\n#endif\n", "text_preview": "#ifndef _FTTRP_H_ #define _FTTRP_H_ #include <complex> #include <fftw3.h> #include \"array_view.h\" namespace fft { enum complex_part { re=0, im=1 }; /** class: spectrum brief: la classe `spectrum` est un *wrapper* pour un signal réel 1D depu", "input_sha256": "de38bb6f1d2f1df05d9f9ad420cbf300024c3377e9f9ccf4b1376a22b2255138", "text_sha256": "1494cb4de27d22a5381a1ea1e5cfcd951bd36579ea817690541004b78d87cc19", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 89, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Artoriaxx/c-Program/blob/147045c2cde76413fe55d04de53ae1f1ab5b3e59/noip/%E6%B4%9B%E8%B0%B7p1600%20%E5%A4%A9%E5%A4%A9%E7%88%B1%E8%B7%91%E6%AD%A5.cpp", "id": "cpp-0-91ba48de6dc03f6caa79b1355d760d5e", "text": "#include<bits/stdc++.h>\nusing namespace std;\nconst int maxn = 300050;\nint n, m;\nstruct node{\n\tint v, next;\n}edge[maxn * 2];\nstruct point{\n\tint s, t;\n}run[maxn];\nvector<int> q1[maxn], q2[maxn], q3[maxn];\nint mark[maxn], t[maxn * 2], ans[maxn];\nint num, head[maxn];\ninline void adde(int u, int v){\n\tedge[++num].next = head[u];\n\tedge[num].v = v;\n\thead[u] = num;\n}\ninline int getnum(){\n\tint ans = 0; bool flag = false; char c;\n\twhile (!isdigit(c = getchar()) && c != '-');\n\tif (c == '-') flag = true; else ans = c - '0';\n\twhile (isdigit(c = getchar())) ans = ans * 10 + c - '0';\n\treturn ans * (flag ? -1 : 1);\n}\nint w[maxn];\nint fa[maxn][25];\ninline void init(){\n\tfor (int j = 1; j <= 23; j++){\n\t\tfor (int i = 1; i <= n; i++){\n\t\t\tfa[i][j] = fa[fa[i][j - 1]][j - 1];\n\t\t}\n\t}\n}\nint dep[maxn];\nvoid build(int u, int f){\n\tfor (int i = head[u]; i; i = edge[i].next){\n\t\tint v = edge[i].v;\n\t\tif (v == f) continue;\n\t\tdep[v] = dep[u] + 1;\n\t\tfa[v][0] = u;\n\t\tbuild(v, u);\n\t}\n}\ninline int lca(int x, int y){\n\tif (dep[x] < dep[y]) swap(x, y);\n\tfor (int i = 23; i >= 0; i--){\n\t\tif(dep[fa[x][i]] >= dep[y]) x = fa[x][i];\n\t}\n\tif (x == y) return x;\n\tfor (int i = 23; i >= 0; i--){\n\t\tif (fa[x][i] != fa[y][i]) x = fa[x][i], y = fa[y][i];\n\t}\n\treturn fa[x][0];\n}\nint maxdep;\nvoid dfs1(int u, int f){\n\tint v, now = dep[u] + w[u], pre = 0;//我们发现在s -> t路径上能够被观测到的点满足dep[x] + w[x] = dep[s]; \n\tif (now <= maxdep) pre = t[now];//所以我们就统计有多少个这样的点，用一个桶下标存储左式，值存储右式。 \n\tfor (int i = head[u]; i; i = edge[i].next){\n\t\tv = edge[i].v;\n\t\tif (v == f) continue;\n\t\tdfs1(v, u);\n\t}//要从子树开始查询 \n\tt[dep[u]] += mark[u];\n\tif (now <= maxdep) ans[u] += t[now] - pre;//由于在dfs的过程中t的值已经改变，所以观测到的点就是变化量 \n\tfor (int i = 0; i < q1[u].size(); i++){\n\t\tt[q1[u][i]]--;//遍历到lca时，把这个影响消除，减去所有到lca的起点的影响 \n\t}\n}\nvoid dfs2(int u, int f){\n\tint now = dep[u] - w[u] + maxn, pre = 0;//在lca -> t路径上，需要满足dep[u] - w[u] = dep[lca] - len \n\tpre = t[now];//由于可能出现负数，把桶下标向右移动3e5 \n\tfor (int i = head[u]; i; i = edge[i].next){\n\t\tint v = edge[i].v;\n\t\tif (v == f) continue;\n\t\tdfs2(v, u);\n\t}\n\tfor (int i = 0; i < q2[u].size(); i++){//q2，q3用于差分 \n\t\tt[q2[u][i] + maxn]++;\n\t}\n\tans[u] += t[now] - pre;//同上 \n\tfor (int i = 0; i < q3[u].size(); i++){\n\t\tt[q3[u][i] + maxn]--;\n\t}\n}\nint main(){\n\tn = getnum(), m = getnum();\n\tfor (int i = 1; i < n; i++){\n\t\tint a = getnum(), b = getnum();\n\t\tadde(a, b), adde(b, a);\n\t}\n\tfor (int i = 1; i <= n; i++){\n\t\tw[i] = getnum();\n\t}\n\tdep[1] = 1;\n\tbuild(1, 0);\n\tinit();\n\tfor (int i = 1; i <= n; i++) maxdep = max(maxdep, dep[i]);\n\tfor (int i = 1; i <= m; i++){\n\t\trun[i].s = getnum(), run[i].t = getnum();\n\t}\n\tfor (int i = 1; i <= m; i++){\n\t\tmark[run[i].s]++;//先处理s -> lca,mark[i]表示第i个节点作为起点多少次  \n\t\tint Lca = lca(run[i].s, run[i].t);\n\t\tq1[Lca].push_back(dep[run[i].s]);//q1[i]存储lca为i的人的起点的深度 \n\t}\n\tdfs1(1, 0);\n\tmemset(t, 0, sizeof(t));\n\tfor (int i = 1; i <= m; i++){\n\t\tint Lca = lca(run[i].s, run[i].t);\n\t\tint dis = dep[run[i].s] + dep[run[i].t] - 2 * dep[Lca];\n\t\tint tmp = dep[run[i].t] - dis;\n\t\tq2[run[i].t].push_back(tmp);\n\t\tq3[Lca].push_back(tmp);\n\t}\n\tdfs2(1, 0);\n\tfor (int i = 1; i <= m; i++){\n\t\tint Lca = lca(run[i].s, run[i].t);\n\t\tif (dep[run[i].s] - dep[Lca] == w[Lca]) --ans[Lca];\n\t}\n\tfor (int i = 1; i <= n; i++){\n\t\tprintf(\"%d \", ans[i]);\n\t}\n\treturn 0;\n}\n", "text_preview": "#include<bits/stdc++.h> using namespace std; const int maxn = 300050; int n, m; struct node{ int v, next; }edge[maxn * 2]; struct point{ int s, t; }run[maxn]; 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/johnlinew/cs35-Data_Structures-Algorithms/blob/17845462a690fa7d1143c25024da34214388f9dd/labs/10/library/linkedBST-inl.h", "id": "cpp-0-75ed5394d5440216975e94ca767876a7", "text": "#include <stdexcept>\n#include \"arrayQueue.h\"\n\n////////////////////////////////////////////////////////////////////////////\n/////////////////////////////BST NODE IMPLEMENTATION////////////////////////\n////////////////////////////////////////////////////////////////////////////\n/**\n * Default constructor for the BSTNode class.  \n *   Does not set the key-value pair, and initializes the subtrees to NULL.\n */\ntemplate <typename K, typename V>\nBSTNode<K,V>::BSTNode() {\n  left = NULL;\n  right = NULL;\n}\n\n\n/**\n * Standard constructor for the BSTNode class.\n * Stores the given key-value pair and initializes the subtrees to NULL.\n * @param k - key for the element (index in the overall BST)\n * @param v - value for the element\n */\ntemplate <typename K, typename V>\nBSTNode<K,V>::BSTNode(K k, V v) {\n  key = k;\n  value = v;\n  left = NULL;\n  right = NULL;\n}\n\n////////////////////////////////////////////////////////////////////////////\n////////////////////////////LinkedBST IMPLEMENTATION////////////////////////\n////////////////////////////////////////////////////////////////////////////\n\n/**\n * Standard constructor for the LinkedBST class.\n *   Constructs an empty tree (size 0, with a NULL root).\n */\ntemplate <typename K, typename V>\nLinkedBST<K,V>::LinkedBST() {\n  size = 0;\n  root = NULL;\n}\n\n/**\n * The LinkedBST destructor uses a recursive helper function which\n * traverses the tree and frees each node on the heap as it traverses.\n */\ntemplate <typename K, typename V>\nLinkedBST<K,V>::~LinkedBST() {\n  traverseAndDelete(root);\n}\n\n/* getSize - returns the size of the BST\n * @return int: the number of key-value pairs in the data structure\n */\ntemplate <typename K, typename V>\nint LinkedBST<K,V>::getSize() {\n  return size;\n}\n\n/* isEmpty  - returns true if the tree is empty\n * @return bool: true if there are no elements in the BST\n */\ntemplate <typename K, typename V>\nbool LinkedBST<K,V>::isEmpty() {\n  return size == 0;\n}\n\n/* insert - inserts the key-value pair into the tree\n * @param key - key for indexing the new element\n * @param value - value associated with the given key\n * @error runtime_error if they key already exists\n */\ntemplate <typename K, typename V>\nvoid LinkedBST<K,V>::insert(K key, V value) {\n  root = insertInSubtree(root, key, value);\n}\n\n/* update - finds the element indexed by the given key and updates \n *          its value to the provided value parameter\n * @param key - key for finding the existing element\n * @param value - the new value to store for the given key\n * @error runtime_error if the key is not found in the BST\n */\ntemplate <typename K, typename V>\nvoid LinkedBST<K,V>::update(K key, V value){\n  updateInSubtree(root, key, value);\n}\n\n/* remove - deletes the element with given key from the tree\n * @param key - index key to search for and remove\n * @error: runtime_error if they key is not found\n */\ntemplate <typename K, typename V>\nvoid LinkedBST<K,V>::remove(K key) {\n  root = removeFromSubtree(root, key);\n}\n\n/* contains - returns true if there exists an element in the BST\n *   with the given key\n * @param key - index key to search for\n * @return bool: true if the given key exists in the BST\n */\ntemplate <typename K, typename V>\nbool LinkedBST<K,V>::contains(K key) {\n  return containsInSubtree(root, key);\n}\n\n/* find - returns the value associated with the given key\n * @param key - index key for element to find\n * @error runtime_error if the key is not found in the BST\n * @return V: value associated with given key\n */\ntemplate <typename K, typename V>\nV LinkedBST<K,V>::find(K key) {\n  return findInSubtree(root, key);\n}\n\n\n/* getMin - returns the smallest key in the data structure\n * @error runtime_error if BST is empty\n * @return K: minimum key in the BST\n */\ntemplate <typename K, typename V>\nK LinkedBST<K,V>::getMin() {\n  if (isEmpty()) {\n    throw std::runtime_error(\"LinkedBST::getMin called on an empty tree.\");\n  }\n  return getMinInSubtree(root);\n}\n\n/* getMax - returns the largest key in the data structure\n * @error runtime_error if BST is empty\n * @return K: maximum key in the BST\n */\ntemplate <typename K, typename V>\nK LinkedBST<K,V>::getMax() {\n  if (isEmpty()) {\n    throw std::runtime_error(\"LinkedBST::getMax called on an empty tree.\");\n  }\n  return getMaxInSubtree(root);\n}\n\n/* getHeight - returns a height for the tree (i.e., largest\n *   depth for any leaf node)\n * @return int: height of tree, -1 if tree is empty\n */\ntemplate <typename K, typename V>\nint LinkedBST<K,V>::getHeight() {\n  return getHeightOfSubtree(root);\n}\n\n\n/* getPreOrder - returns a pointer to an iterator (a queue here) containing\n *               all key-value pairs in the data structure.  Uses a pre-order\n *               traversal to obtain all elements\n * @return Queue< Pair<K,V>>*: a pointer to a dynamically allocated\n *   Queue with key-value pairs.  The caller is responsible for handling\n *   the heap memory deallocation\n */\ntemplate <typename K, typename V>\nQueue< Pair<K,V> >* LinkedBST<K,V>::getPreOrder() {\n  Queue< Pair<K,V> >* it = new ArrayQueue< Pair<K,V> >();\n  buildPreOrder(root, it);\n  return it;\n}\n\n\n/* getInOrder - returns a pointer to an iterator (a queue here) containing\n *               all key-value pairs in the data structure.  Uses an in-order\n *               traversal to obtain all elements\n * @return Queue< Pair<K,V>>*: a pointer to a dynamically allocated\n *   Queue with key-value pairs.  The caller is responsible for handling\n *   the heap memory deallocation\n */\ntemplate <typename K, typename V>\nQueue< Pair<K,V> >* LinkedBST<K,V>::getInOrder() {\n  Queue< Pair<K,V> >* it = new ArrayQueue< Pair<K,V> >();\n  buildInOrder(root, it);\n  return it;\n}\n\n/* getPostOrder - returns a pointer to an iterator (a queue here) containing\n *               all key-value pairs in the data structure.  Uses a post-order\n *               traversal to obtain all elements\n * @return Queue< Pair<K,V>>*: a pointer to a dynamically allocated\n *   Queue with key-value pairs.  The caller is responsible for handling\n *   the heap memory deallocation\n */\ntemplate <typename K, typename V>\nQueue< Pair<K,V> >* LinkedBST<K,V>::getPostOrder() {\n  Queue< Pair<K,V> >* it = new ArrayQueue< Pair<K,V> >();\n  buildPostOrder(root, it);\n  return it;\n}\n\n/* getLevelOrder - returns a pointer to an iterator (a queue here) containing\n *               all key-value pairs in the data structure.  Uses a level-order\n *               traversal to obtain all elements\n * @return Queue< Pair<K,V>>*: a pointer to a dynamically allocated\n *   Queue with key-value pairs.  The caller is responsible for handling\n *   the heap memory deallocation\n */\ntemplate <typename K, typename V>\nQueue< Pair<K,V> >* LinkedBST<K,V>::getLevelOrder() {\n  ArrayQueue< BSTNode<K,V>* > levelQ;\n  Queue< Pair<K,V> >* it = new ArrayQueue< Pair<K,V> >();\n\n  levelQ.enqueue(root);\n  while (!levelQ.isEmpty()) {\n    BSTNode<K,V>* current = levelQ.dequeue();\n    if (current != NULL) {\n      it->enqueue( Pair<K,V>(current->key, current->value) );\n      levelQ.enqueue(current->left);\n      levelQ.enqueue(current->right);\n    }\n  }\n\n  return it;\n}\n\n", "text_preview": "#include <stdexcept> #include \"arrayQueue.h\" //////////////////////////////////////////////////////////////////////////// /////////////////////////////BST NODE IMPLEMENTATION//////////////////////// /////////////////////////////////////////", "input_sha256": "2fea25fc9e24f2beab271fc21dfcaebc0bc6acfd8c03c7e6539862b505207cd4", "text_sha256": 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"Travel & Tourism": 8.034418001691448e-05, "Other / Uncovered Subject": 0.0005731760835084384, "Unknown / Insufficient Content": 9.15318005486737e-05}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.000544485601666346, "Art & Design": 0.0005723540487227823, "Law & Crime": 0.00041837872439725714, "Education & Careers": 0.009495880004630962, "Entertainment & Media": 0.00040532503932429843, "Fashion & Beauty": 0.0002415595326753812, "Business, Economics & Finance": 0.00044384876043273495, "Food & Dining": 0.00019732462504171486, "Games & Gaming": 0.0003746440485180672, "Electronics & Hardware": 0.0004659002418930391, "Health, Healthcare & Wellness": 0.000340171447703726, "History & Geography": 0.00032737687852229955, "Home, Pets & Hobbies": 0.00020843929854398916, "Industry & Production": 0.00021559429956657852, "Language, Literature & Philosophy": 0.00020937617578275982, "Politics & Government": 0.00020566188427252385, "Religion & Spirituality": 0.000325489250566477, "Science, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/felipesilva543/Inteligencia-Artificia/blob/a8e85dfe30e34d22cb19f48b35011f4b3aecd03c/Trabalhos/Trabalho%202/Trab-2-Enviar/374850_374852_378578/NRainhasR/nrainhas.h", "id": "cpp-0-229a3641d628a42200d9e7effd4aeaf7", "text": "#ifndef NRAINHAS\n#define NRAINHAS\n#include <iostream>\n#include <fstream>\n#include <string>\n#include <vector>\n#include <sstream>\n\nusing namespace std;\n\nstring convStr(int i){\n    stringstream ss;\n    ss << i;\n    return ss.str();\n}\n\nclass NRainhas{\n    string nR;\npublic:\n    NRainhas(string _valor) {\n        nR = _valor;\n    }\n    void gegarArquivo(){\n        ofstream f_out;\n        vector<string> vFormulas;\n        string sFormulas(\"\");\n        int numRai = stoi(nR);\n        int nAtomos = numRai*numRai;\n        string txt = \"rainhas\" + nR ;\n\n            f_out.open(txt+\".cnf\");\n            if(! f_out.good())\n                cout << \"Não foi possivel abrir o arquivo!\\n\";\n            else cout << \"Arquivo criado!\\n\";\n\n            //Posicionando Linhas\n            int quebraLinha = 0;\n            for(int i = 0; i <= nAtomos; i++){\n                if(quebraLinha == numRai){\n                    sFormulas += \"0\";\n                    quebraLinha = 0;\n                    vFormulas.push_back(sFormulas);\n                    sFormulas = \"\";\n                }\n                sFormulas += convStr(i+1) + \" \";\n                quebraLinha++;\n            }\n            sFormulas = \"\";\n\n            //Posicionando Colunas\n            int auxColuna = 1;\n            int auxColuna2 = 0;\n            while(auxColuna <= numRai){\n                auxColuna2 = auxColuna;\n                sFormulas += convStr(auxColuna2) + \" \";\n                for(int i = 0; i < numRai-1; i++ ){\n                    auxColuna2 += numRai;\n                    sFormulas += convStr(auxColuna2) + \" \";\n                }\n                sFormulas += \"0\";\n                vFormulas.push_back(sFormulas);\n                sFormulas = \"\";\n                auxColuna++;\n            }\n            sFormulas = \"\";\n\n            //Exclusão de repetição de linhas\n            int auxExLin = 1, cColLi = 1;\n            while (auxExLin <= nAtomos){\n                int auxExLin2 = auxExLin;\n                while(cColLi < numRai){\n                    for(int i = 0; i < (numRai - cColLi); i++){\n                        auxExLin2++;\n                        sFormulas += \"-\" + convStr(auxExLin) + \" -\" + convStr(auxExLin2) + \" 0\";\n                        vFormulas.push_back(sFormulas);\n                        sFormulas = \"\";\n                    }\n                    cColLi++;\n                    auxExLin++;\n                    auxExLin2 = auxExLin;\n                }\n                    auxExLin += 1;\n                    cColLi = 1;\n            }\n            sFormulas = \"\";\n\n            //Exclusão de repetição nas colunas\n            int auxExCol = 1, cColu = 1;\n            int cLinha = 1;\n            while (auxExCol <= nAtomos){\n                int auxExCol2 = auxExCol;\n                while(cColu <= numRai){\n                    for(int i = 0; i < (numRai - cLinha); i++){\n                        auxExCol2 += numRai;\n                        sFormulas += \"-\" + convStr(auxExCol) + \" -\" + convStr(auxExCol2) + \" 0\";\n                        vFormulas.push_back(sFormulas);\n                        sFormulas = \"\";\n                    }\n                    cColu++;\n                    auxExCol++;\n                    auxExCol2 = auxExCol;\n                }\n                    cLinha++;\n                    cColu = 1;\n            }\n            sFormulas = \"\";\n\n            //Exclusão de repetição nas diagonais primarias\n            int auxDiaPri = 1, auxDiapri2 = 0;\n            while(auxDiaPri <= nAtomos){\n                int coluna = 1, linha = 1;\n                auxDiapri2 = auxDiaPri;\n                while(coluna <= numRai){\n                    for(int i = 0; i < (numRai - linha); i++ ){\n                        auxDiapri2 += numRai + 1;\n                        if(auxDiapri2 <= nAtomos){\n                            sFormulas += \"-\" + convStr(auxDiaPri) + \" -\" + convStr(auxDiapri2) + \" 0\";\n                            vFormulas.push_back(sFormulas);\n                            sFormulas = \"\";\n                        }\n                    }\n                    linha++;\n                    auxDiaPri++;\n                    auxDiapri2 = auxDiaPri;\n                    coluna++;\n                }\n            }\n            sFormulas = \"\";\n\n            //Exclusão de repetição nas diagonais secundárias\n            int auxPriCol = 1;\n            int auxDiaSec = 2, auxDiaSec2 = 0;\n            while(auxDiaSec <= nAtomos){\n                int coluna = 1, linha = 1;\n                auxDiaSec2 = auxDiaSec;\n                while(coluna <= numRai){\n                    for(int i = 0; i < linha; i++ ){\n                        auxDiaSec2 += numRai - 1;\n                        if(auxDiaSec2 <= nAtomos){\n                            if(auxDiaSec != ((numRai*auxPriCol)+1)){\n                                sFormulas += \"-\" + convStr(auxDiaSec) + \" -\" + convStr(auxDiaSec2) + \" 0\";\n                                vFormulas.push_back(sFormulas);\n                                sFormulas = \"\";\n                            }\n                        }\n                    }\n                    auxDiaSec++;\n                    linha++;\n                    auxDiaSec2 = auxDiaSec;\n                    coluna++;\n                }\n                auxPriCol++;\n            }\n            sFormulas = \"\";\n\n            //Guardar em arquivo\n            f_out << \"p cnf \" << nAtomos << \" \" << vFormulas.size() << endl;\n            for(string ele : vFormulas){\n                f_out << ele << endl;\n            }\n\n            f_out.close();\n    }\n\n};\n\n\n#endif // NRAINHAS\n\n", "text_preview": "#ifndef NRAINHAS #define NRAINHAS #include <iostream> #include <fstream> #include <string> #include <vector> #include <sstream> using namespace std; string convStr(int i){ stringstream ss; ss << i; return ss.str(); } class NRainhas{ string ", "input_sha256": "2b352301bdea764024c614259978d001cffc2a422b268a9b91a0afc9fdac1b9d", "text_sha256": "229a3641d628a42200d9e7effd4aeaf74741511fdc92d2361f5b0f310fbaa8ce", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 92, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.36532585276276297, "noise_severity": 0.05596136542362944, "missing_content_severity": 0.022002542986392225, "factual_density": 3.1255518053614693, "pretraining_utility": 0.30755259175647076, "math_density": 2.195104266526097, "code_density": 3.849858507315211, "reasoning_density": 0.6676646010859439, "education_level": 2.0635201608616582, "creative_writing_quality": -0.4812586519645337}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.006868644635770305, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9945084956067477, "secondary": 0.9813350748708086, "tertiary": 0.9885023562298734, "structured_records": 0.0036458240977212796, "procedural_steps": 0.021794383053928376, "worked_solution": 0.01698279410620909, "contextual_dialogue": 7.710229139472173e-06, "content_quality": 0.3756450284914762, "noise_severity": 0.9533655288136421, "missing_content_severity": 0.9816645475113398, "factual_density": 0.27129317113455753, "pretraining_utility": 0.49215731846695865, "math_density": 0.0, "code_density": 0.8748820894293425, "reasoning_density": 0.4436128324283798, "education_level": 0.7809796256445194, "creative_writing_quality": 0.9989564924919876, "primary_topic": 0.9216331836840289, "secondary_topic": 0.5328423522599682}, "probability_margin": {"primary": 0.9922913258809417, "secondary": 0.9745893793581025, "tertiary": 0.9849058039928028, "education_level": 0.7217733530046107, "creative_writing_quality": 0.9987930070539002, "primary_topic": 0.8729117035240308, "secondary_topic": 0.3030844269643974}, "raw_probabilities": {"primary": {"Academic Writing": 0.00044102757997534496, "Content Listing": 2.5786377745404113e-05, "Creative Writing": 3.433403045001098e-05, "Customer Support Page": 1.0626660943681963e-05, "Q&A Forum": 1.8344176146621943e-05, "Discussion Thread": 1.0625632354543892e-05, "Comment Section": 1.5707598375977823e-05, "Missing Content": 3.653754678903465e-05, "Knowledge Article": 0.0001575089802582759, "Legal Notices": 5.0190881807347186e-05, "News Article": 6.549618663938583e-06, "Organizational Announcement": 1.2714082074681524e-05, "Nonfiction Writing": 2.0219914609466522e-05, "Organizational About Page": 2.09381020362568e-05, "Personal About Page": 1.8265219523602603e-05, "Personal Blog": 9.816238130661884e-06, "Product Page": 8.64405408308296e-06, "Spam / Ads": 1.4830143743409415e-05, "Structured Data": 0.0005881722116017969, "Technical Writing": 0.9947373082897999, "Transcript / Interview": 2.302025253005456e-05, "Tutorial / How-To Guide": 0.0012832241983599354, "User Reviews": 9.625801138794906e-06, "Other / Unknown": 0.0024459824088582083}, "secondary": {"Academic Writing": 0.004071343799674853, "Content Listing": 8.380282475798877e-05, "Creative Writing": 0.00014115991447766598, "Customer Support Page": 4.39361800307359e-05, "Q&A Forum": 4.13802218760779e-05, "Discussion Thread": 3.108140291760452e-05, "Comment Section": 6.209223989845296e-05, "Missing Content": 0.0002951709720263867, "Knowledge Article": 0.0005281128098650629, "Legal Notices": 0.00020046946864859635, "News Article": 5.32622968580977e-05, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Abacn/AntLabyrinth/blob/816cd8acd9761eb8bfa4e5d571f15638b1dd7cb5/MKK/MKKDynamics/spheres.cpp", "id": "cpp-0-3b5d9f6dde71fe71a1a224f2e5845303", "text": "//===========================================================\n//===========================================================\n//===========================================================\n//\n//  Molecular dynamics simulation of Mk/MKK model\n//\n//===========================================================\n//===========================================================\n//===========================================================\n\n#include <iostream>\n#include <math.h>\n#include <fstream>\n#include <vector>\n#include <string.h>\n#include <string>\n#include <stdlib.h>\n#include <sstream>\n#include <iomanip>\n\n#include \"box.h\"\n#include \"sphere.h\"\n#include \"event.h\"\n#include \"heap.h\"\n#include \"read_input.h\"\n#include \"utility.h\"\n\nint main(int argc, char **argv)\n{\n  read_input input;\n  int mult;\n  //double r, rmin, rmax;\n  const char *dftinput = \"inputfile.dat\", *infname;\n\n  std::cout << \"DIM = \" << DIM << std::endl;\n\n  if (argc < 2) infname = dftinput;\n  else infname = argv[1];\n\n  int error = input.read(infname);\n\n  if (error) return error;\n  if (input.seed == -1)\n    rg.seed(time(0));\n  else\n    rg.seed(input.seed);   // initialize the random number generator\n\n  Box b(input);\n\n  if(strcmp(input.readfile, \"new\")==0)\n    input.readfile[0]=0;\n\n  if (input.readfile[0]) // read in existing configuration\n  {\n    // read the header\n    std::cout << \"Reading in positions of spheres\" << std::endl;\n    b.RecreateSpheres(input.readfile, input.temp);\n  }\n  else\n  {\n    std::cout << \"Creating new positions of spheres\" << std::endl;\n    b.CreateSpheres(input.temp);\n    if (input.outconfig_switch == 1)\n    {\n      b.WriteLastConfiguration(std::string(input.configprefixes) + \"_config_initial.dat\");\n      b.WriteVelocities(std::string(input.configprefixes) + \"_v_initial.dat\");\n      if (0.0 != input.shiftscale)\n        b.WriteShifts(std::string(input.configprefixes) + \"_shifts.dat\");\n    }\n  }\n  std::cout << \"nlist tol = \" << sqrt(b.neighs->getsqrhfsh()) << std::endl;\n  //  Clear output file\n  //    std::ofstream output(input.writefile);\n  //    output.close();\n\n  std::ofstream output(input.datafile);\n  output.precision(10);\n  double tprint = 20 * input.mintime / DIM;\n  double maxtime = input.maxtime / DIM;  // scaled maxtime\n  mult=input.N;\n  double sigma;\n\n  // process all checks\n  b.Process(mult);\n\n  //procedure 1: compress to maxpf\n  if (b.PackingFraction() < input.maxpf)\n  {\n    std::cout << \"Procedure 1: compress to maxpf ...\" << std::endl;\n    while (b.PackingFraction() < input.maxpf) {\n      b.Process(mult);\n      sigma = b.rscale * 2.0;\n      if ((b.gtime + b.rtime) / sigma > tprint)\n      {\n        b.Synchronize(true);\n        auto thermos = b.Pressure();\n        output << (b.gtime + b.rtime) / sigma << '\\t' << b.PackingFraction() << '\\t' << thermos[0] << std::endl;\n        tprint = tprint * 1.1892071150027211;\n      }\n    }\n    if (input.outconfig_switch == 1)\n    {\n      b.WriteLastConfiguration(std::string(input.configprefixes) + \"_config_compressed.dat\");\n      b.WriteVelocities(std::string(input.configprefixes) + \"_v_compressed.dat\");\n    }\n  }\n\n  //reset compression rate to zero;\n  b.Reset();\n  sigma = b.rmeanfin* 2.0;\n  tprint = input.mintime / DIM;\n\n  //procedure 2: equilibrate\n  double MSD=0., rtyp2=0., MQD=0., dtmp;\n  std::cout << \"Procedure 2: equilibrate ...\" << std::endl;\n  double eqtime = std::min(1e3 / DIM, maxtime / 4.0);\n  while ((b.gtime + b.rtime)/sigma < eqtime && MSD < 20.0 / DIM)\n  {\n    b.Process(mult*input.eventspercycle, 1);\n    if((b.gtime + b.rtime)/sigma > tprint)\n    {\n      b.Synchronize(false);\n      MSD = 0.;\n      MQD = 0.;\n      for (int i=0; i<b.N; i++){\n        double dr2 = vector<>::norm_squared(b.s[i].x, b.x0[i]);\n        MSD += dr2;\n        MQD += dr2*dr2;\n      }\n      dtmp = sigma*sigma;\n      MSD = MSD/input.N/dtmp;\n      MQD = MQD/input.N/(dtmp*dtmp);\n      auto thermos = b.Pressure();\n      output << (b.gtime + b.rtime) / sigma << '\\t' << thermos[0];\n      output << '\\t' << MSD << '\\t' << MQD << std::endl;\n      tprint = tprint * 1.1892071150027211;\n    }\n  }\n\n  tprint = input.mintime / DIM;\n  b.StartMeasure(maxtime/4, maxtime*3/4000, tprint, maxtime);\n  std::ofstream outMSD(\"MSD.dat\");\n  outMSD.precision(10);\n\n  //procedure 3: measure MSD and rtyp\n  std::cout << \"Procedure 3: measure MSD and rtyp ...\" << std::endl;\n\n  while ((b.gtime + b.rtime)/sigma < maxtime)\n  {\n    b.Process(0, 1, tprint);\n\n    b.Synchronize(false);\n    // time evolution statistics\n    MSD = 0.;\n    rtyp2 = 0.;\n    MQD = 0.;\n    for (int i=0; i<b.N; i++) {\n      double dr2 = vector<>::norm_squared(b.s[i].x, b.x0[i]);\n      MSD = MSD + dr2;\n      rtyp2 = rtyp2 + log(dr2);\n      MQD += dr2*dr2;\n    }\n    dtmp = sigma*sigma;\n    MSD = MSD/input.N/dtmp;\n    rtyp2 = exp(rtyp2/input.N)/dtmp;\n    MQD = MQD/input.N/(dtmp*dtmp);\n    auto thermos = b.Pressure();\n    outMSD << (b.gtime + b.rtime) / sigma << '\\t' << MSD << '\\t' << rtyp2 << '\\t' << MQD << '\\t' << thermos[0];\n    outMSD << std::endl;\n    tprint = tprint * 1.1892071150027211; // 2^(1/4)\n  }\n  output.close();\n  outMSD.close();\n  b.PrintStatistics(1);\n  b.RunTime();\n\n  if (input.outconfig_switch == 1)\n  {\n    b.WriteLastConfiguration(std::string(input.configprefixes) + \"_config_last.dat\");\n    b.WriteVelocities(std::string(input.configprefixes) + \"_v_last.dat\");\n  }\n  return 0;\n}\n", "text_preview": "//=========================================================== 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{"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.9504287419135666, "noise_severity": 0.36857963809437033, "missing_content_severity": 0.08010795577118131, "factual_density": 3.161434260472379, "pretraining_utility": 0.46634706517142566, "math_density": 2.9397457693693294, "code_density": 3.7655115555106513, "reasoning_density": 0.6013871436443471, "education_level": 3.1212038119534737, "creative_writing_quality": -0.3455778583379751}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": 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2.2614262277893757e-05, "Spam / Ads": 3.728729991759173e-05, "Structured Data": 0.0005805275976800868, "Technical Writing": 0.0033297544468128526, "Transcript / Interview": 5.251513928094699e-05, "Tutorial / How-To Guide": 0.0013651920186253004, "User Reviews": 1.988615132746046e-05, "NONE": 0.9880161891847966}, "structured_records": {"false": 0.9948684237049038, "true": 0.0051315762950962214}, "procedural_steps": {"false": 0.9694810499380235, "true": 0.030518950061976444}, "worked_solution": {"false": 0.9939594474111888, "true": 0.006040552588811176}, "contextual_dialogue": {"false": 0.999994000483318, "true": 5.999516681995979e-06}, "content_quality": {"-2": 0.006799244153173053, "-1": 0.020704462162568524, "0": 0.23811414814832202, "1": 0.48403259868939164, "2": 0.2503495468465448}, "noise_severity": {"0": 0.6910882988745036, "1": 0.26090019067395515, "2": 0.039774314018070786, "3": 0.004817966349608383, "4": 0.0034192300838621174}, "missing_content_severity": {"0": 0.9518560560253632, "1": 0.02255318557670587, "2": 0.01992552670935638, "3": 0.004957209978535587, "4": 0.0007080217100389791}, "factual_density": {"0": 0.017786904210883026, "1": 0.051033968290120295, "2": 0.13236667663276977, "3": 0.3495828645481886, "4": 0.44922958631803833}, "pretraining_utility": {"-2": 0.01035025551393614, "-1": 0.11255408265821419, "0": 0.3746193119980134, "1": 0.40535104080216044, "2": 0.09712530902767585}, "math_density": {"0": 0.022586869661239368, "1": 0.06898701102626824, "2": 0.20589413139651158, "3": 0.3511574561138853, "4": 0.35137453180209555}, "code_density": {"0": 0.00037581356710967, "1": 0.0007242825223241784, "2": 0.018071018081855076, "3": 0.19467030649022737, "4": 0.7861585793384837}, "reasoning_density": {"0": 0.7501337953523132, "1": 0.07119765932209057, "2": 0.07030512790513145, "3": 0.04387444116986562, "4": 0.06448897625059918}, "education_level": {"none": 0.0013989895822368213, "0": 0.001471684291493814, "1": 0.006786829497420249, "2": 0.18031539999362417, "3": 0.4906887356890556, "4": 0.3193383609461694}, "creative_writing_quality": {"not_applicable": 0.9994758041392926, "-2": 7.72819010270359e-05, "-1": 0.00013859701166430414, "0": 0.00022236787618867192, "1": 5.9887812829348e-05, "2": 2.6061258998062648e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.00031064490995681227, "Art & Design": 0.0005148068333744368, "Law & Crime": 0.0006627413477055574, "Education & Careers": 0.0003026354285208205, "Entertainment & Media": 0.0003296149531915884, "Fashion & Beauty": 0.0003239135759793478, "Business, Economics & Finance": 0.00034979797163017215, "Food & Dining": 0.000323525202658847, "Games & Gaming": 0.0006401402271588835, "Electronics & Hardware": 0.00043942813287415526, "Health, Healthcare & Wellness": 0.0004871611575778731, "History & Geography": 0.0005091370361164892, "Home, Pets & Hobbies": 0.00041357015965517996, "Industry & Production": 0.0006223433039719476, "Language, Literature & Philosophy": 0.0003013342682480069, "Politics & Government": 0.00017225616394818426, "Religion & Spirituality": 0.0003105489384653384, "Science, Mathematics & Engineering": 0.39577324160084054, "Relationships & Community": 0.0001760135964282136, "Software Usage & Applications": 0.0005448936752867559, "Programming & Software Engineering": 0.594418443072171, "Sports & Fitness": 0.0003924149289661456, "Vehicles & Transportation": 0.00022019747195732602, "Travel & Tourism": 0.0003393795314279647, "Other / Uncovered Subject": 0.0006708925481955135, "Unknown / Insufficient Content": 0.00045092396369289766}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0005364002966015988, "Art & Design": 0.0005987722535454678, "Law & Crime": 0.000700743706899415, "Education & Careers": 0.0003939979102407471, "Entertainment & Media": 0.0004174789455376701, "Fashion & Beauty": 0.0004136273106176467, "Business, Economics & Finance": 0.00047069424711715835, "Food & Dining": 0.00033678788326745637, "Games & Gaming": 0.0005852303491032025, "Electronics & Hardware": 0.0006063812516163608, "Health, Healthcare & Wellness": 0.000508058747424666, "History & Geography": 0.00048508509577816194, "Home, Pets & Hobbies": 0.0004415332900495943, "Industry & Production": 0.0007129561565958505, "Language, Literature & Philosophy": 0.0003267078612556987, "Politics & Government": 0.0002455806629231831, "Religion & Spirituality": 0.0004545741242598272, "Science, Mathematics & Engineering": 0.4567981069786606, "Relationships & Community": 0.00022171986130952562, "Software Usage & Applications": 0.0005095936061061119, "Programming & Software Engineering": 0.344659056055079, "Sports & Fitness": 0.0004651233221525567, "Vehicles & Transportation": 0.0002835679973102344, "Travel & Tourism": 0.0005815078631368948, "Other / Uncovered Subject": 0.000670026092454613, "NONE": 0.18757668813095685}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999998, "mass_delta": -2.220446049250313e-16, "missing_mass": 2.220446049250313e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}}, "usage": {"input_tokens": 10247, "output_tokens": 19}, "cache": {"status": "unreported", "cached_tokens": null, "cache_write_tokens": null, "input_tokens": 10247, "cached_share": null, "cache_discount_reported": null, "reported_cost_usd": null}, "response_metadata": {"model": "decision-models/baseten-mammoth-27b-latest"}, 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false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "816cd8acd9761eb8bfa4e5d571f15638b1dd7cb5"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 15, "repo_created_at": "2019-02-10T19:18:40.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 0, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "ed2edb9c3ebaf71c45a0832d0a423a7c2c320ac0", "size_bytes": 5320, "file_timestamp": 1733722513, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "816cd8acd9761eb8bfa4e5d571f15638b1dd7cb5", "url": "https://github.com/Abacn/AntLabyrinth/blob/816cd8acd9761eb8bfa4e5d571f15638b1dd7cb5/MKK/MKKDynamics/spheres.cpp"}, "gpt4o_tokens": 1573, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.9504287419135666, "noise_severity": 0.36857963809437033, "missing_content_severity": 0.08010795577118131, "factual_density": 3.161434260472379, "pretraining_utility": 0.46634706517142566, "math_density": 2.9397457693693294, "code_density": 3.7655115555106513, "reasoning_density": 0.6013871436443471, "education_level": 3.1212038119534737, "creative_writing_quality": -0.3455778583379751}, "score_applicability": {"education_level": 0.9986010104177632, "creative_writing_quality": 0.0005241958607074226}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/YunanZzz/Disk_based_vector/blob/b01825bdda35ea54c873d13f569e29fcde969053/demos/demo_ivf_pq_fs_disk.cpp", "id": "cpp-0-7e7fbc32a2ad00cec88ce95c3ad1a02f", "text": "/**\n * Copyright (c) Facebook, Inc. and its affiliates.\n *\n * This source code is licensed under the MIT license found in the\n * LICENSE file in the root directory of this source tree.\n */\n\n#include <faiss/AutoTune.h>\n#include <faiss/Index.h>\n#include <faiss/IndexFlat.h>\n#include <faiss/IndexHNSW.h>\n#include <faiss/IndexIVF.h>\n#include <faiss/IndexIVFFlat.h>\n#include <faiss/IndexIVFPQFastScanDisk.h>\n\n#include <faiss/index_io.h>\n#include <sys/stat.h>\n#include <sys/time.h>\n#include <cassert>\n#include <cstdio>\n#include <cstdlib>\n#include <iostream>\n#include <map>\n#include <random>\n#include <omp.h>\nusing idx_t = faiss::idx_t;\ndouble elapsed() {\n    struct timeval tv;\n    gettimeofday(&tv, NULL);\n    return tv.tv_sec + tv.tv_usec * 1e-6;\n}\n\nfloat* fvecs_read(const char* fname, size_t* d_out, size_t* n_out) {\n    FILE* f = fopen(fname, \"r\");\n    if (!f) {\n        fprintf(stderr, \"could not open %s\\n\", fname);\n        perror(\"\");\n        abort();\n    }\n    int d;\n    fread(&d, 1, sizeof(int), f);\n    assert((d > 0 && d < 1000000) || !\"unreasonable dimension\");\n    fseek(f, 0, SEEK_SET);\n    struct stat st;\n    fstat(fileno(f), &st);\n    size_t sz = st.st_size;\n    assert(sz % ((d + 1) * 4) == 0 || !\"weird file size\");\n    size_t n = sz / ((d + 1) * 4);\n\n    *d_out = d;\n    *n_out = n;\n    float* x = new float[n * (d + 1)];\n    size_t nr = fread(x, sizeof(float), n * (d + 1), f);\n    assert(nr == n * (d + 1) || !\"could not read whole file\");\n\n    // shift array to remove row headers\n    for (size_t i = 0; i < n; i++)\n        memmove(x + i * d, x + 1 + i * (d + 1), d * sizeof(*x));\n\n    fclose(f);\n    return x;\n}\nint* ivecs_read(const char* fname, size_t* d_out, size_t* n_out) {\n    return (int*)fvecs_read(fname, d_out, n_out);\n}\nint main() {\n    double t0 = elapsed();\n    int d = 128;      // dimension\n    int nb = 1000000; // database size\n    int nq = 100;   // nb of queries\n    char* base_filepath =\"/mnt/d/VectorDB/sift/sift/sift_base.fvecs\";\n    char* query_filepath =\"/mnt/d/VectorDB/sift/sift/sift_query.fvecs\";\n    char* ground_truth_filepath=\"/mnt/d/VectorDB/sift/sift/sift_groundtruth.ivecs\";\n    \n    std::string index_store_path  = \"/home/granthe/faiss/faiss/build/sift1M_fs\";\n    //std::string result_path       = \"/mnt/d/VectorDB/RESULT_SET/ivf_pq_disk_wsl.txt\";\n\n    size_t dd; // dimension\n    size_t nt; // the number of vectors\n    float* xb = new float[d * nb];\n    float* xq = new float[d * nq];\n\n    size_t dd2; // dimension\n    size_t nt2; // the number of query\n    xb = fvecs_read(base_filepath, &dd, &nt);\n    xq = fvecs_read(query_filepath, &dd2, &nt2);\n\n    int ratio = 50;\n    {\n        srand((int)time(0));\n        std::vector<float> trainvecs(nb / ratio * d);\n        for (int i = 0; i < nb / ratio; i++) {\n            int rng = (rand() % (nb + 1));\n            // rng=random number in nb\n\n            for (int j = 0; j < d; j++) {\n                // printf(\" setting %d vector's %d data, trianvecs[%d]=xb[%d]\n                // \\n\",i,j,d * i + j,rng * d + j);\n                trainvecs[d * i + j] = xb[rng * d + j];\n            }\n        }\n        int nlist = 8000;\n        int k = 100;\n        // load ground-truth and convert int to long\n        size_t nq2;\n        size_t kk;\n        int* gt_int = ivecs_read(ground_truth_filepath, &kk, &nq2);\n\n        //pq parameters\n        int m = 32;\n        int nbits = 4;\n        // Disk parameters\n        int top_clusters = 5;\n        float estimate_factor = 1.2;\n        float estimate_factor_partial =1.3;\n        float prune_factor = 10;\n\n        int M = 32;\n        faiss::IndexHNSWFlat quantizer(d, M);\n        faiss::IndexIVFPQFastScanDisk index(&quantizer, d, nlist, m, nbits, top_clusters, estimate_factor, prune_factor,index_store_path);   \n        index.set_estimate_factor_partial(estimate_factor_partial);\n        index.set_assign_replicas(2);\n        index.verbose = true;\n\n        printf(\"[%.3f s] IndexIVFPQ_disk start to train\\n\", elapsed() - t0);\n        index.train(nb / ratio, trainvecs.data());\n        printf(\"[%.3f s] IndexIVFPQ_disk train finished\\n\", elapsed() - t0);\n\n        printf(\"[%.3f s] IndexIVFPQ_disk start to add\\n\", elapsed() - t0);\n        int nadd = 10;\n        for(int i = 0; i < nadd; i++){\n            index.add(nb/nadd, xb + nb/nadd * i * d);\n        }\n        printf(\"[%.3f s] IndexIVFPQ_disk add finished\\n\", elapsed() - t0);\n\n        printf(\"[%.3f s] IndexIVFPQ_disk start to reorg\\n\", elapsed() - t0);\n        //index.initial_location(xb);\n        printf(\"[%.3f s] IndexIVFPQ_disk reorg finished\\n\", elapsed() - t0);\n\n        std::vector<double> search_times;\n        std::vector<double> recalls;\n        //omp_set_num_threads(1);\n        { // search xq\n\n            idx_t* I = new idx_t[k * nq];\n            float* D = new float[k * nq];\n            faiss::idx_t* gt;\n            gt = new faiss::idx_t[kk * nq];\n            for (int i = 0; i < kk * nq; i++) {\n                gt[i] = gt_int[i]; // long int / int\n            }\n            delete[] gt_int;\n            //int arr[] = {4, 5, 6, 7, 8, 10};\n            //int arr[] = {5, 7, 9, 11, 13};\n            //int arr[] = {5,10,15,20,25,30};\n            int arr[] = {50,60,70,80,90,100};\n            //int arr[] = { 90,100,120, 140};\n\n            // write_index(index, \"hnsw.index\");\n            // how to get length of array\n\n            for (int i : arr) {\n                printf(\"-----------now, the efs is=%d---------\\n\", i);\n                \n                index.nprobe = i;\n                printf(\"[%.3f s] IndexIVFPQ_disk start to search\\n\", elapsed() - t0);\n                double t1 = elapsed();\n                index.search(nq, xq, k, D, I);\n                double t2 = elapsed();\n                double search_time = t2 - t1;\n                printf(\"[%.3f s] IndexIVFPQ_disk search finished\\n\", elapsed() - t0);\n\n                size_t fcc = faiss::indexIVFPQFastScanDisk_stats.full_cluster_compare;\n                size_t fcr = faiss::indexIVFPQFastScanDisk_stats.full_cluster_rerank;\n                size_t pcc = faiss::indexIVFPQFastScanDisk_stats.partial_cluster_compare;\n                size_t pcr = faiss::indexIVFPQFastScanDisk_stats.partial_cluster_rerank;\n\n                size_t sl  = faiss::indexIVFPQFastScanDisk_stats.pruned;\n\n                double me1 = faiss::indexIVFPQFastScanDisk_stats.memory_pq_elapsed.count() / 1000; \n                double me2 = faiss::indexIVFPQFastScanDisk_stats.memory_2_elapsed.count() / 1000; \n                double dfe = faiss::indexIVFPQFastScanDisk_stats.disk_full_elapsed.count() / 1000; \n                double dpe = faiss::indexIVFPQFastScanDisk_stats.disk_partial_elapsed.count() / 1000; \n\n                \n\n                int repeated = 0;\n                for (int i = 0; i < nq; i++)\n                {\n                    std::unordered_set<size_t> seen_ids;\n\n                    for (int j = 0; j < k; j++)\n                    {\n                        size_t temp_id = I[i * k + j];  // 获取第 i 个查询的第 j 个结果的 ID\n                        \n                        if (seen_ids.find(temp_id) != seen_ids.end())\n                        {\n                            repeated++;\n                        }\n                        else\n                        {\n                            seen_ids.insert(temp_id);\n                        }\n                    }\n                }\n                std::cout << \"repeated = \" << repeated << std::endl;\n\n\n                std::cout << \"full_cluster_compare      :\"<<fcc/nq << std::endl;\n                std::cout << \"full_cluster_rerank       :\"<<fcr/nq << std::endl;\n                std::cout << \"partial_cluster_compare   :\"<<pcc/nq << std::endl;\n                std::cout << \"partial_cluster_rerank    :\"<<pcr/nq << std::endl;\n\n                std::cout << \"AVG rerank ratio(full)    :\" << (double)fcr/(double)fcc << std::endl;\n                std::cout << \"AVG rerank ratio(partital):\" << (double)pcr/(double)pcc << std::endl;\n\n                std::cout << \"Scanned lists total       :\" << sl << std::endl;\n                std::cout << \"Scanned lists per query   :\" << (double)sl/(double)nq << std::endl;\n                std::cout << \"Scanned lists account for :\" << (double)sl /((double)nq*i) << std::endl;\n\n                std::cout << \"memory_1_elapsed        :\" << me1/nq << std::endl;\n                std::cout << \"memory_2_elapsed        :\" << me2/nq << std::endl;\n                std::cout << \"disk_full_elapsed       :\" << dfe/nq << std::endl;\n                std::cout << \"disk_partial_elapsed    :\" << dpe/nq << std::endl\n                          << std::endl\n                          << std::endl;\n                \n\n                faiss::indexIVFPQFastScanDisk_stats.reset();\n                // evaluate result\n                int n2_100 = 0;\n                for (int i = 0; i < nq; i++) {\n                    std::map<float, int> umap;\n                    for (int j = 0; j < k; j++) {\n                        umap.insert({gt[i * 100 + j], 0});\n                    }\n                    for (int l = 0; l < k; l++) {\n                        if (umap.find(I[i * k + l]) != umap.end()) {\n                            n2_100++;\n                        }\n                    }\n                    umap.clear();\n                }\n                printf(\"Intersection R@100 = %.4f\\n\", n2_100 / float(nq * k));\n                double recall = n2_100 / float(nq * k);\n                search_times.push_back(search_time/nq*1000);\n                recalls.push_back(recall);\n            }\n\n            delete[] I;\n            delete[] D;\n        }\n\n        delete[] xb;\n        delete[] xq;\n        // Print the search times and recalls\n        std::cout << \"Search times: [\";\n        for (size_t i = 0; i < search_times.size(); i++) {\n            std::cout << search_times[i];\n            if (i < search_times.size() - 1) {\n                std::cout << \", \";\n            }\n        }\n        std::cout << \"]\" << std::endl;\n\n        std::cout << \"QPS: [\";\n        for (size_t i = 0; i < search_times.size(); i++) {\n            std::cout << (1000.0/search_times[i]);\n            if (i < search_times.size() - 1) {\n                std::cout << \", \";\n            }\n        }\n        std::cout << \"]\" << std::endl;\n\n        std::cout << \"Recalls: [\";\n        for (size_t i = 0; i < recalls.size(); i++) {\n            std::cout << recalls[i];\n            if (i < recalls.size() - 1) {\n                std::cout << \", \";\n            }\n        }\n        std::cout << \"]\" << std::endl;\n        return 0;\n    }\n}", "text_preview": "/** * Copyright (c) Facebook, Inc. and its affiliates. * * This source code is licensed under the MIT license found in the * LICENSE file in the root directory of this source tree. */ #include <faiss/AutoTune.h> #include <faiss/Index.h> #in", "input_sha256": "153f59ed234d3290708d11380b3b4c58d4b6e49759effba1b92ebaf5bbdb1338", "text_sha256": "7e7fbc32a2ad00cec88ce95c3ad1a02fe3088e9770c1ddc354793cc0ec2b34b3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 94, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 2, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.3431110597812992, "noise_severity": 0.4485299489888206, "missing_content_severity": 0.03992272280908355, "factual_density": 3.1172903167755046, "pretraining_utility": 0.6205175318692907, "math_density": 2.004533332706538, "code_density": 3.773043326929802, "reasoning_density": 0.4416732848896032, "education_level": 3.2664981676820033, "creative_writing_quality": -0.4103472817765422}, "api_choices": {"primary": "Technical Writing", "secondary": "Legal Notices", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": ["secondary probability 0.5585 below 0.70"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.3180860718057665, "secondary_choice": "Legal Notices", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9876329402355697, "secondary": 0.5393487370350912, "tertiary": 0.6898565910861768, "structured_records": 0.0042535527991577485, "procedural_steps": 0.1344140086044932, "worked_solution": 0.03503622077823133, "contextual_dialogue": 7.403636353910351e-06, "content_quality": 0.3940843765257075, "noise_severity": 0.6262250425093161, "missing_content_severity": 0.9667310643257637, "factual_density": 0.26440859731292043, "pretraining_utility": 0.5161877501204322, "math_density": 0.055232689039346194, "code_density": 0.810869439108168, "reasoning_density": 0.6319389292586639, "education_level": 0.4289556992873992, "creative_writing_quality": 0.9990136055872648, "primary_topic": 0.9397574407510486, "secondary_topic": 0.46568323531534916}, "probability_margin": {"primary": 0.979050098849, "secondary": 0.17384937834014286, "tertiary": 0.4266704757590678, "education_level": 0.1504602165264846, "creative_writing_quality": 0.9988368408707768, "primary_topic": 0.88843155457152, "secondary_topic": 0.14220018059224204}, "raw_probabilities": {"primary": {"Academic Writing": 0.00010364852444322871, "Content Listing": 3.754135286591289e-05, "Creative Writing": 3.707577395617414e-05, "Customer Support Page": 2.9316124101054074e-05, "Q&A Forum": 2.7058014777659383e-05, "Discussion Thread": 1.74905053643375e-05, "Comment Section": 2.973371594856874e-05, "Missing Content": 5.245408083414838e-05, "Knowledge Article": 0.00012847752488169775, "Legal Notices": 0.00076977056401062, "News Article": 1.3623236698341096e-05, "Organizational Announcement": 2.862182261937055e-05, "Nonfiction Writing": 2.5536640388131996e-05, "Organizational About Page": 2.6329300899453887e-05, "Personal About Page": 1.97012855573131e-05, "Personal Blog": 1.8742789299727687e-05, "Product Page": 1.5558632691347395e-05, "Spam / Ads": 2.2159963002893454e-05, "Structured Data": 0.0002585201625246195, "Technical Writing": 0.9881482343924209, "Transcript / Interview": 2.7836826424988917e-05, "Tutorial / How-To Guide": 0.009098135543421012, "User Reviews": 1.6042676121218105e-05, "Other / Unknown": 0.0010483905467471662}, "secondary": {"Academic Writing": 0.0007238917658784875, "Content Listing": 0.0005344570563459272, "Creative Writing": 0.0006446364624452507, "Customer Support Page": 0.00038613910947180453, "Q&A Forum": 0.00037486933347400205, "Discussion Thread": 0.00033554266660733463, "Comment Section": 0.000601187663411285, "Missing Content": 0.0007073309033561553, "Knowledge Article": 0.001121279062836893, "Legal Notices": 0.5585425396586291, "News Article": 0.0006417241177038792, "Organizational Announcement": 0.0006921544885949461, "Nonfiction Writing": 0.0005248036975624965, "Organizational About Page": 0.0006003045133787815, "Personal About Page": 0.00039550749045343624, "Personal Blog": 0.0003309217629425672, "Product Page": 0.00041349208281004, "Spam / Ads": 0.00043650576610442524, "Structured Data": 0.0007692039365412286, "Technical Writing": 0.03420743765177233, "Transcript / Interview": 0.0006249511719795197, "Tutorial / How-To Guide": 0.011359503583064263, "User Reviews": 0.0003384547361496207, "NONE": 0.38469316131848624}, "tertiary": {"Academic Writing": 0.0005044697353816246, "Content Listing": 0.0002940568183229759, "Creative Writing": 0.0005329893688620133, "Customer Support Page": 0.0002540988866456475, "Q&A Forum": 0.00021178603215049575, "Discussion Thread": 0.0001825568689332227, "Comment Section": 0.0004193629557195707, "Missing Content": 0.0004876449407343064, "Knowledge Article": 0.0007228552576853915, "Legal Notices": 0.2761087573651849, "News Article": 0.00035914101838732215, "Organizational Announcement": 0.0003810867299928479, "Nonfiction Writing": 0.00030474525268032847, "Organizational About Page": 0.00039893104094178647, "Personal About Page": 0.00024916302017551116, "Personal Blog": 0.00023930829383410628, "Product Page": 0.00026778110492787176, "Spam / Ads": 0.00028115107020676666, "Structured Data": 0.0006581260903308865, "Technical Writing": 0.005853226860361227, "Transcript / Interview": 0.0004123252198218768, "Tutorial / How-To Guide": 0.00792945920666571, "User Reviews": 0.00016774373780086696, "NONE": 0.7027792331242527}, "structured_records": {"false": 0.9957464472008423, "true": 0.0042535527991577485}, "procedural_steps": {"false": 0.8655859913955068, "true": 0.1344140086044932}, "worked_solution": {"false": 0.9649637792217687, "true": 0.03503622077823133}, "contextual_dialogue": {"false": 0.9999925963636461, "true": 7.403636353910351e-06}, "content_quality": {"-2": 0.025145672667824498, "-1": 0.1417024988582769, "0": 0.3881639649650782, "1": 0.3548708230424158, "2": 0.09011704046640463}, "noise_severity": {"0": 0.596320803018124, "1": 0.3656743473421746, "2": 0.03305850460617649, "3": 0.0030467876998066844, "4": 0.001899557333718239}, "missing_content_severity": {"0": 0.9713408071549948, "1": 0.019549468485679077, "2": 0.007355112757682275, "3": 0.0013554175985357583, "4": 0.00039919400310816153}, "factual_density": {"0": 0.011354314689066598, "1": 0.048580655022951016, "2": 0.16072482804267713, "3": 0.37010080331402156, "4": 0.4092393989312836}, "pretraining_utility": {"-2": 0.00885652971057773, "-1": 0.061350871470842894, "0": 0.33075725191967636, "1": 0.49848923103651704, "2": 0.10054611586238603}, "math_density": {"0": 0.1661558668945545, "1": 0.23228198643401432, "2": 0.24530381288060524, "3": 0.14338961465199035, "4": 0.21286871913883543}, "code_density": {"0": 0.0002996011202756457, "1": 0.0006216268252057474, "2": 0.013499729118027264, "3": 0.19689392987742405, "4": 0.7886851130590672}, "reasoning_density": {"0": 0.8030893004691257, "1": 0.05398892388195426, "2": 0.07392780331447178, "3": 0.036147134959087744, "4": 0.03284683737536054}, "education_level": {"none": 0.0011595767752725853, "0": 0.0012627557139879612, "1": 0.0039137379167193094, "2": 0.0958646473081727, "3": 0.524129749406166, "4": 0.37366953287968135}, "creative_writing_quality": {"not_applicable": 0.999178004656054, "-2": 0.00011342973244243782, "-1": 0.0002549867604355446, "0": 0.0003411637852771563, "1": 8.028746128226866e-05, "2": 3.2127604508489475e-05}, "primary_topic": {"Adult & Explicit Sexual Content": 0.0001059242554732901, "Art & Design": 0.00015285266873338386, "Law & Crime": 0.00019227610064009466, "Education & Careers": 0.00011307070016202322, "Entertainment & Media": 9.352852140342215e-05, "Fashion & Beauty": 9.770430962677559e-05, "Business, Economics & Finance": 0.00015310396677289134, "Food & Dining": 8.795639870938884e-05, "Games & Gaming": 0.0001605185391347495, "Electronics & Hardware": 0.00033818736118981747, "Health, Healthcare & Wellness": 0.00011955306368462201, "History & Geography": 0.00013981622022984943, "Home, Pets & Hobbies": 9.035072807746179e-05, "Industry & Production": 0.0001071591109835232, "Language, Literature & Philosophy": 9.498457641951548e-05, "Politics & Government": 6.0929742211646796e-05, "Religion & Spirituality": 7.598276774267834e-05, "Science, Mathematics & Engineering": 0.05364290768910361, "Relationships & Community": 4.7920519847240336e-05, "Software Usage & Applications": 0.0011230151993772536, "Programming & Software Engineering": 0.9420744622606236, "Sports & Fitness": 0.00013380289703992946, "Vehicles & Transportation": 6.111797989978346e-05, "Travel & Tourism": 0.00010819657592327944, "Other / Uncovered Subject": 0.0004954344472116477, "Unknown / Insufficient Content": 0.000129243399778528}, "secondary_topic": {"Adult & Explicit Sexual Content": 0.0008585586678842837, "Art & Design": 0.0007607674160928065, "Law & Crime": 0.0035690388075342826, "Education & Careers": 0.0005303587379387296, "Entertainment & Media": 0.0004809229805010908, "Fashion & Beauty": 0.00037239515078068625, "Business, Economics & Finance": 0.0006572412876999634, "Food & Dining": 0.00034715431247392685, "Games & Gaming": 0.00048809896018414624, "Electronics & Hardware": 0.0017819913921908476, "Health, Healthcare & Wellness": 0.0005372676742441758, "History & Geography": 0.0004917095357566747, "Home, Pets & Hobbies": 0.00038260355291865533, "Industry & Production": 0.00041828125433471193, "Language, Literature & Philosophy": 0.0002817270771029819, "Politics & Government": 0.00035545920724177195, "Religion & Spirituality": 0.0005211587363166807, "Science, Mathematics & Engineering": 0.34403369951867063, "Relationships & Community": 0.0001965352607353377, "Software Usage & Applications": 0.002315002277021746, "Programming & Software Engineering": 0.15213542875375874, "Sports & Fitness": 0.0004875107060788234, "Vehicles & Transportation": 0.00025568873900897967, "Travel & Tourism": 0.0005535785511040856, "Other / Uncovered Subject": 0.0009539413315125761, "NONE": 0.4862338801109127}}, "probabilities": {"primary": {"Academic Writing": 0.00010364852444322872, "Content Listing": 3.75413528659129e-05, "Creative Writing": 3.7075773956174147e-05, "Customer Support Page": 2.9316124101054078e-05, "Q&A Forum": 2.7058014777659386e-05, "Discussion Thread": 1.7490505364337502e-05, "Comment Section": 2.9733715948568742e-05, "Missing Content": 5.2454080834148385e-05, "Knowledge Article": 0.00012847752488169778, "Legal Notices": 0.0007697705640106201, "News Article": 1.3623236698341098e-05, "Organizational Announcement": 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Notices": 0.2761087573651849, "News Article": 0.00035914101838732215, "Organizational Announcement": 0.0003810867299928479, "Nonfiction Writing": 0.00030474525268032847, "Organizational About Page": 0.00039893104094178647, "Personal About Page": 0.00024916302017551116, "Personal Blog": 0.00023930829383410628, "Product Page": 0.00026778110492787176, "Spam / Ads": 0.00028115107020676666, "Structured Data": 0.0006581260903308865, "Technical Writing": 0.005853226860361227, "Transcript / Interview": 0.0004123252198218768, "Tutorial / How-To Guide": 0.00792945920666571, "User Reviews": 0.00016774373780086696, "NONE": 0.7027792331242527}, "structured_records": {"false": 0.9957464472008423, "true": 0.0042535527991577485}, "procedural_steps": {"false": 0.8655859913955068, "true": 0.1344140086044932}, "worked_solution": {"false": 0.9649637792217687, "true": 0.03503622077823133}, "contextual_dialogue": {"false": 0.9999925963636461, "true": 7.403636353910351e-06}, "content_quality": {"-2": 0.025145672667824498, "-1": 0.1417024988582769, "0": 0.3881639649650782, "1": 0.3548708230424158, "2": 0.09011704046640463}, "noise_severity": {"0": 0.596320803018124, "1": 0.3656743473421746, "2": 0.03305850460617649, "3": 0.0030467876998066844, "4": 0.001899557333718239}, "missing_content_severity": {"0": 0.9713408071549948, "1": 0.019549468485679077, "2": 0.007355112757682275, "3": 0.0013554175985357583, "4": 0.00039919400310816153}, "factual_density": {"0": 0.0113543146890666, "1": 0.04858065502295102, "2": 0.16072482804267715, "3": 0.3701008033140216, "4": 0.40923939893128364}, "pretraining_utility": {"-2": 0.00885652971057773, "-1": 0.061350871470842894, "0": 0.33075725191967636, "1": 0.49848923103651704, "2": 0.10054611586238603}, "math_density": {"0": 0.16615586689455453, "1": 0.23228198643401438, "2": 0.2453038128806053, "3": 0.14338961465199038, "4": 0.21286871913883548}, "code_density": {"0": 0.00029960112027564576, "1": 0.0006216268252057475, "2": 0.013499729118027265, "3": 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"no_license", "detected_licenses": []}, "commit_id": "b01825bdda35ea54c873d13f569e29fcde969053", "url": "https://github.com/YunanZzz/Disk_based_vector/blob/b01825bdda35ea54c873d13f569e29fcde969053/demos/demo_ivf_pq_fs_disk.cpp"}, "gpt4o_tokens": 2909, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.3431110597812992, "noise_severity": 0.4485299489888206, "missing_content_severity": 0.03992272280908355, "factual_density": 3.1172903167755046, "pretraining_utility": 0.6205175318692907, "math_density": 2.004533332706538, "code_density": 3.773043326929802, "reasoning_density": 0.4416732848896032, "education_level": 3.2664981676820033, "creative_writing_quality": -0.4103472817765422}, "score_applicability": {"education_level": 0.9988404232247274, "creative_writing_quality": 0.000821995343945897}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/zainab-zahid-khokhar/L3-Group-1/blob/728cb9f9e35ce4383ee6524602734388cc96c63b/min-max-heap.cpp", "id": "cpp-0-63a7ef30252f5b7b90f7710895f747a3", "text": "#include \"min-max-heap.hpp\"\n#include <cmath>\n#include <iostream>\n#include <algorithm>\n\n// template <class T>\nbool MinMaxHeap::isMinLevel(int i)\n{\n    return int(floor(log2(i + 1))) % 2 == 0; //if even then true\n}\n\nvoid MinMaxHeap::swap(int i, int j)\n{\n    SensorReading temp = arr[i]; // swap\n    arr[i] = arr[j];\n    arr[j] = temp;\n}\n\nint MinMaxHeap::grandparent(int i)\n{   \n    if(i <=2) return parent(parent(i));\n\n    return -1;\n}\n\nint MinMaxHeap::parent(int i)\n{\n    return (i - 1) / 2;\n}\n\nint MinMaxHeap::left_child(int i)\n{\n    return (2 * i) + 1;\n}\n\nint MinMaxHeap::right_child(int i)\n{\n    return (2 * i ) + 2;\n}\n\nstd::vector<int> MinMaxHeap::get_children(int i)\n{ // returns vector of indexes of children + grand children\n\n    std::vector<int> children;\n\n    if (i < size) //if index is valid\n    { // push children to arrray if the right and left children exist \n        if(right_child(i) <size) children.push_back(right_child(i)); \n        if(left_child(i) <size) children.push_back(left_child(i));\n    }\n    for (int child : children)\n    { // iterate over children and push grand children if they exist \n        if (child < size)\n        {\n            if(right_child(child) <size)children.push_back(right_child(child));\n            if(left_child(child) <size) children.push_back(left_child(child));\n        }\n    }\n\n    return children;\n}\n\n\nint MinMaxHeap::min_descendent(int i) // returns index of min descendent\n{\n    std::vector<int> children = get_children(i); //get all the children\n\n    if (children.empty())\n    { // \n        return -1; //if empty no descendents\n    }\n    int min = children[0]; //assume first value as min\n    for (int child : children) //for the indexes in childrne array \n    {\n        if (arr[child].temperature < arr[min].temperature) //if lesser than min make min\n        {\n            min = child;\n        }\n    }\n\n    return min; //output the min index \n}\n\nint MinMaxHeap::max_descendent(int i)\n{\n    std::vector<int> children = get_children(i); //get children \n\n    if (children.empty())\n    { // no descendents\n        return -1;\n    }\n    int max = children[0];\n    for (int child : children)\n    {\n        if (arr[child].temperature > arr[max].temperature) //if larger then make max\n        {\n            max = child;\n        }\n    }\n\n    return max;\n}\n\nvoid MinMaxHeap::bubble_up(int i)\n{\n\n    if (i == 0)\n        return; // cant bubble up from here its the lowest value \n\n    int parent = this->parent(i); // get parent\n\n    if (isMinLevel(i))\n    { // for min level\n        if (arr[i].temperature > arr[parent].temperature)\n        { // if value is greater than parent shift it to the max level level \n            swap(i, parent);\n            bubble_up_max(parent); // it is now in max level, bubble up among max levels\n        }\n        else\n        {\n            bubble_up_min(i); //else bubble up in min level\n        }\n    }\n    else\n    {\n        if (arr[i].temperature < arr[parent].temperature)\n        { // if value is lesser than parent shift it to a min level\n            swap(i, parent);\n            bubble_up_min(parent); // it is now in min level, bubble up among min levels\n        }\n        else\n        { \n            bubble_up_max(i); //else bubble up in max level\n            // bubble_up_max(parent);\n        }\n    }\n}\n\nvoid MinMaxHeap::bubble_up_min(int i)\n{\n    int grandparent = this->grandparent(i); //get grandparent\n    if(grandparent ==-1) return;\n    while (i > 2 && (arr[i].temperature < arr[grandparent].temperature))\n    { // check for i >2 to make sure i has a grandparent (is a deeper level node)\n        swap(i, grandparent); //if i.temp is lesser than grandparent, then swap \n        i = grandparent;\n        grandparent = this->grandparent(i);\n        if(grandparent ==-1) return;\n    }\n}\n\nvoid MinMaxHeap::bubble_up_max(int i)\n{\n    int grandparent = this->grandparent(i); //get grandparent \n    if(grandparent ==-1) return;\n    while (i > 2 && (arr[i].temperature > arr[grandparent].temperature)) \n    { // if i has a grandparent, if its greater than its grandparent, \n        swap(i, grandparent); // swap \n        i = grandparent;\n        grandparent = this->grandparent(i);\n        if(grandparent ==-1) return;\n    }\n}\n\nvoid MinMaxHeap::insert(SensorReading x)\n{\n    //no resize because its a vector\n    arr.push_back(x); //insert into array \n    size++; //increment size\n    bubble_up(size - 1); //call bubble up on the inserted index\n}\n\nvoid MinMaxHeap::replace(SensorReading x) // replaces if alr there else inserts\n{\n    // delete value\n    //  change temp\n    //  insert\n\n    for (int i = 0; i < size; i++) //traverse the heap \n    {\n        if (this->arr[i].sensorID == x.sensorID) //if sensor ID matches \n        {\n            this->delete_value(i); //delete old sensor value \n            this->insert(x); //insert the new one \n            return;\n        }\n    }\n    this->insert(x); //if not in heap, just insert \n}\n\nvoid MinMaxHeap::trickle_down(int i)\n{\n    if (i < size) //if less than size , i.e. if valid index\n    {\n\n        if (isMinLevel(i)) //if min level, call trickledown min else call trickle down max\n        {\n            trickle_down_min(i);\n        }\n        else\n        {\n            trickle_down_max(i);\n        }\n    }\n}\n\nvoid MinMaxHeap::trickle_down_min(int i)\n{\n    int min; //declare for later use\n    int min_parent;\n\n    while (left_child(i) < size) \n    { // while node has children (left is the most far left one)\n        // return;\n        min = min_descendent(i); //get min descendent of the index being deleted \n        if (min == -1)\n            break;\n        min_parent = parent(min); //get parent of min \n\n        if (arr[min].temperature < arr[i].temperature) //if min i lesser than index\n        {   \n            swap(i,min); //swapc\n            if (min >= 4 * i + 3) // if it is a grandchild \n            { \n                if (arr[min_parent].temperature < arr[min].temperature) // if the parent is lesser than min\n                {\n                    swap(min, min_parent); //swap \n                }\n                // trickle_down_min(min);\n                // i = min;  \n            }\n            // else{\n            //     break;\n            // }\n            i = min;                  \n\n        }\n        else\n        {\n            break;\n        }\n    }\n}\n\nvoid MinMaxHeap::trickle_down_max(int i)\n{\n    int max;\n    int max_parent;\n\n    while (left_child(i)<size)\n    { // while node has children\n        // return;\n        max = max_descendent(i);\n        if (max == -1)\n            break;\n        max_parent = parent(max);\n\n        if (arr[max].temperature > arr[i].temperature)\n        {\n            swap(max, i);\n            if (max >= 4 * i + 3)\n            { // is a grandchild\n                if (arr[max_parent].temperature > arr[max].temperature)\n                {\n                    swap(max, max_parent);\n                }\n\n                i = max;\n            }\n            else\n            {\n                break;\n            }\n        }\n        else\n        {\n            break;\n        }\n    }\n}\n\nvoid MinMaxHeap::delete_value(int i)\n{\n\n    // call trickle down\n    if (i >= size)\n        return;\n\n    if (i == size - 1)\n    {\n        arr.pop_back();\n        size--;\n        return;\n    }\n\n    arr[i] = arr.back();\n    arr.pop_back();\n    size--;\n\n    if (i < size)\n    {\n        if (i > 0 && (isMinLevel(i)) && (arr[i].temperature < arr[parent(i)].temperature))\n        { // if violates property w parents (if it has a aprent)\n            bubble_up(i);\n        }\n        else\n        {\n            trickle_down(i);\n        }\n    }\n}\n\nvoid MinMaxHeap::delete_value(SensorReading Reading){\n    for(int i =0; i <size; i++){\n        if (arr[i].sensorID == Reading.sensorID){\n            delete_value(i);\n        }\n    }\n}\n\nstd::vector<SensorReading> MinMaxHeap::top_k_min(int k)\n{\n    std::vector<SensorReading> min_readings;\n\n    if (size == 0)\n    {\n        return min_readings; //if size is 0 return empty list \n    }\n\n    min_readings.push_back(arr[0]); // \n\n    std::vector<int> counting = {0}; // begin at zero\n\n    while (min_readings.size() < k && !counting.empty())\n    {\n        std::vector<int> Temp;\n        std::vector<int> new_counting;\n        for (int count : counting)\n        { // get all grandchildren of values in count vector\n\n            std::vector<int> children = this->get_children(count);\n            for (int child : children)\n            {\n                // if (child != left_child(count) && child != right_child(count))\n                if(child<size)\n                { // only grandchildren get appended\n                    Temp.push_back(child);\n                    new_counting.push_back(child); // keep appending the grandchildren to new counting vector\n                }\n            }\n        }\n        std::sort(Temp.begin(), Temp.end(), [this](int a, int b)\n                  { return arr[a].temperature < arr[b].temperature; });\n\n        for (int j : Temp)\n        {\n            if (min_readings.size() < k) // if k is greater thna size \n            {\n                min_readings.push_back(arr[j]);\n            } // push the sorted grandchildren into the min readings array\n            else{\n                break;\n            }\n        }\n\n        counting = new_counting; // make new counting the counting vector\n    }\n\n    return min_readings;\n}\n\nstd::vector<SensorReading> MinMaxHeap::top_k_max(int k)\n{\n    std::vector<SensorReading> max_readings; \n\n    if (size == 0)\n    {\n        return max_readings; //if size is zero return empty list \n    }\n    if (size == 1)\n    {\n        max_readings.push_back(arr[0]); \n        return max_readings;\n    }\n\n    if (size == 2)\n    {\n        max_readings.push_back(arr[1]);\n        return max_readings;\n    }\n    if (size == 3)\n    {\n        max_readings.push_back(arr[1]);\n        max_readings.push_back(arr[2]);\n        std::sort(max_readings.begin(), max_readings.end(), [](auto &a, auto &b)\n                  { return a.temperature > b.temperature; });\n        return max_readings;\n    }\n\n    std::vector<int> counting = {1, 2}; // begin at first max level\n\n    while (max_readings.size() < k && !counting.empty())\n    {\n        std::vector<int> Temp;\n        std::vector<int> new_counting;\n        for (int count : counting)\n        { // get all grandchildren of values in count vector\n\n            std::vector<int> children = this->get_children(count);\n            for (int child : children)\n            {\n                // if (child != left_child(count) && child != right_child(count))\n                if(child<size)\n                { // only grandchildren get appended\n                    Temp.push_back(child);\n                    new_counting.push_back(child); // keep appending the grandchildren to new counting vector\n                }\n            }\n        }\n        std::sort(Temp.begin(), Temp.end(), [this](int a, int b)\n                  { return arr[a].temperature > arr[b].temperature; }); //sort in descendimg order based on temp\n\n        for (int j : Temp)\n        {\n            if (max_readings.size() < k) // if k is greater than size, needs mmore readings \n            {\n                max_readings.push_back(arr[j]);\n            }\n            else{\n                break;\n            }\n        }\n\n        counting = new_counting; // make new counting the counting vector\n    }\n\n    return max_readings;\n}\n\nSensorReading MinMaxHeap::maximum_val()\n{\n    if (size == 1)\n    { // if only one element return that\n        return arr[0];\n    }\n    else if (size == 2)\n    { // else go to level 2\n        return arr[1];\n    }\n    else\n    {\n        if (arr[1].temperature > arr[2].temperature)\n        { // if more, check between right and left children and return the max one\n            return arr[1];\n        }\n        else\n        {\n            return arr[2];\n        }\n    }\n}\n\nint MinMaxHeap::get_size()\n{\n\n    return size;\n}\n\nvoid MinMaxHeap::print(){\n\n    for(int i =0; i < size; i++){\n        std::cout << \"Sensor ID: \" << arr[i].sensorID << std::endl;\n        std::cout << \"TimeStamp: \" << arr[i].timestamp << std::endl;\n        std::cout << \"Temperature: \" << arr[i].temperature << std::endl;\n\n   }\n}", "text_preview": "#include \"min-max-heap.hpp\" #include <cmath> #include <iostream> #include <algorithm> // template <class T> bool MinMaxHeap::isMinLevel(int i) { return int(floor(log2(i + 1))) % 2 == 0; //if even then true } void MinMaxHeap::swap(int i, int", "input_sha256": "6bf91a7bff6e53f65bc293182bcf5ba7216fe07e1d17f98d112ca5b0acc2239e", "text_sha256": "63a7ef30252f5b7b90f7710895f747a31c957c774105aadb9fccbd29906a6e02", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 95, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": 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-1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 1.0000000000000002, "mass_delta": 2.220446049250313e-16, "missing_mass": 0.0, "excess_mass": 2.220446049250313e-16, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, 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"file_timestamp": 1745745060, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "728cb9f9e35ce4383ee6524602734388cc96c63b"}}, "parsed_source_meta": {"github_metadata": {"branch": "master", "commit_count": 22, "repo_created_at": "2025-04-14T13:05:33.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "49eb7b0efd0d5661bb69f413c5833c65e932ed66", "size_bytes": 12173, "file_timestamp": 1745745060, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "728cb9f9e35ce4383ee6524602734388cc96c63b", "url": "https://github.com/zainab-zahid-khokhar/L3-Group-1/blob/728cb9f9e35ce4383ee6524602734388cc96c63b/min-max-heap.cpp"}, "gpt4o_tokens": 3030, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": 0.2689277866627333, "noise_severity": 0.1372023847842234, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/munepi0713/Darwin/blob/9b0251c689d1b28a67b4caf6843c69e6c42c2cce/Garnet/include/PicturePerfectEngine/OpenCVOperators/Segment.h", "id": "cpp-0-17d808a3494ece0cb38a3ae4661fa85e", "text": "\n#include <graph.h>\n\n\nstatic\ndouble sq(double a)\n{\n    return a * a;\n}\n\nstatic\ndouble dist(double a, double b)\n{\n    return sqrt(a * a + b * b);\n}\n\nstatic\nvoid NR2(const cv::Mat& src, const cv::Mat& obj, const cv::Mat& bkg, cv::Mat* dst)\n{\n    int dx[4] = { 1, -1, 0, 1 };\n    int dy[4] = { 0,  1, 1, 1 };\n    int n = src.cols;\n    int m = src.rows;\n    //printf(\"%d x %d\\n\", n, m);\n\n    // Calculate histogram of src.\n    int fgbin[256];\n    int bgbin[256];\n    int fgtotal = 0;\n    int bgtotal = 0;\n    memset(fgbin, 0, sizeof(int) * 256);\n    memset(bgbin, 0, sizeof(int) * 256);\n    for (int j = 0; j < m; j++) {\n        for (int i = 0; i < n; i++) {\n            int ip = src.at<unsigned char>(j, i);\n            int io = obj.at<unsigned char>(j, i);\n            int ib = bkg.at<unsigned char>(j, i);\n            if ( io > 0 ) {\n                // Object (Foreground)\n                fgbin[ip]++;\n                fgtotal++;\n            }\n            else if ( ib > 0 ) {\n                // Background\n                bgbin[ip]++;\n                bgtotal++;\n            }\n        }\n    }\n\n    // Calculate frequency of intensities of src.\n    double fgfreq[256], fgsum = 0;\n    double bgfreq[256], bgsum = 0;\n    for (int i = 0; i < 256; i++) {\n        fgfreq[i] = fgbin[i] == 0 ? 0 : -log((double)fgbin[i] / fgtotal);\n        bgfreq[i] = bgbin[i] == 0 ? 0 : -log((double)bgbin[i] / bgtotal);\n        fgsum += (double)fgbin[i] / fgtotal;\n        bgsum += (double)bgbin[i] / bgtotal;\n        //printf(\"[%3d] %.3f(%.3f) %.3f(%.3f)\\n\", i, (double)fgbin[i] / fgtotal, fgfreq[i], (double)bgbin[i] / bgtotal, bgfreq[i]);\n    }\n    //printf(\"total %.3f        %.3f\\n\", fgsum, bgsum);\n\n    // Calcualte sigma;\n    double s1, s2, sigma2;\n    int nn = 0;\n    s1 = s2 = sigma2 = 0;\n    for (int j = 0; j < m; j++) {\n        for (int i = 0; i < n; i++) {\n            int ip = src.at<unsigned char>(j, i);\n            for (int d = 0; d < 4; d++) {\n                int x = i + dx[d];\n                int y = j + dy[d];\n                if ( 0 <= x && 0 <= y && x < n && y < m ) {\n                    int iq = src.at<unsigned char>(x, y);\n                    double delta = ip - iq;\n                    nn++;\n                    delta -= s1;\n                    s1 += delta / nn;\n                    s2 += (nn - 1) * delta * delta / nn;\n                    //printf(\".\");\n                }\n            }\n        }\n    }\n    //printf(\"%f, %f\\n\", s1, s2 / (nn - 1));\n    sigma2 = nn == 0 ? 0 : s2 / (nn - 1);\n    //abort();\n\n    //\n    typedef Graph<int, int, int>  GraphType;\n    \n    int lambda = 10;//10;\n    double alpha = 10;\n    double kappa = 0.5 / sigma2; // 25; // 25;\n    GraphType* g = new GraphType(n * m, n * m * 4); // Appox numbers of vertices and edges.\n    //printf(\"lambda=%.3f, alpha=%.3f, kappa=%.3f\\n\", (double)lambda, alpha, kappa);\n\n    double maxSumB = 0;\n    for (int j = 0; j < m; j++) {\n        for (int i = 0; i < n; i++) {\n            int ip = src.at<unsigned char>(j, i);\n            for (int d = 0; d < 4; d++) {\n                int x = i + dx[d];\n                int y = j + dy[d];\n                double sumB = 0;\n                if ( 0 <= x && 0 <= y && x < n && y < m ) {\n                    int iq = src.at<unsigned char>(x, y);\n\n                    sumB += alpha * exp(- kappa * sq((double)ip - iq)) / dist(dx[d], dy[d]);\n                }\n                if ( sumB > maxSumB ) maxSumB = sumB;\n            }\n        }\n    }\n\n    g->add_node(n * m);\n    for (int j = 0; j < m; j++) {\n        for (int i = 0; i < n; i++) {\n            int ip = src.at<unsigned char>(j, i);\n            int io = obj.at<unsigned char>(j, i);\n            int ib = bkg.at<unsigned char>(j, i);\n\n            // Add edge between neighbors\n            int maxB = 0;\n            for (int d = 0; d < 4; d++) {\n                int x = i + dx[d];\n                int y = j + dy[d];\n                if ( 0 <= x && 0 <= y && x < n && y < m ) {\n                    int hpq, hqp;\n                    int iq = src.at<unsigned char>(x, y);\n                    if ( ip == iq ) {\n                        hpq = hqp = 0;\n                    }\n                    else {\n                        int B;\n                        B = alpha * exp(- kappa * sq((double)ip - iq)) / dist(dx[d], dy[d]);\n#if 0\n                        hpq = hqp = B;\n#else\n                        if ( ip > iq ) {\n                            hpq = B;\n                            hqp = 0;\n                        }\n                        else {\n                            hpq = 0;\n                            hqp = B;\n                        }\n#endif\n                    }\n                    //if ( io == 255 )\n                    //printf(\"H %d, %d | %f %f %f\\n\", hpq, hqp, kappa, sq((double)ip - iq), dist(dx[d], dy[d]));\n                    g->add_edge(j * n + i, y * n + x, hpq, hqp);\n                }\n            }\n\n            // Add edges between a pixel to source and sink.\n            int g0, g1;\n#if 0\n            if ( ip > 0 ) {\n                g1 = 0;\n                g0 = lambda;\n            }\n            else {\n                g1 = lambda;\n                g0 = 0;\n            }\n#elif 1\n            if ( io == 255 ) {\n                g1 = 0;\n                g0 = 1 + maxSumB;\n            }\n            else if ( ib == 255 ) {\n                g1 = 1 + maxSumB;\n                g0 = 0;\n            }\n            else {\n                g1 = bgfreq[ip];\n                g0 = fgfreq[ip];\n            }\n            g0 *= lambda;\n            g1 *= lambda;\n#endif\n            //if ( io == 255 )\n            //printf(\"G %d, %d\\n\", g1, g0);\n            g->add_tweights(j * n + i, g1, g0);\n        }\n    }\n    g->maxflow();\n    for (int j = 0; j < m; j++) {\n        for (int i = 0; i < n; i++) {\n            if ( g->what_segment(j * n + i) == GraphType::SOURCE ) {\n                dst->at<unsigned char>(j, i) = 0;\n            }\n            else {\n                dst->at<unsigned char>(j, i) = 255;\n            }\n        }\n    }\n    delete g;\n}\n\nnamespace PicturePerfectOpenCVOperators\n{\n\nclass Segment : public Operator\n{\n    public :\n\t\tvirtual ResultType  process(const strings& tokens, PacketStore& packets)\n\t\t{\n\t\t\tassertNumberOfArgs(tokens, 5);\n\n\t\t\t// \n\t\t\tPacket& dst  = packets[tokens[1]];\n\t\t\tPacket& src1 = packets[tokens[2]]; // source\n\t\t\tPacket& src2 = packets[tokens[3]]; // object\n\t\t\tPacket& src3 = packets[tokens[4]]; // background\n\n            // Image\n            cv::Mat res(dst.image);\n            cv::Mat src(src1.image);\n            cv::Mat obj_(src2.image);\n            cv::Mat bgk_(src3.image);\n            cv::Mat obj(obj_.rows, obj_.cols, CV_8U);\n            cv::Mat bgk(bgk_.rows, bgk_.cols, CV_8U);\n            \n            cv::threshold(obj_, obj, 0, 1, CV_THRESH_BINARY | CV_THRESH_OTSU);\n            cv::threshold(bgk_, bgk, 0, 1, CV_THRESH_BINARY | CV_THRESH_OTSU);\n\n            NR2(src, obj, bgk, &res);\n            \n\t\t\t// Value\n\t\t\tdst.value = src1.value;\n\n\t\t\treturn RES_NEXT;\n\t\t}\n};\n\n} // end of namespace PicturePerfectOpenCV\n", "text_preview": "#include <graph.h> static double sq(double a) { return a * a; } static double dist(double a, double b) { return sqrt(a * a + b * b); } static void NR2(const cv::Mat& src, const cv::Mat& obj, const cv::Mat& bkg, cv::Mat* dst) { int dx[4] = {", "input_sha256": "25be5ce59ee727ef9352ab3f2a6ba60b464c945d5da1e24c2154be10e9cb82d5", "text_sha256": "17d808a3494ece0cb38a3ae4661fa85e55ebc93f0a4e3762b44ffdf4a82110ce", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 96, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 3, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.5969901082122856, "noise_severity": 0.07270456579134384, "missing_content_severity": 0.08108824670087174, "factual_density": 3.2872584667047975, "pretraining_utility": 0.5529440595351047, "math_density": 3.245953930649177, "code_density": 3.895583282229152, "reasoning_density": 1.090160642188989, "education_level": 3.033363208040564, "creative_writing_quality": -0.35580498638146}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "3", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0034741146494194908, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9982417238793893, "secondary": 0.9916008844174163, "tertiary": 0.995226047529854, "structured_records": 0.002058737724204646, "procedural_steps": 0.02057966303344187, "worked_solution": 0.006570415186864328, "contextual_dialogue": 6.061380174640049e-06, "content_quality": 0.3917655001307111, "noise_severity": 0.9394128618405468, "missing_content_severity": 0.9324264610826069, "factual_density": 0.4060487222539979, "pretraining_utility": 0.4159661355772285, "math_density": 0.37162827554098066, "code_density": 0.9129860685242929, "reasoning_density": 0.09153279817584237, "education_level": 0.4383983962710139, "creative_writing_quality": 0.9993196817856962, "primary_topic": 0.9383717696018512, "secondary_topic": 0.4678114749304578}, "probability_margin": {"primary": 0.997309893152186, "secondary": 0.9875390862297995, "tertiary": 0.9934273361815292, "education_level": 0.2788737288325979, "creative_writing_quality": 0.9991866394798, "primary_topic": 0.8977092410241259, "secondary_topic": 0.10941497161180641}, "raw_probabilities": {"primary": {"Academic Writing": 3.7265347448912465e-05, "Content Listing": 1.6298720683586998e-05, "Creative Writing": 1.2623071625510074e-05, "Customer Support Page": 5.802156610513895e-06, "Q&A Forum": 7.4932045245936706e-06, "Discussion Thread": 5.266861244010352e-06, "Comment Section": 7.953269256709878e-06, "Missing Content": 2.182674443930464e-05, "Knowledge Article": 5.640484441106024e-05, "Legal Notices": 2.3125333168848212e-05, "News Article": 2.9264860896124714e-06, "Organizational Announcement": 6.4327674427626125e-06, "Nonfiction Writing": 7.5875609095708405e-06, "Organizational About Page": 8.429393127218699e-06, "Personal About Page": 7.389168443847331e-06, "Personal Blog": 4.534684049486764e-06, "Product Page": 5.258815734237996e-06, "Spam / Ads": 6.499235122821708e-06, "Structured Data": 0.0002826483677228652, "Technical Writing": 0.9983149853844147, "Transcript / Interview": 9.540751580281707e-06, "Tutorial / How-To Guide": 0.00014020152437843532, "User Reviews": 4.414075342716463e-06, "Other / 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/pichenettes/eurorack/blob/08460a69a7e1f7a81c5a2abcc7189c9a6b7208d4/plaits/dsp/fm/operator.h", "id": "cpp-0-415a461dc075ef5bfe0b56abaf950bb7", "text": "// Copyright 2021 <NAME>.\n//\n// Author: <NAME> (<EMAIL>)\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n// \n// See http://creativecommons.org/licenses/MIT/ for more information.\n//\n// -----------------------------------------------------------------------------\n//\n// FM Operator.\n\n#ifndef PLAITS_DSP_FM_OPERATOR_H_\n#define PLAITS_DSP_FM_OPERATOR_H_\n\n#include <algorithm>\n\n#include \"plaits/dsp/oscillator/sine_oscillator.h\"\n\n#include \"stmlib/dsp/dsp.h\"\n\nnamespace plaits {\n\nnamespace fm {\n\nstruct Operator {\n  enum ModulationSource {\n    MODULATION_SOURCE_EXTERNAL = -2,\n    MODULATION_SOURCE_NONE = -1,\n    MODULATION_SOURCE_FEEDBACK = 0\n  };\n\n  inline void Reset() {\n    phase = 0;\n    amplitude = 0.0f;\n  }\n  \n  uint32_t phase;\n  float amplitude;\n};\n\ntypedef void (*RenderFn)(\n    Operator* ops,\n    const float* f,\n    const float* a,\n    float* fb_state,\n    int fb_amount,\n    const float* modulation,\n    float* out,\n    size_t size);\n\ntemplate<int n, int modulation_source, bool additive>\nvoid RenderOperators(\n    Operator* ops,\n    const float* f,\n    const float* a,\n    float* fb_state,\n    int fb_amount,\n    const float* modulation,\n    float* out,\n    size_t size) {\n  float previous_0, previous_1;\n  \n  if (modulation_source >= Operator::MODULATION_SOURCE_FEEDBACK) {\n    previous_0 = fb_state[0];\n    previous_1 = fb_state[1];\n  }\n\n  uint32_t frequency[n];\n  uint32_t phase[n];\n  float amplitude[n];\n  float amplitude_increment[n];\n\n  const float scale = 1.0f / float(size);\n  for (int i = 0; i < n; ++i) {\n    frequency[i] = static_cast<uint32_t>(std::min(f[i], 0.5f) * 4294967296.0f);\n    phase[i] = ops[i].phase;\n    amplitude[i] = ops[i].amplitude;\n    amplitude_increment[i] = (std::min(a[i], 4.0f) - amplitude[i]) * scale;\n  }\n  \n  const float fb_scale = fb_amount ? float(1 << fb_amount) / 512.0f : 0.0f;\n\n  while (size--) {\n    float pm = 0.0f;\n    if (modulation_source >= Operator::MODULATION_SOURCE_FEEDBACK) {\n      pm = (previous_0 + previous_1) * fb_scale;\n    } else if (modulation_source == Operator::MODULATION_SOURCE_EXTERNAL) {\n      pm = *modulation++;\n    }\n    for (int i = 0; i < n; ++i) {\n      phase[i] += frequency[i];\n      pm = SinePM(phase[i], pm) * amplitude[i];\n      amplitude[i] += amplitude_increment[i];\n      if (i == modulation_source) {\n        previous_1 = previous_0;\n        previous_0 = pm;\n      }\n    }\n    if (additive) {\n      *out++ += pm;\n    } else {\n      *out++ = pm;\n    }\n  }\n  \n  for (int i = 0; i < n; ++i) {\n    ops[i].phase = phase[i];\n    ops[i].amplitude = amplitude[i];\n  }\n  \n  if (modulation_source >= Operator::MODULATION_SOURCE_FEEDBACK) {\n    fb_state[0] = previous_0;\n    fb_state[1] = previous_1;\n  }\n};\n\n}  // namespace fm\n  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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/WEIIEW97/VP/blob/21ff899a7d2c250468df4fa57f112608e63f5f23/include/vp.h", "id": "cpp-0-92d1c699c44aa0562da641a811b55824", "text": "/*\n * Copyright (c) 2022-2025, <NAME>. All rights reserved.\n *\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n *     http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n */\n\n#pragma once\n\n#ifdef _MSC_VER\n#define _USE_MATH_DEFINES\n#endif\n\n#include <Eigen/Dense>\n#include <cmath>\n#include <iostream>\n#include <utility>\n#include <deque>\n\n#include \"../src/kalman_filter.h\"\n\nenum class CameraModel {\n  pinhole_k6,\n  fisheye,\n};\n\ntemplate <typename EigenScalarType>\nEigen::Matrix<EigenScalarType, -1, 2> undistort_points_pinhole(\n    const Eigen::Matrix<EigenScalarType, -1, 2>& distorted_points,\n    const Eigen::Matrix<EigenScalarType, 3, 3>& K,\n    const Eigen::Vector<EigenScalarType, 8>& dist_coefs, int max_iter = 8,\n    EigenScalarType epsilon = 1e-7) {\n  using Eigen::Array;\n  using Eigen::Dynamic;\n  using Eigen::Matrix;\n\n  const int num_points = distorted_points.rows();\n  Matrix<EigenScalarType, -1, 2> undistorted_points(num_points, 2);\n\n  // Extract intrinsic parameters\n  const EigenScalarType fx = K(0, 0);\n  const EigenScalarType fy = K(1, 1);\n  const EigenScalarType cx = K(0, 2);\n  const EigenScalarType cy = K(1, 2);\n\n  // Extract distortion coefficients\n  const EigenScalarType k1 = dist_coefs[0], k2 = dist_coefs[1],\n                        k3 = dist_coefs[4];\n  const EigenScalarType k4 = dist_coefs[5], k5 = dist_coefs[6],\n                        k6 = dist_coefs[7];\n  const EigenScalarType p1 = dist_coefs[2], p2 = dist_coefs[3];\n\n  const Array<EigenScalarType, Dynamic, 1> xn =\n      (distorted_points.col(0).array() - cx) / fx;\n  const Array<EigenScalarType, Dynamic, 1> yn =\n      (distorted_points.col(1).array() - cy) / fy;\n\n  Array<EigenScalarType, Dynamic, 1> xu = xn;\n  Array<EigenScalarType, Dynamic, 1> yu = yn;\n\n  // Newton-Raphson iteration\n  for (int iter = 0; iter < max_iter; ++iter) {\n    const Array<EigenScalarType, Dynamic, 1> x = xu;\n    const Array<EigenScalarType, Dynamic, 1> y = yu;\n    const Array<EigenScalarType, Dynamic, 1> r2 = x.square() + y.square();\n    const Array<EigenScalarType, Dynamic, 1> r4 = r2.square();\n    const Array<EigenScalarType, Dynamic, 1> r6 = r4 * r2;\n\n    // Radial distortion components\n    const Array<EigenScalarType, Dynamic, 1> num_radial =\n        1.0 + r2 * (k1 + r2 * (k2 + r2 * k3));\n    const Array<EigenScalarType, Dynamic, 1> den_radial =\n        1.0 + r2 * (k4 + r2 * (k5 + r2 * k6));\n    const Array<EigenScalarType, Dynamic, 1> radial = num_radial / den_radial;\n\n    // Tangential distortion components\n    const Array<EigenScalarType, Dynamic, 1> xy = x * y;\n    const Array<EigenScalarType, Dynamic, 1> dx_tang =\n        2 * p1 * xy + p2 * (r2 + 2 * x.square());\n    const Array<EigenScalarType, Dynamic, 1> dy_tang =\n        2 * p2 * xy + p1 * (r2 + 2 * y.square());\n\n    const Array<EigenScalarType, Dynamic, 1> x_distorted = x * radial + dx_tang;\n    const Array<EigenScalarType, Dynamic, 1> y_distorted = y * radial + dy_tang;\n    const Array<EigenScalarType, Dynamic, 1> x_err = xn - x_distorted;\n    const Array<EigenScalarType, Dynamic, 1> y_err = yn - y_distorted;\n\n    // Radial derivatives\n    const Array<EigenScalarType, Dynamic, 1> dr_num =\n        2 * k1 * x + 4 * k2 * x * r2 + 6 * k3 * x * r4;\n    const Array<EigenScalarType, Dynamic, 1> dr_den =\n        2 * k4 * x + 4 * k5 * x * r2 + 6 * k6 * x * r4;\n    const Array<EigenScalarType, Dynamic, 1> drdx =\n        (dr_num * den_radial - num_radial * dr_den) / den_radial.square();\n\n    const Array<EigenScalarType, Dynamic, 1> dr_num_y =\n        2 * k1 * y + 4 * k2 * y * r2 + 6 * k3 * y * r4;\n    const Array<EigenScalarType, Dynamic, 1> dr_den_y =\n        2 * k4 * y + 4 * k5 * y * r2 + 6 * k6 * y * r4;\n    const Array<EigenScalarType, Dynamic, 1> drdy =\n        (dr_num_y * den_radial - num_radial * dr_den_y) / den_radial.square();\n\n    // Tangential derivatives\n    const Array<EigenScalarType, Dynamic, 1> dtdx_x = 2 * p1 * y + 4 * p2 * x;\n    const Array<EigenScalarType, Dynamic, 1> dtdx_y = 2 * p1 * x + 2 * p2 * y;\n    const Array<EigenScalarType, Dynamic, 1> dtdy_x = 2 * p2 * y + 2 * p1 * x;\n    const Array<EigenScalarType, Dynamic, 1> dtdy_y = 4 * p1 * y + 2 * p2 * x;\n\n    // Jacobian matrix components\n    const Array<EigenScalarType, Dynamic, 1> J11 = radial + x * drdx + dtdx_x;\n    const Array<EigenScalarType, Dynamic, 1> J12 = x * drdy + dtdx_y;\n    const Array<EigenScalarType, Dynamic, 1> J21 = y * drdx + dtdy_x;\n    const Array<EigenScalarType, Dynamic, 1> J22 = radial + y * drdy + dtdy_y;\n\n    const Array<EigenScalarType, Dynamic, 1> det = J11 * J22 - J12 * J21;\n    const Array<EigenScalarType, Dynamic, 1> inv_det = 1.0 / det.max(epsilon);\n\n    xu += inv_det * (J22 * x_err - J12 * y_err);\n    yu += inv_det * (-J21 * x_err + J11 * y_err);\n\n    if ((x_err.abs().maxCoeff() < epsilon) &&\n        (y_err.abs().maxCoeff() < epsilon)) {\n      break;\n    }\n  }\n\n  undistorted_points.col(0) = xu * fx + cx;\n  undistorted_points.col(1) = yu * fy + cy;\n\n  return undistorted_points;\n}\n\ntemplate <typename EigenScalarType>\nEigen::Matrix<EigenScalarType, -1, 2> undistort_points_fisheye(\n    const Eigen::Matrix<EigenScalarType, -1, 2>& distorted_points,\n    const Eigen::Matrix<EigenScalarType, 3, 3>& K,\n    const Eigen::Vector<EigenScalarType, 4>& dist_coefs, int max_iter = 100,\n    EigenScalarType epsilon = 1e-5) {\n  using Eigen::Array;\n  using Eigen::Dynamic;\n  using Eigen::Matrix;\n\n  const int num_points = distorted_points.rows();\n  Matrix<EigenScalarType, -1, 2> undistorted_points(num_points, 2);\n\n  // Extract intrinsic parameters\n  const EigenScalarType fx = K(0, 0);\n  const EigenScalarType fy = K(1, 1);\n  const EigenScalarType cx = K(0, 2);\n  const EigenScalarType cy = K(1, 2);\n\n  // Extract fisheye distortion coefficients (k1, k2, k3, k4)\n  const EigenScalarType k1 = dist_coefs[0], k2 = dist_coefs[1],\n                        k3 = dist_coefs[4], k4 = dist_coefs[5];\n\n  // Normalized image coordinates\n  const Array<EigenScalarType, Dynamic, 1> xn =\n      (distorted_points.col(0).array() - cx) / fx;\n  const Array<EigenScalarType, Dynamic, 1> yn =\n      (distorted_points.col(1).array() - cy) / fy;\n\n  Array<EigenScalarType, Dynamic, 1> xu = xn;\n  Array<EigenScalarType, Dynamic, 1> yu = yn;\n\n  Array<EigenScalarType, Dynamic, 1> x_err, y_err;\n  bool converged = false;\n  int iter = 0;\n\n  // Newton-Raphson iteration for fisheye model\n  while (!converged && iter < max_iter) {\n    const Array<EigenScalarType, Dynamic, 1> x = xu;\n    const Array<EigenScalarType, Dynamic, 1> y = yu;\n    const Array<EigenScalarType, Dynamic, 1> r =\n        (x.square() + y.square()).sqrt();\n    const Array<EigenScalarType, Dynamic, 1> theta = r.atan();\n\n    // Fisheye distortion polynomial: theta_d = theta*(1 + k1*theta^2 +\n    // k2*theta^4 + k3*theta^6 + k4*theta^8)\n    const Array<EigenScalarType, Dynamic, 1> theta2 = theta.square();\n    const Array<EigenScalarType, Dynamic, 1> theta4 = theta2.square();\n    const Array<EigenScalarType, Dynamic, 1> theta6 = theta4 * theta2;\n    const Array<EigenScalarType, Dynamic, 1> theta8 = theta4.square();\n\n    const Array<EigenScalarType, Dynamic, 1> theta_d =\n        theta * (1.0 + k1 * theta2 + k2 * theta4 + k3 * theta6 + k4 * theta8);\n    const Array<EigenScalarType, Dynamic, 1> scaling =\n        (r > epsilon).select(theta_d / r, 1.0);\n\n    // Distorted points\n    const Array<EigenScalarType, Dynamic, 1> x_distorted = x * scaling;\n    const Array<EigenScalarType, Dynamic, 1> y_distorted = y * scaling;\n\n    // Error\n    x_err = xn - x_distorted;\n    y_err = yn - y_distorted;\n\n    converged = (x_err.abs().maxCoeff() < epsilon) &&\n                (y_err.abs().maxCoeff() < epsilon);\n    if (converged)\n      break;\n\n    // Jacobian computation\n    const Array<EigenScalarType, Dynamic, 1> dr_dx = x / r.max(epsilon);\n    const Array<EigenScalarType, Dynamic, 1> dr_dy = y / r.max(epsilon);\n\n    const Array<EigenScalarType, Dynamic, 1> dtheta_dr =\n        1.0 / (1.0 + r.square());\n\n    const Array<EigenScalarType, Dynamic, 1> dtheta_d_dtheta =\n        1.0 + 3.0 * k1 * theta2 + 5.0 * k2 * theta4 + 7.0 * k3 * theta6 +\n        9.0 * k4 * theta8;\n\n    const Array<EigenScalarType, Dynamic, 1> dtheta_d_dr =\n        dtheta_d_dtheta * dtheta_dr;\n\n    const Array<EigenScalarType, Dynamic, 1> dscaling_dr =\n        (r > epsilon).select((dtheta_d_dr * r - theta_d) / (r.square()), 0.0);\n\n    const Array<EigenScalarType, Dynamic, 1> dscaling_dx = dscaling_dr * dr_dx;\n    const Array<EigenScalarType, Dynamic, 1> dscaling_dy = dscaling_dr * dr_dy;\n\n    // Jacobian matrix components\n    const Array<EigenScalarType, Dynamic, 1> J11 = scaling + x * dscaling_dx;\n    const Array<EigenScalarType, Dynamic, 1> J12 = x * dscaling_dy;\n    const Array<EigenScalarType, Dynamic, 1> J21 = y * dscaling_dx;\n    const Array<EigenScalarType, Dynamic, 1> J22 = scaling + y * dscaling_dy;\n\n    const Array<EigenScalarType, Dynamic, 1> det = J11 * J22 - J12 * J21;\n    const Array<EigenScalarType, Dynamic, 1> inv_det = 1.0 / det.max(epsilon);\n\n    xu += inv_det * (J22 * x_err - J12 * y_err);\n    yu += inv_det * (-J21 * x_err + J11 * y_err);\n\n    iter++;\n  }\n\n  if (!converged) {\n    std::cerr << \"Computation diverged!\" << \"\\n\";\n    std::cerr << \"Result may not be accurate. Use with your own risk.\"\n              << std::endl;\n  }\n\n  undistorted_points.col(0) = xu * fx + cx;\n  undistorted_points.col(1) = yu * fy + cy;\n\n  return undistorted_points;\n}\n\ntemplate <typename EigenScalarType>\nEigen::Matrix<EigenScalarType, -1, 2>\nundistort_points_impl(CameraModel cam_model, auto&&... args) {\n  if (cam_model == CameraModel::pinhole_k6) {\n    return undistort_points_pinhole<EigenScalarType>(\n        std::forward<decltype(args)>(args)...);\n  } else if (cam_model == CameraModel::fisheye) {\n    return undistort_points_fisheye<EigenScalarType>(\n        std::forward<decltype(args)>(args)...);\n  } else {\n    throw std::invalid_argument(\"Unsupported camera model\");\n  }\n}\n\ntemplate <typename EigenScalarType>\nEigen::Matrix<EigenScalarType, -1, 2> undistort_points(CameraModel cam_model,\n                                                       auto&&... args) {\n  return undistort_points_impl<EigenScalarType>(\n      cam_model, std::forward<decltype(args)>(args)...);\n}\n\nclass VP {\npublic:\n  VP(const Eigen::Matrix3f& K, const Eigen::VectorXf& dist_coef,\n     int min_num_pts = 10, CameraModel cam_model = CameraModel::pinhole_k6,\n     bool verbose = false, int window_size = 5)\n      : K_(K), dist_coef_(dist_coef), min_num_pts_(min_num_pts),\n        cam_model_(cam_model), verbose_(verbose), window_size_(window_size) {\n    fx_ = K(0, 0);\n    cx_ = K(0, 2);\n    fy_ = K(1, 1);\n    cy_ = K(1, 2);\n  }\n\nprivate:\n  bool judge_valid(const std::vector<Eigen::MatrixXf>& frame_pts, int thr = 2);\n  Eigen::VectorXf polyfit(const Eigen::VectorXf& x, const Eigen::VectorXf& y);\n  void line_fit(const std::vector<Eigen::MatrixXf>& frame_pts);\n  void compute_vp();\n  Eigen::Vector2f filter_candidates(const std::string& strategy);\n  Eigen::Vector2f temporal_smooth(const Eigen::Vector2f& vp);\n  Eigen::Vector2f kalman_smooth(const Eigen::Vector2f & vp);\n  Eigen::Vector2f estimate_yp(const Eigen::Vector2f& vp);\n  void reload();\n\npublic:\n  Eigen::Vector2f\n  get_yp_estimation(const std::vector<Eigen::MatrixXf>& frame_pts);\n  Eigen::Vector2f\n  get_vp_estimation(const std::vector<Eigen::MatrixXf>& frame_pts);\n  Eigen::Vector2f get_yp_est_by_vp(const Eigen::Vector2f& vp);\n  Eigen::Matrix3f get_K() const;\n  Eigen::VectorXf get_dist() const;\n\nprivate:\n  Eigen::Matrix3f K_;\n  Eigen::VectorXf dist_coef_;\n  int min_num_pts_;\n  bool verbose_;\n  CameraModel cam_model_;\n  std::vector<Eigen::Vector2f> param_lst_;\n  std::vector<Eigen::Vector3f> homo_lst_;\n  std::vector<Eigen::Vector3f> vps_;\n  std::deque<Eigen::Vector2f> vps_trace_;\n  int window_size_;\n  float fx_, cx_, fy_, cy_;\n  bool line_fit_flag_ = true;\n  float r2_thr_ = 0.1f;\n};", "text_preview": "/* * Copyright (c) 2022-2025, <NAME>. All rights reserved. * * Licensed under the Apache License, Version 2.0 (the \"License\"); * you may not use this file except in compliance with the License. * You may obtain a copy of the License at * * ", "input_sha256": "f06517f913c47fc2efce1f4fb35cf966307da83bc5ef1ce388fae0e08faae680", "text_sha256": "92d1c699c44aa0562da641a811b55824e57c020b3565ae5940b56696260eb47a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 97, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": {"main_format": "Legal Notices", "sub_format": null}, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/nhuttantran98/DSA172A01/blob/444f98ab158bdf24471a393dc41d0b9b3b279f14/listLib.h", "id": "cpp-0-13628498fa8a9cb5d70a0f9db2b41df9", "text": "/*\n* =========================================================================================\n* Name        : listLib.h\n* Author      : <NAME>\n* Email       : <EMAIL>\n* Copyright   : Faculty of Computer Science and Engineering - Bach Khoa University\n* Description : library for Assignment 1 - Data structures and Algorithms - Spring 2018\n* =========================================================================================\n*/\n\n#ifndef A01_LISTLIB_H\n#define A01_LISTLIB_H\n\n#include <string>\nusing namespace std;\n\nclass DSAException\n{\n\tint _error;\n\tstring _text;\n\npublic:\n\tDSAException() : _error(0), _text(\"Success\") {}\n\tDSAException(int err) : _error(err), _text(\"Unknown Error\") {}\n\tDSAException(int err, const char *text) : _error(err), _text(text) {}\n\n\tint getError() { return _error; }\n\tstring &getErrorText() { return _text; }\n};\n\ntemplate <class T>\nstruct L1Item\n{\n\tT data;\n\tL1Item<T> *pNext;\n\tL1Item() : pNext(NULL) {}\n\tL1Item(L1Item<T> *p) : pNext(p) {}\n\tL1Item(T &a) : data(a), pNext(NULL) {}\n\tL1Item(T &&a) : data(std::move(a)), pNext(NULL) {}\n};\n\ntemplate <class T>\nclass L1List\n{\n\tL1Item<T> *_pHead; // The head pointer of linked list\n\tsize_t _size;      // number of elements in this list\npublic:\n\tL1List() : _pHead(NULL), _size(0) {}\n\t/*~L1List() {\n\t\t_size = 0;\n\t\tL1Item<T> * temp = _pHead;\n\t\twhile (temp != NULL) {\n\t\t\tdelete _pHead;\n\t\t\t_pHead = temp->pNext;\n\t\t}\n\t};*/\n\t\n\tvoid clean();\n\tbool isEmpty()\n\t{\n\t\treturn _pHead == NULL;\n\t}\n\tsize_t getSize()\n\t{\n\t\treturn _size;\n\t}\n\n\tT &at(int i) {\n\t\tL1Item<T> * temp = _pHead;\n\t\tfor (int j = 0; j < i; j++) {\n\t\t\ttemp = temp->pNext;\n\t\t}\n\t\treturn temp->data;\n\t};\n\n\tT &operator[](int i) {\n\t\tL1Item<T> * temp = _pHead;\n\t\tfor (int j = 0; j < i; j++) {\n\t\t\ttemp = temp->pNext;\n\t\t}\n\t\treturn temp->data;\n\t};\n\n\tbool find(T &a, int &idx);\n\tint insert(int i, T &a);\n\tint remove(int i);\n\n\tint push_back(T &a);\n\tint insertHead(T &a);\n\tint insertHead()\n\t{\n\t\t_pHead = new L1Item<T>(_pHead);\n\t\t_size++;\n\t\treturn 0;\n\t}\n\n\tint removeHead();\n\tint removeLast();\n\n\tvoid reverse();\n\n\tvoid traverse(void(*op)(T &))\n\t{\n\t\tL1Item<T> *p = _pHead;\n\t\twhile (p)\n\t\t{\n\t\t\top(p->data);\n\t\t\tp = p->pNext;\n\t\t}\n\t}\n\tvoid traverse(void(*op)(T &, void *), void *pParam)\n\t{\n\t\tL1Item<T> *p = _pHead;\n\t\twhile (p)\n\t\t{\n\t\t\top(p->data, pParam);\n\t\t\tp = p->pNext;\n\t\t}\n\t}\n};\n\n/// Insert item to the end of the list\n/// Return 0 if success\ntemplate <class T>\nint L1List<T>::push_back(T &a)\n{\n\tif (_pHead == NULL)\n\t{\n\t\t_pHead = new L1Item<T>(a);\n\t}\n\telse\n\t{\n\t\tL1Item<T> *p = _pHead;\n\t\twhile (p->pNext)\n\t\t\tp = p->pNext;\n\t\tp->pNext = new L1Item<T>(a);\n\t}\n\n\t_size++;\n\treturn 0;\n}\n\n/// Insert item to the front of the list\n/// Return 0 if success\ntemplate <class T>\nint L1List<T>::insertHead(T &a)\n{\n\tL1Item<T> *p = new L1Item<T>(a);\n\tp->pNext = _pHead;\n\t_pHead = p;\n\t_size++;\n\treturn 0;\n}\n\n/// Remove the first item of the list\n/// Return 0 if success\ntemplate <class T>\nint L1List<T>::removeHead()\n{\n\tif (_pHead)\n\t{\n\t\tL1Item<T> *p = _pHead;\n\t\t_pHead = p->pNext;\n\t\tdelete p;\n\t\t_size--;\n\t\treturn 0;\n\t}\n\treturn -1;\n}\n\n/// Remove the last item of the list\n/// Return 0 if success\ntemplate <class T>\nint L1List<T>::removeLast()\n{\n\tif (_pHead)\n\t{\n\t\tif (_pHead->pNext)\n\t\t{\n\t\t\tL1Item<T> *prev = _pHead;\n\t\t\tL1Item<T> *pcur = prev->pNext;\n\t\t\twhile (pcur->pNext)\n\t\t\t{\n\t\t\t\tprev = pcur;\n\t\t\t\tpcur = pcur->pNext;\n\t\t\t}\n\t\t\tdelete pcur;\n\t\t\tprev->pNext = NULL;\n\t\t}\n\t\telse\n\t\t{\n\t\t\tdelete _pHead;\n\t\t\t_pHead = NULL;\n\t\t}\n\t\t_size--;\n\t\treturn 0;\n\t}\n\treturn -1;\n}\n/// insert\ntemplate <class T>\nint L1List<T>::insert(int i, T &a)\n{\n\tif (_pHead)\n\t{\n\t\tL1Item<T> *pcur = _pHead;\n\t\tfor (int j = 0; j < i; j++)\n\t\t{\n\t\t\tpcur = pcur->pNext;\n\t\t}\n\t\tL1Item<T> *p = new L1Item<T>(a);\n\t\tp->pNext = pcur->pNext;\n\t\tpcur->pNext = p;\n\t\t_size++;\n\t\treturn 0;\n\t}\n\treturn -1;\n}\n\ntemplate<class T>\nint L1List<T>::remove(int i) {\n\tif (_pHead) {\n\t\tL1Item<T> *pcur = _pHead;\n\t\tL1Item<T> *prev = _pHead;\n\t\tfor (int j = 0; j < i; j++)\n\t\t{\n\t\t\tprev = pcur;\n\t\t\tpcur = pcur->pNext;\n\t\t}\n\t\tprev->pNext = pcur->pNext;\n\t\tdelete pcur;\n\t\treturn 0;\n\t}\n\treturn -1;\n}\n\ntemplate<class T>\nbool L1List<T>::find(T &a, int &idx) {\n\tL1Item<T>* temp = _pHead;\n\tfor (int i = 0; i < _size; i++) {\n\t\tif (idx == i) 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Dipakyadav-19/Disk-Scheduling-algorithms/blob/2bf9674a3ff26d8cd5c93027d1aa238e31fe15fb/main.cpp", "id": "cpp-0-c8994020a1bab7948bffc0dde09d5b5c", "text": "\n#include <bits/stdc++.h>\nusing namespace std;\n\nvoid FCFS(vector<int> p,int head)\n{\n    int seek_time=0;\n            cout<<\"\\n Next position\"<<\"             Head\"<< \"  Traversed tracks\\n\";\n    for(auto current_head:p)\n    {\n        seek_time+=abs(current_head - head);\n\n        cout<<\"      \"<<setw(10)<<current_head<<\"           \"<<setw(8)<<head<<setw(8)<<abs(current_head - head)<<\"\\n\";\n\n        head=current_head;\n\n    }\n    cout<<\"\\nSeek time for FCFS is:\"<<seek_time;\n\n}\nvoid SSTF(vector<int> q,int head1)\n{\n    vector<int> temp;\n    vector<int> p;\n    p=q;\n    int head= head1;\n    int len=p.size();\n    int seek_time=0;\n            cout<<\"\\n Next position\"<<\"             Head\"<< \"  Traversed tracks\\n\";\n\n    while(len>0)\n    {\n        len--;\n        for(auto current_head:p)\n        {\n            temp.push_back(abs(current_head - head));\n        }\n\n        int temp_time= *min_element(temp.begin(), temp.end());\n        seek_time=seek_time+ *min_element(temp.begin(), temp.end());\n        vector<int>::iterator itr = std::find(temp.begin(), temp.end(), temp_time);\n        //distance(v.begin(), itr);\n        cout<<\"      \"<<setw(10)<<p[distance(temp.begin(), itr)]<<\"           \"<<setw(8)<<head<<setw(8)<<temp_time<< \"\\n\";\n\n        head=p[distance(temp.begin(), itr)];\n        auto it = p.begin()+distance(temp.begin(), itr);\n        p.erase(it);\n        temp.clear();\n\n    }\n\n    cout<<\"\\nSeek time for SSTF is:\"<<seek_time;\n\n}\n\n\nvoid SCAN(vector<int>  p,int head ,string Direction )\n{\n        int disk_size=200;\n\n    int temp =head;\n    vector<int> left,right;\n    int seek_time=0;\n    string decide=\"null\";\n            cout<<\"\\n Next position\"<<\"             Head\"<< \"  Traversed tracks\\n\";\n\n    for(auto i:p)\n    {\n        if(i<head) left.push_back(i);\n\n    else\n    {\n        right.push_back(i);\n\n    }\n    }\nsort(left.begin(),left.end());\nreverse(left.begin(),left.end());\nsort(right.begin(),right.end());\n\nif(Direction==\"left\")\n{\n    for(auto i:left){\n    seek_time=seek_time+ abs(temp-i);\n        cout<<\"      \"<<setw(10)<<i<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-i)<< \"\\n\";\n\n        temp=i;\n\n    }\n    seek_time=seek_time+(temp-0);\n        cout<<\"      \"<<setw(10)<<0<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-0)<< \"\\n\";\n    decide=\"right\";\n    temp=0;\n}\nelse\n    {\n        cout<<\"\\n\"\n;     for(auto i:right){\n    seek_time=seek_time+ abs(temp-i);\n                   // cout<<\"temp\"<<temp<<\" i \"<<i<<\"=\"<<abs(temp-i)<<\"seek =\"<<seek_time<<\"\\n\";\n        cout<<\"      \"<<setw(10)<<i<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-i)<< \"\\n\";\n\n    temp=i;\n\n     }\n         seek_time=seek_time+ abs(temp-(disk_size-1));\n\n        decide=\"left\";\n    temp=disk_size-1;\n        cout<<\"      \"<<setw(10)<<0<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-0)<< \"\\n\";\n\n}\nif(decide==\"left\")\n{\n     for(auto i:left){\n    seek_time=seek_time+ abs(temp-i);\n                  // cout<<\"temp\"<<temp<<\" i \"<<i<<\"=\"<<abs(temp-i)<<\"seek =\"<<seek_time<<\"\\n\";\n        cout<<\"      \"<<setw(10)<<i<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-i)<< \"\\n\";\n\n        temp=i;\n     }\n}\nelse\n    {\n        for(auto i:right){\n    seek_time=seek_time+ abs(temp-i);\n               // cout<<\"temp\"<<temp<<\" i \"<<i<<\"=\"<<abs(temp-i)<<\"seek =\"<<seek_time<<\"\\n\";\n        cout<<\"      \"<<setw(10)<<i<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-i)<< \"\\n\";\n\n        temp=i;\n        }\n\n\n}\n\n    cout<<\"\\nSeek time for SCAN is:\"<<seek_time;\n\n}\nvoid CSCAN(vector<int> p,int head)\n{\n        int temp =head;\n    int disk_size=200;\n    vector<int> left,right;\n    int seek_time=0;\n    string decide=\"null\";\n            cout<<\"\\n Next position\"<<\"             Head\"<< \"  Traversed tracks\\n\";\n\n    for(auto i:p)\n    {\n        if(i<head) left.push_back(i);\n\n    else\n    {\n        right.push_back(i);\n\n    }\n    }\nsort(left.begin(),left.end());\nsort(right.begin(),right.end());\n    for(auto i:right){\n    seek_time=seek_time+ abs(temp-i);\n               // cout<<\"temp\"<<temp<<\" i \"<<i<<\"=\" <<abs(temp-i)<<\"seek =\"<<seek_time<<\"\\n\";\n        cout<<\"      \"<<setw(10)<<i<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-i)<< \"\\n\";\n\n        temp=i;\n\n    }\n    seek_time=seek_time+abs(temp-(disk_size-1));\n                //cout<<\"temp\"<<temp<<\" i \"<<disk_size-1<<\"=\" <<abs(temp-(disk_size-1))<<\"seek =\"<<seek_time<<\"\\n\";\n                            cout<<\"      \"<<setw(10)<<disk_size-1<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-0)<< \"\\n\";\n\n    seek_time=seek_time+disk_size-1;\n    cout<<\"      \"<<setw(10)<<0<<\"           \"<<setw(8)<<disk_size-1<<setw(8)<<abs(disk_size-1-0)<< \"\\n\";\n\n\n    temp=0;\n\n    for(auto i:left){\n    seek_time=seek_time+ abs(temp-i);\n               // cout<<\"temp\"<<temp<<\" i \"<<i<<\"=\"<<abs(temp-i)<<\"seek =\"<<seek_time<<\"\\n\";\n        cout<<\"      \"<<setw(10)<<i<<\"           \"<<setw(8)<<temp<<setw(8)<<abs(temp-i)<< \"\\n\";\n\n        temp=i;\n    }\n        cout<<\"\\nSeek time for CSCAN is:\"<<seek_time;\n\n}\nint main()\n{\n        cout.setf(ios:: left);\n\n    vector< int> p= {176, 79, 34, 60, 92, 11, 41, 114};\n    int head=50;\n    FCFS(p,head);\n    SSTF(p,head);\n    SCAN(p,head,\"left\");\n    CSCAN(p,head);\n}\n", "text_preview": "#include <bits/stdc++.h> using namespace std; void FCFS(vector<int> p,int head) { int seek_time=0; cout<<\"\\n Next position\"<<\" Head\"<< \" Traversed tracks\\n\"; for(auto current_head:p) { seek_time+=abs(current_head - head); cout<<\" \"<<setw(10", "input_sha256": "525064e8a74591225642877ca9ca662615767b8a63cff43ddd77775236ee3923", "text_sha256": "c8994020a1bab7948bffc0dde09d5b5cb656110d1ac5a4e5e78236ed0101962b", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 100, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 3, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.1952949213334678, "noise_severity": 0.1546623574561551, "missing_content_severity": 0.02130458816685279, "factual_density": 2.809245386786383, "pretraining_utility": 0.493669362449373, "math_density": 3.0260040689974175, "code_density": 3.8797548790974465, "reasoning_density": 1.0010587075714077, "education_level": 2.0341550851200716, "creative_writing_quality": -0.6998111867815829}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.006948380232263074, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9929325328403299, "secondary": 0.9837897544336579, "tertiary": 0.9910402381542803, "structured_records": 0.02981825161189493, "procedural_steps": 0.0147455213313113, "worked_solution": 0.37058101226046397, "contextual_dialogue": 5.1054158124076215e-06, "content_quality": 0.4257579920062179, "noise_severity": 0.8711147021198707, "missing_content_severity": 0.9822461765276227, "factual_density": 0.32283736872429225, "pretraining_utility": 0.4521488182996458, "math_density": 0.1883367241645143, "code_density": 0.899795732581205, "reasoning_density": 0.16578441035716018, "education_level": 0.8150738344478486, "creative_writing_quality": 0.9986350784025115, "primary_topic": 0.9179395914335204, "secondary_topic": 0.507851078302137}, "probability_margin": {"primary": 0.9894210403070183, "secondary": 0.9777472869489772, "tertiary": 0.9886280269933966, 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0.00040248752909986997, "Technical Writing": 0.9932270106386495, "Transcript / Interview": 2.5247436294845186e-05, "Tutorial / How-To Guide": 0.003805970331631219, "User Reviews": 9.650634856244937e-06, "Other / Unknown": 0.0016748066289139778}, "secondary": {"Academic Writing": 0.0011802550747507955, "Content Listing": 6.564565035285482e-05, "Creative Writing": 0.0001313908799676105, "Customer Support Page": 4.2039706604681595e-05, "Q&A Forum": 3.5409322204574876e-05, "Discussion Thread": 3.493909902470515e-05, "Comment Section": 5.5302222585086884e-05, "Missing Content": 0.00014177783792475868, "Knowledge Article": 0.0008243461752857182, "Legal Notices": 7.23890746814465e-05, "News Article": 3.6328749075699596e-05, "Organizational Announcement": 3.36927437039682e-05, "Nonfiction Writing": 4.4458138676004836e-05, "Organizational About Page": 4.6394088089815603e-05, "Personal About Page": 3.940955806242307e-05, "Personal Blog": 3.4219949356515925e-05, "Product Page": 2.3025831967256542e-05, "Spam / Ads": 3.720168278835519e-05, "Structured Data": 0.001282336793230087, "Technical Writing": 0.006717894383278284, "Transcript / Interview": 5.560000211012247e-05, "Tutorial / How-To Guide": 0.00457993474582153, "User Reviews": 2.0826958202127896e-05, "NONE": 0.9844651813322555}, "tertiary": {"Academic Writing": 0.000586663855725237, "Content Listing": 3.881136959989673e-05, "Creative Writing": 7.345701252177753e-05, "Customer Support Page": 2.0905529304410235e-05, "Q&A Forum": 1.92774884901913e-05, "Discussion Thread": 1.9523475094836908e-05, "Comment Section": 4.5838993936793666e-05, "Missing Content": 0.00015059174717987152, "Knowledge Article": 0.0007328104000987938, "Legal Notices": 6.0570952679684426e-05, "News Article": 2.3285233700576403e-05, "Organizational Announcement": 1.837462398955685e-05, "Nonfiction Writing": 4.067491647579856e-05, "Organizational About Page": 3.886435341057578e-05, "Personal About Page": 2.666803763892985e-05, "Personal Blog": 2.3107563256495892e-05, "Product Page": 1.4797000970105797e-05, "Spam / Ads": 2.576832123853492e-05, "Structured Data": 0.0013906841595946822, "Technical Writing": 0.0027855345711220716, "Transcript / Interview": 3.549616005199398e-05, "Tutorial / How-To Guide": 0.0024011137285520614, "User Reviews": 1.3618940848554487e-05, "NONE": 0.9914135615645187}, "structured_records": {"false": 0.9701817483881051, "true": 0.02981825161189493}, "procedural_steps": {"false": 0.9852544786686886, "true": 0.0147455213313113}, "worked_solution": {"false": 0.629418987739536, "true": 0.37058101226046397}, "contextual_dialogue": {"false": 0.9999948945841876, "true": 5.1054158124076215e-06}, "content_quality": {"-2": 0.03314651747369555, "-1": 0.1806047091821443, "0": 0.4120672431052941, "1": 0.30617039501472887, "2": 0.06801113522413714}, "noise_severity": {"0": 0.8790058947299421, "1": 0.0925961029326016, "2": 0.024414617306000187, "3": 0.002696520214271582, "4": 0.0012868648171845906}, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/microsoft/Sora/blob/72d8e39a98cb17d4082015801f7cc13c8809112e/kernel/bb/Brick11/src/channel_11a.hpp", "id": "cpp-0-f9a2a83171c939512c1908e736b8ebbc", "text": "#pragma once\n\n/*****************************************************\n TChannelEst --\n TChannelEst takes a sequence of time-domain 11a LTS, \n estimate channel on the first LTS, and pass through\n the reset LTS down stream\n******************************************************/\n//\n// The LTS sequence as defined in 802.11a\n// the sequence is presented in frequency domain\n//\nstatic const char LTS_Sequence_11a[64] = {\n\t0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, \n\t1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, \n\t0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, \n\t1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1\n};\n\n//\n// T11aLTS - Processing 11a Long Training Symbols\n//\nDEFINE_LOCAL_CONTEXT(T11aLTS,\n\t\t\t\t\t CF_VecDC, \n                     CF_Channel_11a, \n                     CF_11aSymState, \n                     CF_CFOffset, \n                     CF_PilotTrack, \n                     CF_PhaseCompensate, \n                     CF_FreqCompensate,\n                     CF_Error);\n\ntemplate<TSINK_ARGS>\nclass T11aLTS: public TSink<TSINK_PARAMS>\n{\nprivate:\nstatic const int pre_read   = 16; //16;\nstatic const int skip_cp    = 8;\nstatic const int norm_one\t= 1600;\nstatic const int norm_shift = 8;\n\npublic:\n\tDEFINE_IPORT(COMPLEX16, 160 - pre_read); \t\n\nprivate:\n\tCTX_VAR_RW (ulong, symbol_type ); \n\t// DC\n\tCTX_VAR_RO (vcs, direct_current );\n\t// Channel estimation\n\tCTX_VAR_RW (vcs, ChannelCoeffs, [16] );\n\t// CFO estimation\n\tCTX_VAR_RW (FP_RAD, CFO_est );\n\t// frequency compensation\n\tCTX_VAR_RW (vcs, FreqCoeffs, [16] );\n\t// phase compensation\n    CTX_VAR_RW (FP_RAD, CFO_comp );\n    CTX_VAR_RW (FP_RAD, SFO_comp );\n\tCTX_VAR_RW (vcs, CompCoeffs, [16] );\n\t// pilot tracker\n    CTX_VAR_RW (FP_RAD, CFO_tracker );\n    CTX_VAR_RW (FP_RAD, SFO_tracker );\n\t// error\n\tCTX_VAR_RW (ulong, error_code );\n\nprotected:\n\tvcs LTS_seq[16];\n\n\t// build up the LTS LUT\n\t// 1 maps to norm_one\n\t// 0 maps to - norm_one\n\tFINL void __init_lut () {\n\t\tfor (int i=0; i<64; i++) {\n\t\t\tif ( LTS_Sequence_11a[i] ) {\n\t\t\t\tLTS_seq[i/4][i%4].re = norm_one;\n\t\t\t} else {\n\t\t\t\tLTS_seq[i/4][i%4].re = -norm_one;\n\t\t\t}\n\n\t\t\tLTS_seq[i/4][i%4].im = 0;\n\t\t}\n\t}\n\nprotected:\n\tFINL void __init () {\n\t}\n\nprotected:\n\tFINL\n\tvoid _fine_grained_frequency_estimation ( vcs* input )\n\t{\n\t\t// vcs temp[128];\n\t\t// coarse frequency compensation to a temp buffer\n\t\t// Kun: disabled\n\t\t// FrequencyShift<128/vcs::size>(temp, input, 0, CFO_est );\n\n\t\t// Fine grained frequency estimation\n\t\tFP_RAD arg = FreqOffsetEstimate<64/vcs::size> (input, input+16);\n\n        _dump_text ( \"CFO estimated %d\\n\", arg );        \n\n        // adjust the CFO estimation\n\t\tCFO_est = arg; \n\t\n\t\t// set the phase compensate parameters\n//\t\tCFO_comp = CFO_est * 144;\n\n\t\tCFO_comp = 0;\n\t\tSFO_comp = 0;\n\n//\t\tCFO_tracker = CFO_est * 80;\n\t\tCFO_tracker = 0;\n\t\tSFO_tracker = 0;\n\n\t\t// fine grained frequency compensation\n\t\t// setup the frequency compensation ceofficients\n\t\tBuildFrequencyShiftCoeffs<64> ((COMPLEX16*) FreqCoeffs, 0, CFO_est );\n\t\t_dump_symbol<64>(\"freq comp coeffs\", (COMPLEX16*) FreqCoeffs );\n\t\t\n\t\t// frequency shift LTS; prepare for channel estimation\n\t\tFrequencyShift<64/vcs::size> ( input, input,  FreqCoeffs );\n        _dump_symbol<64>(\"After Freq comp\", (COMPLEX16*) input );\n        \n\t}\n\n\tFINL\n\tvoid _channel_estimation ( vcs* input ) {\n\t\tvcs fft_out[16];\n\t\t\n\t\t// FFT first\n\t\tFFT<64>(input, fft_out);\n        _dump_symbol<64>(\"After FFT\", (COMPLEX16*) fft_out );\n        \n\t\t// positive subcarriers\n\t\tvi re, im;\n\t\tvi rre, rim;\n\t\t\t\n\t\tfor (int i=0; i<7; i++) {\n\t\t\tvi e = shift_right (SquaredNorm (fft_out[i]), norm_shift); \n\n\t\t\t// compute the 1/h\n\t\t\tconj_mul ( re, im, LTS_seq[i], fft_out[i] ); \n\n\t\t\t// normalization\n\t\t\tfor ( int j=0; j<4; j++ ) {\n\t\t\t\tif ( e[j] != 0 ) {\n\t\t\t\t\trre[j] = re[j] / e[j];\n\t\t\t\t\trim[j] = im[j] / e[j];\t\t\t\t\t\n\t\t\t\t} else {\n\t\t\t\t\trre[j] = 0;\n\t\t\t\t\trim[j] = 0;\n\t\t\t\t}\n\t\t\t}\n\n\t\t\tpack(ChannelCoeffs[i], rre, rim );\n\t\t}\n\n\t\t// negative subcarriers\n\t\tfor (int i=9; i<16; i++) {\n\t\t\tvi e = shift_right (SquaredNorm (fft_out[i]), norm_shift); \n\n\t\t\t// compute the 1/h\n\t\t\tconj_mul ( re, im, LTS_seq[i], fft_out[i] ); \n\n\t\t\t// normalization\n\t\t\tfor ( int j=0; j<4; j++ ) {\n\t\t\t\tif ( e[j] != 0 ) {\n\t\t\t\t\trre[j] = re[j] / e[j];\n\t\t\t\t\trim[j] = im[j] / e[j];\t\t\t\t\t\n\t\t\t\t} else {\n\t\t\t\t\trre[j] = 0;\n\t\t\t\t\trim[j] = 0;\n\t\t\t\t}\t\n\t\t\t}\n\n\t\t\tpack (ChannelCoeffs[i], rre, rim );\n\t\t}\n\t\n\t}\n\t\npublic:\n\tREFERENCE_LOCAL_CONTEXT(T11aLTS);\n\n\tSTD_TSINK_CONSTRUCTOR(T11aLTS)\n\t\tBIND_CONTEXT(CF_11aSymState::symbol_type, symbol_type )\n\t\tBIND_CONTEXT(CF_VecDC::direct_current, direct_current )\t\t\n\t\tBIND_CONTEXT(CF_CFOffset::CFO_est,        CFO_est)\t\t\n\t\tBIND_CONTEXT(CF_FreqCompensate::Coeffs,   FreqCoeffs)\t\t\t\t\n\t\tBIND_CONTEXT(CF_PhaseCompensate::CFO_comp, CFO_comp)\n\t\tBIND_CONTEXT(CF_PhaseCompensate::SFO_comp, SFO_comp)\t\t\n\t\tBIND_CONTEXT(CF_PhaseCompensate::CompCoeffs,  CompCoeffs)\t\t\n\t\tBIND_CONTEXT(CF_PilotTrack::CFO_tracker,    CFO_tracker)\n\t\tBIND_CONTEXT(CF_PilotTrack::SFO_tracker,    SFO_tracker)\t\t\n\t\tBIND_CONTEXT(CF_Channel_11a::ChannelCoeffs, ChannelCoeffs)\n\t\tBIND_CONTEXT(CF_Error::error_code,\t\t  error_code)\t\t\n\t{ \n\t\t__init (); \n\t\t__init_lut ();\n\t}\n\n\tSTD_TSINK_RESET() { \n\t\t__init ();\n\t}\n\n\tSTD_TSINK_FLUSH() { }\n\n\tBOOL_FUNC_PROCESS (ipin)\n\t{\n\t\twhile (ipin.check_read())\n\t\t{\n\t\t\tvcs * pvi = (vcs*) ipin.peek ();\n\t\t\tpvi += (skip_cp / vcs::size);\n\n            _dump_text ( \"dc <%d,%d>\\n\", direct_current[0].re, direct_current[0].im );\n            _dump_symbol<128> (\"LTS\", (COMPLEX16*) pvi );\n\n            rep_shift_right<64/vcs::size>(pvi, pvi, 1);\n\t\t\t_fine_grained_frequency_estimation ( pvi );\n\t\t\t_channel_estimation ( pvi );\t\n            _dump_symbol<64> (\"Channel\", (COMPLEX16*) ChannelCoeffs );\n\n\t\t\tipin.pop ();\n\n\t\t\t// we have done LTS\n\t\t\t// update the symbol state\n\t\t\tsymbol_type = CF_11aSymState::SYMBOL_OFDM_DATA;\n\t\t}\n\t\t\n\t\treturn true;\n\t};\n};\n\n\nDEFINE_LOCAL_CONTEXT(TChannelEst, CF_Channel_11a, CF_Error);\ntemplate<TFILTER_ARGS>\nclass TChannelEst : public TFilter<TFILTER_PARAMS>\n{\nprivate:\n\tstatic const int norm_one = 1600;\n\tstatic const int norm_shift = 8;\n\nprotected:\n\tvcs LTS_seq[16];\n\tbool est_ctrl;\n\nprivate:\t\n\tCTX_VAR_RW (vcs, ChannelCoeffs, [16] );\n\n\tCTX_VAR_RW (ulong, error_code );\n\t\n\tpublic:\n\t\tDEFINE_IPORT(COMPLEX16, 64); \t// \n\t\tDEFINE_OPORT(COMPLEX16, 64); \t// passthru the sample downstream\n\n\tprotected:\n\t\tFINL\n\t\tvoid _channel_est ( vcs* input ) {\n\t\t\tvcs fft_out[16];\n\t\t\t\n\t\t\t// FFT first\n\t\t\tFFT<64>(input, fft_out);\n\t\t\t// positive subcarriers\n\t\t\tvi re, im;\n\t\t\tvi rre, rim;\n\n\t\t\t\t\n\t\t\tfor (int i=0; i<7; i++) {\n\t\t\t\tvi e = shift_right (SquaredNorm (fft_out[i]), norm_shift); \n\n\t\t\t\t// compute the 1/h\n\t\t\t\tconj_mul ( re, im, LTS_seq[i], fft_out[i] ); \n\n\t\t\t\t// normalization\n\t\t\t\tfor ( int j=0; j<4; j++ ) {\n\t\t\t\t\tif ( e[j] != 0 ) {\n\t\t\t\t\t\trre[j] = re[j] / e[j];\n\t\t\t\t\t\trim[j] = im[j] / e[j];\t\t\t\t\t\n\t\t\t\t\t} else {\n\t\t\t\t\t\trre[j] = 0;\n\t\t\t\t\t\trim[j] = 0;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tpack(ChannelCoeffs[i], rre, rim );\n\t\t\t}\n\n\t\t\t// negative subcarriers\n\t\t\tfor (int i=9; i<16; i++) {\n\t\t\t\tvi e = shift_right (SquaredNorm (fft_out[i]), norm_shift); \n\n\t\t\t\t// compute the 1/h\n\t\t\t\tconj_mul ( re, im, LTS_seq[i], fft_out[i] ); \n\n\t\t\t\t// normalization\n\t\t\t\tfor ( int j=0; j<4; j++ ) {\n\t\t\t\t\tif ( e[j] != 0 ) {\n\t\t\t\t\t\trre[j] = re[j] / e[j];\n\t\t\t\t\t\trim[j] = im[j] / e[j];\t\t\t\t\t\n\t\t\t\t\t} else {\n\t\t\t\t\t\trre[j] = 0;\n\t\t\t\t\t\trim[j] = 0;\n\t\t\t\t\t}\t\n\t\t\t\t}\n\n\t\t\t\tpack (ChannelCoeffs[i], rre, rim );\n\t\t\t}\n\n#if 0\n\t\t\t// debug\n\t\t\tprintf ( \"LTS \" );\t\t\t\n\t\t\tfor ( int i=0; i<64; i++) {\n\t\t\t\tif ( i%8 == 0 ) printf (\"\\n\");\t\t\t\t\n\t\t\t\tprintf ( \"<%d, %d> , \", fft_out[i/4][i%4].re, fft_out[i/4][i%4].im);\n\t\t\t}\n\t\t\tprintf ( \"\\n\" );\n\n\t\t\tprintf ( \"channels: \" );\t\t\t\n\t\t\tfor ( int i=0; i<64; i++) {\n\t\t\t\tif ( i%8 == 0 ) printf (\"\\n\");\t\t\t\t\n\t\t\t\tprintf ( \"<%d, %d> , \", ChannelCoeffs[i/4][i%4].re, ChannelCoeffs[i/4][i%4].im);\n\t\t\t}\n\t\t\tprintf ( \"\\n\" );\n#endif\t\t\t\n\t\t}\n\n\n\tprotected:\n\t\tFINL void __init () {\n\t\t\test_ctrl = false;\n\t\t}\n\n\t\t// build up the LTS LUT\n\t\t// 1 maps to norm_one\n\t\t// 0 maps to - norm_one\n\t\tFINL void __init_lut () {\n\t\t\tfor (int i=0; i<64; i++) {\n\t\t\t\tif ( LTS_Sequence_11a[i] ) {\n\t\t\t\t\tLTS_seq[i/4][i%4].re = norm_one;\n\t\t\t\t} else {\n\t\t\t\t\tLTS_seq[i/4][i%4].re = -norm_one;\n\t\t\t\t}\n\n\t\t\t\tLTS_seq[i/4][i%4].im = 0;\n\t\t\t}\n\t\t}\n\t\t\n\tpublic:\n\t\tREFERENCE_LOCAL_CONTEXT(TChannelEst );\n\t\t\n\t\tSTD_TFILTER_CONSTRUCTOR(TChannelEst )\n\t\t\tBIND_CONTEXT(CF_Channel_11a::ChannelCoeffs, ChannelCoeffs)\n\t\t\tBIND_CONTEXT(CF_Error::error_code,\t\t  error_code)\t\t\n\t\t{ \n\t\t\t__init ();\n\t\t\t__init_lut ();\n\t\t}\n\t\n\t\tSTD_TFILTER_RESET() { \n\t\t\t__init ();\n\t\t}\n\t\t\n\t\tSTD_TFILTER_FLUSH() { }\n\t\n\t\tBOOL_FUNC_PROCESS (ipin)\n\t\t{\n\t\t\twhile (ipin.check_read())\n\t\t\t{\n\t\t\t\tvcs* pi = (vcs*)ipin.peek ();\n\t\t\t\tvcs* po = (vcs*) opin().append();\n\t\t\t\t\n\t\t\t\t// passthru\n\t\t\t\trep_memcpy<16>(po, pi);\n\n\t\t\t\t// only estimate channel on the first LTS\n\t\t\t\t// N.B. We need to copy samples downstream first, as\n\t\t\t\t// FFT operations used in _channel_est will destory\n\t\t\t\t// the input data\n\t\t\t\tif ( !est_ctrl ) {\n\t\t\t\t\t_channel_est ( pi );\n\t\t\t\t\test_ctrl = true;\n\t\t\t\t}\n\t\t\t\t\n\t\t\t\tNext()->Process(opin());\n\t\t\t\tipin.pop();\n\t\t\t}\n\t\t\treturn true;\n\t\t}\n\n};\n\n/*****************************************************\n TFineCFOEst --\n TFineCFOEst takes two time-domain 11a LTSs, and \n perform fine grained CFO estimation.\n The original samples are passed through to downstream\n bricks\n******************************************************/\nDEFINE_LOCAL_CONTEXT(TFineCFOEst, CF_CFOffset, CF_PilotTrack, CF_PhaseCompensate, CF_Error);\ntemplate<TFILTER_ARGS>\nclass TFineCFOEst : public TFilter<TFILTER_PARAMS>\n{\nprivate:\t\n\tCTX_VAR_RW (FP_RAD, CFO_est );\n\t\n    CTX_VAR_RW (FP_RAD, CFO_comp );\n    CTX_VAR_RW (FP_RAD, SFO_comp );\n\tCTX_VAR_RW (vcs, CompCoeffs, [16] );\n\n    CTX_VAR_RW (FP_RAD, CFO_tracker );\n    CTX_VAR_RW (FP_RAD, SFO_tracker );\n\n\tCTX_VAR_RW (ulong, error_code   );\n\t\npublic:\n\tDEFINE_IPORT(COMPLEX16, 128); \t// \n\tDEFINE_OPORT(COMPLEX16, 128); \t// passthru the sample downstream\n\nprotected:\n\tFINL\n\tvoid _coarse_cfo_comp ( vcs* input ) {\n\t\tvcs compCoeffs[32];\n\t\tCOMPLEX16* pCoeffs = (COMPLEX16*) compCoeffs;\n\t\tFP_RAD delta = 0;\n\t\tfor (int i=0; i<128; i++) {\n\t\t\tpCoeffs[i].re = ucos  (delta);\n\t\t\tpCoeffs[i].im = -usin (delta);\n\t\t\tdelta += CFO_est;\n\t\t}\n\n\t\trep_mul<32> ( input, input, compCoeffs );\t\n\t}\n\n\tFINL\n\tvoid _build_phase_coeff ( COMPLEX16* pcoeffs, FP_RAD ave ) {\n\t\tFP_RAD th = ave;\n\t\tint i;\n\t\tfor (i = 0; i < 64; i++)\n\t\t{\n\t\t\tpcoeffs[i].re =  ucos(th);\n\t\t\tpcoeffs[i].im = -usin(th);\t\t\t\n\t\t}\n\t}\n\t\t\n\tFINL\n\tvoid _fine_cfo_est ( vcs* input ) {\n\t\tvi re, im;\n\t\tint sum_re = 0;\n\t\tint sum_im = 0;\n\t\t\t\n\t\tfor (int i=0; i<16; i++) {\n\t\t\tconj_mul (re, im, input[i+16], input[i] );\n\n\t\t\tvi sr = hadd (shift_right(re, 6));\n\t\t\tvi si = hadd (shift_right(im, 6));\n\n\t\t\tsum_re += sr[0]; sum_im += si[0];\n\t\t}\n\n\t\tFP_RAD arg = uatan2 (sum_im, sum_re);\n\t\targ >>= 6;\n\n\t\tCFO_est += arg; // adjust the CFO estimation\n\t\n\t\t// set the phase compensate parameters\n\t\tCFO_comp = CFO_est * 144;\n\t\tSFO_comp = 0;\n\n\t\tCFO_tracker = CFO_est * 80;\n\t\tSFO_tracker = 0;\n\t\t\n\t\t//printf ( \"Fine CFO estimation: %lf (%lf) CFO comp %lf CFO tracker % lf\\n\", \n\t\t//\t\tfprad2rad(CFO_est),\n\t\t//\t\tfprad2rad(arg),\n\t\t//\t\tfprad2rad(CFO_comp),\n\t\t//\t\tfprad2rad(CFO_tracker) );\n\n\t\t_build_phase_coeff ( (COMPLEX16*) CompCoeffs, CFO_comp );\n\t}\n\t\nprotected:\n\tFINL void __init () {\n\t}\n\npublic:\n\tREFERENCE_LOCAL_CONTEXT(TFineCFOEst );\n\t\n\tSTD_TFILTER_CONSTRUCTOR(TFineCFOEst )\n\t\tBIND_CONTEXT (CF_CFOffset::CFO_est,              CFO_est)\t\t\n\t\tBIND_CONTEXT (CF_PhaseCompensate::CFO_comp,      CFO_comp)\n\t\tBIND_CONTEXT (CF_PhaseCompensate::SFO_comp,      SFO_comp)\t\t\n\t\tBIND_CONTEXT (CF_PhaseCompensate::CompCoeffs,  CompCoeffs)\t\t\n\t\tBIND_CONTEXT (CF_PilotTrack::CFO_tracker,    CFO_tracker)\n\t\tBIND_CONTEXT (CF_PilotTrack::SFO_tracker,    SFO_tracker)\t\t\n\t\tBIND_CONTEXT(CF_Error::error_code,\t\t      error_code)\t\t\n\t{ \n\t\t__init ();\n\t}\n\t\n\tSTD_TFILTER_RESET() { \n\t\t__init ();\n\t}\n\t\t\n\tSTD_TFILTER_FLUSH() { }\n\t\n\tBOOL_FUNC_PROCESS (ipin)\n\t{\n\t\twhile (ipin.check_read())\n\t\t{\n\t\t\tvcs* pi = (vcs*)ipin.peek ();\n\n\t\t\t// compensate coarse CFO first \n\t\t\t_coarse_cfo_comp (pi);\n\t\t\t\t\n\t\t\t// fine CFO estimation\n\t\t\t_fine_cfo_est ( pi );\n\t\t\tvcs* po = (vcs*) opin().append();\n\n\t\t\trep_memcpy<32> (po, pi);\n\t\t\tNext()->Process(opin());\n\t\t\tipin.pop();\n\t\t}\n\t\treturn true;\n\t}\n};\n\n/*****************************************************\n TChannelEqualization --\n TChannelEqualization performs equalization on the \n incoming data symbol. The equalized results are\n passed downstream.\n******************************************************/\n\nDEFINE_LOCAL_CONTEXT(TChannelEqualization, CF_Channel_11a, CF_Error);\ntemplate<TFILTER_ARGS>\nclass TChannelEqualization : public TFilter<TFILTER_PARAMS>\n{\nstatic const int norm_shift = 8;\n\t\nprivate:\t\n\tCTX_VAR_RW (vcs, ChannelCoeffs, [16] );\n\tCTX_VAR_RW (ulong, error_code );\n\t\npublic:\n\tDEFINE_IPORT(COMPLEX16, 64); \t// \n\tDEFINE_OPORT(COMPLEX16, 64); \t// passthru the sample downstream\n\nprotected:\n\tFINL\n\tvoid _channel_equalize ( vcs* input, vcs* output ) {\n\t\tvi re, im;\n\t\tvi rre, rim;\n\n        set_zero(output[7]);\n        set_zero(output[8]);\n\t\t\n\t\t// positive subcarriers\t\t\t\t\n\t\tfor (int i=0; i<7; i++) {\n\t\t\tmul (re, im, input[i], ChannelCoeffs[i] );\n\n\t\t\tre = shift_right (re, norm_shift);\n\t\t\tim = shift_right (im, norm_shift);\n\n\t\t\tpack (output[i], re, im );\n\t\t}\n\n\t\t// negative subcarriers\n\t\tfor (int i=9; i<16; i++) {\n\t\t\tmul (re, im, input[i], ChannelCoeffs[i] );\n\n\t\t\tre = shift_right (re, norm_shift);\n\t\t\tim = shift_right (im, norm_shift);\n\n\t\t\tpack (output[i], re, im );\n\t\t}\n\t}\n\t\t\npublic:\n\tREFERENCE_LOCAL_CONTEXT(TChannelEqualization);\n\t\n\tSTD_TFILTER_CONSTRUCTOR(TChannelEqualization)\n\t\tBIND_CONTEXT(CF_Channel_11a::ChannelCoeffs, ChannelCoeffs)\n\t\tBIND_CONTEXT(CF_Error::error_code,\t\t  error_code)\t\t\n\t{ }\n\t\n\tSTD_TFILTER_RESET() { }\n\t\n\tBOOL_FUNC_PROCESS (ipin)\n\t{\n\t\twhile (ipin.check_read())\n\t\t{\n\t\t\tvcs* pi = (vcs*)ipin.peek();\n\t\t\tvcs* po = (vcs*)opin().append();\n\n//            _dump_symbol<64> ( \"before equalization\", (COMPLEX16*) pi);\n            \n\t\t\t_channel_equalize ( pi, po );\n\n            _dump_symbol<64> ( \"After equalization\",  (COMPLEX16*) po);\n\n\t\t\tNext()->Process(opin());\n\t\t\tipin.pop();\n\t\t}\n\t\treturn true;\n\t}\n};\n\n/*****************************************************\n TFreqCompensation --\n TFreqCompensation compensates the frequency offset of\n the incoming symbol to remove ICI\n******************************************************/\n\nDEFINE_LOCAL_CONTEXT(TFreqCompensation, CF_FreqCompensate, CF_Error);\ntemplate<TFILTER_ARGS>\nclass TFreqCompensation : public TFilter<TFILTER_PARAMS>\n{\n\t\nprivate:\t\n\tCTX_VAR_RO (vcs, FreqCoeffs, [16] );\n\tCTX_VAR_RW (ulong, error_code );\n\npublic:\n\tDEFINE_IPORT(COMPLEX16, 64); \t// \n\tDEFINE_OPORT(COMPLEX16, 64); \t// passthru the sample downstream\n\n\t\t\npublic:\n\tREFERENCE_LOCAL_CONTEXT(TFreqCompensation);\n\t\n\tSTD_TFILTER_CONSTRUCTOR(TFreqCompensation)\n\t\tBIND_CONTEXT(CF_FreqCompensate::Coeffs, FreqCoeffs)\n\t\tBIND_CONTEXT(CF_Error::error_code, error_code)\t\t\n\t{ }\n\t\n\tSTD_TFILTER_RESET() { }\n\t\n\tBOOL_FUNC_PROCESS (ipin)\n\t{\n\t\twhile (ipin.check_read())\n\t\t{\n\t\t\tvcs* pi = (vcs*)ipin.peek ();\n\t\t\tvcs* po = (vcs*) opin().append();\n\n            rep_shift_right<64/vcs::size>(pi, pi, 1);\n\t\t\tFrequencyShift<64/vcs::size> ( po, pi, FreqCoeffs );\n//\t\t\t_dump_symbol<64>(\"freq comp coeffs\", (COMPLEX16*) FreqCoeffs );\t\t\t\n\t\t\t_dump_symbol<64> (\"After freq compensation\", (COMPLEX16*) po);\n\n\t\t\tNext()->Process(opin());\n\t\t\tipin.pop();\n\t\t}\n\t\treturn true;\n\t}\n};\n\n", "text_preview": "#pragma once /***************************************************** TChannelEst -- TChannelEst takes a sequence of time-domain 11a LTS, estimate channel on the first LTS, and pass through the reset LTS down stream **************************", "input_sha256": "bd85aec219ff17902699d3d28dab44f0fe7d3bd0979fca3c0abfc79f7f983181", "text_sha256": "f9a2a83171c939512c1908e736b8ebbc235637432d627cedde3bbe0d9f3dfb03", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 101, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Nelarius/playground/blob/49446fa92157ad61f48696f28f8baa04aa8bf8fe/src/utils/sparse_graph.h", "id": "cpp-0-bf08ad7acc2637a8c226b517ce9eefa7", "text": "#ifndef SPARSEGRAPH_H_INCLUDED\n#define SPARSEGRAPH_H_INCLUDED\n\n#include <vector>\n#include <list>\n#include <cstdlib>\n#include <cassert>\n\nnamespace graph {\n\ntemplate< typename T >\nstruct node {\n    // a valid index is >= 0\n    int index;\n    T   value;\n    \n    using value_type = T;\n};\n\ntemplate< typename T >\nstruct edge {\n    int from;\n    int to;\n    T cost;\n};\n\ntemplate<>\nstruct edge< void > {\n    int from;\n    int to;\n};\n\n/**\n* @class sparse_graph\n* @brief A sparse graph container.\n* In the following comments, N is going to denote the number of nodes,\n* and E the number of edges a node has, on average.\n*/\ntemplate< typename N, typename E > \nclass sparse_graph {\n        \n    public:\n        using node_type = N;\n        using edge_type = E;\n        \n    private:\n        using node_vector = std::vector< node_type >;\n        using edge_list = std::list< edge_type >;\n        using edge_list_vector = std::vector< edge_list >;\n        \n    public:\n        \n        sparse_graph( bool is_digraph );\n        sparse_graph() = default;\n        ~sparse_graph() = default;\n        \n        /**\n        * @brief Get the number of nodes.\n        * Has O(1) time complexity.\n        */\n        std::size_t number_of_nodes() const;\n        /**\n        * @brief Get the number of edges.\n        * Has O(1) time complexity.\n        */\n        std::size_t number_of_edges() const;\n        /**\n        * @brief Check whether the graph contains any nodes.\n        */\n        bool is_empty() const;\n        /**\n        * @brief Check whether the graph is a bidirectional graph.\n        * @returns true, is the graph is a bidirectional graph.\n        */\n        bool is_digraph() const;\n        \n        N& at( int index );\n        const N& at( int index ) const;\n        \n        N& operator[]( int index );\n        const N& operator[]( int index ) const;\n        /**\n        * @brief Add a new node with a value.\n        * @param value The value to add to the node.\n        * @returns The index of the node.\n        * This operation has O(1) time complexity.\n        */\n        int add_node( typename N::value_type value );\n        /**\n        * @brief Remove a node at index i.\n        * This operation has O(N*E) time complexity. \n        */\n        void remove_node( int i );\n        /**\n        * @brief Add an edge uniquely to the graph.\n        * @param e The edge to add.\n        * If the graph is a digraph, then an edge in the opposite bidirection \n        * is added as well. Note that if the edge already exists, then nothing will * * be added.\n        * \n        * Adding an edge has O( E ) time complexity, where E is the number of edges\n        * between nodes, on average.\n        */\n        void add_edge( edge_type e );\n        /**\n        * @brief Check whether a node has outgoing edges.\n        * @returns true if the node has no outgoing edges, false otherwise\n        * Has O(E) time complexity.\n        */\n        bool is_leaf( int i ) const;\n        \n        using edge_iterator = typename edge_list::iterator;\n        using const_edge_iterator = typename edge_list::const_iterator;\n        \n        class node_iterator {\n            public:\n                node_iterator( int i, sparse_graph& owner )\n                :   position_{ i },\n                    owner_{ owner }\n                    {}\n                node_iterator()                                     = delete;\n                node_iterator( const node_iterator& )               = default;\n                node_iterator( node_iterator&&  )                   = default;\n                node_iterator& operator=( const node_iterator& )    = default;\n                node_iterator& operator=( node_iterator&& )         = default;\n                ~node_iterator()                                    = default;\n                \n                bool operator==( const node_iterator& n ) const {\n                    return position_ == n.position_;\n                }\n                \n                bool operator!=( const node_iterator& n ) const {\n                    return position_ != n.position_;\n                }\n                \n                node_iterator& operator++() {\n                    position_++;\n                    return *this;\n                }\n                \n                N& operator*() {\n                    return owner_.nodes_.at( position_ );\n                }\n                \n                edge_iterator begin() {\n                    return owner_.edges_[ position_ ].begin();\n                }\n                \n                const_edge_iterator begin() const {\n                    return owner_.edges[ position_ ].begin();\n                }\n                \n                edge_iterator end() {\n                    return owner_.edges_[ position_ ].end();\n                }\n                \n                const_edge_iterator end() const {\n                    return owner_.edges_[ position_ ].end();\n                }\n                \n            private:\n                int             position_;\n                sparse_graph&   owner_;\n        };\n        \n        class const_node_iterator {\n            public:\n                const_node_iterator( int i, const sparse_graph& owner )\n                :   position_{ i },\n                    owner_{ owner }\n                    {}\n                const_node_iterator()                                           = delete;\n                const_node_iterator( const const_node_iterator& )               = default;\n                const_node_iterator( const_node_iterator&&  )                   = default;\n                const_node_iterator& operator=( const const_node_iterator& )    = default;\n                const_node_iterator& operator=( const_node_iterator&& )         = default;\n                ~const_node_iterator()                                          = default;\n                \n                bool operator==( const const_node_iterator& n ) const {\n                    return position_ == n.position_;\n                }\n                \n                bool operator!=( const const_node_iterator& n ) const {\n                    return position_ != n.position_;\n                }\n                \n                const_node_iterator& operator++() {\n                    position_++;\n                    return *this;\n                }\n                \n                const N& operator*() const {\n                    return owner_.nodes_.at( position_ );\n                }\n                \n                const_edge_iterator begin() const {\n                    return owner_.edges_[ position_ ].begin();\n                }\n                \n                const_edge_iterator end() const {\n                    return owner_.edges_[ position_ ].end();\n                }\n                \n            private:\n                int                 position_;\n                const sparse_graph& owner_;\n        };\n        \n        friend class node_iterator;\n        friend class const_node_iterator;\n        \n        node_iterator iterator_at( int index );\n        const_node_iterator iterator_at( int index ) const;\n        node_iterator begin();\n        const_node_iterator begin() const;\n        node_iterator end();\n        const_node_iterator end() const;\n        \n    private:\n        // METHODS\n        // Check if there is only one of the given edge\n        bool is_unique_( int, int );\n        // remove all edges pointing to invalid nodes\n        // this operation has O( N*E ) time complexity\n        void cull_invalid_edges_();\n        \n        // FIELDS\n        node_vector      nodes_{};\n        edge_list_vector edges_{};\n        std::size_t      edge_count_{ 0u };\n        bool             is_digraph_{ false };\n        const int        invalid_node_index_{ -1 };\n};\n\ntemplate< typename N, typename E >\nsparse_graph< N, E >::sparse_graph( bool is_digraph )\n:   nodes_(),\n    edges_(),\n    edge_count_( 0u ),\n    is_digraph_( is_digraph ),\n    invalid_node_index_( -1 )\n    {}\n    \ntemplate< typename N, typename E >\nbool sparse_graph< N, E >::is_unique_( int from, int to ) {\n    for ( const edge_type& e: iterator_at( from ) ) {\n        if ( e.to == to ) {\n            return false;\n        }\n    }\n    return true;\n}\n\ntemplate< typename N, typename E >\nvoid sparse_graph< N, E >::cull_invalid_edges_() {\n    // it points to list of edges\n    for ( auto it = edges_.begin(); it != edges_.end(); it++ ) {\n        // e points to edge_type\n        for ( auto e = it->begin(); e != it->end(); e++ ) {\n            if ( nodes_[ e->from ] == invalid_node_index_ || nodes_[ e->to ] == invalid_node_index_ ) {\n                e = it->erase( e );\n                edge_count_--;\n            }\n        }\n    }\n}\n\ntemplate< typename N, typename E >\nbool sparse_graph< N, E >::is_empty() const {\n    return nodes_.empty();\n}\n   \ntemplate< typename N, typename E >\nbool sparse_graph< N, E >::is_digraph() const {\n    return is_digraph_;\n}\n  \ntemplate< typename N, typename E >\nstd::size_t sparse_graph< N, E >::number_of_nodes() const {\n    return nodes_.size();\n}\n\ntemplate< typename N, typename E >\nstd::size_t sparse_graph< N, E >::number_of_edges() const {\n    return edge_count_;\n}\n\ntemplate< typename N, typename E >\nN& sparse_graph< N, E >::operator[]( int i ) {\n    return nodes_[i];\n}\n\ntemplate< typename N, typename E >\nconst N& sparse_graph< N, E >::operator[]( int i ) const {\n    return nodes_[i];\n}\n\ntemplate< typename N, typename E >\nint sparse_graph< N, E >::add_node( typename N::value_type val ) {\n    nodes_.push_back( node_type{ int (nodes_.size()), val } );\n    edges_.emplace_back();\n    return nodes_.size() - 1u;\n}\n\ntemplate< typename N, typename E >\nvoid sparse_graph< N, E >::remove_node( int index ) {\n    nodes_[ index ] = invalid_node_index_;\n    cull_invalid_edges_();\n}\n\ntemplate< typename N, typename E >\nvoid sparse_graph< N, E >::add_edge( edge_type e ) {\n    assert( e.from >= 0 && e.to >= 0 );\n    assert( e.from < int (nodes_.size()) && e.to < int (nodes_.size()) );\n    if ( is_unique_( e.from, e.to ) ) {\n        edges_[ e.from ].push_back( e );\n        edge_count_++;\n    }\n    \n    if ( is_digraph_ ) {\n        if ( is_unique_( e.to, e.from ) ) {\n            edges_[ e.to ].push_back( e );\n            edge_count_++;\n        }\n    }\n}\n\ntemplate< typename N, typename E >\nbool sparse_graph< N, E >::is_leaf( int i ) const {\n    if ( edges_[i].size() == 0u ) {\n        return true;\n    }\n    return false;\n}\n\ntemplate< typename N, typename E >\ntypename sparse_graph< N, E >::node_iterator sparse_graph< N, E >::begin() {\n    return node_iterator( 0, *this );\n}\n\ntemplate< typename N, typename E >\ntypename sparse_graph< N, E >::const_node_iterator sparse_graph< N, E >::begin() const {\n    return const_node_iterator( 0, *this );\n}\n\ntemplate< typename N, typename E >\ntypename sparse_graph< N, E >::node_iterator sparse_graph< N, E >::end() {\n    return node_iterator( -1, *this );\n}\n\ntemplate< typename N, typename E >\ntypename sparse_graph< N, E >::const_node_iterator sparse_graph< N, E >::end() const {\n    return const_node_iterator( -1, *this );\n}\n\ntemplate< typename N, typename E >\nN& sparse_graph< N, E >::at( int i ) {\n    return nodes_[i];\n}\n\ntemplate< typename N, typename E >\nconst N& sparse_graph< N, E >::at( int i ) const {\n    return nodes_[i];\n}\n\ntemplate< typename N, typename E >\ntypename sparse_graph< N, E >::node_iterator sparse_graph< N, E >::iterator_at( int i ) {\n    assert( i < int( nodes_.size() ) );\n    return node_iterator( i, *this );\n}\n\ntemplate< typename N, typename E >\ntypename sparse_graph< N, E >::const_node_iterator sparse_graph< N, E >::iterator_at( int i ) const {\n    assert( i < int( nodes_.size() ) );\n    return const_node_iterator( i, *this );\n}\n\n}   // namespace graph\n\n#endif // SPARSEGRAPH_H_INCLUDED", "text_preview": "#ifndef SPARSEGRAPH_H_INCLUDED #define SPARSEGRAPH_H_INCLUDED #include <vector> #include <list> #include <cstdlib> #include <cassert> namespace graph { template< typename T > struct node { // a valid index is >= 0 int index; T value; using ", "input_sha256": "93bfb8284cad02908f39773bba31b47ba9c77edfe77a14e0f4a002e9a5bf0798", "text_sha256": 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{"content_quality": 0.557004742062002, "noise_severity": 0.07351482157443014, "missing_content_severity": 0.020526773310312087, "factual_density": 3.3952972229068474, "pretraining_utility": 0.563405328695542, "math_density": 1.3434300948370665, "code_density": 3.835865437240638, "reasoning_density": 0.5566330391130447, "education_level": 2.31061515049608, "creative_writing_quality": -0.19928708965210842}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.01456875684304526, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9960232567753999, "secondary": 0.9782689945519691, "tertiary": 0.9934711756055813, "structured_records": 0.017191124107261726, "procedural_steps": 0.0025611704858404904, "worked_solution": 0.004368859011007511, "contextual_dialogue": 7.271011885582723e-06, "content_quality": 0.39506250122617814, "noise_severity": 0.9387376486879749, "missing_content_severity": 0.9828943555747399, "factual_density": 0.4960810190890397, "pretraining_utility": 0.45109688400773984, "math_density": 0.0, "code_density": 0.8632211977005317, "reasoning_density": 0.5361391340724627, "education_level": 0.6331503214524882, "creative_writing_quality": 0.9985147616930983, "primary_topic": 0.9578870025013143, "secondary_topic": 0.7646862095334102}, "probability_margin": {"primary": 0.9942817235392286, "secondary": 0.9666771088221803, "tertiary": 0.9905591823944171, "education_level": 0.4106705206372052, "creative_writing_quality": 0.9981517033586462, "primary_topic": 0.9221531598017981, "secondary_topic": 0.6124447075159271}, "raw_probabilities": {"primary": {"Academic Writing": 5.1846379033968855e-05, "Content Listing": 2.1552694524212385e-05, "Creative Writing": 2.0021937552395484e-05, "Customer Support Page": 9.220716139922582e-06, "Q&A Forum": 1.193425726895293e-05, "Discussion Thread": 8.691623176036008e-06, "Comment Section": 1.3789192714297814e-05, "Missing Content": 4.4125310491978166e-05, "Knowledge Article": 0.00019730428237824888, "Legal Notices": 4.0476507120399334e-05, "News Article": 6.0579188873780575e-06, "Organizational Announcement": 1.1255725964737879e-05, "Nonfiction Writing": 1.5349295217374965e-05, "Organizational About Page": 1.3740674398266382e-05, "Personal About Page": 1.5304574830452346e-05, "Personal Blog": 8.53103835668718e-06, "Product Page": 9.387772176642153e-06, "Spam / Ads": 9.456660937641645e-06, "Structured Data": 0.001182072577989797, "Technical Writing": 0.9961889544097583, "Transcript / Interview": 1.691704896185177e-05, "Tutorial / How-To Guide": 0.000189465671110378, "User Reviews": 7.31286048061452e-06, "Other / Unknown": 0.0019072308705296779}, "secondary": {"Academic Writing": 0.0002404922792166797, "Content Listing": 9.245966865829973e-05, "Creative Writing": 0.0001061786466947677, "Customer Support Page": 6.768301097963896e-05, "Q&A Forum": 4.852125321996707e-05, "Discussion Thread": 4.3956029706440484e-05, "Comment Section": 7.838143363445353e-05, "Missing Content": 0.0002567075319858741, "Knowledge Article": 0.0015744195122885875, "Legal Notices": 0.00021803955522118349, "News Article": 7.126767165138244e-05, "Organizational Announcement": 6.024112924729704e-05, "Nonfiction Writing": 6.878608096750839e-05, "Organizational About Page": 8.655707487061491e-05, "Personal About Page": 7.236667812053579e-05, "Personal Blog": 5.1084141149698165e-05, "Product Page": 4.899192462442951e-05, "Spam / Ads": 5.734669520059093e-05, "Structured Data": 0.004315318825407495, "Technical Writing": 0.012497344290123485, "Transcript / Interview": 8.47433440090335e-05, "Tutorial / How-To Guide": 0.0006494322848295262, "User Reviews": 3.522782588884596e-05, "NONE": 0.9791744531123038}, "tertiary": {"Academic Writing": 9.636346395678983e-05, "Content Listing": 5.097060589498536e-05, "Creative Writing": 5.323040996241341e-05, "Customer Support Page": 2.7049936001849698e-05, "Q&A Forum": 1.796801381285757e-05, "Discussion Thread": 1.532614732249526e-05, "Comment Section": 4.047291637319433e-05, "Missing Content": 0.00018568415777432497, "Knowledge Article": 0.0005846766215772262, "Legal Notices": 0.00016561208189056365, "News Article": 3.5188907461913985e-05, "Organizational Announcement": 2.7407461783777267e-05, "Nonfiction Writing": 3.988349770047508e-05, "Organizational About Page": 4.962287003866242e-05, "Personal About Page": 3.068022427241339e-05, "Personal Blog": 2.374860592364172e-05, "Product Page": 2.3636230643809876e-05, "Spam / Ads": 2.7944933917201917e-05, "Structured Data": 0.001151003997204647, "Technical Writing": 0.0031840275609316664, "Transcript / Interview": 3.090127813927513e-05, "Tutorial / How-To Guide": 0.00037950147326078497, "User Reviews": 1.5888648806258167e-05, "NONE": 0.9937432099553487}, "structured_records": {"false": 0.9828088758927382, "true": 0.017191124107261726}, "procedural_steps": {"false": 0.9974388295141595, "true": 0.0025611704858404904}, "worked_solution": {"false": 0.9956311409889925, "true": 0.004368859011007511}, "contextual_dialogue": {"false": 0.9999927289881144, "true": 7.271011885582723e-06}, "content_quality": {"-2": 0.014569934192783381, "-1": 0.109376374550994, "0": 0.3219975765529539, "1": 0.4125912444079745, "2": 0.14146487029529406}, "noise_severity": {"0": 0.932044889829096, "1": 0.06439744536724602, "2": 0.0021754511695450847, "3": 0.0007623806683577729, "4": 0.0006198329657551553}, "missing_content_severity": {"0": 0.9864387622713714, "1": 0.008078828341507353, "2": 0.004318461968139705, "3": 0.0008447686434014081, "4": 0.0003191787755802764}, "factual_density": {"0": 0.0027216998034020013, "1": 0.00566908347562826, "2": 0.08580367176726761, "3": 0.4052013839181244, "4": 0.5006041610355777}, "pretraining_utility": {"-2": 0.00865233510300246, "-1": 0.07625583990673185, "0": 0.369170520125114, "1": 0.43487677092202437, "2": 0.11104453394312719}, "math_density": {"0": 0.43469679522599264, "1": 0.19663722520754093, "2": 0.12117888257667248, "3": 0.08551328348299513, "4": 0.16197381350679885}, "code_density": {"0": 0.0003387777893057184, "1": 0.0006478958307600107, "2": 0.01958451125586429, "3": 0.12166674159813046, "4": 0.8577620735259395}, "reasoning_density": {"0": 0.726546825230082, "1": 0.10307662250366509, "2": 0.09293166964110125, "3": 0.04208645317343006, "4": 0.03535842945172177}, "education_level": {"none": 0.0007147956258626711, "0": 0.0009047387695529574, "1": 0.004117682176956284, "2": 0.6942919345437402, "3": 0.28362141390653495, "4": 0.016349434977352813}, "creative_writing_quality": {"not_applicable": 0.9987623014109153, "-2": 0.00011982768795343562, "-1": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/tarasglek/jsonbench/blob/848feee5d4643b0f3c41dd3886b6a4f2f3c62e74/pipejson/pipejson.cpp", "id": "cpp-0-83bf9ecdfb53d5226d60a1e891e4e8f3", "text": "/*\nA line-by-line multiplexer. This takes input on stdin, and feeds it to stdin of a user-requested number of processes\nUnfortunately, it's memory-bound atm due to number of copies imposed by pipe\n\n */\n#include <vector>\n#include <unistd.h>\n#include <fcntl.h>\n#include <utility>\n#include <stdio.h>\n#include <string.h>\n#include <sys/types.h>\n#include <stdlib.h>\n#include <sys/wait.h>\n#include <errno.h>\n\nusing namespace std;\n// todo look at rust flatpipes\n\nstatic long wrotesum = 0;\nstatic long readsum = 0;\n\nstruct FDBuffer {\n  enum Status {\n    READY,\n    BLOCKED,\n    INPUT_EOF\n  };\n\n  int fd;\n  char *line_buffer;\n  size_t line_buffer_size;\n  ssize_t fwd_size;\n  char *outbuf;\n  size_t fwdsofar;\n  pid_t pid;\n  FDBuffer(int fd = -1, pid_t pid = 0):\n    fd(fd), line_buffer(NULL), line_buffer_size(0), fwd_size(0), outbuf(NULL), fwdsofar(0), pid(pid)\n  {\n  }\n  \n  void malloc(size_t size) {\n    line_buffer = (char*)::malloc(size);\n    line_buffer_size = size;\n  }\n\n  Status write_outstanding() {\n    if (!outbuf)\n      return READY;\n    const int todo_bytes = fwd_size - fwdsofar;\n    int wrote = write(fd, outbuf, todo_bytes);\n\n    if (wrote != -1) {\n      wrotesum += wrote;\n      fwdsofar += wrote;\n    }\n    if (wrote == todo_bytes) {\n      outbuf = NULL;\n      fwdsofar = fwd_size = 0;\n      return READY;\n    } \n    if (wrote == -1) {\n      if (errno == EAGAIN) {\n        //can't write anything else to socket\n      } else {// IO ERROR\n        perror(\"write\");\n        _exit(1);\n      }\n\n    } else if (wrote < todo_bytes) {\n      outbuf += wrote;\n    } else if (wrote > todo_bytes) {\n      fprintf(stderr, \"Impossible write()ed more than asked for\\n\");\n      _exit(1);\n    }\n    return BLOCKED;\n  }\n  // move data from line_buffer into fd\n  // return -1 when EOF\n  // return 0 when fd wont accept any more bytes\n  // return 1 when ready to write more\n  Status fwd(FILE *input) {\n    if (write_outstanding() == BLOCKED)\n      return BLOCKED;\n    \n    fwd_size = getline(&line_buffer, &line_buffer_size, input);\n    // input EOF\n    if (fwd_size == -1)\n      return INPUT_EOF;\n\n    readsum += fwd_size;\n    outbuf = line_buffer;\n    fwdsofar = 0;\n    return write_outstanding();\n  }\n\n  // not using a destructor cos copy constructors make a mess of fd-management\n  void close() {\n    ::close(fd);\n    free(line_buffer);\n    fd = -1;\n  }\n};\ntypedef vector<FDBuffer> fd_vector;\n\nvoid\nselect_wait(fd_vector &sockets)\n{\n  fd_set wrds;\n  struct timeval tv;\n  int retval;\n\n  /* Watch stdin (fd 0) to see when it has input. */\n  FD_ZERO(&wrds);\n  int maxfd = 0;\n  for(fd_vector::iterator it = sockets.begin();it != sockets.end();it++) {\n    if (it->fd == -1)\n      continue;\n    FD_SET(it->fd, &wrds);\n    maxfd = max(it->fd, maxfd);\n  }\n  maxfd++;\n\n  /* Wait up to five seconds. */\n  tv.tv_sec = 5;\n  tv.tv_usec = 0;\n\n  retval = select(maxfd, NULL, &wrds, NULL, &tv);\n  /* Don't rely on the value of tv now! */\n\n  if (retval == -1)\n    perror(\"select()\");\n  else if (retval) {\n    //printf(\"time to write\\n\");\n  }\n}\n\nint main(int argc, char **argv) {\n  fd_vector workers;\n  fd_vector::iterator pipe_iterator;\n  if (argc < 3) {\n    fprintf(stderr, \"Usage; %s <process number> <cmd + args>\", argv[0]);\n    _exit(1);\n  }\n  int worker_count = atoi(argv[1]);\n  for(int worker = 0;worker < worker_count;worker++) {\n    // multiple of 2\n    int pipes[2] = {0,0};\n    if (pipe(pipes)) {\n      perror(\"pipe\");\n      _exit(1);\n    }\n\n    int pid = fork();\n    if (pid == -1) {\n      perror(\"fork\");\n      _exit(1);\n    }\n\n\n    if (pid == 0) {    /* Child reads from pipe */\n      close(pipes[1]);          /* Close unused write end */\n#define STDIN 0\n      if (dup2(pipes[0], STDIN) == -1) {\n        perror(\"dup2\");\n        _exit(1);\n      }\n      close(pipes[0]);\n      execv(argv[2], argv+2);\n      fprintf(stderr, \"Failed to launch subprocess: %s\\n\", argv[2]);\n      _exit(1);\n      // process is replaced with child\n    }\n    // parent\n    close(pipes[0]);          /* Close unused read end */    \n\n    int outfd = pipes[1];\n    int flags = fcntl(outfd, F_GETFL, 0);\n    if (fcntl(outfd, F_SETFL, flags | O_NONBLOCK) == -1) \n      perror(\"fcntl\");\n    // max buffer size on linux\n    if (fcntl(outfd, F_SETPIPE_SZ, 1048576) == -1) \n      perror(\"fcntl\");\n    // max size if one bumps /proc/sys/fs/pipe-max-size\n    fcntl(outfd, F_SETPIPE_SZ, 16777216);\n\n    FDBuffer child(outfd, pid);\n    workers.push_back(child);\n  }\n\n  pipe_iterator = workers.begin();\n  bool pipes_full = false;\n  while(true) {\n    const int pipe_status = pipe_iterator->fwd(stdin);\n    if (pipe_status == FDBuffer::INPUT_EOF)\n      break;\n    else if (pipe_status == FDBuffer::BLOCKED) {\n      pipe_iterator++;\n      if (pipe_iterator == workers.end()) {\n        pipe_iterator = workers.begin();\n        // require one full everything-is-blocked iteration before giving up and trying to select\n        if (pipes_full) {\n          select_wait(workers);\n        }\n        pipes_full = true;\n      }\n    } else if (pipe_status == FDBuffer::READY) {\n      pipes_full = false;\n      continue;\n    }\n  }\n  for(pipe_iterator = workers.begin();pipe_iterator != workers.end(); pipe_iterator++) {\n    while(pipe_iterator->write_outstanding() == FDBuffer::BLOCKED)\n      select_wait(workers);\n    pipe_iterator->close();\n  } \n\n\n  while (true) {\n    pid_t done = wait(NULL);\n    if (done == -1) {\n      if (errno == ECHILD) break; // no more child processes\n    }\n  }\n  fprintf(stderr, \"read:%ld wrote:%ld diff:%d\\n\", readsum, wrotesum, (int)(readsum - wrotesum));\n}\n", "text_preview": "/* A line-by-line multiplexer. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/samyakpiya/Modeling-the-Gearball/blob/0cc4cc01f02f2eda523ca6e1ebeb4c346acee2fd/astar.cpp", "id": "cpp-0-056707b21eb362e49e9f7a5c711acfc9", "text": "#ifndef ASTAR\n#define ASTAR\n\n#include \"gearball.cpp\"\n#include \"node.cpp\"\n#include <queue> // for min priority_queue\n#include <vector>\n#include <stack>\n#include <algorithm> // needed erase(), count()\n\n// We used Admissible Heurisitc 2\n// Admissible Heuristic 1 (intial): The number of pieces out of position divided by 36. 36 is the number\n// of pieces that go out of position when one rotation is performed on a solved gearball.\n// AH1 is not good in our case because of how our code prints the ball. If all the faces were solved but\n// the front face changed from red to blue in the final solved state, every single piece would be out of position.\n\n// Admissible Heuristic 2 (final): The number of pieces that do not match the color of the center piece\n// divided by 32 and then multiplied by 10 (i.e. the number of pieces that do not match the center piece\n// color when a rotation is performed)\n\nclass AStar\n{\n\npublic:\n    // Evaluates the h-value of a gearball state and returns it\n    // uses admissible heuristic 2\n    float heuristic(Gearball ball, int movePerformed)\n    {\n        Side *currentBall = ball.getSides();\n\n        float piecesOutOfPos = 0;\n        string centerPieceColor;\n        string currPieceColor;\n\n        // iterate through each face of the gearball\n        for (int sides = 0; sides < SIDES; sides++)\n        {\n            centerPieceColor = currentBall[sides].getPiece(2, 2).getColor();\n            for (int i = 0; i < ROWS; i++)\n            {\n                for (int j = 0; j < COLUMNS; j++)\n                {\n\n                    currPieceColor = currentBall[sides].getPiece(i, j).getColor();\n                    if (currPieceColor == \" \")\n                    {\n                        continue;\n                    }\n                    else\n                    {\n                        if (currPieceColor != centerPieceColor)\n                        {\n                            piecesOutOfPos++;\n                        }\n                    }\n                }\n            }\n        }\n\n        float hValue = (piecesOutOfPos / 32) * 10;\n\n        // For testing:\n        // cout << \"The pieces that are out of position are: \" << piecesOutOfPos << endl;\n        // cout << \"The h Values for turning the Gearball \" << intToMovePerformed(movePerformed) << \" is: \" << hValue << endl;\n\n        return hValue;\n    }\n\n    string intToMovePerformed(int movePerformed)\n    {\n        switch (movePerformed)\n        {\n        case ROTATE_TCW:\n            return \"Top Clockwise\";\n        case ROTATE_BCW:\n            return \"Bottom Clockwise\";\n        case ROTATE_LCW:\n            return \"Left Clockwise\";\n        case ROTATE_RCW:\n            return \"Right Clockwise\";\n        case ROTATE_TCCW:\n            return \"Top Counter Clockwise\";\n        case ROTATE_BCCW:\n            return \"Bottom Counter Clockwise\";\n        case ROTATE_LCCW:\n            return \"Left Counter Clockwise\";\n        case ROTATE_RCCW:\n            return \"Right Counter Clockwise\";\n        default:\n            return \"Wrong rotation int entered!\";\n        }\n    }\n\n    // Trace back to the parent node and print the sequence of moves that solved the gearball\n    void retracePath(Node &child, Gearball &ball, int nodesExpanded)\n    {\n        cout << \"The gearball has been solved! The number of nodes expanded to solve the gearball is: \" << nodesExpanded << \".\" << endl\n             << endl;\n\n        cout << \"Gearball state before solving: \" << endl;\n        ball.printBall();\n\n        cout << \"Steps taken to solve the gearball are as follows: \" << endl\n             << endl;\n\n        stack<int> movesPerformed;\n        // Trace back to the parent node while storing the moves performed on the stack\n        while (child.parent != NULL)\n        {\n            movesPerformed.push(child.movePerformed);\n            child = *child.parent;\n        }\n\n        // Print the sequence while popping the stack\n        int movePerformed;\n        int rotationsPerformed = 1;\n        while (!movesPerformed.empty())\n        {\n            movePerformed = movesPerformed.top();\n            cout << \"S\" << rotationsPerformed << \".\" << intToMovePerformed(movePerformed) << endl;\n            ball.rotate(movePerformed);\n            ball.printBall();\n            movesPerformed.pop();\n            rotationsPerformed++;\n        }\n\n        return;\n    }\n\n    // AStar algorithm to pathfind a sequence of moves that solves the current gearball\n    // returns the number of nodes expanded when solving the ball\n    int solve(Gearball &ball, bool print = true)\n    {\n        Node startNode = Node(ball);\n\n        // the set of nodes to be evaluated\n        vector<Node> openSet;\n\n        // the set of nodes already evaluated\n        vector<Node> closedSet;\n        int closedSetIndex = 0;\n\n        // Each node has 4 children at max\n        Node children[4];\n\n        // add the start node to OPEN\n        openSet.push_back(startNode);\n\n        // loop\n        while (!openSet.empty())\n        {\n            // node = node in OPEN with the lowest f_cost\n            Node node = openSet.at(0);\n            for (int i = 1; i < openSet.size(); i++)\n            {\n                if (openSet[i].getFCost() < node.getFCost() || (openSet[i].getFCost() == node.getFCost() && openSet[i].hCost < node.hCost))\n                {\n                    node = openSet[i];\n                }\n            }\n\n            // remove current node from openSet\n            openSet.erase(remove(openSet.begin(), openSet.end(), node));\n\n            // add current node to closedSet\n            closedSet.push_back(node);\n\n            // if current is the target node //path has been found\n            //    return\n            if (node.ball.isSolved())\n            {\n\n                // Trace back to the parent node and print the moves performed to solve the gearball\n                if (print)\n                    retracePath(node, ball, closedSet.size());\n\n                return closedSet.size();\n            }\n\n            // make copies of the current node to create children\n            Node nodeTCCW = node;\n            Node nodeBCCW = node;\n            Node nodeLCCW = node;\n            Node nodeRCCW = node;\n\n            // Each gearball state has 4 neighbor states (reached by top, bottom, left, or right clockwise rotations)\n            children[0] = (Node(ROTATE_TCCW, nodeTCCW.ball.rotate(ROTATE_TCCW)));\n            children[1] = (Node(ROTATE_BCCW, nodeBCCW.ball.rotate(ROTATE_BCCW)));\n            children[2] = (Node(ROTATE_LCCW, nodeLCCW.ball.rotate(ROTATE_LCCW)));\n            children[3] = (Node(ROTATE_RCCW, nodeRCCW.ball.rotate(ROTATE_RCCW)));\n\n            // foreach child of the current node\n            for (Node child : children)\n            {\n\n                // if child is not traversable or child is in CLOSED\n                if ((count(closedSet.begin(), closedSet.end(), child)))\n                {\n                    // skip to the next child\n                    continue;\n                }\n\n                float newCostTochild = node.gCost + 10;\n\n                // if new path to child is shorter OR child is not in OPEN\n                if (newCostTochild < child.gCost || !(count(openSet.begin(), openSet.end(), child)))\n                {\n                    // set f_cost of child\n                    child.gCost = newCostTochild;\n                    child.hCost = heuristic(child.ball, child.movePerformed);\n\n                    // set parent of child to current\n                    child.parent = &closedSet[closedSetIndex];\n\n                    // if child is not in openSet\n                    if (!(count(openSet.begin(), openSet.end(), child)))\n                    {\n                        // add child to openSet\n                        openSet.push_back(child);\n                    }\n                }\n            }\n            closedSetIndex++;\n        }\n        return closedSet.size();\n    }\n\n    // constructor\n    AStar()\n    {\n    }\n};\n\n#endif", "text_preview": "#ifndef ASTAR #define ASTAR #include \"gearball.cpp\" #include \"node.cpp\" #include <queue> // for min priority_queue #include <vector> #include 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/waynejo/android-ndk-gif/blob/df5ce8f8058f81f4b5418f023f0874ff02b79bc8/library/androidndkgif/jni/BaseGifEncoder.cpp", "id": "cpp-0-b9fc5f804938c75909aaecf12c34ba8e", "text": "/*\n * Copyright (c) 2015 waynejo\n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy of\n * this software and associated documentation files (the \"Software\"), to deal in\n * the Software without restriction, including without limitation the rights to\n * use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of\n * the Software, and to permit persons to whom the Software is furnished to do so,\n * subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in all\n * copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS\n * FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR\n * COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER\n * IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n * CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n */\n\n#include <stdio.h>\n#include <stdint.h>\n#include <string.h>\n#include <vector>\n#include \"BaseGifEncoder.h\"\n\nusing namespace std;\n\nBaseGifEncoder::BaseGifEncoder() {\n\twidth = 1;\n\theight = 1;\n\tframeNum = 0;\n\tlastColorReducedPixels = NULL;\n\tlastRootColor = 0;\n\tuseDither = true;\n}\n\nvoid BaseGifEncoder::qsortColorHistogram(uint32_t* imageColorHistogram, int32_t maxColor, uint32_t from, uint32_t to)\n{\n\tif (to == from) {\n\t\treturn ;\n\t}\n\tuint32_t middle = from + ((to - from) >> 1);\n\tuint32_t shift = maxColor << 3;\n\tuint32_t pivot = ((imageColorHistogram[middle]) >> shift) & 0xFF;\n\tuint32_t i = from;\n\tuint32_t k = to;\n\twhile (i <= k) {\n\t\twhile (((imageColorHistogram[i] >> shift) & 0xFF) < pivot && i <= k) {\n\t\t\t++i;\n\t\t}\n\t\twhile (((imageColorHistogram[k] >> shift) & 0xFF) > pivot && i <= k && 1 < k) {\n\t\t\t--k;\n\t\t}\n\t\tif (i <= k) {\n\t\t\tuint32_t temp = imageColorHistogram[k];\n\t\t\timageColorHistogram[k] = imageColorHistogram[i];\n\t\t\timageColorHistogram[i] = temp;\n\t\t\t++i;\n\t\t\t--k;\n\t\t}\n\t}\n\tif (from < k && -1 != k) {\n\t\tqsortColorHistogram(imageColorHistogram, maxColor, from, k);\n\t}\n\tif (i < to) {\n\t\tqsortColorHistogram(imageColorHistogram, maxColor, i, to);\n\t}\n}\n\nvoid BaseGifEncoder::updateColorHistogram(Cube* nextCube, Cube* maxCube, int32_t maxColor, uint32_t* imageColorHistogram)\n{\n\tqsortColorHistogram(imageColorHistogram, maxColor, maxCube->colorHistogramFromIndex, maxCube->colorHistogramToIndex);\n\tuint32_t median = maxCube->colorHistogramFromIndex + ((maxCube->colorHistogramToIndex - maxCube->colorHistogramFromIndex) >> 1);\n\tnextCube->colorHistogramFromIndex = maxCube->colorHistogramFromIndex;\n\tnextCube->colorHistogramToIndex = median;\n\n\tif (GET_COLOR(imageColorHistogram[nextCube->colorHistogramFromIndex], maxColor) !=\n\t\tGET_COLOR(imageColorHistogram[maxCube->colorHistogramToIndex], maxColor)) {\n\t\t\tif (GET_COLOR(imageColorHistogram[nextCube->colorHistogramFromIndex], maxColor) != GET_COLOR(imageColorHistogram[nextCube->colorHistogramToIndex], maxColor)) {\n\t\t\t\tif (GET_COLOR(imageColorHistogram[median], maxColor) == GET_COLOR(imageColorHistogram[median + 1], maxColor)) {\n\t\t\t\t\twhile (GET_COLOR(imageColorHistogram[nextCube->colorHistogramToIndex], maxColor) == GET_COLOR(imageColorHistogram[median], maxColor)) {\n\t\t\t\t\t\t--median;\n\t\t\t\t\t}\n\t\t\t\t\tnextCube->colorHistogramToIndex = median;\n\t\t\t\t}\n\t\t\t} else {\n\t\t\t\twhile (GET_COLOR(imageColorHistogram[nextCube->colorHistogramToIndex], maxColor) == GET_COLOR(imageColorHistogram[median], maxColor)) {\n\t\t\t\t\t++median;\n\t\t\t\t}\n\t\t\t\tnextCube->colorHistogramToIndex = median;\n\t\t\t}\n\t}\n\tmaxCube->colorHistogramFromIndex = maxCube->colorHistogramToIndex > median + 1 ? median + 1 : maxCube->colorHistogramToIndex;\n\tnextCube->cMin[maxColor] = GET_COLOR(imageColorHistogram[nextCube->colorHistogramFromIndex], maxColor);\n\tnextCube->cMax[maxColor] = GET_COLOR(imageColorHistogram[nextCube->colorHistogramToIndex], maxColor);\n\tmaxCube->cMin[maxColor] = GET_COLOR(imageColorHistogram[maxCube->colorHistogramFromIndex], maxColor);\n\tmaxCube->cMax[maxColor] = GET_COLOR(imageColorHistogram[maxCube->colorHistogramToIndex], maxColor);\n}\n\nvoid BaseGifEncoder::computeColorTable(uint32_t* pixels, Cube* cubes, uint32_t pixelNum)\n{\n\tuint32_t colors[COLOR_MAX][Cube::COLOR_RANGE] = {0, };\n\tuint32_t* pixelBegin = pixels;\n\n\tvector<uint32_t> colorHistogramMemory;\n\tif (0 != frameNum && NULL != lastColorReducedPixels) {\n\t\tcolorHistogramMemory.resize(pixelNum * 2 * sizeof(uint32_t));\n\t\tmemcpy(&colorHistogramMemory[0], pixels, pixelNum * sizeof(uint32_t));\n\t\tmemcpy(&colorHistogramMemory[pixelNum], lastColorReducedPixels, pixelNum * sizeof(uint32_t));\n\t\tpixelNum *= 2;\n\t} else {\n\t\tcolorHistogramMemory.resize(pixelNum * sizeof(uint32_t));\n\t\tmemcpy(&colorHistogramMemory[0], pixels, pixelNum * sizeof(uint32_t));\n\t}\n\tuint32_t *colorHistogram = &colorHistogramMemory[0];\n\tpixels = colorHistogram;\n\tuint32_t* last = colorHistogram + pixelNum;\n\n\twhile (last != pixels) {\n\t\tuint8_t r = (*pixels) & 0xFF;\n\t\tuint8_t g = ((*pixels) >> 8) & 0xFF;\n\t\tuint8_t b = ((*pixels) >> 16) & 0xFF;\n\t\t++colors[RED][r];\n\t\t++colors[GREEN][g];\n\t\t++colors[BLUE][b];\n\t\t++pixels;\n\t}\n\n\tuint32_t cubeIndex = 0;\n\tCube* cube = &cubes[cubeIndex];\n\tfor (uint32_t i = 0; i < COLOR_MAX; ++i) {\n\t\tcube->cMin[i] = 255;\n\t\tcube->cMax[i] = 0;\n\t}\n\tfor (uint32_t i = 0; i < 256; ++i) {\n\t\tfor (uint32_t color = 0; color < COLOR_MAX; ++color) {\n\t\t\tif (0 != colors[color][i]) {\n\t\t\t\tcube->cMax[color] = cube->cMax[color] < i ? i : cube->cMax[color];\n\t\t\t\tcube->cMin[color] = cube->cMin[color] > i ? i : cube->cMin[color];\n\t\t\t}\n\t\t}\n\t}\n\tcube->colorHistogramFromIndex = 0;\n\tcube->colorHistogramToIndex = pixelNum - 1;\n\tuint32_t comparingColorList[COLOR_MAX] = {GREEN, RED, BLUE};\n\tfor (cubeIndex = 1; cubeIndex < 255; ++cubeIndex) {\n\t\tuint32_t maxDiff = 0;\n\t\tuint32_t maxColor = GREEN;\n\t\tCube* maxCube = cubes;\n\t\tfor (uint32_t i = 0; i < cubeIndex; ++i) {\n\t\t\tCube* cube = &cubes[i];\n\t\t\tfor (uint32_t colorIdx = 0; colorIdx < COLOR_MAX; ++colorIdx) {\n\t\t\t\tuint32_t comparingColor = comparingColorList[colorIdx];\n\t\t\t\tuint32_t comparingDiff = cube->cMax[comparingColor] - cube->cMin[comparingColor];\n\t\t\t\tif (comparingColor == lastRootColor) {\n\t\t\t\t\tcomparingDiff = comparingDiff * 11 / 10; // multiply 110% to reduce color blinking from difference of root color.\n\t\t\t\t}\n\n\t\t\t\tif (comparingDiff > maxDiff) {\n\t\t\t\t\tmaxDiff = comparingDiff;\n\t\t\t\t\tmaxColor = comparingColor;\n\t\t\t\t\tmaxCube = cube;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t\tif (1 == cubeIndex) {\n\t\t\tlastRootColor = maxColor;\n\t\t}\n\t\tif (1 >= maxDiff) {\n\t\t\tbreak;\n\t\t}\n\t\tCube* nextCube = &cubes[cubeIndex];\n\t\tfor (int32_t color = 0; color < COLOR_MAX; ++color) {\n\t\t\tif (color == maxColor) {\n\t\t\t\tupdateColorHistogram(nextCube, maxCube, maxColor, colorHistogram);\n\t\t\t} else {\n\t\t\t\tnextCube->cMax[color] = maxCube->cMax[color];\n\t\t\t\tnextCube->cMin[color] = maxCube->cMin[color];\n\t\t\t}\n\t\t}\n\t}\n\tfor (uint32_t i = 0; i < 255; ++i) {\n\t\tCube* cube = &cubes[i];\n\t\tfor (int32_t color = 0; color < COLOR_MAX; ++color) {\n\t\t\tqsortColorHistogram(colorHistogram, color, cube->colorHistogramFromIndex, cube->colorHistogramToIndex);\n\t\t\tuint32_t median = cube->colorHistogramFromIndex + ((cube->colorHistogramToIndex - cube->colorHistogramFromIndex) >> 1);\n\t\t\tif (median < pixelNum) {\n\t\t\t\tcube->color[color] = GET_COLOR(colorHistogram[median], color);\n\t\t\t}\n\t\t}\n\t}\n}\n\nvoid BaseGifEncoder::reduceColor(Cube* cubes, uint32_t cubeNum, uint32_t* pixels)\n{\n\tconst int32_t ERROR_PROPAGATION_DIRECTION_NUM = 4;\n\tconst int32_t ERROR_PROPAGATION_DIRECTION_X[] = {1, -1, 0, 1};\n\tconst int32_t ERROR_PROPAGATION_DIRECTION_Y[] = {0, 1, 1, 1};\n\tconst int32_t ERROR_PROPAGATION_DIRECTION_WEIGHT[] = {7, 3, 5, 1};\n\n\tuint32_t pixelNum = width * height;\n\tuint32_t* last = pixels + pixelNum;\n\tuint8_t* pixelOut = (uint8_t*)pixels;\n\tuint32_t* colorReducedPixelOut = lastColorReducedPixels;\n\tfor (uint32_t y = 0; y < height; ++y) {\n\t\tfor (uint32_t x = 0; x < width; ++x) {\n\t\t\tif (0 == (*pixels >> 24)) {\n\t\t\t\t*pixelOut = 255; //l transparent color\n\t\t\t\t*colorReducedPixelOut = 0;\n\t\t\t} else {\n\t\t\t\tCube* cube = cubes;\n\t\t\t\tuint32_t r = (*pixels) & 0xFF;\n\t\t\t\tuint32_t g = ((*pixels) >> 8) & 0xFF;\n\t\t\t\tuint32_t b = ((*pixels) >> 16) & 0xFF;\n\n\t\t\t\tCube* closestColorCube = cube;\n\t\t\t\tint32_t diffR = cube->color[RED] - r;\n\t\t\t\tint32_t diffG = cube->color[GREEN] - g;\n\t\t\t\tint32_t diffB = cube->color[BLUE] - b;\n\t\t\t\tuint32_t closestDifference = diffR * diffR + diffG * diffG + diffB * diffB;\n\t\t\t\tCube* lastCube = cube + cubeNum;\n\n\t\t\t\tfor (Cube* testCube = cube; testCube != lastCube; ++testCube) {\n\t\t\t\t\tdiffR = testCube->color[RED] - r;\n\t\t\t\t\tdiffG = testCube->color[GREEN] - g;\n\t\t\t\t\tdiffB = testCube->color[BLUE] - b;\n\t\t\t\t\tuint32_t difference = diffR * diffR + diffG * diffG + diffB * diffB;\n\n\t\t\t\t\tif (difference < closestDifference) {\n\t\t\t\t\t\tclosestDifference = difference;\n\t\t\t\t\t\tclosestColorCube = testCube;\n\t\t\t\t\t}\n\t\t\t\t}\n\n\t\t\t\tuint32_t closestColor = closestColorCube - cube;\n\t\t\t\tcube = &cubes[closestColor];\n\t\t\t\t*pixelOut = closestColor;\n\t\t\t\t*colorReducedPixelOut = (0xFF000000 | (cube->color[BLUE] << 16) | (cube->color[GREEN] << 8) | cube->color[RED]);\n\t\t\t\tif (useDither) {\n\t\t\t\t\tdiffR = r - (uint32_t)cube->color[RED];\n\t\t\t\t\tdiffG = g - (uint32_t)cube->color[GREEN];\n\t\t\t\t\tdiffB = b - (uint32_t)cube->color[BLUE];\n\t\t\t\t\tfor (int directionId = 0; directionId < ERROR_PROPAGATION_DIRECTION_NUM; ++directionId) {\n\t\t\t\t\t\tuint32_t* pixel = pixels + ERROR_PROPAGATION_DIRECTION_X[directionId] + ERROR_PROPAGATION_DIRECTION_Y[directionId] * width;\n\t\t\t\t\t\tif (x + ERROR_PROPAGATION_DIRECTION_X[directionId] >= width ||\n\t\t\t\t\t\t\ty + ERROR_PROPAGATION_DIRECTION_Y[directionId] >= height || 0 == (*pixels >> 24)) {\n\t\t\t\t\t\t\t\tcontinue;\n\t\t\t\t\t\t}\n\t\t\t\t\t\tint32_t weight = ERROR_PROPAGATION_DIRECTION_WEIGHT[directionId];\n\t\t\t\t\t\tint32_t dstR = ((int32_t)((*pixel) & 0xFF) + (diffR * weight + 8) / 16);\n\t\t\t\t\t\tint32_t dstG = (((int32_t)((*pixel) >> 8) & 0xFF) + (diffG * weight + 8) / 16);\n\t\t\t\t\t\tint32_t dstB = (((int32_t)((*pixel) >> 16) & 0xFF) + (diffB * weight + 8) / 16);\n\t\t\t\t\t\tint32_t dstA = (int32_t)(*pixel >> 24);\n\t\t\t\t\t\tint32_t newR = MIN(255, MAX(0, dstR));\n\t\t\t\t\t\tint32_t newG = MIN(255, MAX(0, dstG));\n\t\t\t\t\t\tint32_t newB = MIN(255, MAX(0, dstB));\n\t\t\t\t\t\t*pixel = (dstA << 24) | (newB << 16) | (newG << 8) | newR;\n\t\t\t\t\t}\n\t\t\t\t}\n\t\t\t}\n\t\t\t++pixels;\n\t\t\t++pixelOut;\n\t\t\t++colorReducedPixelOut;\n\t\t}\n\t}\n}\n", "text_preview": "/* * Copyright (c) 2015 waynejo * * Permission is hereby granted, free of charge, to any person obtaining a copy of * this software and associated documentation files (the \"Software\"), to deal in * the Software without restriction, includin", "input_sha256": "27aebb544913dd1d7a94bcc756f046bf0928c7dafdeb1bb0b72ca472a494e4c3", "text_sha256": "b9fc5f804938c75909aaecf12c34ba8ea8d25a47e66425f9b453e9690eea85d7", "run_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/jaidurn/Design-Tests-take-2/blob/f2a8d29faf1be99937d19ad1b3c807fe2b031d82/Design%20Tests/Line.cpp", "id": "cpp-0-cc5eae35ed63529d41a0db79e424bb56", "text": "#include \"Line.h\"\n#include <iostream>\n\n// Checks if a line has an undefined slope.\nbool isLineUndefined(const Line line)\n{\n\treturn (line.start.getX() == line.end.getX());\n}\n\n// Calculates the slope of a line.\nfloat getSlope(const Line line)\n{\n\tfloat slope = 0;\n\n\tif (!isLineUndefined(line))\n\t{\n\t\tVector2D distance = line.end - line.start;\n\n\t\tslope = distance.getY() / distance.getX();\n\t}\n\n\treturn slope;\n}\n\n// Gets the point at which two lines intersect\nVector2D intersectionPoint(const Line a, const Line b)\n{\n\tVector2D point{ 0, 0 };\n\n\t// The if both lines aren't undefined\n\tif (!isLineUndefined(a) && !isLineUndefined(b))\n\t{\n\t\tfloat slopeA = getSlope(a);\n\t\tfloat slopeB = getSlope(b);\n\t\tfloat yInterceptA = a.start.getY() - (a.start.getX() * slopeA);\n\t\tfloat yInterceptB = b.start.getY() - (b.start.getX() * slopeB);\n\n\t\tfloat tempPointX = ((slopeA * a.start.getX()) - (slopeB * b.start.getX()) + b.start.getY() - a.start.getY()) / (slopeA - slopeB);\n\t\tfloat tempPointY = (slopeA * (tempPointX - a.start.getX())) + a.start.getY();\n\n\t\ttempPointX = round(tempPointX);\n\t\ttempPointY = round(tempPointY);\n\n\t\tpoint.setX(tempPointX);\n\t\tpoint.setY(tempPointY);\n\t}\n\telse\n\t{\n\t\tif (isLineUndefined(a) && !isLineUndefined(b))\n\t\t{\n\t\t\tfloat slopeB = getSlope(b);\n\t\t\tfloat intersectB = b.start.getY() - (slopeB * b.start.getX());\n\n\t\t\tfloat tempPointX = a.start.getX();\n\t\t\tfloat tempPointY = (slopeB * a.start.getX()) + intersectB;\n\n\t\t\tpoint.setX(tempPointX);\n\t\t\tpoint.setY(tempPointY);\n\t\t}\n\t\telse if (!isLineUndefined(a) && isLineUndefined(b))\n\t\t{\n\t\t\tfloat slopeA = getSlope(a);\n\n\t\t\tfloat intersectA = a.start.getY() - (slopeA * a.start.getX());\n\n\t\t\tfloat tempPointX = b.start.getX();\n\t\t\tfloat tempPointY = (slopeA * b.start.getX()) + intersectA;\n\n\t\t\tpoint.setX(tempPointX);\n\t\t\tpoint.setY(tempPointY);\n\t\t}\n\t}\n\n\treturn point;\n}\n\n// Checks to see if two line segments intersect\nbool linesIntersect(const Line a, const Line b)\n{\n\tbool intersection = false;\n\n\tif (!isLineUndefined(a) && !isLineUndefined(b))\n\t{\n\t\tfloat slopeA = getSlope(a);\n\t\tfloat slopeB = getSlope(b);\n\n\t\t// Find the Y intersepts of each line\n\t\tfloat interseptA = a.start.getY() - (slopeA * a.start.getX());\n\t\tfloat interseptB = b.start.getY() - (slopeB * b.start.getX());\n\n\t\t// If we have the same slope and the same intercept\n\t\tif ((slopeA == slopeB) && (interseptA == interseptB))\n\t\t{\n\t\t\t// Check if the lines are actually touching\n\t\t\tif (pointOnLine(a, b.start) || pointOnLine(a, b.end))\n\t\t\t{\n\t\t\t\tintersection = true;\n\t\t\t}\n\t\t}\n\t\t// Make sure we're not checking a parallel line for intersecting points\n\t\telse if (slopeA != slopeB)\n\t\t{\n\t\t\t// Get the intersection point of the line\n\t\t\tVector2D intersect = intersectionPoint(a, b);\n\n\t\t\t// Make sure the point is on the line segment\n\t\t\tif (pointOnLine(a, intersect) && pointOnLine(b, intersect))\n\t\t\t{\n\t\t\t\tintersection = true;\n\t\t\t}\n\t\t}\n\n\t}\n\telse if((!isLineUndefined(a) && isLineUndefined(b)) ||\n\t\t\t(isLineUndefined(a) && !isLineUndefined(b)))\n\t{\n\t\tif (isLineUndefined(a))\n\t\t{\n\t\t\t// Get the intersection point of the line\n\t\t\tVector2D intersect = intersectionPoint(a, b);\n\n\t\t\tif (pointOnLine(a, intersect) && pointOnLine(b, intersect))\n\t\t\t{\n\t\t\t\tintersection = true;\n\t\t\t}\n\n\t\t}\n\t\telse if (isLineUndefined(b))\n\t\t{\n\t\t\t// Get the intersection point of the line\n\t\t\tVector2D intersect = intersectionPoint(a, b);\n\n\t\t\tif (pointOnLine(a, intersect) && pointOnLine(b, intersect))\n\t\t\t{\n\t\t\t\tintersection = true;\n\t\t\t}\n\t\t}\n\t}\n\telse\n\t{\n\t\t// If the lines are both undefined and share the same x coordinates\n\t\tif (a.start.getX() == b.start.getX())\n\t\t{\n\t\t\t// Check if the lines are actually touching\n\t\t\tif (pointOnLine(a, b.start) || pointOnLine(a, b.end))\n\t\t\t{\n\t\t\t\tintersection = true;\n\t\t\t}\n\t\t}\n\t}\n\n\treturn intersection;\n}\n\n// Checks if a point is on a line\n//TODO: Fix this function!\nbool pointOnLine(const Line line, const Vector2D point)\n{\n\tbool onLine = false;\n\n\tif (!isLineUndefined(line))\n\t{\n\t\tif(getSlope(line) == 0)\n\t\t{\n\t\t\t// Find the point on a line with no slope\n\t\t\tfloat smallestPointX = line.start.getX();\n\t\t\tfloat largestPointX = line.end.getX();\n\n\t\t\tif(line.end.getX() < line.start.getX())\n\t\t\t{\n\t\t\t\tsmallestPointX = line.end.getX();\n\t\t\t\tlargestPointX = line.start.getX();\n\t\t\t}\n\n\t\t\tif(smallestPointX <= point.getX() && point.getX() <= largestPointX)\n\t\t\t{\n\t\t\t\tonLine = true;\n\t\t\t}\n\t\t}\n\t\telse\n\t\t{\n\t\t\tfloat distanceA = totalDistance(line.start, point);\n\t\t\tfloat distanceB = totalDistance(line.end, point);\n\n\t\t\tfloat distanceTotal = distanceA + distanceB;\n\n\t\t\tif (distanceTotal == totalDistance(line))\n\t\t\t{\n\t\t\t\tonLine = true;\n\t\t\t}\n\t\t}\n\t}\n \telse\n\t{\n\t\tif(line.start.getX() == point.getX())\n\t\t{\n\t\t\t//Find the smaller part of the line\n\t\t\tif(line.start.getY() < line.end.getY())\n\t\t\t{\n\t\t\t\tif(line.start.getY() <= point.getY() && point.getY() <= line.end.getY())\n\t\t\t\t{\n\t\t\t\t\tonLine = true;\n\t\t\t\t}\n\t\t\t}\n\t\t\telse\n\t\t\t{\n\t\t\t\tif(line.end.getY() <= point.getY() && point.getY() <= line.start.getY())\n\t\t\t\t{\n\t\t\t\t\tonLine = true;\n\t\t\t\t}\n\t\t\t}\n\t\t}\n\t}\n\n\n\treturn onLine;\n}\n\n// Returns the total distance of the line\nfloat totalDistance(const Line line)\n{\n\tfloat totalDistance = 0;\n\n\tfloat 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/luobuyu/Code-test-daily/blob/96681c0309e67186e27b83707d58cf155af6089b/ICPC/2021xunlianlianmeng/fifth/J.cpp", "id": "cpp-0-3f48e6f01fa277a52b35320746377930", "text": "#include <bits/stdc++.h>\n#define ll long long\n#define lll long long\nnamespace FAST_IO\n{\n\n    inline char nextChar()\n    {\n        static char buf[1000000], *p1 = buf, *p2 = buf;\n        return p1 == p2 && (p2 = (p1 = buf) + fread(buf, 1, 1000000, stdin), p1 == p2) ? EOF : *p1++;\n    }\n#define getch getchar\n    template <class T>\n    inline void read(T &x)\n    {\n        T flag = 1;\n        x = 0;\n        char ch = getch();\n        while (ch < '0' || ch > '9')\n        {\n            if (ch == '-')\n                flag = -1;\n            ch = getch();\n        }\n        while (ch >= '0' && ch <= '9')\n        {\n            x = (x << 3) + (x << 1) + (ch ^ 48), ch = getch();\n        }\n        x *= flag;\n    }\n\n    template <class T, class... _T>\n    inline void read(T &x, _T &...y)\n    {\n        return read(x), read(y...);\n    }\n\n    inline void print128(lll x)\n    {\n        if (x < 0)\n            putchar('-'), x = -x;\n        if (x > 9)\n            print128(x / 10);\n        putchar(x % 10 + '0');\n    }\n\n} // namespace FAST_IO\nusing namespace std;\nusing namespace FAST_IO;\nconst ll mod = 1e9 + 7;\nconst int INF = 0x3f3f3f3f;\nconst ll INF_LL = 0x3f3f3f3f3f3f3f3f;\nconst double eps = 1e-5;\nconst int maxn = 1e2 + 10;\nconst int maxm = 2e7 + 10;\nint t, n, m, k;\nchar s[maxn][maxn];\nint dx[9] = {0, 0, 0, 1, -1, -1, -1, 1, 1};\nint dy[9] = {0, 1, -1, 0, 0, -1, 1, -1, 1};\nstruct Node\n{\n    int x, y, vx, vy, k, d;\n    bool check()\n    {\n        if (x < 1 || y < 1 || x > n || y > m || s[x][y] == '#')\n            return 0;\n        if (abs(vx) > 7 || abs(vy) > 7)\n            return 0;\n        return 1;\n    }\n    int hash() { return x + y * 50 + (vx + 8) * 50 * 50 + (vy + 8) * 50 * 50 * 15 + (k + 1) * 50 * 50 * 15 * 15; }\n};\nqueue<Node> q;\nbool mp[maxm];\n\nbool bfs()\n{\n    while (!q.empty())\n    {\n        auto out = q.front();\n        q.pop();\n        for (int i = 0; i < 9; i++)\n        {\n            auto newp = out;\n            newp.vx += dx[i];\n            newp.vy += dy[i];\n            newp.x += newp.vx;\n            newp.y += newp.vy;\n            newp.d++;\n            if (!newp.check())\n                continue;\n            if (newp.k == s[newp.x][newp.y] - '0')\n                newp.k++;\n            if (newp.k == k + 2 && s[newp.x][newp.y] - '0' == k + 1 && newp.vx == 0 && newp.vy == 0)\n            {\n                printf(\"%d\\n\", newp.d);\n                return true;\n            }\n            // cout << newp.hash() << endl;\n            if (mp[newp.hash()])\n                continue;\n            q.push(newp);\n            mp[newp.hash()] = 1;\n        }\n    }\n    return false;\n}\nint main()\n{\n// #define COMP_DATA\n#ifndef ONLINE_JUDGE\n    freopen(\"in.txt\", \"r\", stdin);\n#endif\n    read(n, m, k);\n    for (int i = 1; i <= n; i++)\n    {\n        scanf(\"%s\", s[i] + 1);\n        for (int j = 1; j <= m; j++)\n        {\n            if (s[i][j] == '0')\n            {\n                q.push({i, j, 0, 0, 1, 0});\n                mp[q.front().hash()] = 1;\n            }\n        }\n    }\n    if (!bfs())\n    {\n        printf(\"impossible\\n\");\n    }\n    return 0;\n}", "text_preview": "#include <bits/stdc++.h> #define ll long long #define lll long long namespace FAST_IO { inline char nextChar() { static char buf[1000000], *p1 = buf, *p2 = buf; return p1 == p2 && (p2 = (p1 = buf) + fread(buf, 1, 1000000, stdin), p1 == p2) ", "input_sha256": "3b15678aa87260c5a934bc444fbc5b964bd5b610cc6bf6fae7d136bc165ed24f", "text_sha256": "3f48e6f01fa277a52b35320746377930c6ad9a1d266ebd1f12eeaf990ea90ec8", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 105, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.7593926659016252, "noise_severity": 0.02957828946055802, "missing_content_severity": 0.012050746532313408, "factual_density": 3.4229101114971865, "pretraining_utility": 0.5576206599336027, "math_density": 2.492478065109739, "code_density": 3.935111884776607, "reasoning_density": 1.9989091051006116, "education_level": 2.269497754313101, "creative_writing_quality": -0.2713970192139945}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/adityaanantjadhav/Design-And-AnalysisOfAlgorithm/blob/f2b7dfb1d09f490ffdc241056ffc34e67f8a8210/Assignment4TravellingSalesmanProblemIterative.cpp", "id": "cpp-0-4113c61634ed31bf7ae3cace2b33d8fb", "text": "#include <iostream>\nusing namespace std;\n\nvoid printmatrix(int arr[][50], int v) // PRINTS THE MATRIX\n{\n    cout << endl;\n    for (int m = 0; m < v; m++)\n    {\n        for (int n = 0; n < v; n++)\n        {\n            cout << \"|\" << arr[m][n] << \" \";\n            if (arr[m][n] != 9999)\n                cout << \"  \";\n            if (arr[m][n] / 10 == 0)\n                cout << \" \";\n            cout << \"|\";\n        }\n        cout << endl;\n    }\n}\n\nint colmin(int mat[][50], int v)\n{ // MINIMIZES THE COLUMNS AND RETURNS SUM OF MINIMUM VALUES\n    int sum = 0;\n    for (int i = 0; i < v; i++)\n    {\n        int min = 9999;\n        for (int j = 0; j < v; j++)\n        {\n            if (min > mat[j][i])\n            {\n                min = mat[j][i];\n            }\n        }\n        if (min != 9999)\n            sum = min + sum;\n        for (int j = 0; j < v; j++)\n        {\n            if (mat[j][i] != 9999)\n                mat[j][i] = mat[j][i] - min;\n        }\n    }\n    return sum;\n}\n\nint rowmin(int mat[][50], int v)\n{ // MINIMIZES THE COLUMNS AND RETURNS SUM OF MINIMUM VALUES\n    int sum = 0;\n    for (int i = 0; i < v; i++)\n    {\n        int min = 9999;\n        for (int j = 0; j < v; j++)\n        {\n            if (min > mat[i][j])\n            {\n                min = mat[i][j];\n            }\n        }\n        if (min != 9999)\n            sum = min + sum;\n        for (int j = 0; j < v; j++)\n        {\n            if (mat[i][j] != 9999)\n                mat[i][j] = mat[i][j] - min;\n        }\n    }\n    return sum;\n}\n\nint main()\n{\n    int v, e, initial = 0;\n    int sum = 0;\n    cout << \"enter the no. of vertices::\";\n    cin >> v;\n    int mat[50][50];\n\n    for (int i = 0; i < v; i++)\n    {\n        for (int j = 0; j < v; j++)\n        {\n            mat[i][j] = 9999;\n        }\n    }\n\n    cout<<\"enter the matrix values::\\n\";\n    for (int m = 0; m < v; m++)\n    {\n        for (int n = 0; n < v; n++)\n        {\n            cout<<\"row\"<<m<<\"column\"<<n<<\"::\";\n            cin >> mat[m][n];\n        }\n    }\n\n    cout << \"enter the starting point::\";\n    cin >> initial;\n\n    int arr[50][50];\n    int arr2[50][50];\n\n    for (int m = 0; m < v; m++)\n    {\n        for (int n = 0; n < v; n++)\n        {\n            arr[m][n] = 9999;\n            arr2[m][n] = 9999;\n        }\n    }\n\n    int visited[v]; // KEEPS TRACK OF VISITED VERTICES\n    int route[v];   // KEEPS TRACK OF ROUTE\n\n    for (int i = 0; i < v; i++)\n    { // INITIALIZING ALL VERTEX NOT VISITED EXCEPT INITIAL\n        visited[i] = 0;\n    }\n    visited[initial] = 1;\n\n    cout << \"row minimization:\\n\";\n    sum += rowmin(mat, v);\n    printmatrix(mat, v);\n\n    cout << \"column minimization:\\n\";\n    sum += colmin(mat, v);\n    printmatrix(mat, v);\n\n    cout << \"\\n cost is:\" << sum << endl\n         << endl;\n\n    int start = initial;\n    int sum1 = 0;\n    int b = 0;\n\n    for (int e = 0; e < v; e++)\n    { // looping v times to visit all vertices and return to initial vertex\n        if (e == v - 1)\n            visited[initial] = 0; // we can visit initial vertex only at end\n\n        int min = 999999;\n        int minpoint;\n        for (int i = 0; i < v; i++)\n        {\n            if (i != start && visited[i] == 0)\n            {\n                for (int l = 0; l < v; l++)\n                { // initialising another matrix arr as original matrix\n                    for (int m = 0; m < v; m++)\n                    {\n                        arr[l][m] = mat[l][m];\n                    }\n                }\n                for (int l = 0; l < v; l++)\n                { // making the row and column infinite\n                    arr[start][l] = 9999;\n                    arr[l][i] = 9999;\n                }\n                arr[i][start] = 9999;\n\n                printmatrix(arr, v);\n\n                sum1 = rowmin(arr, v);\n                sum1 = sum1 + sum + colmin(arr, v) + mat[start][i]; // storing cost into sum1\n                cout << \"cost of \" << start << \" to \" << i << \":: \" << sum1 << endl;\n\n                if (min > sum1)\n                { // finding the vertex with minimum cost\n                    min = sum1;\n                    minpoint = i;\n                    for (int l = 0; l < v; l++)\n                    {\n                        for (int m = 0; m < v; m++)\n                        {\n                            arr2[l][m] = arr[l][m]; // storing the matrix of minimum cost into another matrix\n                        }\n                    }\n                }\n            }\n        }\n        cout << \"\\nfinal minimum cost b/w \" << start << \" to \" << minpoint << \" is::\" << min << endl;\n        start = minpoint;\n        visited[start] = 1;\n        route[b++] = start;\n        sum = min;\n        for (int i = 0; i < v; i++)\n        { // making the matrix with minimum cost as original matrix\n            for (int j = 0; j < v; j++)\n            {\n                mat[i][j] = arr2[i][j];\n            }\n        }\n    }\n    cout << endl\n         << \"path is::\\n\"\n         << initial;\n    for (int i = 0; i < v; i++)\n    {\n        cout << \" -> \" << route[i];\n    }\n}", "text_preview": "#include <iostream> using namespace std; void printmatrix(int arr[][50], int v) // PRINTS THE MATRIX { cout << endl; for (int m = 0; m < v; m++) { for (int n = 0; n < v; n++) { cout << \"|\" << arr[m][n] << \" \"; if (arr[m][n] != 9999) cout <<", "input_sha256": "415e9346629f22ce521d84840266b2596cb5624152ef12ad3211a0668d1fdada", "text_sha256": "4113c61634ed31bf7ae3cace2b33d8fba97693ed03cc33c45742a7450216715a", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 109, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": {"main_topic": "Science, Mathematics & Engineering", "sub_topic": null}}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.2770312447257773, "noise_severity": 0.028582242288598184, "missing_content_severity": 0.006802477930013635, "factual_density": 2.9502847231598697, "pretraining_utility": 0.5065770601763373, "math_density": 3.1756814868039993, "code_density": 3.8365450692878666, "reasoning_density": 2.025734950718191, "education_level": 2.0278578535781797, "creative_writing_quality": -0.4917050285933769}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "Science, Mathematics & Engineering"}, "review_reasons": ["secondary_topic probability 0.4162 below 0.70", "secondary_topic vs NONE margin 0.0572 below 0.10"], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/rabienrose/3d_vision/blob/3555c4d8ddfec6574f1d94bc81e3a443dcee67d1/visual_map/src/aslam_cv2/aslam_cv_matcher/include/aslam/matcher/matching-problem-frame-to-frame.h", "id": "cpp-0-ef9fba8cfb80aa7bcafb1a49b26d6f55", "text": "#ifndef ASLAM_CV_MATCHING_PROBLEM_FRAME_TO_FRAME_H_\n#define ASLAM_CV_MATCHING_PROBLEM_FRAME_TO_FRAME_H_\n\n/// \\addtogroup Matching\n/// @{\n///\n/// @}\n\n#include <map>\n#include <memory>\n#include <vector>\n\n#include <aslam/common/macros.h>\n#include <aslam/common/memory.h>\n#include <aslam/common/pose-types.h>\n#include <aslam/common/feature-descriptor-ref.h>\n#include <Eigen/Core>\n\n#include \"aslam/matcher/match.h\"\n#include \"aslam/matcher/matching-problem.h\"\n\nnamespace aslam {\nclass VisualFrame;\n\n/// \\class MatchingProblem\n/// \\brief Defines the specifics of a matching problem.\n/// The problem is assumed to have two visual frames (apple_frame and banana_frame) filled with\n/// keypoints and binary descriptors and a rotation matrix taking vectors from the banana frame\n/// into the apple frame. The problem matches banana features against apple features.\n///\n/// Coordinate Frames:\n///   A:  apple frame\n///   B:  banana frame\nclass MatchingProblemFrameToFrame : public MatchingProblem {\npublic:\n  ASLAM_POINTER_TYPEDEFS(MatchingProblemFrameToFrame);\n  ASLAM_DISALLOW_EVIL_CONSTRUCTORS(MatchingProblemFrameToFrame);\n  ASLAM_ADD_MATCH_TYPEDEFS(FrameToFrame);\n  friend class MatcherTest;\n\n  MatchingProblemFrameToFrame() = delete;\n\n  /// \\brief Constructor for a frame-to-frame matching problem.\n  ///\n  /// @param[in]  apple_frame                                 Apple frame.\n  /// @param[in]  banana_frame                                Banana frame.\n  /// @param[in]  q_A_B                                       Quaternion taking vectors from\n  ///                                                         the banana frame into the\n  ///                                                         apple frame.\n  /// @param[in]  image_space_distance_threshold_pixels       Max image space distance threshold\n  ///                                                         for two pairs to become match\n  ///                                                         candidates.\n  /// @param[in]  hamming_distance_threshold                  Max hamming distance for two pairs\n  ///                                                         to become candidates.\n  MatchingProblemFrameToFrame(const VisualFrame& apple_frame,\n                              const VisualFrame& banana_frame,\n                              const aslam::Quaternion& q_A_B,\n                              double image_space_distance_threshold_pixels,\n                              int hamming_distance_threshold);\n  virtual ~MatchingProblemFrameToFrame() {};\n\n  virtual size_t numApples() const;\n  virtual size_t numBananas() const;\n\n  /// Get a short list of candidates in list a for index b\n  ///\n  /// \\param[in] frame_banana_keypoint_index The index of b queried for candidates.\n  /// \\param[out] candidates  Candidates from the apple frame keypoints that could\n  ///                         potentially match the given keypoint from the banana frame.\n  virtual void getAppleCandidatesForBanana(int frame_banana_keypoint_index, Candidates* candidates);\n\n  inline double computeMatchScore(int hamming_distance) {\n    return static_cast<double>(384 - hamming_distance) / 384.0;\n  }\n\n  inline int computeHammingDistance(int banana_index, int apple_index) {\n    CHECK_LT(apple_index, static_cast<int>(apple_descriptors_.size()))\n        << \"No descriptor for this apple.\";\n    CHECK_LT(banana_index, static_cast<int>(banana_descriptors_.size()))\n        << \"No descriptor for this banana.\";\n    CHECK_LT(apple_index, static_cast<int>(valid_apples_.size()))\n        << \"No valid flag for this apple.\";\n    CHECK_LT(banana_index, static_cast<int>(valid_bananas_.size()))\n        << \"No valid flag for this apple.\";\n    CHECK(valid_apples_[apple_index]) << \"The given apple is not valid.\";\n    CHECK(valid_bananas_[banana_index]) << \"The given banana is not valid.\";\n\n    const common::FeatureDescriptorConstRef& apple_descriptor =\n        apple_descriptors_[apple_index];\n    const common::FeatureDescriptorConstRef& banana_descriptor =\n        banana_descriptors_[banana_index];\n\n    CHECK_NOTNULL(apple_descriptor.data());\n    CHECK_NOTNULL(banana_descriptor.data());\n\n    return common::GetNumBitsDifferent(banana_descriptor, apple_descriptor);\n  }\n\n  /// \\brief Gets called at the beginning of the matching problem.\n  /// Creates a y-coordinate LUT for all apple keypoints and projects all banana keypoints into the\n  /// apple frame.\n  virtual bool doSetup();\n\nprivate:\n  /// The apple frame.\n  const VisualFrame& apple_frame_;\n  /// The banana frame.\n  const VisualFrame& banana_frame_;\n  /// Rotation matrix taking vectors from the banana frame into the apple frame.\n  aslam::Quaternion q_A_B_;\n  /// Map mapping y coordinates in the image plane onto keypoint indices of apple keypoints.\n  std::multimap<size_t, size_t> y_coordinate_to_apple_keypoint_index_map_;\n\n  /// Index marking apples as valid or invalid.\n  std::vector<bool> valid_apples_;\n  /// Index marking bananas as valid or invalid.\n  std::vector<bool> valid_bananas_;\n\n  /// The banana keypoints projected into the apple frame, expressed in the apple frame.\n  Aligned<std::vector, Eigen::Vector2d> A_projected_keypoints_banana_;\n\n  /// The apple descriptors.\n  std::vector<common::FeatureDescriptorConstRef> apple_descriptors_;\n\n  /// The banana descriptors.\n  std::vector<common::FeatureDescriptorConstRef> banana_descriptors_;\n\n  /// Descriptor size in bytes.\n  size_t descriptor_size_bytes_;\n\n  /// Half width of the vertical band used for match lookup in pixels.\n  int vertical_band_halfwidth_pixels_;\n\n  /// Pairs with image space distance >= image_space_distance_threshold_pixels_ are\n  /// excluded from matches.\n  double squared_image_space_distance_threshold_px_sq_;\n\n  /// Pairs with descriptor distance >= hamming_distance_threshold_ are\n  /// excluded from matches.\n  int hamming_distance_threshold_;\n\n  /// The heigh of the apple frame.\n  size_t image_height_apple_frame_;\n};\n}\n#endif //ASLAM_CV_MATCHING_PROBLEM_FRAME_TO_FRAME_H_\n", "text_preview": "#ifndef ASLAM_CV_MATCHING_PROBLEM_FRAME_TO_FRAME_H_ #define ASLAM_CV_MATCHING_PROBLEM_FRAME_TO_FRAME_H_ /// \\addtogroup Matching /// @{ /// /// @} #include <map> #include <memory> #include <vector> #include <aslam/common/macros.h> #include ", "input_sha256": "a13b2a7085175fdf84ea0af95fa61223cb9cc79216fad0e2683af383167a650c", "text_sha256": "ef9fba8cfb80aa7bcafb1a49b26d6f559583bf07c6da82d0bceab0f97260bbd3", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 110, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/apsknight/snippets/blob/596edd2fee92db41ab031e1ffc59cdd08ad529d8/assignments/DAA/Assignment-8/tic-tac-toe.cpp", "id": "cpp-0-94bb93fee153cc71a73fdec1e3ecc8e9", "text": "/**\n * <NAME>\n * 16CS01011, Design and Analysis of Algorithms\n */\n\n#include <bits/stdc++.h>\nusing namespace std;\n\nclass node{\n    public:\n        string board;\n        int level;\n        vector<string> parent;\n};\n\nbool win(string board, char player) {\n    if (board[0] == player && board[4] == player && board[8] == player) {\n        return true;\n    }\n    else if (board[2] == player && board[4] == player && board[6] == player) {   \n        return true;\n    }\n    for(int i = 0; i < 3; i++) {\n        if (board[0+i] == player && board[3+i] == player && board[6+i] == player) {\n            return true;\n        }\n        else if (board[0 + 3*i] == player && board[1 + 3*i] == player && board[2 + 3*i] == player) {  \n            return true;\n        }\n    }\n\n    return false;\n}\n\nint main() {\n    // off;\n    string board = \"000000000\";\n    // memset(board, 0, sizeof board);\n\n    cout << \"Enter Board Configuration: \" << endl;\n    for(int i = 0; i < 9; i++) {\n        cin >> board[i];\n    }\n\n    node n;\n    n.board = board;\n    n.level = 0;\n    queue< node > q;\n    q.push(n);\n\n    bool o = false, t = false;\n    while(!q.empty()) {\n        string x = q.front().board;\n        int level = q.front().level;\n        q.pop();\n        if (win(x, '1') && !o) {\n            cout << \"Player 1 win in steps: \" << level << endl;\n            q.front().parent.push_back(x);\n            for(auto i : q.front().parent) {\n                if (i == board) continue;\n                for(int j = 0; j < 9; j++) {\n                    cout << i[j] << \" \";\n                    if (j == 2 || j == 5 || j == 8) cout << endl;\n           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      c.board = y;\n                c.level = level;\n                c.parent = q.front().parent;\n                c.parent.push_back(x);\n                q.push(c);\n                y = x;                \n            }\n        }\n        if (one == two + 1)\n        for(int i = 0; i < 9; i++) {\n            if (x[i] == '0') {\n                z[i] = '2';\n                // cout << z << endl;\n                node c;\n                c.board = z;\n                c.level = level;\n                c.parent = q.front().parent;\n                c.parent.push_back(x);\n                q.push(c);                \n                q.push(c);                \n                z = x;\n            }\n        }\n        // for(int i = 0; i < 9; i++) {\n        //     cout << y[i] << ' ';\n        //     if (i == 2 || i == 5 || i == 8) cout << endl;\n        // }\n        // cout << endl;\n        // for(int i = 0; i < 9; i++) {\n        //     cout << z[i] << ' ';\n        //     if (i == 2 || 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/nghialuffy/BaiTapOOP3/blob/3a64f63197e2f60f2e04a579a6e3bb5ad352f6a5/Bai_Tap_03/Bai_Tap_03/ListNode.cpp", "id": "cpp-0-b6ad081ecf7458cc47ef18aaeb758e70", "text": "#include \"ListNode.h\"\n#include<iostream>\n#include<string>\nusing namespace std;\n\nNode *ListNode::CreatNode(SinhVien sv)\n{\n\tNode *p = new Node(sv, NULL, NULL);\n\tthis->size = 0;\n\treturn p;\n}\n\nListNode * ListNode::InsertHead(SinhVien sv)\n{\n\tNode *temp = new Node(sv, NULL, NULL);\n\tif (this->size == 0) {\n\t\tthis->head = this->tail = temp;\n\t}\n\telse {\n\t\ttemp->next = this->head;\n\t\tthis->head->pre = temp;\n\t\tthis->head = temp;\n\t}\n\tthis->head->pre = this->tail;\n\tthis->tail->next = this->head;\n\n\tthis->size++;\n\treturn this;\n}\n\nListNode * ListNode::InsertTail(SinhVien sv)\n{\n\tNode *temp = new Node(sv, NULL, NULL);\n\tif (this->size == 0) {\n\t\tthis->head = this->tail = temp;\n\t}\n\telse {\n\t\tthis->tail->next = temp;\n\t\ttemp->pre = this->tail;\n\t\tthis->tail = temp;\n\t}\n\tthis->head->pre = this->tail;\n\tthis->tail->next = this->head;\n\tthis->size++;\n\treturn this;\n}\n\nListNode * ListNode::InsertK(SinhVien sv, int k)\n{\n\tif (k <= 0)\n\t\tthis->InsertHead(sv);\n\telse if (k > this->size - 1) {\n\t\tthis->InsertTail(sv);\n\t}\n\telse {\n\t\tNode *temp = new Node(sv, NULL, NULL);\n\t\tNode *temph = this->head, *tempt, *tempr = new Node(sv, NULL, NULL);\n\t\tint i = 1;\n\t\tdo {\n\t\t\ti++;\n\t\t\ttemph = temph->next;\n\t\t} while (temph != this->head && i != k);\n\t\ttempt = temph->next;\n\t\ttempr->next = tempt;\n\t\ttempr->pre = temph;\n\t\ttemph->next = tempr;\n\t\ttempt->pre = tempr;\n\t\tthis->size++;\n\t}\n\treturn this;\n}\n\nListNode * ListNode::DeleteHead()\n{\n\tthis->head->data.setLop(\"None\");\n\tif (this->size != 0) {\n\t\tthis->head = this->head->next;\n\t\tthis->head->pre = this->tail;\n\t\tthis->tail->next = this->head;\n\t\tthis->size--;\n\t}\n\treturn this;\n}\n\nListNode * ListNode::DeleteTail()\n{\n\tthis->tail->data.setLop(\"None\");\n\tif (this->size != 0) {\n\t\tthis->tail = this->tail->pre;\n\t\tthis->head->pre = this->tail;\n\t\tthis->tail->next = this->head;\n\t\tthis->size--;\n\t}\n\treturn this;\n}\n\nListNode * ListNode::DeleteK(int k)\n{\n\tNode *temph = this->head, *tempt;\n\tint i = 1;\n\tif (k <= 0) {\n\t\tthis->DeleteHead();\n\t}\n\telse if (k >= this->size - 1) {\n\t\tthis->DeleteTail();\n\t}\n\telse {\n\t\twhile (temph != NULL && i != k) {\n\t\t\ti++;\n\t\t\ttemph = temph->next;\n\t\t}\n\t\ttemph->data.setLop(\"None\");\n\t\ttempt = temph->next->next;\n\t\ttemph->next = tempt;\n\t\ttempt->pre = temph;\n\t\tthis->size--;\n\t}\n\treturn this;\n}\n\nNode * ListNode::Search(string mssv)\n{\n\tNode *temph = this->head;\n\tdo {\n\t\tif (temph->data.getMSSV() == mssv)\n\t\t\treturn temph;\n\t\ttemph = temph->next;\n\t} while (temph != this->head);\n\treturn NULL;\n}\n\nSinhVien & ListNode::Index(int i)\n{\n\tSinhVien *temp = new SinhVien;\n\tNode *temph = this->head;\n\tif (i < 0 || i >= this->size) {\n\t\tcout << \"Index Error\" << endl;\n\t\treturn *temp;\n\t}\n\tint j = 0;\n\twhile (j < i) {\n\t\tj++;\n\t\ttemph = temph->next;\n\t}\n\treturn temph->data;\n}\n\nListNode * ListNode::Update(SinhVien sv, string mssv)\n{\n\tNode *temph = this->head;\n\tint i = 0;\n\tdo {\n\t\tif (temph->data.getMSSV() == mssv) {\n\t\t\tbreak;\n\t\t}\n\t\ti++;\n\t\ttemph = temph->next;\n\t} while (temph != this->head);\n\tif (i >= this->size) {\n\t\tcout << \"Can't Update\" << endl;\n\t\treturn this;\n\t}\n\tthis->DeleteK(i);\n\tthis->InsertK(sv, i);\n\treturn this;\n}\n\nbool ListNode::Dec(string left, string right)\n{\n\tif (left < right) {\n\t\treturn true;\n\t}\n\telse\n\t\treturn false;\n}\n\nbool ListNode::Inc(string left, string right)\n{\n\tif (left > right) {\n\t\treturn true;\n\t}\n\telse\n\t\treturn false;\n}\n\nbool(*pp)(string left, string right);\n\nListNode * ListNode::Sort(bool(*pp)(string left, string right))\n{\n\tNode *temp;\n\tSinhVien data;\n\tbool check = false;\n\twhile (!check) {\n\t\tcheck = true;\n\t\ttemp = this->head;\n\t\tdo {\n\t\t\tif (pp(temp->data.getMSSV(), temp->next->data.getMSSV())) {\n\t\t\t\tdata = temp->getData();\n\t\t\t\ttemp->SetData(temp->next->getData());\n\t\t\t\ttemp->next->SetData(data);\n\t\t\t\tcheck = false;\n\t\t\t}\n\t\t\ttemp = temp->next;\n\t\t} while (temp->next != this->head);\n\n\t}\n\treturn this;\n}\n\nint ListNode::getSize()\n{\n\treturn this->size;\n}\n\nvoid ListNode::Show()\n{\n\tif (this->head) {\n\t\tNode *temph = this->head;\n\t\tdo {\n\t\t\ttemph->data.Show();\n\t\t\ttemph = temph->next;\n\t\t} while (temph != this->head);\n\t}\n\telse {\n\t\tcout << \"List Empty\" << endl;\n\t}\n\n}\n\nListNode::ListNode()\n{\n\tthis->head = NULL;\n\tthis->tail = NULL;\n\tthis->size = 0;\n}\n\nListNode::~ListNode()\n{\n}\n\nListNode& ListNode::operator=(const ListNode & l1)\n{\n\tNode *temp = l1.head;\n\tif (l1.size != 0) {\n\t\tdo {\n\t\t\tthis->DeleteHead();\n\t\t} while (this->size != 0);\n\t\tdo {\n\t\t\tthis->InsertTail(temp->data);\n\t\t\ttemp->next;\n\t\t} while (temp->next != l1.head);\n\t}\n\treturn *this;\n}\n", "text_preview": "#include \"ListNode.h\" #include<iostream> #include<string> using namespace std; Node *ListNode::CreatNode(SinhVien sv) { Node *p = new Node(sv, NULL, NULL); this->size = 0; return p; } ListNode * ListNode::InsertHead(SinhVien sv) { Node *tem", "input_sha256": "74ee83f8d726ba64b2364d6efd939d4175a86379f4881e96f4e5ee5a47ac8802", "text_sha256": "b6ad081ecf7458cc47ef18aaeb758e70679cbd2f35935221d44f43d90c515b76", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 111, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 0, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 0, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.15266496254808457, "noise_severity": 0.015553178193050906, "missing_content_severity": 0.03665129173162329, "factual_density": 3.386538197355024, "pretraining_utility": 0.49700676251075326, "math_density": 0.597346457080665, "code_density": 3.8971904630698764, "reasoning_density": 0.9927195705474039, "education_level": 2.0377535013513612, "creative_writing_quality": -0.5957012370430916}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.0040673664161872125, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9964187361458766, "secondary": 0.9930362630831742, "tertiary": 0.9972804715174565, "structured_records": 0.002577268425731945, "procedural_steps": 0.005752457778964286, "worked_solution": 0.013291511200240538, "contextual_dialogue": 5.3225243191826196e-06, "content_quality": 0.41432916198011915, "noise_severity": 0.9870390181724575, "missing_content_severity": 0.9694572568903139, "factual_density": 0.4887818311291865, 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0.00012103831504432703, "Legal Notices": 2.4013436293995376e-05, "News Article": 4.71697555274606e-06, "Organizational Announcement": 1.0855077260694589e-05, "Nonfiction Writing": 1.1122941642422332e-05, "Organizational About Page": 1.1350015419434025e-05, "Personal About Page": 1.3550626349194545e-05, "Personal Blog": 7.836269435032208e-06, "Product Page": 6.7959241415252915e-06, "Spam / Ads": 1.1426561473114634e-05, "Structured Data": 0.00037947229675291147, "Technical Writing": 0.9965679554731317, "Transcript / Interview": 1.557960914527257e-05, "Tutorial / How-To Guide": 0.0008838811387800971, "User Reviews": 6.467127107584488e-06, "Other / Unknown": 0.0017663105008319632}, "secondary": {"Academic Writing": 7.078046748110966e-05, "Content Listing": 4.227182045990249e-05, "Creative Writing": 4.4381360964562386e-05, "Customer Support Page": 2.1802406769650964e-05, "Q&A Forum": 1.692211710424529e-05, "Discussion Thread": 1.3038126237150808e-05, "Comment Section": 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"Discussion Thread": 7.206797091579143e-06, "Comment Section": 1.3669291262557674e-05, "Missing Content": 9.378326784919129e-05, "Knowledge Article": 9.274079416715998e-05, "Legal Notices": 3.496763000011269e-05, "News Article": 1.3349930389601076e-05, "Organizational Announcement": 1.2861737738285417e-05, "Nonfiction Writing": 1.3371400188802882e-05, "Organizational About Page": 2.1456324622160105e-05, "Personal About Page": 1.2527633150511618e-05, "Personal Blog": 1.028120935987485e-05, "Product Page": 9.533670585217799e-06, "Spam / Ads": 1.388243944484581e-05, "Structured Data": 0.00019767709044373496, "Technical Writing": 0.001383452403748534, "Transcript / Interview": 1.4278865340794303e-05, "Tutorial / How-To Guide": 0.0005481586371577655, "User Reviews": 6.87554099350872e-06, "NONE": 0.9973937852042292}, "structured_records": {"false": 0.9974227315742681, "true": 0.002577268425731945}, "procedural_steps": {"false": 0.9942475422210357, "true": 0.005752457778964286}, "worked_solution": {"false": 0.9867084887997595, "true": 0.013291511200240538}, "contextual_dialogue": {"false": 0.9999946774756808, "true": 5.3225243191826196e-06}, "content_quality": {"-2": 0.043220145717232095, "-1": 0.18862973010342202, "0": 0.4014188729481541, "1": 0.3057275183764129, "2": 0.06100373285477895}, "noise_severity": {"0": 0.9872100537407191, "1": 0.010828729958853268, "2": 0.0013839217123090574, "3": 0.0003525735428946058, "4": 0.00022472104522392627}, "missing_content_severity": {"0": 0.9805494091728015, "1": 0.005730254690362064, "2": 0.010737846349257498, "3": 0.0024846148075696405, "4": 0.000497874980009327}, "factual_density": {"0": 0.007074213247219283, "1": 0.011072835023017865, "2": 0.10493097160462865, "3": 0.3420845013777882, "4": 0.5348374787473461}, "pretraining_utility": {"-2": 0.00970105644886104, "-1": 0.08599234813070186, "0": 0.3919241230508124, "1": 0.42236372120007226, "2": 0.09001875116955246}, "math_density": {"0": 0.7543155102665272, "1": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/jonturner53/grafalgo/blob/ce74b51ccfa5165e2dee6a097a34ebf77bccb593/cpp/graphAlgorithms/match/match_eg.cpp", "id": "cpp-0-3b15e30579d18137817fc35e746f5779", "text": "/** @file match_eg.cpp\n * \n *  @author <NAME>\n *  @date 2011\n *  This is open source software licensed under the Apache 2.0 license.\n *  See http://www.apache.org/licenses/LICENSE-2.0 for details.\n */\n\n#include \"match_eg.h\"\n\nnamespace grafalgo {\n\n/** Find a maximum size matching.\n *  @param[in] g is an undirected graph\n *  @param[in,out] matchingEdge[u] is (on return) the matching edge incident\n *  to u, or 0 if u is unmatched; if matchingEdge is not all zero initially, it\n *  is assumed to represent a valid initial matching\n */\nmatch_eg::match_eg(const Graph& g, edge *matchingEdge)\n\t\t   : match_egc(g,matchingEdge) {\n\t// Create initial maximal (not maximum) matching\n\tfor (edge e = gp->first(); e != 0; e = gp->next(e)) {\n\t\tvertex u = gp->left(e); vertex v = gp->right(e);\n\t\tif (mEdge[u] == 0 && mEdge[v] == 0) {\n\t\t\tmEdge[u] = mEdge[v] = e;\n\t\t}\n\t}\n\t// Make it maximum\n\tedge e;\n\twhile ((e = findpath()) != 0) augment(e);\n}\n\n/** Search for an augmenting path.\n *  @return an unmatched edge on the augmenting path or 0 if\n *  no augmenting path is found; on success, the list in the augpath data\n *  structure that includes the returned edge defines the augmenting path.\n */\nedge match_eg::findpath() {\n\tblossoms->clear();\n\tList q(gp->M()); // list of edges to be processed in main loop\n\tfor (vertex u = 1; u <= gp->n(); u++) {\n\t\tpEdge[u] = 0; origin[u] = u; state[u] = unreached;\n\t\tif (mEdge[u] == 0) {\n\t\t\tstate[u] = even;\n\t\t\tfor (edge e = gp->firstAt(u); e != 0;\n\t\t\t\t  e = gp->nextAt(u,e))\n\t\t\t\tq.addLast(e);\n\t\t}\n\t}\n\n\twhile (!q.empty()) {\n\t\tedge e = q.first(); q.removeFirst();\n\t\tvertex u = gp->left(e); vertex up = base(u);\n\t\tif (state[up] != even) { u = gp->right(e); up = base(u); }\n\t\tvertex v = gp->mate(u,e); vertex vp = base(v);\n\t\tif (up == vp) continue; // skip internal edges in a blossom\n\t\tif (state[vp] == odd) continue;\n\t\tif (state[vp] == unreached) {\n\t\t\t// v is not contained in a blossom and is matched\n\t\t\t// so extend tree and add newly eligible edges to q\n\t\t\tvertex w = gp->mate(v,mEdge[v]);\n\t\t\tstate[v] = odd;  pEdge[v] = e;\n\t\t\tstate[w] = even; pEdge[w] = mEdge[v];\n\t\t\tfor (edge ee = gp->firstAt(w); ee != 0;\n\t\t\t     \t  ee = gp->nextAt(w,ee)) {\n\t\t\t\tif ((ee != mEdge[w]) && !q.member(ee))\n\t\t\t\t\tq.addLast(ee);\n\t\t\t}\n\t\t\tcontinue;\n\t\t}\n\t\t// up and vp are both even\n\t\tvertex a = nca(up,vp);\n\t\tif (a == 0) {\n\t\t\t// up, vp are different trees - construct path & return\n\t\t\tedge ee = augpath->join(\n\t\t\t\t\taugpath->reverse(path(u,root(up))),e);\n\t\t\treturn augpath->join(ee,path(v,root(vp)));\n\t\t}\n\t\t// up and vp are in same tree - collapse blossom\n\t\tvertex x = up;\n\t\twhile (x != a) {\n\t\t\torigin[blossoms->link(blossoms->find(x),\n\t\t\t\t\t      blossoms->find(a))] = a;\n\t\t\tx = gp->mate(x,pEdge[x]); // x now odd\n\t\t\torigin[blossoms->link(x,blossoms->find(a))] = a;\n\t\t\tbridge[x].e = e; bridge[x].v = u;\n\t\t\tfor (edge ee = gp->firstAt(x); ee != 0;\n\t\t\t     \t  ee = gp->nextAt(x,ee)) {\n\t\t\t\tif (!q.member(ee)) q.addLast(ee);\n\t\t\t}\n\t\t\tx = base(gp->mate(x,pEdge[x]));\n\t\t}\n\t\tx = vp;\n\t\twhile (x != a) {\n\t\t\torigin[blossoms->link(blossoms->find(x),\n\t\t\t\t\t      blossoms->find(a))] = a;\n\t\t\tx = gp->mate(x,pEdge[x]); // x now odd\n\t\t\torigin[blossoms->link(x,blossoms->find(a))] = a;\n\t\t\tbridge[x].e = e; bridge[x].v = v;\n\t\t\tfor (edge ee = gp->firstAt(x); ee != 0;\n\t\t\t     \t  ee = gp->nextAt(x,ee)) {\n\t\t\t\tif (!q.member(ee)) q.addLast(ee);\n\t\t\t}\n\t\t\tx = 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/rolccs/dj-universe/blob/2ea310ce0bcb87c7ec1e3c4aeed9a56634996250/audio-engine/src/audio_engine.cpp", "id": "cpp-0-31b8ceb1226d8be8779ec40fd4c16fa3", "text": "#include \"audio_engine.h\"\n#include <fftw3.h>\n#include <aubio/aubio.h>\n#include <algorithm>\n#include <numeric>\n#include <cmath>\n\nnamespace DJUniverse {\n\nclass AudioEngine::Impl {\npublic:\n    uint32_t sampleRate;\n    uint32_t fftSize;\n    uint32_t hopSize;\n    \n    // FFTW objects\n    fftwf_complex* fftIn;\n    fftwf_complex* fftOut;\n    fftwf_plan fftPlan;\n    \n    // Aubio objects for BPM detection\n    aubio_tempo_t* tempo;\n    aubio_onset_t* onset;\n    aubio_pitch_t* pitch;\n    fvec_t* aubioBuffer;\n    fvec_t* tempoOutput;\n    \n    // Lock-free queues for real-time processing\n    std::atomic<bool> isProcessing{false};\n    std::mutex resultsMutex;\n    BPMResult latestBPM;\n    PitchResult latestPitch;\n    SpectrumData latestSpectrum;\n    \n    Impl(uint32_t sr) : sampleRate(sr), fftSize(2048), hopSize(512) {\n        // Initialize FFTW\n        fftIn = fftwf_alloc_complex(fftSize);\n        fftOut = fftwf_alloc_complex(fftSize);\n        fftPlan = fftwf_plan_dft_1d(fftSize, fftIn, fftOut, FFTW_FORWARD, FFTW_ESTIMATE);\n        \n        // Initialize Aubio\n        aubioBuffer = new_fvec(hopSize);\n        tempoOutput = new_fvec(2);\n        \n        tempo = new_aubio_tempo(\"default\", fftSize, hopSize, sampleRate);\n        onset = new_aubio_onset(\"default\", fftSize, hopSize, sampleRate);\n        pitch = new_aubio_pitch(\"default\", fftSize, hopSize, sampleRate);\n        \n        aubio_pitch_set_unit(pitch, \"Hz\");\n    }\n    \n    ~Impl() {\n        // Cleanup FFTW\n        fftwf_destroy_plan(fftPlan);\n        fftwf_free(fftIn);\n        fftwf_free(fftOut);\n        \n        // Cleanup Aubio\n        del_aubio_tempo(tempo);\n        del_aubio_onset(onset);\n        del_aubio_pitch(pitch);\n        del_fvec(aubioBuffer);\n        del_fvec(tempoOutput);\n    }\n};\n\nAudioEngine::AudioEngine(uint32_t sampleRate) \n    : pImpl(std::make_unique<Impl>(sampleRate)) {}\n\nAudioEngine::~AudioEngine() = default;\n\nbool AudioEngine::initialize() {\n    if (!pImpl->fftPlan || !pImpl->tempo || !pImpl->onset || !pImpl->pitch) {\n        return false;\n    }\n    return true;\n}\n\nvoid AudioEngine::shutdown() {\n    pImpl->isProcessing = false;\n}\n\nBPMResult AudioEngine::analyzeBPM(const AudioBuffer& buffer) {\n    BPMResult result;\n    result.isValid = false;\n    \n    if (buffer.left.empty()) {\n        return result;\n    }\n    \n    try {\n        // Process audio in chunks\n        std::vector<double> beatTimes;\n        size_t processedSamples = 0;\n        double currentTime = 0.0;\n        \n        while (processedSamples + pImpl->hopSize < buffer.left.size()) {\n            // Copy audio data to aubio buffer\n            for (uint32_t i = 0; i < pImpl->hopSize; ++i) {\n                pImpl->aubioBuffer->data[i] = buffer.left[processedSamples + i];\n            }\n            \n            // Detect tempo\n            aubio_tempo_do(pImpl->tempo, pImpl->aubioBuffer, pImpl->tempoOutput);\n            \n            // Check if beat detected\n            if (pImpl->tempoOutput->data[0] != 0) {\n                beatTimes.push_back(currentTime);\n            }\n            \n            processedSamples += pImpl->hopSize;\n            currentTime = (double)processedSamples / pImpl->sampleRate;\n        }\n        \n        // Calculate BPM from beat times\n        if (beatTimes.size() >= 2) {\n            std::vector<double> intervals;\n            for (size_t i = 1; i < beatTimes.size(); ++i) {\n                intervals.push_back(beatTimes[i] - beatTimes[i-1]);\n            }\n            \n            // Calculate median interval\n            std::sort(intervals.begin(), intervals.end());\n            double medianInterval = intervals[intervals.size() / 2];\n            \n            result.bpm = 60.0 / medianInterval;\n            result.confidence = std::min(1.0, (double)beatTimes.size() / 32.0);\n            result.beatTimes = beatTimes;\n            result.isValid = true;\n        }\n        \n    } catch (const std::exception& e) {\n        result.isValid = false;\n    }\n    \n    return result;\n}\n\nPitchResult AudioEngine::analyzePitch(const AudioBuffer& buffer) {\n    PitchResult result;\n    result.isValid = false;\n    \n    if (buffer.left.empty()) {\n        return result;\n    }\n    \n    try {\n        std::vector<double> pitchValues;\n        std::vector<double> confidenceValues;\n        \n        size_t processedSamples = 0;\n        \n        while (processedSamples + pImpl->hopSize < buffer.left.size()) {\n            // Copy audio data\n            for (uint32_t i = 0; i < pImpl->hopSize; ++i) {\n                pImpl->aubioBuffer->data[i] = buffer.left[processedSamples + i];\n            }\n            \n            // Detect pitch\n            smpl_t pitch = aubio_pitch_do(pImpl->pitch, pImpl->aubioBuffer);\n            smpl_t confidence = aubio_pitch_get_confidence(pImpl->pitch);\n            \n            if (confidence > 0.5 && pitch > 50.0) { // Filter out low confidence and very low frequencies\n                pitchValues.push_back(pitch);\n                confidenceValues.push_back(confidence);\n            }\n            \n            processedSamples += pImpl->hopSize;\n        }\n        \n        if (!pitchValues.empty()) {\n            // Calculate median pitch\n            std::sort(pitchValues.begin(), pitchValues.end());\n            result.fundamentalFreq = pitchValues[pitchValues.size() / 2];\n            \n            // Calculate average confidence\n            result.confidence = std::accumulate(confidenceValues.begin(), confidenceValues.end(), 0.0) / confidenceValues.size();\n            \n            // Calculate harmonics (simplified)\n            result.harmonics.clear();\n            for (int i = 2; i <= 5; ++i) {\n                result.harmonics.push_back(result.fundamentalFreq * i);\n            }\n            \n            result.isValid = true;\n        }\n        \n    } catch (const std::exception& e) {\n        result.isValid = false;\n    }\n    \n    return result;\n}\n\nSpectrumData AudioEngine::analyzeSpectrum(const AudioBuffer& buffer) {\n    SpectrumData result;\n    \n    if (buffer.left.empty() || buffer.left.size() < pImpl->fftSize) {\n        return result;\n    }\n    \n    try {\n        // Copy audio data to FFT input\n        for (uint32_t i = 0; i < pImpl->fftSize; ++i) {\n            pImpl->fftIn[i][0] = i < buffer.left.size() ? buffer.left[i] : 0.0f;\n            pImpl->fftIn[i][1] = 0.0f;\n        }\n        \n        // Execute FFT\n        fftwf_execute(pImpl->fftPlan);\n        \n        // Calculate magnitudes and phases\n        result.magnitudes.resize(pImpl->fftSize / 2);\n        result.phases.resize(pImpl->fftSize / 2);\n        result.frequencies.resize(pImpl->fftSize / 2);\n        \n        for (uint32_t i = 0; i < pImpl->fftSize / 2; ++i) {\n            float real = pImpl->fftOut[i][0];\n            float imag = pImpl->fftOut[i][1];\n            \n            result.magnitudes[i] = std::sqrt(real * real + imag * imag);\n            result.phases[i] = std::atan2(imag, real);\n            result.frequencies[i] = (float)i * pImpl->sampleRate / pImpl->fftSize;\n        }\n        \n        result.timestamp = buffer.timestamp;\n        \n    } catch (const std::exception& e) {\n        result.magnitudes.clear();\n        result.phases.clear();\n        result.frequencies.clear();\n    }\n    \n    return result;\n}\n\nTransitionAnalysis AudioEngine::analyzeTransition(const AudioBuffer& buffer1, const AudioBuffer& buffer2) {\n    TransitionAnalysis result;\n    \n    try {\n        // Analyze both buffers\n        BPMResult bpm1 = analyzeBPM(buffer1);\n        BPMResult bpm2 = analyzeBPM(buffer2);\n        SpectrumData spectrum1 = analyzeSpectrum(buffer1);\n        SpectrumData spectrum2 = analyzeSpectrum(buffer2);\n        \n        // Beat matching accuracy\n        if (bpm1.isValid && bpm2.isValid) {\n            double bpmDiff = std::abs(bpm1.bpm - bpm2.bpm);\n            result.beatMatchAccuracy = std::max(0.0, 1.0 - bpmDiff / 20.0); // 20 BPM tolerance\n        }\n        \n        // Spectral similarity\n        if (!spectrum1.magnitudes.empty() && !spectrum2.magnitudes.empty()) {\n            size_t minSize = std::min(spectrum1.magnitudes.size(), spectrum2.magnitudes.size());\n            double correlation = 0.0;\n            double norm1 = 0.0, norm2 = 0.0;\n            \n            for (size_t i = 0; i < minSize; ++i) {\n                correlation += spectrum1.magnitudes[i] * spectrum2.magnitudes[i];\n                norm1 += spectrum1.magnitudes[i] * spectrum1.magnitudes[i];\n                norm2 += spectrum2.magnitudes[i] * spectrum2.magnitudes[i];\n            }\n            \n            if (norm1 > 0 && norm2 > 0) {\n                result.spectralSimilarity = correlation / (std::sqrt(norm1) * std::sqrt(norm2));\n            }\n        }\n        \n        // Overall transition quality\n        result.transitionQuality = (result.beatMatchAccuracy + result.spectralSimilarity) / 2.0;\n        result.isSmooth = result.transitionQuality > 0.7;\n        \n    } catch (const std::exception& e) {\n        result.beatMatchAccuracy = 0.0;\n        result.spectralSimilarity = 0.0;\n        result.transitionQuality = 0.0;\n        result.isSmooth = false;\n    }\n    \n    return result;\n}\n\ndouble AudioEngine::evaluateBeatMatching(const std::vector<double>& track1Beats, const std::vector<double>& track2Beats) {\n    if (track1Beats.empty() || track2Beats.empty()) {\n        return 0.0;\n    }\n    \n    // Find the best alignment between beat sequences\n    double maxCorrelation = 0.0;\n    const double maxOffset = 2.0; // seconds\n    const double step = 0.01; // 10ms steps\n    \n    for (double offset = -maxOffset; offset <= maxOffset; offset += step) {\n        double correlation = 0.0;\n        int matches = 0;\n        \n        for (double beat1 : track1Beats) {\n            double adjustedBeat = beat1 + offset;\n            \n            // Find closest beat in track2\n            auto it = std::lower_bound(track2Beats.begin(), track2Beats.end(), adjustedBeat);\n            \n            if (it != track2Beats.end()) {\n                double diff = std::abs(*it - adjustedBeat);\n                if (diff < 0.1) { // 100ms tolerance\n                    correlation += 1.0 - diff / 0.1;\n                    matches++;\n                }\n            }\n        }\n        \n        if (matches > 0) {\n            correlation /= matches;\n            maxCorrelation = std::max(maxCorrelation, correlation);\n        }\n    }\n    \n    return maxCorrelation;\n}\n\ndouble AudioEngine::evaluatePitchAccuracy(double targetBPM, double actualBPM) {\n    double diff = std::abs(targetBPM - actualBPM);\n    return std::max(0.0, 1.0 - diff / 10.0); // 10 BPM tolerance for perfect score\n}\n\ndouble AudioEngine::evaluateTransitionSmootness(const TransitionAnalysis& transition) {\n    return transition.transitionQuality;\n}\n\nvoid AudioEngine::setSampleRate(uint32_t sampleRate) {\n    pImpl->sampleRate = sampleRate;\n}\n\nvoid AudioEngine::setFFTSize(uint32_t fftSize) {\n    pImpl->fftSize = fftSize;\n}\n\nvoid AudioEngine::setHopSize(uint32_t hopSize) {\n    pImpl->hopSize = hopSize;\n}\n\nvoid AudioEngine::processAudioAsync(const AudioBuffer& buffer) {\n    if (pImpl->isProcessing.load()) {\n        return; // Skip if already processing\n    }\n    \n    pImpl->isProcessing = true;\n    \n    std::thread([this, buffer]() {\n        try {\n            BPMResult bpm = analyzeBPM(buffer);\n            PitchResult pitch = analyzePitch(buffer);\n            SpectrumData spectrum = analyzeSpectrum(buffer);\n            \n            std::lock_guard<std::mutex> lock(pImpl->resultsMutex);\n            pImpl->latestBPM = bpm;\n            pImpl->latestPitch = pitch;\n            pImpl->latestSpectrum = spectrum;\n            \n        } catch (...) {\n            // Error handling\n        }\n        \n        pImpl->isProcessing = false;\n    }).detach();\n}\n\nbool AudioEngine::getLatestResults(BPMResult& bpm, PitchResult& pitch, SpectrumData& spectrum) {\n    std::lock_guard<std::mutex> lock(pImpl->resultsMutex);\n    \n    bpm = pImpl->latestBPM;\n    pitch = pImpl->latestPitch;\n    spectrum = pImpl->latestSpectrum;\n    \n    return bpm.isValid || pitch.isValid || !spectrum.magnitudes.empty();\n}\n\n} // namespace DJUniverse", "text_preview": "#include \"audio_engine.h\" #include <fftw3.h> #include <aubio/aubio.h> #include <algorithm> 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/z80/mapper/blob/e39c6c1270b39ae734f53da815901dd6d55cd3c7/software.miling/3dparty/camlib/src/ballcutter.cpp", "id": "cpp-0-df203c83c316b43dd293cf161e3d8e53", "text": "/*\n *  Copyright 2010 <NAME> (<EMAIL> \"at\" gmail.com)\n *  \n *  This file is part of OpenCAMlib.\n *\n *  OpenCAMlib is free software: you can redistribute it and/or modify\n *  it under the terms of the GNU General Public License as published by\n *  the Free Software Foundation, either version 3 of the License, or\n *  (at your option) any later version.\n *\n *  OpenCAMlib is distributed in the hope that it will be useful,\n *  but WITHOUT ANY WARRANTY; without even the implied warranty of\n *  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n *  GNU General Public License for more details.\n *\n *  You should have received a copy of the GNU General Public License\n *  along with OpenCAMlib.  If not, see <http://www.gnu.org/licenses/>.\n*/\n\n#include <iostream>\n#include <sstream>\n#include <string>\n\n#include <boost/foreach.hpp>\n\n#include \"ballcutter.hpp\"\n\nnamespace ocl\n{\n\nBallCutter::BallCutter() {\n    std::cout << \" usage: BallCutter( double diameter, double length )\\n\";\n    assert(0);\n}\n\nBallCutter::BallCutter(double d, double l) {\n    diameter = d;               assert( diameter>0.0 );\n    radius = d/2.0;\n    length = l;                 assert( length>0.0 );\n    normal_length = radius;\n    xy_normal_length = 0.0;\n    center_height = radius;\n}\n\n// drop-cutter methods: vertex and facet are handled in base-class\n\n// drop-cutter edgeDrop \nCC_CLZ_Pair BallCutter::singleEdgeDropCanonical(const Point& u1, const Point& u2) const {\n    // the plane of the line will slice the spherical cutter at\n    // a distance d==u1.y==u2.y from the center of the cutter\n    // here the radius of the circular section is s:\n    double s = sqrt( square(radius) - square( u1.y ) );            \n    Point normal(u2.z - u1.z, -(u2.x - u1.x), 0); // (dz, -du) is a normal to the line \n    normal.xyNormalize();\n    if (normal.y < 0)  // flip normal so it points upward\n        normal = -1*normal;\n    Point cc( -s*normal.x, u1.y, 0); // from (0,u1.y,0) we go a distance -s in the normal direction\n    cc.z_projectOntoEdge(u1,u2);\n    double cl_z = cc.z + s*normal.y - radius;\n    return CC_CLZ_Pair( cc.x , cl_z);\n}\n\n// push-cutter: vertex and facet handled in base-class\nbool BallCutter::generalEdgePush(const Fiber& f, Interval& i,  const Point& p1, const Point& p2) const {\n    bool result = false;\n    if ( isZero_tol( (p2-p1).xyNorm() ) ) { // this would be a vertical edge\n        return result;\n    }\n    \n    const Point ufp1 = f.p1 + Point(0,0,radius); // take a fiber which is raised up by radius\n    const Point ufp2 = f.p2 + Point(0,0,radius); // and intersect it with a cylinder around the edge p1-p2\n    // Ray : P(t) = O + t*V    from point O, in direction V\n    // Cylinder [A, B, r]   from point A to point B, radius r\n    // Point P on infinite cylinder if ((P - A) x (B - A))^2 = r^2 * (B - A)^2\n    // expand : ((O - A) x (B - A) + t * (V x (B - A)))^2 = r^2 * (B - A)^2\n    // equation in the form (X + t * Y)^2 = d , where: \n    //  X = (O - A) x (B - A)\n    //  Y = V x (B - A)\n    //  d = r^2 * (B - A)^2\n    // expand the equation :\n    // t^2 * (Y . Y) + t * (2 * (X . Y)) + (X . X) - d = 0\n    // => second order equation in the form : a*t^2 + b*t + c = 0 where\n    // a = (Y . Y)\n    // b = 2 * (X . Y)\n    // c = (X . X) - d\n    Point ab = p2-p1;                           // axis of the cylinder\n    Point ao = (ufp1 - p1);                     // cyl start to ray start\n    Point ao_x_ab = ao.cross(ab);               // cross product\n    Point v_x_ab  = (ufp2-ufp1).cross(ab);      // cross product\n    double ab2 = ab.dot(ab);                    // dot product\n    double a = v_x_ab.dot(v_x_ab);              // dot product\n    double b = 2 * ( v_x_ab.dot(ao_x_ab) );     // dot product\n    double c = ao_x_ab.dot(ao_x_ab) - (radius*radius * ab2);\n    // solve second order equation : a*t^2 + b*t + c = 0\n    // t = (-b +/- sqrt( b^2 - 4ac ) )   / 2a\n    double discr = b*b-4*a*c;\n    double t1, t2;\n    if ( isZero_tol( discr ) ) { // tangent case, only one root\n        t1= -b/(2*a); \n        if ( calcCCandUpdateInterval( t1, p1, p2, f, i ) ) \n            result = true;\n    } else if ( discr > 0.0 ) { // two roots\n        t1 = (-b + sqrt( discr))/(2*a);\n        t2 = (-b - sqrt( discr))/(2*a);\n        if ( calcCCandUpdateInterval( t1, p1, p2, f, i ) ) \n            result = true;\n        if ( calcCCandUpdateInterval( t2, p1, p2, f, i ) ) \n            result = true;\n    } \n    return result;\n}\n\n// t is a position along the fiber\n// p1-p2 is the edge\n// Interval& i is updated\nbool BallCutter::calcCCandUpdateInterval( double t, const Point& p1, const Point& p2, const Fiber& f, Interval& i) const {\n    Point cl_center = f.point(t) + Point(0,0,radius); // center of ball is here\n    CCPoint cc_tmp = cl_center.closestPoint(p1,p2); // cc-point on the edge, point on edge closest to center\n    cc_tmp.type = EDGE_BALL;\n    // require contact with lower hemishphere\n    return i.update_ifCCinEdgeAndTrue( t, cc_tmp, p1, p2, ((cl_center-cc_tmp).z >=0) );\n}\n    \nstd::string BallCutter::str() const {\n    std::ostringstream o;\n    o << *this; \n    return o.str();\n}\n\nstd::ostream& operator<<(std::ostream &stream, BallCutter c) {\n\tstream << \"BallCutter(d=\" << c.diameter << \", r=\" << c.radius << \", L=\" << c.length << \")\";\n\treturn stream;\n}\n\n} // end namespace\n// end file ballcutter.cpp\n", "text_preview": "/* * Copyright 2010 <NAME> (<EMAIL> \"at\" gmail.com) * * This file is part of OpenCAMlib. * * OpenCAMlib is free software: you can redistribute it and/or modify * it under the terms of the GNU General Public License as published by * the Fre", "input_sha256": "4d0fce487ec950e5ceb8f5daaed9156011c339b6f17dca2c412268f99880c697", "text_sha256": "df203c83c316b43dd293cf161e3d8e53383ba0d39a5c756604a184d9a87726e9", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 114, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "needs_review", "training_eligible": false, "label": null, "candidate_label": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/LeparaLaMapara/2_year_computer_science/blob/a2360fa422ebf3a28c6bb1a6e7cdee36d3e26c9c/MerkelMain.cpp", "id": "cpp-0-a8e5507086b15ee071f1b418023bb396", "text": "#include \"MerkelMain.h\"\n#include <iostream>\n#include <algorithm>\n\nMerkelMain::MerkelMain()\n{\n}\n\nvoid MerkelMain::init()\n{\n    std::string command;\n    std::cout << \"Advisorbot> Please enter a command, or help for a list of commands\" << std::endl;\n\n    while (true)\n    {\n        std::cout << \"Advisorbot> \";\n        std::getline(std::cin, command);\n\n        if (command.empty())\n            continue;\n\n        std::istringstream iss(command);\n        std::vector<std::string> tokens(std::istream_iterator<std::string>{iss}, std::istream_iterator<std::string>());\n\n        if (tokens.empty())\n            continue;\n\n        std::string cmd = tokens[0];\n\n        if (cmd == \"help\")\n        {\n            if (tokens.size() == 1)\n            {\n                printHelp();\n            }\n            else if (tokens.size() == 2)\n            {\n                printHelpCommand(tokens[1]);\n            }\n            else\n            {\n                std::cout << \"Invalid command. Usage: help or help <cmd>\" << std::endl;\n            }\n        }\n        else if (cmd == \"prod\")\n        {\n            listAvailableProducts();\n        }\n        else if (cmd == \"min\")\n        {\n            if (tokens.size() == 3)\n            {\n                findMinimum(tokens[1], tokens[2]);\n            }\n            else\n            {\n                std::cout << \"Invalid command. Usage: min product bid/ask\" << std::endl;\n            }\n        }\n        else if (cmd == \"max\")\n        {\n            if (tokens.size() == 3)\n            {\n                findMaximum(tokens[1], tokens[2]);\n            }\n            else\n            {\n                std::cout << \"Invalid command. Usage: max product bid/ask\" << std::endl;\n            }\n        }\n        else if (cmd == \"avg\")\n        {\n            if (tokens.size() == 4)\n            {\n                int timesteps = std::stoi(tokens[3]);\n                calculateAverage(tokens[1], tokens[2], timesteps);\n            }\n            else\n            {\n                std::cout << \"Invalid command. Usage: avg product bid/ask timesteps\" << std::endl;\n            }\n        }\n        else if (cmd == \"predict\")\n        {\n            if (tokens.size() == 3)\n            {\n                if (tokens[1] == \"max\")\n                {\n                    predictMinMax(tokens[2], \"bid\", true);\n                }\n                else if (tokens[1] == \"min\")\n                {\n                    predictMinMax(tokens[2], \"ask\", false);\n                }\n                else\n                {\n                    std::cout << \"Invalid command. Usage: predict max/min product bid/ask\" << std::endl;\n                }\n            }\n            else\n            {\n                std::cout << \"Invalid command. Usage: predict max/min product bid/ask\" << std::endl;\n            }\n        }\n        else if (cmd == \"time\")\n        {\n            displayCurrentTime();\n        }\n        else if (cmd == \"step\")\n        {\n            goToNextTimeStep();\n        }\n        else if (cmd == \"custom\")\n        {\n            processCustomCommand();\n        }\n        else if (cmd == \"optimize\")\n        {\n            optimizeExchangeCode();\n        }\n        else\n        {\n            std::cout << \"Invalid command. Type 'help' for a list of available commands.\" << std::endl;\n        }\n    }\n}\n\nvoid MerkelMain::printHelp()\n{\n    std::cout << \"Available commands:\" << std::endl;\n    std::cout << \"  help                           : List all available commands\" << std::endl;\n    std::cout << \"  help <cmd>                     : Output help for the specified command\" << std::endl;\n    std::cout << \"  prod                           : List available products\" << std::endl;\n    std::cout << \"  min <product> <bid/ask>        : Find the minimum bid or ask for a product\" << std::endl;\n    std::cout << \"  max <product> <bid/ask>        : Find the maximum bid or ask for a product\" << std::endl;\n    std::cout << \"  avg <product> <bid/ask> <timesteps> : Compute the average bid or ask for a product over a number of timesteps\" << std::endl;\n    std::cout << \"  predict max <product> <bid/ask>      : Predict the maximum bid or ask for a product in the next time step\" << std::endl;\n    std::cout << \"  predict min <product> <bid/ask>      : Predict the minimum bid or ask for a product in the next time step\" << std::endl;\n    std::cout << \"  time                           : State the current time in the dataset\" << std::endl;\n    std::cout << \"  step                           : Move to the next time step\" << std::endl;\n    std::cout << \"  custom                         : Execute your own custom command\" << std::endl;\n    std::cout << \"  optimize                       : Optimize the exchange code\" << std::endl;\n}\n\nvoid MerkelMain::printHelpCommand(const std::string& command)\n{\n    // Implement help command for each specific command\n}\n\nvoid MerkelMain::listAvailableProducts()\n{\n    std::vector<std::string> products = orderBook.getKnownProducts();\n\n    std::cout << \"Available products:\" << std::endl;\n    for (const std::string& product : products)\n    {\n        std::cout << \"  \" << product << std::endl;\n    }\n}\n\nvoid MerkelMain::findMinimum(const std::string& product, const std::string& type)\n{\n    std::vector<OrderBookEntry> orders = orderBook.getOrders(OrderBookType::stringToOrderBookType(type), product, currentTime);\n\n    if (orders.empty())\n    {\n        std::cout << \"No \" << type << \" entries found for \" << product << \" in the current time step.\" << std::endl;\n        return;\n    }\n\n    double minPrice = OrderBook::getLowPrice(orders);\n\n    std::cout << \"The min \" << type << \" for \" << product << \" is \" << minPrice << std::endl;\n}\n\n\nvoid MerkelMain::findMaximum(const std::string& product, const std::string& type)\n{\n    std::vector<OrderBookEntry> orders = orderBook.getOrders(OrderBookType::stringToOrderBookType(type), product, currentTime);\n\n    if (orders.empty())\n    {\n        std::cout << \"No \" << type << \" entries found for \" << product << \" in the current time step.\" << std::endl;\n        return;\n    }\n\n    double maxPrice = OrderBook::getHighPrice(orders);\n\n    std::cout << \"The max \" << type << \" for \" << product << \" is \" << maxPrice << std::endl;\n}\n\n\nvoid MerkelMain::calculateAverage(const std::string& product, const std::string& type, int timesteps)\n{\n    std::vector<OrderBookEntry> orders;\n    std::string timestamp = currentTime;\n\n    for (int i = 0; i < timesteps; i++)\n    {\n        std::vector<OrderBookEntry> currentOrders = orderBook.getOrders(OrderBookType::stringToOrderBookType(type), product, timestamp);\n\n        if (!currentOrders.empty())\n        {\n            orders.insert(orders.end(), currentOrders.begin(), currentOrders.end());\n        }\n\n        timestamp = orderBook.getNextTime(timestamp);\n    }\n\n    if (orders.empty())\n    {\n        std::cout << \"No \" << type << \" entries found for \" << product << \" over the specified number of time steps.\" << std::endl;\n        return;\n    }\n\n    double total = 0.0;\n    for (const OrderBookEntry& order : orders)\n    {\n        total += order.price;\n    }\n\n    double average = total / orders.size();\n\n    std::cout << \"The average \" << product << \" \" << type << \" price over the last \" << timesteps << \" timesteps was \" << average << std::endl;\n}\n\n\nvoid MerkelMain::predictMinMax(const std::string& product, const std::string& type, bool isMax)\nvoid MerkelMain::predictMinMax(const std::string& product, const std::string& type, bool isMax)\n{\n    std::vector<OrderBookEntry> currentOrders = orderBook.getOrders(OrderBookType::stringToOrderBookType(type), product, currentTime);\n\n    if (currentOrders.empty())\n    {\n        std::cout << \"No \" << type << \" entries found for \" << product << \" in the current time step.\" << std::endl;\n        return;\n    }\n\n    double prediction = 0.0;\n\n    // Perform prediction using an appropriate algorithm\n    // For example, using moving average\n    double sum = 0.0;\n    int count = 0;\n    for (const OrderBookEntry& order : currentOrders)\n    {\n        sum += order.price;\n        count++;\n    }\n\n    if (count > 0)\n    {\n        double average = sum / count;\n        prediction = isMax ? average + 0.1 : average - 0.1;\n    }\n\n    std::string predictionType = isMax ? \"max\" : \"min\";\n\n    std::cout << \"The \" << predictionType << \" \" << type << \" for \" << product << \" in the next time step might be \" << prediction << std::endl;\n}\n\nvoid MerkelMain::displayCurrentTime()\n{\n    std::cout << \"Current time: \" << currentTime << std::endl;\n}\n\nvoid MerkelMain::gotoNextTimeframe()\n{\n    currentTime = orderBook.getNextTime(currentTime);\n    std::cout << \"Now at \" << currentTime << std::endl;\n}\n\n\nvoid MerkelMain::processCustomCommand()\n{\n    // Implement your own custom command\n}\n\nvoid MerkelMain::optimizeExchangeCode()\n{\n    // Implement optimizing the exchange code\n}\n", "text_preview": "#include \"MerkelMain.h\" #include <iostream> #include <algorithm> MerkelMain::MerkelMain() { } void MerkelMain::init() { std::string command; std::cout << \"Advisorbot> Please enter a command, or help for a list of commands\" << std::endl; whi", "input_sha256": "c800d5c1ef2629e9a01d998501536ede355d108eaab355192991947cb6afb0ab", "text_sha256": "a8e5507086b15ee071f1b418023bb396d33ab552c142e870b9775852174c055c", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 115, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": 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false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a2360fa422ebf3a28c6bb1a6e7cdee36d3e26c9c"}}, "parsed_source_meta": {"github_metadata": {"branch": "main", "commit_count": 20, "repo_created_at": "2023-05-14T07:35:48.000Z", "is_fork": false, "is_org_owned": false, "forked_from": null, "stars": 0, "forks": 1, "issues": 0, "pull_requests": 0}, "file_meta": {"content_id": "2724aa0a6a90a0a0e312cca67fd78bea874ea6e3", "size_bytes": 8834, "file_timestamp": 1689190127, "language": "c++", "is_vendor": false, "license_type": "no_license", "detected_licenses": []}, "commit_id": "a2360fa422ebf3a28c6bb1a6e7cdee36d3e26c9c", "url": "https://github.com/LeparaLaMapara/2_year_computer_science/blob/a2360fa422ebf3a28c6bb1a6e7cdee36d3e26c9c/MerkelMain.cpp"}, "gpt4o_tokens": 2119, "dataset_split": "cpp", "probability_weighted_scores": {"content_quality": -0.2540921332991905, "noise_severity": 0.48384076879502064, "missing_content_severity": 0.5441610037336981, "factual_density": 2.8373339094246433, "pretraining_utility": 0.2493416412301117, "math_density": 1.272808354755995, "code_density": 3.6810670776729872, "reasoning_density": 0.5295931448107739, "education_level": 1.9945940138264144, "creative_writing_quality": -0.5979512167966293}, "score_applicability": {"education_level": 0.998429969006485, "creative_writing_quality": 0.0020757717329330026}}
{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/pixel-pixel/mod2_lab6_grph/blob/701261cb40c52335c05fedcbe2aaf284314bfc02/main.cpp", "id": "cpp-0-66d104644060c94716623694bd4fe8d1", "text": "#include <iostream>\n#include <fstream>\n\nusing namespace std;\n\n//Однозв'язний граф\n//Вершини графа - індекси(починаючи від нуля)\n//При об'єднанні вершин графу(наприклад переміщуємо вершину 3 до вершини 5),\n//то вершина 3 зникає, а індекси пересуваються:\n//вершина 4 тепер стає вершиною 3, 5->4, 6->5 і т.д.\n//Якщо граф має 3 вершини(0, 1, 2) і ви з'єднуєте наприклад вершину 1 із вершиною 5,\n//то розмір графу автоматично збвльшується до 6 (адже індексація з нуля: 5+1 = 6)\n//При видалені ребере розмір зменшуватись не буде.\n\nstruct Graph{\n    int size;\n    int matrix_size = 2;\n    bool **matrix = new bool* [matrix_size];\n\n    Graph(int size){\n        for(int i = 0; i < matrix_size; i++){\n            matrix[i] = new bool [matrix_size];\n        }\n        clear();\n\n        this->size = size;\n        if(size > matrix_size) change_matrix_size(size / 2 * 3);\n    }\n\n    void change_matrix_size(int new_size){\n        bool **new_matrix = new bool* [new_size];\n\n        for(int i = 0; i < new_size; i++){\n            new_matrix[i] = new bool [new_size];\n\n            for(int j = 0; j < new_size; j++){\n                if(i < size && j < size) new_matrix[i][j] = matrix[i][j];\n                else new_matrix[i][j] = false;\n            }\n        }\n        swap(matrix, new_matrix);\n\n        for(int i = 0; i < size; i++){\n            delete [] new_matrix[i];\n        }\n\n        matrix_size = new_size;\n    }\n\n    void add_edge(int from, int to){\n        int max;\n        if(from > to) max = from;\n        else max = to;\n\n        if(max+1 > matrix_size){\n            change_matrix_size((max+1)/2*3);\n            size = max+1;\n        }\n\n        matrix[from][to] = true;\n    }\n\n    void del_edge(int from, int to){\n        if(from < size && to < size){\n            matrix[from][to] = false;\n        }\n    }\n\n    void merge(int old, int in){\n        for(int i = 0; i < size; i++){\n            if(i != old){\n                if(matrix[i][old]) matrix[i][in] = true;\n                if(matrix[old][i]) matrix[in][i] = true;\n            }\n        }\n\n        if(old == size-1){\n            for(int i = 0; i < size; i++){\n                matrix[i][old] = false;\n            }\n            for(int j = 0; j < size; j++){\n                matrix[old][j] = false;\n            }\n        } else {\n            for (int i = old; i < size - 1; i++) {\n                for (int j = 0; j < size; j++) {\n                    matrix[i][j] = matrix[i + 1][j];\n                }\n            }\n            for (int i = 0; i < size; i++) {\n                for (int j = old; j < size - 1; j++) {\n                    matrix[i][j] = matrix[i][j + 1];\n                }\n            }\n        }\n        size--;\n    }\n\n    int* matrix_to_arr(){\n        int arr_size = 0;\n        for(int i = 0; i < size; i++){\n            for(int j = 0; j < size; j++){\n                if(matrix[i][j]) arr_size++;\n            }\n        }\n\n        ++arr_size *=2;\n\n        int * arr = new int[arr_size];\n        int index = 0;\n        arr[index++] = arr_size;\n        arr[index++] = size;\n\n        for(int i = 0; i < size; i++){\n            for(int j = 0; j < size; j++){\n                if(matrix[i][j]) {\n                    arr[index++] = i;\n                    arr[index++] = j;\n                }\n            }\n        }\n        return arr;\n    }\n\n    void arr_to_matrix(int *arr){\n        clear();\n        int n = arr[0];\n        for(int i = 2; i < n; i+=2){\n            add_edge(arr[i], arr[i+1]);\n        }\n        delete [] arr;\n    }\n\n    void write_matrix(){\n        ofstream out;\n        out.open(\"/home/andrew/CLionProjects/mod2_lab6_grph/file\", ios::binary);\n        int *arr = matrix_to_arr();\n        int n = arr[0];\n        if(out.is_open()) {\n            out.write((char*)&(*arr), sizeof(int)*n);\n            out.close();\n        }\n        delete [] arr;\n    }\n\n    void read_matrix(){\n        ifstream in;\n        in.open(\"/home/andrew/CLionProjects/mod2_lab6_grph/file\", ios::binary);\n        if(in.is_open()){\n            in.seekg(0, ios::end);\n            int n = in.tellg() / 4;\n\n            in.seekg(0, ios::beg);\n\n            int *arr = new int[n];\n            in.read((char*)&(*arr), sizeof(int) * n);\n            arr_to_matrix(arr);\n            in.close();\n        }\n    }\n\n    void clear(){\n        for(int i = 0; i < size; i++){\n            for(int j = 0; j < size; j++){\n                matrix[i][j] = false;\n            }\n        }\n    }\n\n    void print(){\n        for(int j = 0; j < size; j++){\n            for(int i = 0; i < size; i++){\n                cout << matrix[i][j] << \"\\t\";\n            }\n            cout << endl;\n        }\n        cout << endl;\n    }\n};\n\nint main() {\n    Graph gr(2);\n    gr.add_edge(0, 3);\n    gr.add_edge(3, 2);\n    gr.add_edge(2, 2);\n    gr.write_matrix();\n    gr.read_matrix();\n    gr.print();\n}\n", "text_preview": "#include <iostream> #include <fstream> using namespace std; //Однозв'язний граф //Вершини графа - індекси(починаючи від нуля) //При об'єднанні вершин графу(наприклад переміщуємо вершину 3 до вершини 5), //то вершина 3 зникає, а індекси пере", "input_sha256": "dbd9aa07a553484af1795937a79e349ff196a191b3572ef61fce124ee86b6a70", "text_sha256": "66d104644060c94716623694bd4fe8d1ea7cb70a6d3ee6c81940309390b63650", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 116, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 1, "math_density": 1, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 0.35143064338023133, "noise_severity": 0.025733055760449118, "missing_content_severity": 0.009094423676542543, "factual_density": 3.249646729320066, "pretraining_utility": 0.4935843449991315, "math_density": 1.9780856406825684, "code_density": 3.720010633184368, "reasoning_density": 0.8533703032097688, "education_level": 2.0295004263205927, "creative_writing_quality": -0.4216834156846549}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.010725254666312023, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.9945797006420384, "secondary": 0.9521123230232664, "tertiary": 0.9633038931098528, "structured_records": 0.009689176763179872, "procedural_steps": 0.007149880463515674, "worked_solution": 0.04818053068354591, "contextual_dialogue": 6.51611302449295e-06, "content_quality": 0.3052421348736055, "noise_severity": 0.9785557868662924, "missing_content_severity": 0.9924213136028812, "factual_density": 0.37470560776672146, "pretraining_utility": 0.4495605967850149, "math_density": 0.0, "code_density": 0.7666755276536399, "reasoning_density": 0.2888580806585259, "education_level": 0.8122087100246609, "creative_writing_quality": 0.9979917546960947, "primary_topic": 0.953368966439435, "secondary_topic": 0.5251925849164798}, "probability_margin": {"primary": 0.9928558336114222, "secondary": 0.9371373819319041, "tertiary": 0.9507133105128265, "education_level": 0.7585840584845004, "creative_writing_quality": 0.9974884123272989, "primary_topic": 0.9129926336172739, "secondary_topic": 0.18691810233985723}, "raw_probabilities": {"primary": {"Academic Writing": 6.860515223943712e-05, "Content Listing": 2.1551437259582177e-05, "Creative Writing": 3.1513917432374985e-05, "Customer Support Page": 9.652293288179064e-06, "Q&A Forum": 1.589132885055868e-05, "Discussion Thread": 1.3007812770144367e-05, "Comment Section": 1.9074332550835736e-05, "Missing Content": 3.892097030158326e-05, "Knowledge Article": 0.00017931071242175225, "Legal Notices": 2.878574442489186e-05, "News Article": 6.477039595492574e-06, "Organizational Announcement": 1.2781188205432152e-05, "Nonfiction Writing": 1.9796429599180687e-05, "Organizational About Page": 1.4474809217778348e-05, "Personal About Page": 1.8579235497118225e-05, "Personal Blog": 1.129620063190952e-05, "Product Page": 8.303032831272042e-06, "Spam / Ads": 1.4264498149778918e-05, "Structured Data": 0.0007536408351934397, "Technical Writing": 0.9948055464486201, "Transcript / Interview": 2.2942402922472413e-05, "Tutorial / How-To Guide": 0.0019497128371979465, "User Reviews": 9.362119962393388e-06, "Other / Unknown": 0.0019265092208362365}, "secondary": {"Academic Writing": 0.0015625210593047192, "Content Listing": 0.00011872536968437204, "Creative Writing": 0.00023487960053047706, "Customer Support Page": 0.00011820308114545436, "Q&A Forum": 9.973138177772498e-05, "Discussion Thread": 8.479257128556449e-05, "Comment Section": 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0.9518194693164541, "true": 0.04818053068354591}, "contextual_dialogue": {"false": 0.9999934838869755, "true": 6.51611302449295e-06}, "content_quality": {"-2": 0.03234534136769117, "-1": 0.14236253881719713, "0": 0.3593782956482533, "1": 0.3733437834009061, "2": 0.09257004076595232}, "noise_severity": {"0": 0.9776491705200803, "1": 0.019980535623868683, "2": 0.00163261160473942, "3": 0.00046343207814569885, "4": 0.00027425017316612616}, "missing_content_severity": {"0": 0.9947617185543366, "1": 0.002312236158551292, "2": 0.0021687026462828053, "3": 0.0005845883378918142, "4": 0.00017275430293754975}, "factual_density": {"0": 0.009848486567865305, "1": 0.02745065126636714, "2": 0.13100520178247677, "3": 0.366596967044418, "4": 0.4650986933388728}, "pretraining_utility": {"-2": 0.010079040802909764, "-1": 0.08152656275641458, "0": 0.3901812215078668, "1": 0.4411573605042521, "2": 0.07705581442855677}, "math_density": {"0": 0.1962808195245815, "1": 0.23979551076915057, "2": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/Masttf/XCPC/blob/903053d39040cc7c5aa023bb6c822c26b9f80086/niuke/2025%E5%AF%92%E5%81%87/2/mm.cpp", "id": "cpp-0-7e67b32a5f977939a7e26fd9e1e30db4", "text": "#include<bits/stdc++.h>\n#define int long long\nusing namespace std;\n#define dbg(x...) \\\ndo { \\\n    cout << #x << \" -> \"; \\\n    err(x); \\\n} while (0)\n\nvoid err() {\n    cout << endl << endl;\n}\n \ntemplate<class T, class... Ts>\nvoid err(T arg, Ts ... args) {\n    cout << fixed << setprecision(10) << arg << ' ';\n    err(args...);\n}\ntemplate<const int T>\nstruct ModInt {\n    const static int mod = T;\n    int x;\n    ModInt(int x = 0) : x(x < 0 ? x % mod + mod : x % mod) {}\n    // ModInt(long long x) : x(int(x < 0 ? x % mod + mod : x % mod)) {} \n    int val() { return x; }\n    ModInt operator + (const ModInt &a) const { \n        int x0 = x + a.x;\n        return ModInt(x0 < mod ? x0 : x0 - mod); \n    }\n    ModInt operator - (const ModInt &a) const {\n        int x0 = x - a.x;\n        return ModInt(x0 < 0 ? x0 + mod : x0);\n    }\n    ModInt operator * (const ModInt &a) const {\n        return ModInt(1LL * x * a.x % mod);\n    }\n    ModInt operator / (const ModInt &a) const {\n        return *this * a.inv();\n    }\n    bool operator == (const ModInt &a) const {\n        return x == a.x;\n    }\n    bool operator != (const ModInt &a) const {\n        return x != a.x;\n    }\n    void operator += (const ModInt &a) {\n        x += a.x;\n        if (x >= mod) x -= mod;\n    }\n    void operator -= (const ModInt &a) {\n        x -= a.x;\n        if (x < 0) x += mod;\n    }\n    void operator *= (const ModInt &a) {\n        x = 1LL * x * a.x % mod;\n    }            \n    void operator /= (const ModInt &a) {\n        *this = *this / a;\n    }\n    friend ModInt operator + (int y, const ModInt &a){\n        int x0 = y + a.x;\n        return ModInt(x0 < mod ? x0 : x0 - mod);\n    }\n    friend ModInt operator - (int y, const ModInt &a){\n        int x0 = y - a.x;\n        return ModInt(x0 < 0 ? x0 + mod : x0);\n    }            \n    friend ModInt operator * (int y, const ModInt &a){\n        return ModInt(1LL * y * a.x % mod);\n    }\n    friend ModInt operator / (int y, const ModInt &a){\n        return ModInt(y) / a;\n    }\n    friend ostream &operator<<(ostream &os, const ModInt &a) {\n        return os << a.x;\n    }\n    friend istream &operator>>(istream &is, ModInt &t){\n        return is >> t.x;\n    }                        \n                \n    ModInt pow(long long n) const {\n    ModInt res(1), mul(x);\n        while(n){\n            if (n & 1) res *= mul;\n            mul *= mul;\n            n >>= 1;\n        }\n        return res;\n    }\n                \n    ModInt inv() const {\n        int a = x, b = mod, u = 1, v = 0;\n        while (b) {\n            int t = a / b;\n            a -= t * b; swap(a, b);\n            u -= t * v; swap(u, v);\n        }\n        if (u < 0) u += mod;\n        return u;\n    }\n                \n};\nconstexpr int mod = 1e9 + 7;\nusing mint = ModInt<mod>;\nint statu[100];\nmap<int, int> m;\nmint dp[2][100][100], p[10];\nvector<int> g[100];\nvector<array<int, 3>> to[2048];\nint tot = 0;\nvoid init(){\n    for(int i = 0; i < (1 << 9); i++){\n        int cnt = 0;\n        for(int j = 0; j < 9; j++){\n            if(i >> j & 1){\n                cnt++;\n            }\n        }\n        if(cnt == 3){\n            tot++;\n            int x = i * 2;\n            statu[tot] = x;\n            m[x] = tot;\n        }\n    }\n    for(int i = 1; i <= tot; i++){\n        for(int j = 1; j <= tot; j++){\n            if(statu[i] & statu[j])continue;\n            g[i].push_back(j);\n        }\n    }\n    p[0] = 1;\n    for(int i = 1; i <= 3; i++){\n        p[i] = p[i - 1] * i;\n    }\n    int mx = 0;\n    for(int i = 1; i <= 9; i++){\n        mx |= (1 << i);\n    }\n    for(int i = 0; i < (1 << 9); i++){\n        int cnt = 0;\n        for(int j = 0; j < 9; j++){\n            if(i >> j & 1){\n                cnt++;\n            }\n        }\n        if(cnt <= 3){\n            int x = i * 2;\n            for(int j = 1; j <= tot; j++){\n                if(statu[j] & x)continue;\n                for(auto v : g[j]){\n                    if(statu[v] & x)continue;\n                    int u = mx - statu[j] - statu[v];\n                    u = m[u];\n                    to[x].push_back({j, v, u});\n                }\n            }\n        }\n    }\n    return ;\n}\nvoid solve(){\n    int n; cin >> n;\n    vector<string> a(4);\n    for(int i = 1; i <= 3; i++){\n        cin >> a[i];\n        a[i] = ' ' + a[i];\n    }\n    vector<int> c_c(10);\n    for(int i = 1; i <= n; i++){\n        c_c.assign(10, 0);\n        for(int j = 1; j <= 3; j++){\n            if(a[j][i] == '?')continue;\n            c_c[a[j][i] - '0']++;\n            if(c_c[a[j][i] - '0'] >= 2){\n                cout << 0 << '\\n';\n                return ;\n            }\n        }\n    }\n    for(int i = 0; i < 2; i++){\n        for(int j = 0; j <= tot; j++){\n            for(int k = 0; k <= tot; k++){\n                dp[i][j][k] = 0;\n            }\n        }\n    }\n    auto get = [&](int i) -> pair<int, int>{\n        int cnt = 0;\n        int val = 0;\n        for(int j = 1; j <= 3; j++){\n            if(a[j][i] == '?')cnt++;\n            else{\n                int x = a[j][i] - '0';\n                val |= (1 << x);\n            }\n        }\n        return {cnt, val};\n    };\n    auto [c1, v1] = get(1);\n    auto [c2, v2] = get(2);\n    for(int i = 1; i <= tot; i++){\n        int x = statu[i];\n        if((v1 & x) != v1)continue;\n        for(auto j : g[i]){\n            int y = statu[j];\n            if((v2 & y) != v2)continue;\n            dp[0][i][j] += p[c1] * p[c2];\n        }\n    }\n    for(int i = 3, now = 1; i <= n; i++, now ^= 1){\n        auto [cnt, val] = get(i);\n        for(int j = 1; j <= tot; j++){\n            for(auto v : g[j]){\n                dp[now][j][v] = 0;\n            }\n        }\n        for(auto [j, v, u] : to[val]){\n            dp[now][v][u] += dp[now ^ 1][j][v] * p[cnt];\n        }\n    }\n    mint ans = 0;\n    int now = n % 2;\n    for(int i = 1; i <= tot; i++){\n        for(auto v : g[i]){\n            ans += dp[now][i][v];\n        }\n    }\n    cout << ans << '\\n';\n    return ;\n}\nsigned main(){\n    ios::sync_with_stdio(false);\n    cin.tie(0);\n    int t = 1;\n    init();\n    cin >> t;\n    while(t--)solve();\n    return 0;\n}\n/*\nC(3, 9)\n3 * 4 * 7\n12 * 7\n84\n*/", "text_preview": "#include<bits/stdc++.h> #define int long long using namespace std; #define dbg(x...) \\ do { \\ cout << #x << \" -> \"; \\ err(x); \\ } while (0) void err() { cout << endl << endl; } template<class T, class... 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/helenchg/Maze_Robotics/blob/bed52e4fe32a01b65bcc30cab88b518ae4349919/threeSensorsRobot/MazePieceSolver/MazePieceSolver.ino", "id": "cpp-0-2defe732ced28f3642b90a5d512f80b9", "text": "/***********************************************************************\n* Author: <NAME>\n* Last time modified: February 7th, 2016\n* Description: Maze Line Solver Testing Each Piece of Algorithm\n* Initial line following code from SparkFun Electronics RedBot Library Examples.\n***********************************************************************/\n#include <RedBot.h>\nRedBotSensor left = RedBotSensor(A3);   // initialize a left sensor object on A3\nRedBotSensor center = RedBotSensor(A6); // initialize a center sensor object on A6\nRedBotSensor right = RedBotSensor(A7);  // initialize a right sensor object on A7\n\n#define THRESHOLD 900\n#define SPEED 80  // sets the nominal speed. Set to any number from 0 - 255.\n\nRedBotEncoder encoder = RedBotEncoder(A2, 10);  // initializes encoder on pins A2 and 10\nRedBotMotors motors;\nint leftSpeed;   // variable used to store the leftMotor speed\nint rightSpeed;  // variable used to store the rightMotor speed\nint countsPerRev = 192;   // 4 pairs of N-S x 48:1 gearbox = 192 ticks per wheel rev\nfloat wheelDiam = 2.56;  // diam = 65mm / 25.4 mm/in\nfloat wheelCirc = PI * wheelDiam; // Redbot wheel circumference = pi*D\nint lCount; // variables used to store the left and right encoder counts.\n// left negative, right negative == spin CW\n// left positive, right positive == spin CCW\n// left negative == forward\n// left positive == backward\n// right negative == backward\n// right positive == forward\n\nboolean decideAgain = true;\n//int rightSensor;\n//int leftSensor;\n//int centerSensor;\n\nenum state { // States\n  start,        // 0\n  straight,     // 1\n  turnLeft,     // 2\n  turnRight,    // 3\n  uTurn,        // 4\n  decideLeft,   // 5\n  decideRight,  // 6\n  finished      // 7\n};\nstate nextState = start;\nstate prevState = start;\n\n\n\nvoid setup()\n{\n  Serial.begin(9600);\n  delay(2000);\n  Serial.println(\"IR Sensor Readings: \");\n  delay(500);\n}\n\nvoid loop()\n{\n  Serial.print(left.read());\n  Serial.print(\"\\t\");  // tab character\n  Serial.print(center.read());\n  Serial.print(\"\\t\");  // tab character\n  Serial.print(right.read());\n  Serial.println();\n\n  //if ((right.read() > THRESHOLD) && (center.read() > THRESHOLD ) && (left.read() > THRESHOLD)) {\n//    if(decideAgain){\n      //goForwardDecide();  \n//    }\n  //}\n  // BOTH NEGATIVE = CW\n  // BOTH POSITIVE = CCW\n\n  leftSpeed = SPEED;\n  rightSpeed = -SPEED;\n    motors.leftMotor(leftSpeed);\n    motors.rightMotor(rightSpeed);\n}\n\n\n\n/********************************************\n* Go forward an inch to decide next movement.\n*/\nvoid goForwardDecide() {\n  // move forward and decide based on values read.\n  Serial.println(\"Move forward an inch\");\n  driveDistance(1, -SPEED, SPEED); //drive forward an inch\n  delay(200); //delay\n  if ((right.read() < THRESHOLD) && (center.read() < THRESHOLD ) && (left.read() < THRESHOLD)) {\n    leftSpeed = -(SPEED + 50);\n    rightSpeed = SPEED - 50;\n  }\n  else if ((right.read() < THRESHOLD) && (center.read() > THRESHOLD) && (left.read() < THRESHOLD)) {\n    leftSpeed = -SPEED;\n    rightSpeed = SPEED;\n  }\n  else {\n    leftSpeed = 0;\n    rightSpeed = 0;\n  }\n  delay(200);\n  //driveDistance(2, SPEED, -SPEED);\n  //delay(200);\n  motors.leftMotor(leftSpeed);\n  motors.rightMotor(rightSpeed);\n  motors.brake();\n  decideAgain = false;\n}\n\n\n/**\n* Drive distance function from RedBot library examples\n* distance in inches\n* leftPower must be negative to drive forward\n* rightPower must be positive to drive forward\n*/\nvoid driveDistance(float distance, int leftPower, int rightPower)\n{\n  long lCount = 0;\n  long rCount = 0;\n  float numRev;\n  // debug\n  Serial.print(\"driveDistance() \");\n  Serial.print(distance);\n  Serial.print(\" inches at \");\n  Serial.print(leftPower);\n  Serial.println(\" left power.\");\n  Serial.print(rightPower);\n  Serial.println(\" right power.\");\n  numRev = (float)distance / wheelCirc;\n  Serial.println(numRev, 3);\n  encoder.clearEnc(BOTH);  // clear the encoder count\n  motors.leftMotor(leftPower);\n  motors.rightMotor(rightPower);\n\n  while (abs(lCount) < numRev*countsPerRev)\n  {\n//     while the left encoder is less than the target count -- debug print \n//     the encoder values and wait -- this is a holding loop.\n    lCount = encoder.getTicks(LEFT);\n    rCount = encoder.getTicks(RIGHT);\n    Serial.print(lCount);\n    Serial.print(\"\\t\");\n    Serial.print(rCount);\n    Serial.print(\"\\t\");\n    Serial.println(numRev*countsPerRev);\n  }\n  // now apply \"brakes\" to stop the motors.\n  motors.brake();\n}\n", "text_preview": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/JaRoLoz/librpf/blob/4515a37c3f43819ec37ca53aaa5328c45d5718ee/NG.cpp", "id": "cpp-0-35db73e2b95d408d1354be6791ae08ad", "text": "#include \"NG.hpp\"\n\nuint32_t NG::CalculateHash(const uint8_t lut[LUT_SIZE], const std::string_view& name)\n{\n\tuint32_t result = 0;\n\tfor (size_t i = 0; i < name.size(); i++)\n\t{\n\t\tuint8_t lutIndex = name[i];\n\t\tuint32_t temp = static_cast<uint32_t>(1025) * (static_cast<uint32_t>(lut[lutIndex]) + result);\n\t\tresult = (temp >> 6) ^ temp & 0xFFFFFFFF;\n\t}\n\n\treturn static_cast<uint32_t>(32769) * (((static_cast<uint32_t>(9) * result) >> 11) ^ (static_cast<uint32_t>(9) * result));\n}\n\nsize_t NG::GetNGKeyIndex(const uint8_t lut[LUT_SIZE], const std::string_view& name, const size_t size)\n{\n\tsize_t hash = (size_t) CalculateHash(lut, name);\n\tsize_t index = (hash + size + ((size_t)101 - 40)) % 0x65;\n\treturn index;\n}\n\nvoid NG::DecryptRoundA\n(\n\tconst uint32_t decryptTable[DECRYPT_TABLES_SIZE2][DECRYPT_TABLES_SIZE3],\n\tconst uint32_t ngKey[NG_KEY_SIZE_UINT32],\n\tuint8_t buffer[NG_BLOCK_SIZE]\n)\n{\n\tuint32_t x0 = decryptTable[0][buffer[0]]\n\t\t^ decryptTable[1][buffer[1]]\n\t\t^ decryptTable[2][buffer[2]]\n\t\t^ decryptTable[3][buffer[3]]\n\t\t^ ngKey[0];\n\tuint32_t x1 = decryptTable[4][buffer[4]]\n\t\t^ decryptTable[5][buffer[5]]\n\t\t^ decryptTable[6][buffer[6]]\n\t\t^ decryptTable[7][buffer[7]]\n\t\t^ ngKey[1];\n\tuint32_t x2 = decryptTable[8][buffer[8]]\n\t\t^ decryptTable[9][buffer[9]]\n\t\t^ decryptTable[10][buffer[10]]\n\t\t^ decryptTable[11][buffer[11]]\n\t\t^ ngKey[2];\n\tuint32_t x3 = decryptTable[12][buffer[12]]\n\t\t^ decryptTable[13][buffer[13]]\n\t\t^ decryptTable[14][buffer[14]]\n\t\t^ decryptTable[15][buffer[15]]\n\t\t^ ngKey[3];\n\n\tuint32_t* buffer32 = reinterpret_cast<uint32_t*>(buffer);\n\tbuffer32[0] = x0;\n\tbuffer32[1] = x1;\n\tbuffer32[2] = x2;\n\tbuffer32[3] = x3;\n}\n\nvoid NG::DecryptRoundB\n(\n\tconst uint32_t decryptTable[DECRYPT_TABLES_SIZE2][DECRYPT_TABLES_SIZE3],\n\tconst uint32_t ngKey[NG_KEY_SIZE_UINT32],\n\tuint8_t buffer[NG_BLOCK_SIZE]\n)\n{\n\tuint32_t x0 = decryptTable[0][buffer[0]]\n\t\t^ decryptTable[7][buffer[7]]\n\t\t^ decryptTable[10][buffer[10]]\n\t\t^ decryptTable[13][buffer[13]]\n\t\t^ ngKey[0];\n\tuint32_t x1 = decryptTable[1][buffer[1]]\n\t\t^ decryptTable[4][buffer[4]]\n\t\t^ decryptTable[11][buffer[11]]\n\t\t^ decryptTable[14][buffer[14]]\n\t\t^ ngKey[1];\n\tuint32_t x2 = decryptTable[2][buffer[2]]\n\t\t^ decryptTable[5][buffer[5]]\n\t\t^ decryptTable[8][buffer[8]]\n\t\t^ decryptTable[15][buffer[15]]\n\t\t^ ngKey[2];\n\tuint32_t x3 = decryptTable[3][buffer[3]]\n\t\t^ decryptTable[6][buffer[6]]\n\t\t^ decryptTable[9][buffer[9]]\n\t\t^ decryptTable[12][buffer[12]]\n\t\t^ ngKey[3];\n\n\tuint32_t* buffer32 = reinterpret_cast<uint32_t*>(buffer);\n\tbuffer32[0] = x0;\n\tbuffer32[1] = x1;\n\tbuffer32[2] = x2;\n\tbuffer32[3] = x3;\n}\n\nvoid NG::DecryptBlock\n(\n\tconst uint32_t decryptTables[DECRYPT_TABLES_SIZE1][DECRYPT_TABLES_SIZE2][DECRYPT_TABLES_SIZE3], \n\tconst uint32_t ngKey[NG_KEY_SIZE_UINT32], \n\tuint8_t buffer[NG_BLOCK_SIZE]\n)\n{\n\tuint32_t subKeys[17][4] = { 0 };\n\tfor (size_t i = 0; i < 17; i++)\n\t{\n\t\tsubKeys[i][0] = ngKey[i * 4];\n\t\tsubKeys[i][1] = ngKey[i * 4 + 1];\n\t\tsubKeys[i][2] = ngKey[i * 4 + 2];\n\t\tsubKeys[i][3] = ngKey[i * 4 + 3];\n\t}\n\n\tDecryptRoundA(decryptTables[0], subKeys[0], buffer);\n\tDecryptRoundA(decryptTables[1], subKeys[1], buffer);\n\n\tfor (size_t i = 2; i < 16; i++)\n\t{\n\t\tDecryptRoundB(decryptTables[i], subKeys[i], buffer);\n\t}\n\n\tDecryptRoundA(decryptTables[16], subKeys[16], buffer);\n}\n\nuint8_t* NG::Decrypt\n(\n\tconst uint8_t lut[LUT_SIZE],\n\tconst uint32_t decryptTables[DECRYPT_TABLES_SIZE1][DECRYPT_TABLES_SIZE2][DECRYPT_TABLES_SIZE3],\n\tconst uint8_t ngKeys[NG_KEYS_SIZE1][NG_KEYS_SIZE2],\n\tconst uint8_t* data,\n\tconst size_t dataSize,\n\tconst std::string_view& name,\n\tconst size_t entrySize\n)\n{\n\tsize_t ngKeyIndex = GetNGKeyIndex(lut, name, entrySize);\n\tconst uint32_t* ngKey = reinterpret_cast<const uint32_t*>(ngKeys[ngKeyIndex]); // size uint32_t[68]\n\tuint8_t* decryptedData = new uint8_t[dataSize];\n\tmemcpy(decryptedData, data, dataSize);\n\n\tfor (size_t i = 0; i < (dataSize / NG_BLOCK_SIZE); i++)\n\t{\n\t\tsize_t offset = i * NG_BLOCK_SIZE;\n\t\tuint8_t* decryptedPtr = decryptedData + offset;\n\n\t\tDecryptBlock(decryptTables, ngKey, decryptedPtr);\n\t}\n\n\treturn decryptedData;\n}", "text_preview": "#include \"NG.hpp\" uint32_t NG::CalculateHash(const uint8_t lut[LUT_SIZE], const std::string_view& name) { uint32_t result = 0; for (size_t i = 0; i < name.size(); i++) { uint8_t lutIndex = name[i]; uint32_t temp = static_cast<uint32_t>(1025", "input_sha256": "64e188e737d1b7e4a18feb0699f8c8c37dd7d06b2f3b8f66e98bd0be0c645306", "text_sha256": "35db73e2b95d408d1354be6791ae08adeb3205b138b11ee3bb11956b0b0ca175", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 119, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": "https://inference.baseten.co/v1/systemone", "status": "ok", "training_eligible": true, "label": {"primary": {"main_format": "Technical Writing", "sub_format": null}, "secondary": null, "tertiary": null, "content_structure": []}, "candidate_label": null, "topic": {"primary": {"main_topic": "Programming & Software Engineering", "sub_topic": null}, "secondary": null}, "scores": {"content_quality": 1, "noise_severity": 0, "missing_content_severity": 0, "factual_density": 4, "pretraining_utility": 0, "math_density": 4, "code_density": 4, "reasoning_density": 0, "education_level": 2, "creative_writing_quality": null}, "score_expectations": {"content_quality": 1.0594368161793712, "noise_severity": 0.009020082827109428, "missing_content_severity": 0.03961501027534111, "factual_density": 3.410176584281033, "pretraining_utility": 0.36850057717562273, "math_density": 3.0006543008935584, "code_density": 3.8268877786170292, "reasoning_density": 0.21756638350213053, "education_level": 2.6918234681844684, "creative_writing_quality": -0.28639428571727427}, "api_choices": {"primary": "Technical Writing", "secondary": "NONE", "tertiary": "NONE", "education_level": "2", "creative_writing_quality": "not_applicable", "primary_topic": "Programming & Software Engineering", "secondary_topic": "NONE"}, "review_reasons": [], "format_slot_diagnostics": {"comparable": true, "secondary_tertiary_identical": false, "secondary_tertiary_max_abs_gap": 0.002526005652972785, "secondary_choice": "NONE", "tertiary_choice": "NONE"}, "confidence": {"primary": 0.996927992860071, "secondary": 0.9959378782252976, "tertiary": 0.9985737102110083, "structured_records": 0.0027280827151072305, "procedural_steps": 0.010849098287091812, "worked_solution": 0.0036248460566504037, "contextual_dialogue": 5.321151457816571e-06, "content_quality": 0.5296204740108797, "noise_severity": 0.9924832643107422, "missing_content_severity": 0.9669874914372157, "factual_density": 0.5084804869008609, "pretraining_utility": 0.4380207393620048, "math_density": 0.16721191741129826, "code_density": 0.8557398155141911, "reasoning_density": 0.8186946804148912, "education_level": 0.3385949410245898, "creative_writing_quality": 0.9994508493275965, "primary_topic": 0.9650394184012901, "secondary_topic": 0.6652946850119325}, "probability_margin": {"primary": 0.9950613545090592, "secondary": 0.9937487700925143, "tertiary": 0.9978802863381444, "education_level": 0.05770045594310247, "creative_writing_quality": 0.9993318906864898, "primary_topic": 0.943997942901634, "secondary_topic": 0.4733437237161797}, "raw_probabilities": {"primary": {"Academic Writing": 5.8711794739829956e-05, "Content Listing": 1.9944825437356688e-05, "Creative Writing": 1.844279953886703e-05, "Customer Support Page": 7.0218740609836305e-06, "Q&A Forum": 1.0606384568041887e-05, "Discussion Thread": 6.51840813166855e-06, "Comment Section": 1.198416903244857e-05, "Missing Content": 2.9237908104906886e-05, "Knowledge Article": 8.891604200925034e-05, "Legal Notices": 3.1305909086483113e-05, "News Article": 4.010866535847372e-06, "Organizational Announcement": 8.785516439064647e-06, "Nonfiction Writing": 1.258477927178725e-05, "Organizational About Page": 1.1310515099222294e-05, "Personal About Page": 1.0378458814655073e-05, "Personal Blog": 5.746572350705463e-06, "Product Page": 6.6801700190151795e-06, "Spam / Ads": 9.733680331426243e-06, "Structured Data": 0.00042305805419936193, "Technical Writing": 0.9970559931575681, "Transcript / Interview": 1.4188018669524768e-05, "Tutorial / How-To Guide": 0.00015470238474603998, "User Reviews": 5.4990627364533235e-06, "Other / Unknown": 0.0019946386485089505}, "secondary": {"Academic Writing": 0.00012722669600245053, "Content Listing": 3.0200363256609924e-05, "Creative Writing": 4.062322118809079e-05, "Customer Support Page": 1.4582863594018349e-05, "Q&A Forum": 1.2998401143909355e-05, "Discussion Thread": 9.977874471599119e-06, "Comment Section": 2.4497173074316725e-05, "Missing Content": 0.00010116978860374506, "Knowledge Article": 0.00012113491363940077, "Legal Notices": 4.492632920831225e-05, "News Article": 1.8262763102688846e-05, "Organizational Announcement": 1.5323697819830242e-05, "Nonfiction Writing": 1.551135369975501e-05, "Organizational About Page": 2.8694666237391702e-05, "Personal About Page": 1.5882163271480203e-05, "Personal Blog": 1.247777446287474e-05, "Product Page": 1.0196618706408066e-05, "Spam / Ads": 1.4424766361467771e-05, "Structured Data": 0.0006852214792521652, "Technical Writing": 0.0023583632067292457, "Transcript / Interview": 1.9366378957237705e-05, "Tutorial / How-To Guide": 0.00016338996720224451, "User Reviews": 8.414240771306543e-06, "NONE": 0.9961071332992435}, "tertiary": {"Academic Writing": 4.759792709917888e-05, "Content Listing": 1.622460058842808e-05, "Creative Writing": 2.3882542107583893e-05, "Customer Support Page": 7.458925678284384e-06, "Q&A Forum": 6.749376377302032e-06, "Discussion Thread": 4.873866842938366e-06, "Comment Section": 1.1421530366890836e-05, "Missing Content": 6.14192927337386e-05, "Knowledge Article": 4.7094993986179315e-05, "Legal Notices": 3.35017025775545e-05, "News Article": 1.0920656115131915e-05, "Organizational Announcement": 8.383848594474306e-06, "Nonfiction Writing": 9.545598980203144e-06, "Organizational About Page": 2.253964858432541e-05, "Personal About Page": 8.88347840169568e-06, "Personal Blog": 7.088655397480993e-06, "Product Page": 7.067510455885593e-06, "Spam / Ads": 7.782354753006897e-06, "Structured Data": 0.00018481690913519052, "Technical Writing": 0.00075285261407188, "Transcript / Interview": 9.685925086342483e-06, "Tutorial / How-To Guide": 7.192581929690027e-05, "User Reviews": 5.143270553211501e-06, "NONE": 0.9986331389522163}, "structured_records": {"false": 0.9972719172848927, "true": 0.0027280827151072305}, "procedural_steps": {"false": 0.9891509017129082, "true": 0.010849098287091812}, "worked_solution": {"false": 0.9963751539433496, "true": 0.0036248460566504037}, "contextual_dialogue": {"false": 0.9999946788485422, "true": 5.321151457816571e-06}, "content_quality": {"-2": 0.007966441983100996, "-1": 0.0190609016770294, "0": 0.1904881782004247, "1": 0.4705383544562873, "2": 0.3119461236831577}, "noise_severity": {"0": 0.9922701497410905, "1": 0.006908985258910776, "2": 0.00048563656870070215, "3": 0.00020108929439468468, "4": 0.00013413913690329835}, "missing_content_severity": {"0": 0.978926566179187, "1": 0.007141014020314678, "2": 0.009773910411881992, "3": 0.0037078621232020255, "4": 0.0004506472654140915}, "factual_density": {"0": 0.005773664708829015, "1": 0.010464611596104347, "2": 0.0928741966493701, "3": 0.3495865287965976, "4": 0.5413009982490989}, "pretraining_utility": {"-2": 0.01313608175290696, "-1": 0.12666510428917188, "0": 0.42991510522349696, "1": 0.3391295724982398, "2": 0.09115413623618435}, "math_density": {"0": 0.04801453332894073, "1": 0.08423611121127288, "2": 0.11711611324666212, "3": 0.32034700566353635, "4": 0.43028623654958803}, "code_density": {"0": 0.0003186112776238581, "1": 0.0006926890623780341, "2": 0.020413711878701005, "3": 0.12893228532793918, "4": 0.8496427024533578}, "reasoning_density": {"0": 0.9154061399940612, "1": 0.012829537544944111, "2": 0.03093980375763955, "3": 0.020440836371513216, "4": 0.02038368233184192}, "education_level": {"none": 0.0010996072214372787, "0": 0.0012079955841881455, "1": 0.004373057499702532, "2": 0.44882911752049154, "3": 0.39112866157738907, "4": 0.15336156059679132}, "creative_writing_quality": {"not_applicable": 0.9995423744396638, "-2": 6.916357529847623e-05, "-1": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/NIVANorge/Mobius2/blob/fcb200bd0877203a8eb30ae31e1340cf13d250c9/src/model_specific/magic_special.cpp", "id": "cpp-0-ae769a870e62d7e04e623d41037d1a45", "text": "\n#include \"../external_computations.h\"\n#include \"MAGIC_core.h\"\n\nextern \"C\" DLLEXPORT void\nmagic_core(Value_Access *values) {\n\t\n\tauto &out_conc_ca = values[0];\n\tauto &out_conc_mg = values[1];\n\tauto &out_conc_na = values[2];\n\tauto &out_conc_k = values[3];\n\tauto &out_conc_nh4 = values[4];\n\tauto &out_conc_so4 = values[5];\n\tauto &out_conc_cl = values[6];\n\tauto &out_conc_no3 = values[7];\n\tauto &out_conc_f = values[8];\n\tauto &out_conc_po4 = values[9];\n\tauto &out_conc_all_so4 = values[10];\n\tauto &out_conc_all_f = values[11];\n\tauto &out_ph = values[12];\n\tauto &out_exch_ca = values[13];\n\tauto &out_exch_mg = values[14];\n\tauto &out_exch_na = values[15];\n\tauto &out_exch_k = values[16];\n\tauto &out_exch_so4 = values[17];\n\tauto &out_bss = values[18];\n\tauto &out_conc_hco3 = values[19];\n\tauto &out_conc_co3 = values[20];\n\tauto &out_all_doc = values[21];\n\tauto &out_all_dic = values[22];\n\tauto &out_conc_al = values[23];\n\tauto &out_all_al = values[24];\n\tauto &out_org_al = values[25];\n\tauto &io_conc_h = values[26];\n\tauto &io_ionic_strength = values[27];\n\tauto &in_total_ca = values[28];\n\tauto &in_total_mg = values[29];\n\tauto &in_total_na = values[30];\n\tauto &in_total_k = values[31];\n\tauto &in_total_nh4 = values[32];\n\tauto &in_total_so4 = values[33];\n\tauto &in_total_cl = values[34];\n\tauto &in_total_no3 = values[35];\n\tauto &in_total_f = values[36];\n\tauto &in_total_po4 = values[37];\n\tauto &par_max_diff = values[38];\n\tauto &par_is_soil = values[39];\n\tauto &par_depth = values[40];\n\tauto &par_temperature = values[41];\n\tauto &par_pco2 = values[42];\n\tauto &par_conc_doc = values[43];\n\tauto &par_log10_aloh3_equilibrium_const = values[44];\n\tauto &par_h_aloh3_exponent = values[45];\n\tauto &par_pk1doc = values[46];\n\tauto &par_pk2doc = values[47];\n\tauto &par_pk3doc = values[48];\n\tauto &par_pk1aldoc = values[49];\n\tauto &par_pk2aldoc = values[50];\n\tauto &par_porosity = values[51];\n\tauto &par_bulk_density = values[52];\n\tauto &par_cec = values[53];\n\tauto &par_so4_half_sat = values[54];\n\tauto &par_so4_max_cap = values[55];\n\tauto &par_log10_ca_al = values[56];\n\tauto &par_log10_mg_al = values[57];\n\tauto &par_log10_na_al = values[58];\n\tauto &par_log10_k_al = values[59];\n\n\tmagic_input input = {};\n\tmagic_param param = {};\n\tmagic_output result = {};\n\t\n\tinput.total_Ca  = *in_total_ca;\n\tinput.total_Mg  = *in_total_mg;\n\tinput.total_Na  = *in_total_na;\n\tinput.total_K   = *in_total_k;\n\tinput.total_NH4 = *in_total_nh4;\n\tinput.total_SO4 = *in_total_so4;\n\tinput.total_Cl  = *in_total_cl;\n\tinput.total_NO3 = *in_total_no3;\n\tinput.total_F   = *in_total_f;\n\tinput.total_PO4 = *in_total_po4;\n\t\n\tparam.Depth              = *par_depth;\n\tparam.Temperature        = *par_temperature;\n\tparam.PartialPressureCO2 = *par_pco2;\n\tparam.conc_DOC           = *par_conc_doc;\n\tparam.Log10AlOH3EquilibriumConst = *par_log10_aloh3_equilibrium_const;\n\tparam.HAlOH3Exponent     = *par_h_aloh3_exponent;\n\tparam.pK1DOC             = *par_pk1doc;\n\tparam.pK2DOC             = *par_pk2doc;\n\tparam.pK3DOC             = *par_pk3doc;\n\tparam.pK1AlDOC           = *par_pk1aldoc;\n\tparam.pK2AlDOC           = *par_pk2aldoc;\n\tparam.Porosity           = *par_porosity;\n\tparam.BulkDensity        = *par_bulk_density;\n\tparam.CationExchangeCapacity = *par_cec;\n\tparam.SO4HalfSat         = *par_so4_half_sat;\n\tparam.SO4MaxCap          = *par_so4_max_cap;\n\tparam.Log10CaAlSelectCoeff = *par_log10_ca_al;\n\tparam.Log10MgAlSelectCoeff = *par_log10_mg_al;\n\tparam.Log10NaAlSelectCoeff = *par_log10_na_al;\n\tparam.Log10KAlSelectCoeff  = *par_log10_k_al;\n\t\n\t// NOTE: time_step and comp_index were only used in an error message, but we had to disable that for now since we can't throw errors from model code anymore.\n\ts64 time_step = 0;\n\ts64 comp_index = 0;\n\t\n\tbool is_soil = par_is_soil.bool_at(0);\n\tdouble conv = *par_max_diff;\n\t\n\tdouble h_est = *io_conc_h;\n\tdouble ionic_strength = *io_ionic_strength;\n\t\n\tMagicCore(input, param, result, is_soil, conv, h_est, ionic_strength, time_step, comp_index);\n\t\n\t// NOTE: Converting mmol to meq\n\t*out_conc_ca = 2.0*result.conc_Ca;\n\t*out_conc_mg = 2.0*result.conc_Mg;\n\t*out_conc_na = result.conc_Na;\n\t*out_conc_k = result.conc_K;\n\t*out_conc_nh4 = result.conc_NH4;\n\t*out_conc_so4 = 2.0*result.conc_SO4;\n\t*out_conc_cl = result.conc_Cl;\n\t*out_conc_no3 = result.conc_NO3;\n\t*out_conc_f = result.conc_F;\n\t*out_conc_po4 = 3.0*result.conc_PO4;\n\t\n\t*out_conc_all_so4 = result.all_SO4 * 2.0;\n\t*out_conc_all_f = result.all_F;\n\t\n\t*out_ph = result.pH;\n\t\n\t// NOTE: Converting fraction to percent\n\t*out_exch_ca = 100.0*result.exchangeable_Ca;\n\t*out_exch_mg  = 100.0*result.exchangeable_Mg;\n\t*out_exch_na  = 100.0*result.exchangeable_Na;\n\t*out_exch_k   = 100.0*result.exchangeable_K;\n\t*out_exch_so4 = 100.0*result.exchangeable_SO4;\n\t*out_bss      = 100.0*result.BaseSaturationSoil;\n\t\n\t*out_conc_hco3 = result.conc_HCO3,\n\t*out_conc_co3  = result.conc_CO3,\n\t\n\t*out_all_doc = result.all_DOC,\n\t*out_all_dic = result.all_DIC,\n\t\n\t*out_conc_al = result.conc_Al;\n\t*out_all_al = result.all_Al;\n\t*out_org_al = result.org_Al;\n\t\n\t*io_conc_h = result.conc_H;\n\t*io_ionic_strength = result.IonicStrength;\n}\n\n\nextern \"C\" DLLEXPORT void\nmagic_core_initial(Value_Access *values) {\n\tauto &out_conc_SO4 = values[0];\n\tauto &out_conc_F = values[1];\n\tauto &out_conc_H = values[2];\n\tauto &out_log10_ca_al = values[3];\n\tauto &out_log10_mg_al = values[4];\n\tauto &out_log10_na_al = values[5];\n\tauto &out_log10_k_al = values[6];\n\tauto &out_ph = values[7];\n\tauto &out_ionic_str = values[8];\n\tauto &out_exch_so4 = values[9];\n\tauto &in_conc_Ca = values[10];\n\tauto &in_conc_Mg = values[11];\n\tauto &in_conc_Na = values[12];\n\tauto &in_conc_K = values[13];\n\tauto &in_conc_NH4 = values[14];\n\tauto &in_all_SO4 = values[15];\n\tauto &in_conc_Cl = values[16];\n\tauto &in_conc_NO3 = values[17];\n\tauto &in_all_F = values[18];\n\tauto &in_conc_PO4 = values[19];\n\tauto &in_exch_Ca = values[20];\n\tauto &in_exch_Mg = values[21];\n\tauto &in_exch_Na = values[22];\n\tauto &in_exch_K = values[23];\n\tauto &par_max_diff = values[24];\n\tauto &par_is_soil = values[25];\n\tauto &par_depth = values[26];\n\tauto &par_temperature = values[27];\n\tauto &par_pco2 = values[28];\n\tauto &par_conc_doc = values[29];\n\tauto &par_log10_aloh3_equilibrium_const = values[30];\n\tauto &par_h_aloh3_exponent = values[31];\n\tauto &par_pk1doc = values[32];\n\tauto &par_pk2doc = values[33];\n\tauto &par_pk3doc = values[34];\n\tauto &par_pk1aldoc = values[35];\n\tauto &par_pk2aldoc = values[36];\n\tauto &par_porosity = values[37];\n\tauto &par_bulk_density = values[38];\n\tauto &par_cec = values[39];\n\tauto &par_so4_half_sat = values[40];\n\tauto &par_so4_max_cap = values[41];\n\t\n\tmagic_init_input  input = {};\n\tmagic_param       param = {};\n\tmagic_init_result result = {};\n\t\n\tinput.conc_Ca = *in_conc_Ca / 2.0;\n\tinput.conc_Mg = *in_conc_Mg / 2.0;\n\tinput.conc_Na = *in_conc_Na;\n\tinput.conc_K  = *in_conc_K;\n\tinput.conc_NH4 = *in_conc_NH4;\n\tinput.all_SO4 = *in_all_SO4 / 2.0;\n\tinput.conc_Cl = *in_conc_Cl;\n\tinput.conc_NO3 = *in_conc_NO3;\n\tinput.all_F = *in_all_F;\n\tinput.conc_PO4 = *in_conc_PO4 / 3.0;\n\tinput.exchangeable_Ca = *in_exch_Ca * 0.01;\n\tinput.exchangeable_Mg = *in_exch_Mg * 0.01;\n\tinput.exchangeable_Na = *in_exch_Na * 0.01;\n\tinput.exchangeable_K  = *in_exch_K * 0.01;\n\t\n\tparam.Depth              = *par_depth;\n\tparam.Temperature        = *par_temperature;\n\tparam.PartialPressureCO2 = *par_pco2;\n\tparam.conc_DOC           = *par_conc_doc;\n\tparam.Log10AlOH3EquilibriumConst = *par_log10_aloh3_equilibrium_const;\n\tparam.HAlOH3Exponent     = *par_h_aloh3_exponent;\n\tparam.pK1DOC             = *par_pk1doc;\n\tparam.pK2DOC             = *par_pk2doc;\n\tparam.pK3DOC             = *par_pk3doc;\n\tparam.pK1AlDOC           = *par_pk1aldoc;\n\tparam.pK2AlDOC           = *par_pk2aldoc;\n\tparam.Porosity           = *par_porosity;\n\tparam.BulkDensity        = *par_bulk_density;\n\tparam.CationExchangeCapacity = *par_cec;\n\tparam.SO4HalfSat         = *par_so4_half_sat;\n\tparam.SO4MaxCap          = *par_so4_max_cap;\n\t\n\tbool is_soil = par_is_soil.bool_at(0);\n\tdouble conv = *par_max_diff;\n\t\n\tMagicCoreInitial(input, param, result, is_soil, conv);\n\t\n\t*out_conc_SO4 = result.conc_SO4 * 2.0;\n\t*out_conc_F = result.conc_F;\n\t*out_conc_H = result.conc_H;\n\t*out_log10_ca_al = result.Log10CaAlSelectCoeff;\n\t*out_log10_mg_al = result.Log10MgAlSelectCoeff;\n\t*out_log10_na_al = result.Log10NaAlSelectCoeff;\n\t*out_log10_k_al = result.Log10KAlSelectCoeff;\n\t*out_ph = result.pH;\n\t*out_ionic_str = result.IonicStrength;\n\t*out_exch_so4 = 100.0*result.exchangeable_SO4;\n}", "text_preview": "#include \"../external_computations.h\" #include \"MAGIC_core.h\" extern \"C\" DLLEXPORT void magic_core(Value_Access *values) { auto &out_conc_ca = values[0]; auto &out_conc_mg = values[1]; auto &out_conc_na = values[2]; auto &out_conc_k = value", "input_sha256": "485bb235eefd623d88ddef9d1ce6f3fc3242172820bc28459129fb286d43bab6", "text_sha256": "ae769a870e62d7e04e623d41037d1a4510525d5a278b631da2bd0e1697833814", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 120, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": 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0.19548385171969804, "Sports & Fitness": 0.0005174611965297224, "Vehicles & Transportation": 0.0004315525476814608, "Travel & Tourism": 0.0007013375355097802, "Other / Uncovered Subject": 0.004931726781352171, "NONE": 0.39407111798108535}}, "probability_diagnostics": {"primary": {"raw_sum": 0.9999999999999997, "mass_delta": -3.3306690738754696e-16, "missing_mass": 3.3306690738754696e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "secondary": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "tertiary": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "structured_records": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "procedural_steps": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "worked_solution": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "contextual_dialogue": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "content_quality": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "noise_severity": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "missing_content_severity": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "factual_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "pretraining_utility": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "math_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "code_density": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "reasoning_density": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "education_level": {"raw_sum": 1.0, "mass_delta": 0.0, "missing_mass": 0.0, "excess_mass": 0.0, "valid": true, "normalized": false}, "creative_writing_quality": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, "primary_topic": {"raw_sum": 0.9999999999999999, "mass_delta": -1.1102230246251565e-16, "missing_mass": 1.1102230246251565e-16, "excess_mass": 0.0, "valid": true, "normalized": false}, 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/sambayless/monosat/blob/fa810d432ae24b6abd5612725c3bf7c0871c07d2/src/monosat/dgl/DijkstraAllPairs.h", "id": "cpp-0-006499b5a4a7a1c082bca73198d3142c", "text": "/**************************************************************************************************\n The MIT License (MIT)\n\n Copyright (c) 2014, <NAME>\n\n Permission is hereby granted, free of charge, to any person obtaining a copy of this software and\n associated documentation files (the \"Software\"), to deal in the Software without restriction,\n including without limitation the rights to use, copy, modify, merge, publish, distribute,\n sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is\n furnished to do so, subject to the following conditions:\n\n The above copyright notice and this permission notice shall be included in all copies or\n substantial portions of the Software.\n\n THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT\n NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,\n DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT\n OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n **************************************************************************************************/\n\n#ifndef DIJKSTRA_ALLPAIRS_H_\n#define DIJKSTRA_ALLPAIRS_H_\n\n#include <vector>\n#include \"monosat/dgl/alg/Heap.h\"\n#include \"Graph.h\"\n#include \"monosat/core/Config.h\"\n#include \"AllPairs.h\"\n\n\nnamespace dgl {\ntemplate<typename Weight, typename Graph = DynamicGraph<Weight>, class Status = AllPairs::NullStatus>\nclass DijkstraAllPairs : public AllPairs {\npublic:\n\n    Graph& g;\n    Status& status;\n    int last_modification;\n    int last_addition;\n    int last_deletion;\n    int history_qhead;\n\n    int last_history_clear;\n\n    std::vector<int> sources;\n    int INF;\n\n    std::vector<int> check;\n    const int reportPolarity;\n\n    std::vector<std::vector<int>> dist;\n    std::vector<std::vector<int>> prev;\n\n    std::vector<int>* dist_ptr = nullptr;\n\n    struct DistCmp {\n        std::vector<int>** _dist;\n\n        bool operator()(int a, int b) const{\n            return (**_dist)[a] < (**_dist)[b];\n        }\n\n        DistCmp(std::vector<int>** d) :\n                _dist(d){\n        };\n    };\n\n    alg::Heap<DistCmp> q;\n\npublic:\n    int stats_full_updates;\n    int stats_fast_updates;\n    int stats_fast_failed_updates;\n    int stats_skip_deletes;\n    int stats_skipped_updates;\n    int stats_num_skipable_deletions;\n    double mod_percentage;\n\n    double stats_full_update_time;\n    double stats_fast_update_time;\n\n    DijkstraAllPairs(Graph& graph, Status& _status, int _reportPolarity = 0) :\n            g(graph), status(_status), last_modification(-1), last_addition(-1), last_deletion(-1), history_qhead(0),\n            last_history_clear(\n                    0), INF(0), reportPolarity(0), q(DistCmp(&dist_ptr)){\n\n        mod_percentage = 0.2;\n        stats_full_updates = 0;\n        stats_fast_updates = 0;\n        stats_skip_deletes = 0;\n        stats_skipped_updates = 0;\n        stats_full_update_time = 0;\n        stats_fast_update_time = 0;\n        stats_num_skipable_deletions = 0;\n        stats_fast_failed_updates = 0;\n    }\n\n    void addSource(int s) override{\n        assert(!std::count(sources.begin(), sources.end(), s));\n        sources.push_back(s);\n\n        last_modification = -1;\n        last_addition = -1;\n        last_deletion = -1;\n    }\n\n    void setNodes(int n){\n\n        check.reserve(n);\n\n        INF = g.nodes() + 1;\n        if(dist.size() < n){\n            dist.resize(n);\n\n            for(int i = 0; i < dist.size(); i++)\n                dist[i].resize(n);\n\n            prev.resize(n);\n            for(int i = 0; i < dist.size(); i++)\n                prev[i].resize(n);\n        }\n    }\n\n    int64_t num_updates = 0;\n\n    int numUpdates() const override{\n        return num_updates;\n    }\n\n    void update() override{\n        stats_full_updates++;\n\n        if(last_modification > 0 && g.getCurrentHistory() == last_modification){\n            stats_skipped_updates++;\n            return;\n        }\n\n        if(last_deletion == g.nDeletions()){\n            stats_num_skipable_deletions++;\n        }\n        INF = g.nodes() + 1;\n        stats_full_updates++;\n\n        setNodes(g.nodes());\n        q.clear();\n        for(int i = 0; i < g.nodes(); i++){\n            for(int j = 0; j < g.nodes(); j++){\n                prev[i][j] = -1;\n                dist[i][j] = INF;\n            }\n            dist[i][i] = 0;\n        }\n\n        for(int s = 0; s < sources.size(); s++){\n            int source = sources[s];\n            q.clear();\n            dist_ptr = &dist[source];\n            dist[source][source] = 0;\n            q.insert(source);\n            while(q.size()){\n                int u = q.peekMin();\n                if(dist[source][u] == INF)\n                    break;\n\n                q.removeMin();\n                for(int i = 0; i < g.nIncident(u); i++){\n                    if(!g.edgeEnabled(g.incident(u, i).id))\n                        continue;\n                    int v = g.incident(u, i).node;\n                    int alt = dist[source][u] + 1;\n                    if(alt < dist[source][v]){\n                        dist[source][v] = alt;\n                        prev[source][v] = u;\n                        if(!q.inHeap(v))\n                            q.insert(v);\n                        else\n                            q.decrease(v);\n                    }\n                }\n            }\n        }\n\n        for(int i = 0; i < sources.size(); i++){\n            int s = sources[i];\n            for(int u = 0; u < g.nodes(); u++){\n                if(dist[s][u] >= INF && reportPolarity < 1){\n                    status.setReachable(s, u, false);\n                    status.setMininumDistance(s, u, false, INF);\n                }else if(dist[s][u] < INF && reportPolarity > -1){\n                    status.setReachable(s, u, true);\n                    status.setMininumDistance(s, u, true, dist[s][u]);\n                }\n            }\n        }\n        assert(dbg_uptodate());\n        num_updates++;\n        last_modification = g.getCurrentHistory();\n        last_deletion = g.nDeletions();\n        last_addition = g.nAdditions();\n\n        history_qhead = g.historySize();\n        last_history_clear = g.nHistoryClears();\n\n    }\n\n    void getPath(int from, int to, std::vector<int>& path) override{\n        update();\n        assert(dist[from][to] < INF);\n        int d = dist[from][to];\n        path.clear();\n        path.push_back(to);\n        while(prev[from][to] != -1){\n            int p = prev[from][to];\n            path.push_back(p);\n            to = p;\n        }\n\n        for(int i = 0; i < path.size(); i++){\n            if(i >= path.size() - i - 1)\n                break;\n            int t = path[i];\n            int e = path[path.size() - i - 1];\n            path[i] = e;\n            path[path.size() - i - 1] = t;\n        }\n    }\n\n    bool dbg_path(int from, int to){\n\n        return true;\n    }\n\n    void drawFull(){\n    }\n\n    bool dbg_uptodate(){\n\n        return true;\n    }\n\n    bool connected_unsafe(int from, int t) override{\n        return dist[from][t] < INF;\n    }\n\n    bool connected_unchecked(int from, int t) override{\n        assert(last_modification == g.getCurrentHistory());\n        return connected_unsafe(from, t);\n    }\n\n    bool connected(int from, int t) override{\n        if(last_modification != g.getCurrentHistory())\n            update();\n\n        assert(dbg_uptodate());\n\n        return dist[from][t] < INF;\n    }\n\n    int distance(int from, int t) override{\n        if(connected(from, t))\n            return dist[from][t];\n        else\n            return INF;\n    }\n\n    int distance_unsafe(int from, int t) override{\n        if(connected_unsafe(from, t))\n            return dist[from][t];\n        else\n            return INF;\n    }\n};\n};\n#endif\n", "text_preview": "/************************************************************************************************** The MIT License (MIT) Copyright (c) 2014, <NAME> Permission is hereby granted, free of charge, to any person obtaining a copy of this softwa", "input_sha256": "ccead1324667d4e76bf66725d59bcab987c418a42498d7512ee1fb53a6a613cc", "text_sha256": "006499b5a4a7a1c082bca73198d3142c53959a60d062c331226f05c8e9c9c2ba", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 121, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": "baseten", "endpoint": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/LujainBakh/sudoku-parallel/blob/358a8ce10feaf638c969209a5456947f5e88b6f2/critical.cpp", "id": "cpp-0-8e6c4169f58fb9d76c0cac0b38f1370b", "text": "#include <iostream>\n#include <string>\n#include <vector>\n#include <array>\n#include <tuple>\n#include <chrono> // Include chrono for timing\n\nusing std::cout;\nusing std::cin;\nusing std::string;\nusing std::to_string;\nusing std::vector;\nusing std::array;\nusing std::tuple;\nusing std::make_tuple;\nusing std::get;\n\narray<array<int, 9>, 9> sudoku;\nbool solved = false;\n\nint checkrow(int row, int num, array<array<int, 9>, 9> sudoku)\n{\n    for (int column = 0; column < 9; column++)\n        if (sudoku[row][column] == num)\n            return 0;\n    return 1;\n}\n\nint checkcolumn(int column, int num, array<array<int, 9>, 9> sudoku)\n{\n    for (int row = 0; row < 9; row++)\n        if (sudoku[row][column] == num)\n            return 0;\n    return 1;\n}\n\nint checkgrid(int grid_row, int grid_column, int num, array<array<int, 9>, 9> sudoku)\n{\n    for (int row = grid_row * 3; row < (grid_row + 1) * 3; row++)\n        for (int column = grid_column * 3; column < (grid_column + 1) * 3; column++)\n            if (sudoku[row][column] == num)\n                return 0;\n    return 1;\n}\n\nvoid draw(array<array<int, 9>, 9> sudoku)\n{\n    for (int row = 0; row < 9; row++)\n    {\n        for (int column = 0; column < 9; column++)\n        {\n            cout << to_string(sudoku[row][column]) + \" \";\n            if (column == 2 || column == 5)\n                cout << \"| \";\n        }\n        cout << \"\\n\";\n        if (row == 2 || row == 5)\n            cout << \"---------------------\\n\";\n    }\n}\n\ntuple<bool, array<array<vector<int>, 9>, 9>> alternatives(array<array<int, 9>, 9> sudoku)\n{\n    array<array<vector<int>, 9>, 9> alt;\n    for (int row = 0; row < 9; row++)\n        for (int column = 0; column < 9; column++)\n            if (sudoku[row][column] == 0)\n            {\n                for (int n = 1; n <= 9; n++)\n                    if (checkrow(row, n, sudoku) && checkcolumn(column, n, sudoku) && checkgrid(row / 3, column / 3, n, sudoku))\n                        alt[row][column].push_back(n);\n                if (alt[row][column].empty())\n                    return make_tuple(false, alt);\n            }\n    return make_tuple(true, alt);\n}\n\nbool fill(array<array<int, 9>, 9> &sudoku, array<array<vector<int>, 9>, 9> alt)\n{\n    bool flag = false;\n    for (int row = 0; row < 9; row++)\n        for (int column = 0; column < 9; column++)\n            if (alt[row][column].size() == 1 && alt[row][column][0] != 0)\n            {\n                sudoku[row][column] = alt[row][column][0];\n                alt[row][column].clear();\n                flag = true;\n            }\n    return flag;\n}\n\ntuple<int, int> best(array<array<vector<int>, 9>, 9> alt)\n{\n    for (int i = 2; i <= 9; i++)\n        for (int row = 0; row < 9; row++)\n            for (int column = 0; column < 9; column++)\n                if (alt[row][column].size() == i)\n                    return make_tuple(row, column);\n    return make_tuple(-1, -1);\n}\n\nvoid solve(array<array<int, 9>, 9> state, array<array<vector<int>, 9>, 9> alt)\n{\n    if (solved) // Check if already solved\n        return;\n\n    tuple<bool, array<array<vector<int>, 9>, 9>> val;\n\n    while (fill(state, alt))\n    {\n        val = alternatives(state);\n        if (!get<0>(val))\n            return;\n        else\n            alt = get<1>(val);\n    }\n\n    tuple<int, int> pos = best(alt);\n    int row = get<0>(pos);\n    int column = get<1>(pos);\n\n    if (row == -1 && column == -1)\n    {\n        // If no empty cells remain and sudoku is valid\n        #pragma omp critical // Protect shared variable update\n        {\n            if (!solved) // Double-check within critical section\n            {\n                sudoku = state;\n                solved = true;\n            }\n        }\n    }\n    else\n    {\n        #pragma omp parallel for shared(sudoku, solved) // Use OpenMP parallelization\n        for (int i = 0; i < alt[row][column].size(); i++)\n        {\n            if (solved) // Check flag to avoid unnecessary computation\n                continue;\n\n            array<array<int, 9>, 9> local_state = state; // Create a thread-local copy of the state\n            local_state[row][column] = alt[row][column][i];\n            val = alternatives(local_state);\n\n            if (get<0>(val))\n            {\n                #pragma omp critical // Protect recursive solve\n                {\n                    if (!solved) // Re-check flag inside critical section\n                        solve(local_state, get<1>(val));\n                }\n            }\n        }\n    }\n}\n\nint main()\n{\n    int row, column;\n    tuple<bool, array<array<vector<int>, 9>, 9>> val;\n\n    string num;\n    cout << \"To see an example, press 'y'. To enter your own sudoku, press 'n'.\\n\";\n    cin >> num;\n\n    if (num == \"y\")\n    {\n        // Example Sudoku\n        array<array<int, 9>, 9> d = {\n            0, 0, 0, 6, 2, 0, 0, 0, 3,\n            0, 7, 0, 0, 8, 9, 0, 1, 0,\n            0, 0, 3, 1, 0, 0, 6, 0, 0,\n            0, 0, 4, 0, 0, 0, 7, 6, 0,\n            0, 0, 0, 0, 4, 0, 0, 0, 0,\n            0, 9, 2, 0, 0, 0, 5, 0, 0,\n            0, 0, 6, 0, 0, 3, 1, 0, 0,\n            0, 2, 0, 5, 9, 0, 0, 8, 0,\n            3, 0, 0, 0, 6, 8, 0, 0, 0\n        };\n        sudoku = d;\n    }\n    else if (num == \"n\")\n    {\n        cout << \"Please enter your sudoku, filling unknown cells with 0. Enter each number followed by pressing `Enter`. Fill the Sudoku row by row, starting from the top-left cell.\\n\";\n        for (row = 0; row < 9; row++)\n            for (column = 0; column < 9; column++)\n            {\n                cin >> num;\n                if (stoi(num) >= 0 && stoi(num) <= 9)\n                    sudoku[row][column] = stoi(num);\n                else\n                {\n                    cout << \"Invalid number.\";\n                    return 0;\n                }\n            }\n    }\n    else\n    {\n        cout << \"Invalid character.\";\n        return 0;\n    }\n\n    cout << \"Sudoku to be solved:\\n\";\n    draw(sudoku);\n\n    // Start timer using chrono\n    auto start_time = std::chrono::steady_clock::now();\n\n    cout << \"Solving...\\n\";\n    val = alternatives(sudoku);\n    if (get<0>(val))\n        solve(sudoku, get<1>(val));\n\n    // End timer\n    auto end_time = std::chrono::steady_clock::now();\n\n    if (solved)\n    {\n        cout << \"Sudoku solved!\\n\";\n        draw(sudoku);\n    }\n    else\n        cout << \"Sudoku could not be solved.\\n\";\n\n    // Calculate and print elapsed time\n    std::chrono::duration<double> elapsed_seconds = end_time - start_time;\n    cout << \"Time taken to solve the Sudoku: \" << elapsed_seconds.count() << \" seconds.\\n\";\n\n    return 0;\n}", "text_preview": "#include <iostream> #include <string> #include <vector> #include <array> #include <tuple> #include <chrono> // Include chrono for timing using std::cout; using std::cin; using std::string; using std::to_string; using std::vector; using std:", "input_sha256": "f2720321cec97637d8a23afc2a5969da919d4242bef6ed4e7a1dbc9913b90ac7", "text_sha256": "8e6c4169f58fb9d76c0cac0b38f1370b39fa808f5c036c946998ed6d84235850", "run_sha256": "0dbe3046118ab5f0eea428b55f67ac75d558dc1c7da585927c21fa1685f6e177", "line_number": 123, "input_policy": "text-and-optional-url-v1", "label_policy_version": 12, "requested_model": "decision-models/baseten-mammoth-27b-latest", "backend": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/jfur1/csci-2270/blob/0458ab4bde052ab103f145460de4423818af6b54/Assignment1/assignment1_final.cpp", "id": "cpp-0-01613708e03db1748a4131aac33cd957", "text": "/*\nAssignment 1\n<NAME>\nCSCI 2270\n*/\n#include <iostream>\n#include <fstream>\n\nusing namespace std;\n\nstruct market{\n\tstring type;\n\tstring buyorsell;\n\tint price;\n}available[100];\n\nvoid parse(string line, string temp[3]){ // Separates each piece of info from about an item from a line into a temporary array\n\tstring word;\n\tfor(int i = 0; i <= line.length()-1; i++){ // Traverses through the line, looking for a comma\n\t\tif(line[i] == ','){ \n\t\t\tif(word == \"chicken\" || word == \"truck\" || word == \"dresser\" || word == \"microwave\" || word == \"bike\"){\n\t\t\t\ttemp[0] = word; // 1st spot in array stores the type of item trying to be listed\n\t\t\t\tword = \"\";\n\t\t\t}\n\t\t\telse if(word == \" for sale\" || word == \" wanted\"){\n\t\t\t\ttemp[1] = word.substr(1, word.length()); // 2nd spot in temporary array stores the item's listing status (i.e \"for sale\")\n\t\t\t\tword = \"\";\n\t\t\t}\n\t\t}\n\t\telse // \"word\" continues adding characters to itself from the line until a comma is found\n\t\t\tword += line[i];\n\t}\n\ttemp[2] = word.substr(1, word.length()); // After the line is traversed, the 3rd spot in the temp array hold the item's price\n}\n\nvoid deleter(market available[100], int index){ // Function deletes element in a given array starting at a given index, both given as parameters\n\tint numElements = 100;\n\tfor(int i = index; i < numElements-2; i++){ \n\t\tavailable[i].type = available[i+1].type;\n\t\tavailable[i].buyorsell = available[i+1].buyorsell;\n\t\tavailable[i].price = available[i+1].price;\n\t}\n\tnumElements = numElements - 1;\n}\n\nbool sort(string temp[3], market available[100], int count){ // Determines if a new item has any matches that already exist in \"available\" array\n\tfor(int i = 0; i < count; i++){ // Loops through \"available\" searching for matching item type\n\t\tif(temp[0] == available[i].type && temp[1] != available[i].buyorsell){ // If item types match AND the two items' listing statuses do not match\n\t\t\tif(temp[1] == \"wanted\" && stoi(temp[2]) >= available[i].price){ // If person who wants item will pay greater than or equal to \"for sale\" price...\n\t\t\t\tcout << temp[0] << \" \" << available[i].price << endl;\n\t\t\t\tdeleter(available, i); // ... then the item is sold and the match is removed from the array.\n\t\t\t\treturn true; // returns true if the match was deleted from the array\n\t\t\t}\n\t\t\telse if(temp[1] == \"for sale\" && available[i].price >= stoi(temp[2])){\n\t\t\t\tcout << temp[0] << \" \" << temp[2] << endl;\n\t\t\t\tdeleter(available, i);\n\t\t\t\treturn true;\n\t\t\t}\n\t\t}\n\t}\nreturn false;\n}\n\n\nint main(int argc, char* argv[]){\n\t\n\tstring temp[3]; // temporary string array to hold item's type, listing status and price.\n\tint count, count2 = 0; // \"count\" keeps track of total iterations, while \"count2\" keeps track of the size of the \"available\" array.\n\tstring line;\n\tfstream myfile;\n\tmyfile.open(argv[1]);\n\n\tif(myfile.is_open()){\n\t\twhile(getline(myfile, line)){\n\t\t\tparse(line, temp); // After reading in a line from the .txt file, it is sorted into an array using the \"parse\" function.\n\t\t\tif(sort(temp, available, count) == false){ // If no preexisting matches were found for the new item, it gets added to the \"available\" array.\n\t\t\t\tavailable[count2].type = temp[0];\n\t\t\t\tavailable[count2].buyorsell = temp[1];\n\t\t\t\tavailable[count2].price = stoi(temp[2]);\n\t\t\t\tcount2++; // Keeps track of how many items are in the \"avaiable\" array\n\t\t\t}\n\t\t\telse{\n\t\t\t\tcount2 = count2 - 1; // Executes only if item match was found. 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/YanfuOu/OdometryAndPID/blob/b665d44db46579757cdf0460a53a6e117e329007/V7-2-2%20Skills%20100pt-solved%20ring%20issue/src/drive_to.cpp", "id": "cpp-0-0469edbc6f5cc2ea5820ea361f2172d7", "text": "#include \"vex.h\"\n#include \"turn_to.h\"\n\ndouble test = 0; //does nothing\n\n//#include \"main.cpp\"\n//the program calculates the amount of power needed to move to a given target\n// ---------------------------PID Forward distance loop ------------------\ndouble drive_toCalc(double KP, double KI, double KD, bool isForward, double target_distanceX, double target_distanceY, bool extra2, double XGlobal, double yGlobal) {\n\n  //PID\n  /* decent tuning values double KP = 0.05; double KI = 0.05; double KD = 0.02;\n  */\n  //if the front limit switch sensed the goal made contact\n        //sensing using back limit switch \n    if(backGrabberLimitS.pressing() || backGrabberLimitS2.pressing())\n    {\n      backGrabberPneum.set(false);//close\n      //backGrabberPneum = true; \n      Brain.Screen.setCursor(6,2); Brain.Screen.setPenColor(green); Brain.Screen.print(\"LS = true\");\n\n    }\n    else\n    {\n        //keeps the arm down\n        //backArm.spin(fwd, 20, pct); \n    }\n    \n  //sensing using front limit switch \n    if(frontGrabberLimitS12.pressing() )\n    {\n      frontGrabPneumatic.set(false); //closes\n      Brain.Screen.setCursor(7,2); Brain.Screen.setPenColor(blue); Brain.Screen.print(\"pneu true\");\n      //return 0; \n    }\n    else\n    {\n      frontArm.spin(fwd, -20, pct); \n    }\n\n  double intergral = 0;\n  double derivative = 0;\n  double error = 500;\n  double previous_error = 0; \n  double power1 = 0;\n  double max_error = 7;\n\n  double drive_CurrentDeg; \n\n  double error_bound = 180;\n\n  double counter = 0; \n\n  //double target_distance = distance_toCalc(target_distanceX, target_distanceY, false,  XGlobal, yGlobal);\n  double delta_X = 0;\n  double delta_Y = 0;\n  double distance_to_point = 0; \n\n  //change in x, y\n  delta_X = target_distanceX - XGlobal;\n  delta_Y = target_distanceY - yGlobal; \n\n  //printf(\"drive delta_X %f\\n\", delta_X);\n  //printf(\"drive delta_Y %f\\n\", delta_Y);\n\n  //applying the distance formula\n  distance_to_point = sqrt( pow(delta_X, 2) + pow(delta_Y, 2)); //only provides the magnitude\n\n  //printf(\"drive target_distance %f\\n\", distance_to_point);\n\n  //wheel radius =2\"; d[in inches] = r* theata[rads]; d = r * ( theata[deg] * pi / 180); ( 180/ r * pi) * d[inches] = target deg; 28.64 * d[inches] = target_degrees; in inches\n\n  //double target_degrees = distance_to_point * 28.648; \n\n  //------sets the directions of the vector\n  //if the robot is driving backwards(aka back is front)\n  if(isForward == false)\n  {\n    distance_to_point *= -1;\n    \n  }\n  else\n  {\n\n    \n    //if the distance is ever negative(when it has to go backward); flip the sign of distance_to_point\n    if( delta_Y < 0)\n    {\n      if(target_distanceX < 0) //if delta_X is also negative(when in quad 3), don't flip it\n      {\n        printf(\"##########target_distanceX is <0\");\n      }\n      else if(target_distanceX > 0)\n      {\n        printf(\"##########target_distanceX is >0\");\n      }\n      else\n      {\n\n        printf(\"^^^^^^^^^^drive_to: driving backwards\");\n        distance_to_point *= -1; //flipp the sign if negative\n      }\n    }\n  }\n\n  double target_degrees = distance_to_point; \n  double rightDeg = RotationRight.position(degrees);\n\n  //while loop should run until within max_error range\n  //since the outside loop already continueouly sends in values and utilizes this to calculate, there is no need for this to be a while loop\n  //this program only computes and returns the power amount and does not do anything\n  while(fabs(error) > max_error){\n    /*rightDeg = RotationRight.position(degrees);\n    //calculates current degrees by averaging both wheels\n    drive_CurrentDeg = rightDeg; \n    error = target_degrees - drive_CurrentDeg; */\n    \n    //since target_distance passed in is how much the robot must travel, it basically does what the calculations for error does\n    error = target_degrees;\n\n    //printf(\"drive error %f\\n\", error);\n\n    //when within the error bound, let KI grow to give a final push\n    if( fabs(error) < error_bound){ //caution\n      intergral += error;\n    }\n    //reset intergral if it turns negative\n    if(error*previous_error < 0){\n      intergral = 0;\n    }\n    //calculates the derivative(for self correction)\n    derivative = previous_error - error; \n\n    previous_error = error; \n\n    //calculates power\n    power1 = error*KP + intergral*KI + derivative*KD ; \n    //Brain.Screen.setCursor(10, 3); Brain.Screen.print(\"drive power:\");\n    //Brain.Screen.setCursor(10, 20); Brain.Screen.print(power1);\n\n    printf(\"drive power1 %f\\n\", power1);\n    printf(\"drive max_error: %f\\n\", max_error);\n    printf(\"drive error: %f\\n\", error);  \n    //return 0 if within range\n    if(fabs(error) < max_error)\n    {\n      Brain.Screen.setCursor(11, 3); Brain.Screen.setFillColor(yellow); Brain.Screen.print(\"out of PID\");\n      printf(\"------------------------driving done!!------------------- \");\n      return 0;\n    }\n \n\n\n     //since this program only does calculations, no need for movement\n    //gets it out of loop if stuck CAUTION: SAFETY MECHANISM\n    if(counter > 10000)\n    {\n      error  = max_error; \n      return power1 = 0; \n      printf(\"------------------------driving done!!------------------- \");\n    }\n    /*if(delta_X < 0.15*target_distanceX )\n    {\n      if(delta_Y < 0.15*target_distanceY)\n      {\n        Brain.Screen.setCursor(11, 3); Brain.Screen.setFillColor(green); Brain.Screen.print(\"out of PID\");\n        printf(\"------------------------turning done!! into exit loop-------------------\\n \");\n        return 0;\n      }\n    } */\n    return power1; \n\n    counter ++; \n\n    //CAUTION\n    //drive_CurrentDeg = 0;\n  };\n\n  //once outside the loop, stop and hold the motor\n  leftDrive1.stop(hold);\n  leftDrive2.stop(hold);\n  leftDrive3.stop(hold);\n  rightDrive1.stop(hold);\n  rightDrive2.stop(hold); \n  rightDrive3.stop(hold); \n  Brain.Screen.setCursor(11, 3); Brain.Screen.setFillColor(blue); Brain.Screen.print(\"out of PID\");\n\n  return 0; //return 0 to trip stopping mechanism\n\n  //resets everything\n  error = 0; previous_error = 0; intergral = 0; derivative = 0; power1 = 0; target_degrees = 0; target_degrees = 0; \n  \n  \n};", "text_preview": "#include \"vex.h\" #include \"turn_to.h\" double test = 0; //does nothing //#include \"main.cpp\" //the program calculates the amount of power needed to move to a given target // ---------------------------PID Forward distance loop --------------", "input_sha256": 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{"source_group": "cpp", "source": "openbmb/UltraData-Stack-v3-L2/cpp", "domain": "github.com", "language": "cpp", "url": "https://github.com/DerekOyama/CS-Data-Structure-Implementations/blob/2d4fbf689b84c0c1a9b14d613eb5a8a7051b9c81/cs133/assign7/graph.hpp", "id": "cpp-0-a161253ea787f177b6f2dd08ebd3c1f1", "text": "#pragma once\n/*\n * graph.hpp\n * Adjacency-list graph implementation\n */\n#include <climits> // For INT_MAX, INT_MIN\n#include <list>\n#include <vector>\n#include <queue>\n#include <iostream>\nusing namespace std;\n\nclass graph\n{\npublic:\n   /* graph(n)\n       Construct a graph with n nodes and no edges, initially.\n    */\n   graph(int n)\n   {\n      edges.resize(n);\n   }\n\n   /* add_edge(a,b)\n       Add an edge from node a to node b. Note that self edges are not allowed,\n       so attempting add_edge(a,a) should be ignored. Similarly, this is not\n       a multigraph, so if an edge a -> b already exists, a second one should\n       be ignored.\n       Should run in O(E) time in the worst case. \n    */\n   void add_edge(int a, int b)\n   {\n      if (has_edge(a, b) || a == b)\n      {\n         return;\n      }\n      edges[a].push_back(b);\n   }\n\n   /* has_edge(a,b)\n       Returns true if there is an edge from a to b. Should return false if\n       either a or b is out-of-range (< 0 or >= count_nodes()).\n       Should run in O(E) time.\n    */\n   bool has_edge(int a, int b)\n   {\n      if (a >= edges.size() || b >= edges.size())\n      {\n         return false;\n      }\n      for (int i : edges[a])\n      {\n         if (i == b)\n         {\n            return true;\n         }\n      }\n      return false;\n   }\n\n   /* count_nodes()\n       Returns the number of nodes in this graph.\n       Should run in O(1) time\n    */\n   int count_nodes()\n   {\n      return edges.capacity();\n   }\n\n   /* count_edges()\n       Returns the total number of edges in this graph.\n       Should run in O(E) time.\n    */\n   int count_edges()\n   {\n      int ans = 0;\n      for (vector<int> i : edges)\n      {\n         ans += i.size();\n      }\n      return ans;\n   }\n\n   /* count_edges(n)\n       Returns the number of outbound edges from node n.\n       Should run in O(E) time\n    */\n   int count_edges(int n)\n   {\n      return edges[n].size();\n   }\n\n   /* bfs(n)\n       Perform a breadth-first search, starting at node n, and returning a\n       vector that gives the distance to every other node. (If a node is \n       unreachable from n, then set its distance to INT_MAX.)\n       Should run in O(E + N) time.\n    */\n   vector<int> bfs(int n)\n   {\n      //1.  Mark all vertices as *unexplored*.\n      //2.  Enqueue the starting vertex\n      //3.  While the queue is NOT empty:\n      //a.  Let a = Dequeue()\n      //b.  (Search) If a is the target node, stop\n      //c.  Mark a as *explored*\n      //d.  For each vertex b, which is adjacent to a (a ———> b) and is\n      //         NOT explored yet:\n      //i.  Enqueue(b)\n      vector<int> distances(edges.size(), INT_MAX);\n      vector<int> parent(edges.size());\n      vector<bool> explored(edges.size(), false);\n      queue<int> q;\n      q.push(n);\n      distances[n] = 0;\n      do\n      {\n         int a = q.front();\n         q.pop();\n         explored[a] = true;\n         for (int i : edges[a])\n         {\n            if (!explored[i])\n            {\n               parent[i] = a;\n               q.push(i);\n               distances[i] = distances[parent[i]] +1;\n            }\n         }\n      } while (!q.empty());\n      for (int i = 0; i < distances.size(); i++)\n      {\n         cout << \" \" << distances[i] << \" \";\n      }\n      return distances;\n   }\n\n   /* is_connected(a,b)\n       Returns true if a path exists from node a to b. \n       Should run in O(E + N) time.\n    */\n   bool is_connected(int a, int b)\n   {\n      vector<bool> explored(edges.size(), false);\n      queue<int> q;\n      q.push(a);\n      while (!q.empty())\n      {\n         int c = q.front();\n         q.pop();\n         if (c == b)\n         {\n            return true;\n         }\n         explored[c] = true;\n         for (int i : edges[c])\n         {\n            if (!explored[i])\n            {\n               q.push(i);\n            }\n         }\n      }\n      return false;\n   }\n\nprivate:\n   vector<std::vector<int>> edges;\n\n   // Add any private data/function members you need.\n   // g++ -o assign7_test assign7_test.cpp graph.hpp\n};\n", "text_preview": "#pragma once /* * graph.hpp * Adjacency-list graph implementation */ #include <climits> // For INT_MAX, INT_MIN #include <list> #include <vector> #include <queue> #include <iostream> using namespace std; class graph { public: /* graph(n) Co", "input_sha256": "6ae1816c1ddf37ef8e09c51bf0090bca715438a059b9e04c031a47bf5dd5fc09", "text_sha256": "a161253ea787f177b6f2dd08ebd3c1f184ad7faf53c149a1c6e16783097d7a0f", "run_sha256": 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