text stringlengths 0 1.25M | meta stringlengths 47 1.89k |
|---|---|
import numpy as np
import os
import random
import sys
import math
import cv2
import pickle as pkl
from pykalman import KalmanFilter
from copy import deepcopy
import matplotlib.pyplot as pl
import matplotlib.animation as animation
from time import sleep
import random
from scipy.stats import multivariate_normal
import js... | {"hexsha": "e713302b1caadef1c4b96657d3832b9fca4b4721", "size": 17550, "ext": "py", "lang": "Python", "max_stars_repo_path": "gym_collision_avoidance/envs/utils/DataHandlerLSTM.py", "max_stars_repo_name": "mlodel/gym-exploration-2d", "max_stars_repo_head_hexsha": "57a54e3d68d2722cda13e9defd4bd81171e9a621", "max_stars_re... |
import boto3
import itertools
import numpy as np
from .matrix import BigMatrix
from .matrix_utils import load_mmap, chunk, generate_key_name_uop, constant_zeros
from .matrix_init import local_numpy_init
import concurrent.futures as fs
import math
import os
import pywren
from pywren.executor import Executor
import pywre... | {"hexsha": "e2973b1db8b9898d7e48ae6a25d86611aa805910", "size": 3937, "ext": "py", "lang": "Python", "max_stars_repo_path": "numpywren/uops.py", "max_stars_repo_name": "cloudbutton/lithops-array", "max_stars_repo_head_hexsha": "5e74b881c7db95eccdccf986f1e3b0dc44603889", "max_stars_repo_licenses": ["Apache-2.0"], "max_st... |
[STATEMENT]
lemma (in domain) pirreducible_roots:
assumes "p \<in> carrier (poly_ring R)" and "pirreducible (carrier R) p" and "degree p \<noteq> 1"
shows "roots p = {#}"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. roots p = {#}
[PROOF STEP]
proof (rule ccontr)
[PROOF STATE]
proof (state)
goal (1 subgoal):
1... | {"llama_tokens": 1782, "file": null, "length": 16} |
import numpy as np
import tensorflow as tf
def get_distribution_strategy(num_gpus, all_reduce_alg=None):
"""Return a DistributionStrategy for running the model.
Args:
num_gpus: Number of GPUs to run this model.
all_reduce_alg: Specify which algorithm to use when performing all-reduce.
See tf.contrib... | {"hexsha": "73d43f06360bb385a88e3234b3b212396917d7ff", "size": 1684, "ext": "py", "lang": "Python", "max_stars_repo_path": "utils.py", "max_stars_repo_name": "Sy-Zhang/video_reloc", "max_stars_repo_head_hexsha": "1632fe0928f929622c4a32b9c331b911af72592d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 81, "max_... |
# QuEST_jl/src/base/calculations.jl
#
function calcDensityInnerProduct(ϱ1 ::QuEST_Types.Qureg, ϱ2 ::QuEST_Types.Qureg) ::Qreal
w = ccall(:calcDensityInnerProduct,
Qreal,
(QuEST_Types.Qureg, QuEST_Types.Qureg),
ϱ1,
ϱ2)
return Qreal(w)
end
function calcE... | {"hexsha": "296e049dcb3fca7eca245bfe75af8832b77fbaf1", "size": 5260, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/calculations.jl", "max_stars_repo_name": "ketitalabs/QuEST.jl", "max_stars_repo_head_hexsha": "c657c5c78aebbd62fff52b73bf4db7ecd5f7f8e4", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
! ##################################################################################################################################
! Begin MIT license text.
! ___________________________________________________________________________... | {"hexsha": "14d9e0a45ca74d9443d1793a960270c5397c89df", "size": 8441, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Source/UTIL/READ_DOF_TABLES.f90", "max_stars_repo_name": "dr-bill-c/MYSTRAN-general-purpose-finite-element-computer-program", "max_stars_repo_head_hexsha": "307d62953924e3945b22b2fcdb7963a8be330... |
[STATEMENT]
lemma auxm2_lm0246:
assumes "(\<forall>r< n.(nth_bit a r + nth_bit b r \<le> 1))"
shows "(nth_bit (a+b) n) = (nth_bit a n + nth_bit b n) mod 2"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (a + b) \<exclamdown> n = (a \<exclamdown> n + b \<exclamdown> n) mod 2
[PROOF STEP]
using assms no_carry
[PR... | {"llama_tokens": 323, "file": "DPRM_Theorem_Diophantine_Binary_And", "length": 2} |
# Run line by line in the REPL, or ctrl + enter in VSCode
# First code section
using MechanicalUnits
m_air = 1000kg; c_p = 1.00kJ/(kg*K)
@import_expand ~W # Watt = Joule / Second is not exported by default. Several: (u1, u2,..)
Q_cp(T1, T2) = m_air * c_p * (T2 - T1) |> (kW*h)
Q_cp(20°C, 985°C)
dm |> upreferred
prefer... | {"hexsha": "e635bcd37df384c4c5074c71fe403bbca89ee3a4", "size": 1439, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "example/readme_example.jl", "max_stars_repo_name": "hustf/MechanicalUnits", "max_stars_repo_head_hexsha": "52660a9dc49d0dc7c42a2fc17b25b303ada67a4d", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
\documentclass{article}
\let\ifluatex\relax
\usepackage{todonotes}
\usepackage{xspace}
\usepackage{url}
\usepackage{listings}
\usepackage{geometry}
\usepackage{enumitem}
\geometry{a4paper, margin=1in}
\usepackage{inconsolata}
\usepackage{hyperref}
\usepackage[tight]{minitoc}
\input{solidity-latex-highlighting}
\inpu... | {"hexsha": "e354e1a9a5e32719e19275382be2563c381abb1c", "size": 55138, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "use-cases/tutorial/main.tex", "max_stars_repo_name": "gordonpace/contractLarva", "max_stars_repo_head_hexsha": "80e4f5e6c7b1aa782fb04072bf11b265b9c327e7", "max_stars_repo_licenses": ["Apache-2.0"],... |
-----------------------------------------------------------------------------
-- |
-- Module : Data.Packed.Vector
-- Copyright : (c) Alberto Ruiz 2009
-- License : GPL
--
-- Maintainer : Alberto Ruiz <aruiz@um.es>
-- Stability : provisional
--
-- Random vectors and matrices.
--
----------------------... | {"hexsha": "dabb17d72b859b4059537317683806d2e9328426", "size": 1678, "ext": "hs", "lang": "Haskell", "max_stars_repo_path": "benchmarks/hmatrix-0.15.0.1/lib/Data/Packed/Random.hs", "max_stars_repo_name": "curiousleo/liquidhaskell", "max_stars_repo_head_hexsha": "a265c044159480b3ddedbbf4982736a33ec8872c", "max_stars_rep... |
"""KAGRA/LIGO Foton related utilities.
"""
import numpy as np
import kontrol.core.controlutils
import kontrol.logger
def tf2foton(
tf, expression="zpk", root_location="s", decimal_places=6,
itol=1e-25, epsilon=1e-25):
"""Convert a single transfer function to foton expression.
Parameters
... | {"hexsha": "6997930eb07d17891b25a07dc8061992f278c88d", "size": 8868, "ext": "py", "lang": "Python", "max_stars_repo_path": "kontrol/core/foton.py", "max_stars_repo_name": "terrencetec/kontrol", "max_stars_repo_head_hexsha": "ba6461784e38d01399efeb7a42911259f9254db0", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
/*!
* Copyright (C) tkornuta, IBM Corporation 2015-2019
*
* 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
*
* http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by app... | {"hexsha": "047a8a852eee9273275079e335d7b1616751edf1", "size": 3645, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/tests/char_encoder_test.cpp", "max_stars_repo_name": "kant/mi-algorithms", "max_stars_repo_head_hexsha": "7e510577f57cb5e7d36c9d2506b61395739b0bef", "max_stars_repo_licenses": ["Apache-2.0"], "m... |
program nemo2d
use globals_mod
use setup_mod
use mesh_mod, only: mesh_t
use mesh_mod, only: mesh_init
use mesh_mod, only: mesh_dump
use kernel_mod, only: kernel_init
use timedisc_mod, only: evolve
use timestep_mod, only: calc_timestep
use setup_mod, only: setup_init
# ifdef _OPENMP
USE OMP_LIB, only: omp_get_thre... | {"hexsha": "01094308c0e68a6bc986222f9fd766bbbe938a7d", "size": 2549, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "source/nemo2d_prog.f90", "max_stars_repo_name": "jmark/nemo2d", "max_stars_repo_head_hexsha": "a508f192d0f6da49e485ee9c8d1c049dbb81d033", "max_stars_repo_licenses": ["MIT"], "max_stars_count": n... |
#!/usr/local/bin/python3
'''
This script trains a new (predictor) model and stores result in the datadir.
Text feature extraction needs to be separately.
'''
import sys
import argparse
import os
import numpy as np
from sklearn.ensemble import RandomForestClassifier as RF
from sklearn.svm import SVC
from sklearn.ex... | {"hexsha": "b0c429e023bf66d7a17b7f541d8209d43e7419b0", "size": 3415, "ext": "py", "lang": "Python", "max_stars_repo_path": "train.py", "max_stars_repo_name": "futurice/spice-hate_speech_detection", "max_stars_repo_head_hexsha": "ddaed64428a931f4f22748a611b5865ed037a52f", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
[STATEMENT]
lemma find_path_fields[THEN mp]:
"find_path_ty_f P ty = Some path \<longrightarrow> (\<exists>fs. fields_in_path_f path = fs)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. find_path_ty_f P ty = Some path \<longrightarrow> (\<exists>fs. fields_in_path_f path = fs)
[PROOF STEP]
by (force) | {"llama_tokens": 118, "file": "LightweightJava_Lightweight_Java_Proof", "length": 1} |
# VERSION 2
# Add user id as feature
#
#
#
import tensorflow as tf
import tensorflow.contrib as tc
import numpy as np
import tensorflow.contrib.keras as keras
# import tensorflow.contrib as slim
VERSION = "v3"
IS_TRAINING = True
NUM_EPOCHS = 1000000
# IS_TRAINING = False
# NUM_EPOCHS = 1
LEARNING_RATE = 0.01
def re... | {"hexsha": "04534b3f1e2f383b86a1f33d224bd4d62697a41a", "size": 16011, "ext": "py", "lang": "Python", "max_stars_repo_path": "scripts/dl_v3.py", "max_stars_repo_name": "Lucklyric/Expedia-Recommendation", "max_stars_repo_head_hexsha": "d0496fec5305b02d4e17785e6ea5d635e51e92c1", "max_stars_repo_licenses": ["Apache-2.0"], ... |
# python train_mixup.py --dataset cifar10 --model resnet18 --data_augmentation --mixup
import pdb
import argparse
import numpy as np
from tqdm import tqdm
import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.backends.cudnn as cudnn
from torch.optim.lr_scheduler import MultiStepLR
from... | {"hexsha": "48da46e2da824b38794ff2f50194641e46539b18", "size": 9142, "ext": "py", "lang": "Python", "max_stars_repo_path": "train_mixup.py", "max_stars_repo_name": "Ivanyan0516/nndl-pj", "max_stars_repo_head_hexsha": "c4f8d8e1cfaeffe01a8a7fd949e5932dc59a7c69", "max_stars_repo_licenses": ["ECL-2.0"], "max_stars_count": ... |
%
% revised at Jan. 30th; 2009
% we have added two parts: the unitary operator and the
% project operator
%
% revised on Mar. 22th, 2009
% to use the environment of theorem and law to improve it
%
% revised at July 27th, 2009
% finish the part of project operator and the unitary operator
%
% revised at Aug. 10th, 2009
... | {"hexsha": "d6b73dea86f65da4c1c21c43357b3b43f19df458", "size": 61264, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "theory/physics/operator.tex", "max_stars_repo_name": "murfreesboro/fenglai-note", "max_stars_repo_head_hexsha": "7bdf943f681e54948cd68775a31e4c93a53a13f8", "max_stars_repo_licenses": ["MIT"], "max_... |
SUBROUTINE scrv1(x1,y1,x2,y2,a,b)
!
IMPLICIT NONE
!
! Subroutine arguments
!
REAL :: a,b,x1,x2,y1,y2
!
! Local variables
!
REAL :: alog
REAL :: xx,xx1,xx2
!
! author : amare retta
! + + + purpose + + +
! program to compute parameters for an s-curve.
! + + + argument declarations + + +
! + + + lo... | {"hexsha": "58c3e2d1e2b2359237373bb00503453d7a23d406", "size": 691, "ext": "f90", "lang": "FORTRAN", "max_stars_repo_path": "Project Documents/Source Code Original/Scrv1.f90", "max_stars_repo_name": "USDA-ARS-WMSRU/upgm-standalone", "max_stars_repo_head_hexsha": "1ae5dee5fe2cdba97b69c19d51b1cf61ceeab3d7", "max_stars_re... |
import pandas as pd
import numpy as np
# Load the dataset in a dataframe object and include only four features as mentioned
df = pd.read_csv("data3.csv")
include = ['Longeivity','Length of screen name','Does the profile have a description','Length of the description','Does the profile have a URL','Followee count of t... | {"hexsha": "6b4612c64937cc36b0e070962d212e981ef4a9b8", "size": 1505, "ext": "py", "lang": "Python", "max_stars_repo_path": "final_app/lr.py", "max_stars_repo_name": "PCOXX/DeCORE", "max_stars_repo_head_hexsha": "0e664901cbe67fe491f24adb3fa3149a4061db48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 2, "max_st... |
# © 2021 ushita37
import numpy as np
F = open('stationName.csv')
ekimeiList = F.readline().replace('\n', '').split(',')
uchimawaList = ['uchimawari', '内回り', 'inner', 'inner_track', 'inner-track', 'innertrack','うちまわり','ウチマワリ','ｳﾁﾏﾜﾘ']
sotomawaList = ['sotomawari', '外回り', 'outer', 'outer_track', 'outer-track', 'outer... | {"hexsha": "120eb922e76f2d26404108910f88f7e574868d4b", "size": 2540, "ext": "py", "lang": "Python", "max_stars_repo_path": "early_works/yamanote_search.py", "max_stars_repo_name": "ushita37/shortest_path", "max_stars_repo_head_hexsha": "a04eea43480809f528fa4b8d5878b23f11e58c1b", "max_stars_repo_licenses": ["MIT"], "max... |
import sys
import logging
import numpy as np
import pickle
import argparse
import torch
import torch.nn.functional as F
import utils
import os
def get_args():
parser = argparse.ArgumentParser()
utils.add_shared_args(parser)
parser.add_argument('--resume-dir1', type=str, default=None)
parser.add_ar... | {"hexsha": "49a6491ebf51c7563112faa7521910b090d9bb22", "size": 4500, "ext": "py", "lang": "Python", "max_stars_repo_path": "BNN/calc_pred_diff.py", "max_stars_repo_name": "fshp971/mcmc-unlearning", "max_stars_repo_head_hexsha": "3113dedca6de33bcaf316b804cb9c1e636db7fd5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import numpy as np
from wolf_agent import WoLFAgent
from matrix_game_local_only import MatrixGame_local
from matrix_game_mec_only import MatrixGame_mec
from matrix_game import MatrixGame
from queue_relay import QueueRelay
import matplotlib.pyplot as plt
from gpd import GPD ## TLIU
from dataToExcel import DTE ... | {"hexsha": "900d11aedc3aa70ad91e5ffd4183fa0f21043a15", "size": 14868, "ext": "py", "lang": "Python", "max_stars_repo_path": "draw_num_ue.py", "max_stars_repo_name": "T610/MEC", "max_stars_repo_head_hexsha": "351b83362e0c8a7128bd95d20de2a720a87b5c48", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_sta... |
/*
* Copyright (c) CERN 2013-2015
*
* Copyright (c) Members of the EMI Collaboration. 2010-2013
* See http://www.eu-emi.eu/partners for details on the copyright
* holders.
*
* Licensed under the Apache License, Version 2.0 (the "License");
* you may not use this file except in compliance with the License.
*... | {"hexsha": "c574927ff892ec750270ba78db01f5f868986198", "size": 2473, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "src/config/ServerConfig.cpp", "max_stars_repo_name": "cern-fts/fts3", "max_stars_repo_head_hexsha": "cf9eb5c9f52728929965edf58a86381eec0c4e88", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars_... |
'''
File name: nodes.py
Author: Joke Durnez
Date created: 10/31/2017
Date last modified: 10/31/2017
Python Version: 2.7
Description: Script to clean timeseries
Project: Psychosis
'''
from nilearn.input_data import NiftiMasker, NiftiMapsMasker, NiftiLabelsMasker
from nipype.utils.filemanip i... | {"hexsha": "9ae99d9e3882955a509b97b7d2fc3caa8a699bdb", "size": 17138, "ext": "py", "lang": "Python", "max_stars_repo_path": "postbids/bin/scripts/timeseries_clean.py", "max_stars_repo_name": "jokedurnez/Psychosis", "max_stars_repo_head_hexsha": "1887ecb374b001de7f6aadb028c04e09e5bc4f7d", "max_stars_repo_licenses": ["MI... |
import numpy as np
import numba
@numba.njit(cache=True)
def maxprominencedip(events, start, end, top, n):
"""
Find the negative peaks with the maximum prominence in arrays.
For computing the prominence, maxima occuring on the border of the array
are ignored, unless both the left and right maxima o... | {"hexsha": "16e387c30c550c4f2dae24588eed8d6146257c60", "size": 5176, "ext": "py", "lang": "Python", "max_stars_repo_path": "maxprominencedip.py", "max_stars_repo_name": "Gattocrucco/sipmfilter", "max_stars_repo_head_hexsha": "74215d6c53b998808fc6c677b46030234d996bdf", "max_stars_repo_licenses": ["CC-BY-4.0", "MIT"], "m... |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import pytest
import numpy as np
from . import variables
def test_softmax_categorical_deterministic() -> None:
toke... | {"hexsha": "5b39fd7bd17de7dc1eb5c94861d2c9a22f3b9636", "size": 2803, "ext": "py", "lang": "Python", "max_stars_repo_path": "nevergrad/instrumentation/test_variables.py", "max_stars_repo_name": "vishalshar/nevergrad", "max_stars_repo_head_hexsha": "07b5b332786ce5ff831dfabee892bb9397838f70", "max_stars_repo_licenses": ["... |
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
#
# This source code is licensed under the BSD license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "... | {"hexsha": "6f2f6abfdbce77aae6bafc31ff519d406ff38990", "size": 25478, "ext": "py", "lang": "Python", "max_stars_repo_path": "fairscale/utils/testing.py", "max_stars_repo_name": "zhaojuanmao/fairscale", "max_stars_repo_head_hexsha": "61ece000bd1b70029270e2dccab66ffa2ca16d51", "max_stars_repo_licenses": ["MIT", "Apache-2... |
in_channel = 10
out_channel = 5
num_V = 6
num_E = 7
adj = [0. 1. 0. 0. 0. 0.;
1. 0. 0. 1. 1. 1.;
0. 0. 0. 0. 0. 1.;
0. 1. 0. 0. 1. 0.;
0. 1. 0. 1. 0. 1.;
0. 1. 1. 0. 1. 0.]
ne = [[2], [1,4,5,6], [6], [2,5], [2,4,6], [2,3,5]]
struct NewLayer <: MessagePassing
weight
end
NewLayer(m... | {"hexsha": "88c00c45294529ea45aabd66117446056dd8edc1", "size": 1510, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/layers/msgpass.jl", "max_stars_repo_name": "ChrisRackauckas/GeometricFlux.jl", "max_stars_repo_head_hexsha": "aa427da343cb0f40d12d3b7ddc3535e762346ccd", "max_stars_repo_licenses": ["MIT"], "ma... |
#
# Copyright 2016 The BigDL Authors.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | {"hexsha": "ce38f6d8b62e20a276c962e10b0f173dab295523", "size": 4701, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/dllib/test/bigdl/nn/test_load_model.py", "max_stars_repo_name": "DirkFi/BigDL", "max_stars_repo_head_hexsha": "7493209165c046116470b9a1e1c8f527915d6f1e", "max_stars_repo_licenses": ["Apache... |
function main()
call_your_code()
end
function call_your_code()
l::Int64 = 3
arrx = Vector{Float64}([5,5,5])
arry = Vector{Float64}([2,3,7])
s::Float64 = 5
# x = ccall((:__mod_julfort_MOD_dot, "./mod_julfort.so"),
# Float64,
# (Ref{Int64}, Ptr{Float... | {"hexsha": "9f4735c4fd20b541c66469e569e1c6bfeced64b2", "size": 584, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "julia-learning-src/advance/10-calling_fortran.jl", "max_stars_repo_name": "nunesmelo/djs-office-hours", "max_stars_repo_head_hexsha": "aa5cc3dfe3072c5608bf25f5dab27cbfa3664713", "max_stars_repo_lice... |
(*section {* Denotational semantics for \ac{algebra} *}*)
text {*
\label{sec:theory-algebra-dlist}
In the following we present the denotational semantics for \ac{algebra} in terms of sets of distinct lists.
*}
(*<*)
theory Algebra_of_Temporal_Faults_dlist
imports
Algebra_of_Temporal_Faults Sliceable_dlist Dlist_fi... | {"author": "andredidier", "repo": "phd", "sha": "113f7c8b360a3914a571db13d9513e313954f4b2", "save_path": "github-repos/isabelle/andredidier-phd", "path": "github-repos/isabelle/andredidier-phd/phd-113f7c8b360a3914a571db13d9513e313954f4b2/thesis/algebra/Algebra_of_Temporal_Faults_dlist.thy"} |
#include <iostream>
#include <numbers>
#include <Eigen/Geometry>
#include "functional_mesh.h"
///////////////////////////////////////////////////////////////
// CFunctionalMesh
///////////////////////////////////////////////////////////////
CFunctionalMesh::CFunctionalMesh()
{
SetColorFunct... | {"hexsha": "d4acfc251a828fc716fce5bfe3f4b8b5254c66ff", "size": 15126, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "functional_mesh.cpp", "max_stars_repo_name": "RomanFesenko/SurfaceViewer", "max_stars_repo_head_hexsha": "e8f27946ae3bce125b4c6639d9315e4652f401e5", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
#!/usr/bin/env python
import os
import sys
import numpy as np
import random
from pyspark.sql import SparkSession
from pyspark.sql.types import StructType, StructField, LongType, DoubleType
import operator
import re
spark = SparkSession.builder \
.master('local[*]') \
.enableHiveSupport() \
... | {"hexsha": "57d194d2143790d759d7410dc103e0cd45c37507", "size": 4299, "ext": "py", "lang": "Python", "max_stars_repo_path": "projet_data_analitics/exercice_1.py", "max_stars_repo_name": "Zouhairhajji/Data_science", "max_stars_repo_head_hexsha": "17b4f4f7bad759e1ca5849f106c058567c9142d7", "max_stars_repo_licenses": ["Apa... |
from gemmi import cif
import numpy as np
from pyxtal import pyxtal
def cif_data(cif_file):
"""
Loads a cif file and extracts loops and items as data and metadata.
returns:
- metadata : cif header.
- data : atom loops
"""
data = cif.read_file(cif_file)
block = data.sole_block(... | {"hexsha": "e7a6cd9e1096d917541bae052113a368471e396c", "size": 2916, "ext": "py", "lang": "Python", "max_stars_repo_path": "rmctools/builder.py", "max_stars_repo_name": "jorobledo/RMCtools", "max_stars_repo_head_hexsha": "6ff5ebb35ac70f9275c23ab33d5576397784064d", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
[STATEMENT]
lemma length_nat: "length (nat_to_bv n) = length_nat n"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (nat_to_bv n) = length_nat n
[PROOF STEP]
apply (simp add: length_nat_def)
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. length (nat_to_bv n) = (LEAST na. n < 2 ^ na)
[PROOF STEP]
apply (rule... | {"llama_tokens": 1148, "file": "RSAPSS_Word", "length": 15} |
// Copyright (c) 2006, Giovanni P. Deretta
//
// This code may be used under either of the following two licences:
//
// 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 restric... | {"hexsha": "a7c6092a58fc0353c326731664e30a403bf97b03", "size": 5436, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "boost/coroutine/detail/context_windows.hpp", "max_stars_repo_name": "erikfrey/coroutine", "max_stars_repo_head_hexsha": "fe1b9e12f96e320446da1ef49015955162edb17f", "max_stars_repo_licenses": ["BSL-1... |
[STATEMENT]
lemma pref_gcd_lq: assumes "u \<le>p v" shows "(gcd \<^bold>|u\<^bold>| \<^bold>|u\<inverse>\<^sup>>v\<^bold>|) = gcd \<^bold>|u\<^bold>| \<^bold>|v\<^bold>|"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. gcd \<^bold>|u\<^bold>| \<^bold>|u\<inverse>\<^sup>>v\<^bold>| = gcd \<^bold>|u\<^bold>| \<^bold>|v... | {"llama_tokens": 359, "file": "Combinatorics_Words_CoWBasic", "length": 2} |
[STATEMENT]
lemma Suc_le_length_iff:
"(Suc n \<le> length xs) = (\<exists>x ys. xs = x # ys \<and> n \<le> length ys)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. (Suc n \<le> length xs) = (\<exists>x ys. xs = x # ys \<and> n \<le> length ys)
[PROOF STEP]
by (metis Suc_le_D[of n] Suc_le_mono[of n] Suc_length_co... | {"llama_tokens": 156, "file": null, "length": 1} |
subroutine tripck(na,nc,nerr,nn,noutpt,nttyo,n1,n2,n3,
$ qdup12,qdup13,qdup23,unam1,unam2,unam3)
c
c This suboutine checks the species triplets that were read
c from the DATA0 file for illegal combinations.
c
c The only legal combinations here correspond to ternary
c systems and maximal third... | {"hexsha": "f935fc5fbd756e615aa6e576a11a48820610a1b3", "size": 17514, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "src/eqpt/src/tripck.f", "max_stars_repo_name": "39alpha/eq3_6", "max_stars_repo_head_hexsha": "4ff7eec3d34634f1470ae5f67d8e294694216b6e", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_stars_c... |
#!/usr/bin/env python
# coding: utf-8
# # Sample Statistics
# Reading: Emile-Geay Chapter 4.I and 4.II (p51-58)
#
# Other resources:
# https://en.wikipedia.org/wiki/Sampling_distribution
#
# https://en.wikipedia.org/wiki/Central_limit_theorem
# In[2]:
get_ipython().run_line_magic('reset', '')
import numpy as np... | {"hexsha": "751a5c42aa7cdd2bc37591d1dcdaf07787d63f6d", "size": 11229, "ext": "py", "lang": "Python", "max_stars_repo_path": "_build/jupyter_execute/content/Module01/M01_N05_SampleStatistics.py", "max_stars_repo_name": "cdds-uiuc/simles-book", "max_stars_repo_head_hexsha": "79f0fe1133d44f6b94b4bdcd0f05ff65434240c9", "ma... |
classdef medianFilter < EBSDFilter
properties
numNeighbours % number of neigbours to consider (default 1)
end
methods
function F = medianFilter(varargin)
%
F.numNeighbours = get_option(varargin,'neighbours',1);
addlistener(F,'isHex','PostSet',@check);
... | {"author": "mtex-toolbox", "repo": "mtex", "sha": "f0ce46a720935e9ae8106ef919340534bca1adcb", "save_path": "github-repos/MATLAB/mtex-toolbox-mtex", "path": "github-repos/MATLAB/mtex-toolbox-mtex/mtex-f0ce46a720935e9ae8106ef919340534bca1adcb/EBSDAnalysis/EBSDSmoothing/medianFilter.m"} |
function tests = test_ft_preproc_rereference
% MEM 1gb
% WALLTIME 00:10:00
% DEPENDENCY ft_preproc_rereference
if nargout
% assume that this is called by RUNTESTS
tests = functiontests(localfunctions);
else
% assume that this is called from the command line
fn = localfunctions;
for i=1:numel(fn)
feval(f... | {"author": "fieldtrip", "repo": "fieldtrip", "sha": "c2039be598a02d86b39aae76bfa7aaa720f9801c", "save_path": "github-repos/MATLAB/fieldtrip-fieldtrip", "path": "github-repos/MATLAB/fieldtrip-fieldtrip/fieldtrip-c2039be598a02d86b39aae76bfa7aaa720f9801c/test/test_ft_preproc_rereference.m"} |
#include <boost/shared_array.hpp>
#include <iostream>
using namespace std;
int main()
{
boost::shared_array<int> p1{new int[1]};
{
boost::shared_array<int> p2{p1};
p2[0] = 1;
}
cout << p1[0] << '\n';
}
| {"hexsha": "489c36145773edc181d1533e408f9c38d6699750", "size": 220, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "Boost/smartptrs/sharedarray.cpp", "max_stars_repo_name": "Fernal73/LearnCpp", "max_stars_repo_head_hexsha": "100aa80e447fe7735d7f8217c2ec72ae32ed1c7f", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import glob
import math
from os import path, mkdir
import random
import cv2
import dlib
from imutils import face_utils
import numpy as np
import pandas as pd
from skimage.util import random_noise
DATASET_PATH = "C:\\youtube_faces"
class DetectorInput:
"""dlib input container class."""
def __init__(self, file... | {"hexsha": "23819c9e7ca5e7a64eb9943ca97f0a7faf073f7c", "size": 4094, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess_landmarks.py", "max_stars_repo_name": "karashiiro/facial-landmark-tracking", "max_stars_repo_head_hexsha": "787b5663be4f824f631145913136e06a6f970f23", "max_stars_repo_licenses": ["MIT"]... |
function [] = sb_fix_unwrap_manual(reset_flag,ix_reduced_list)
% [] = sb_fix_unwrap_manual(reset_flag)
% script to manually fix unwrapping errors in Small Baseline processing
%
% This function allows to manually fix unwrapping errors by iterating
% the results of ps_plot('rsb'). You can add add or subtract an integer n... | {"author": "dbekaert", "repo": "StaMPS", "sha": "c159eb81b16c446e0e8fdef7dd435eb22e0240ed", "save_path": "github-repos/MATLAB/dbekaert-StaMPS", "path": "github-repos/MATLAB/dbekaert-StaMPS/StaMPS-c159eb81b16c446e0e8fdef7dd435eb22e0240ed/matlab/sb_fix_unwrap_manual_new.m"} |
import numpy as np
from forkan import dataset_path
from forkan.datasets.dsprites import load_dsprites, load_dsprites_one_fixed
from forkan.datasets.image import load_unlabeled_image_dataset
from forkan.datasets.mnist import load_mnist
def load_set(name):
return np.load('{}/{}.npz'.format(dataset_path, name))['da... | {"hexsha": "2d25a63b33f7c3148b41deee874f6126afe45809", "size": 733, "ext": "py", "lang": "Python", "max_stars_repo_path": "forkan/datasets/__init__.py", "max_stars_repo_name": "llach/dl-toolkit", "max_stars_repo_head_hexsha": "33ae3d48ce6f24fc0c254b93ed3f4b8a767ffea5", "max_stars_repo_licenses": ["Unlicense"], "max_sta... |
import numpy as np
# Source: https://zpl.fi/aligning-point-patterns-with-kabsch-umeyama-algorithm/
def get_similarity_transform(A, B):
"""
Compute the optimal similarity transform from B to A.
Namely, compute rotation `R`, translation `t` and scale `c` that best align points `B` into
reference points `... | {"hexsha": "8523bf0948782db1a5f32f74f20de57a2b32130e", "size": 1784, "ext": "py", "lang": "Python", "max_stars_repo_path": "preprocess_custom_data/gonzalo/get_similarity_transform.py", "max_stars_repo_name": "shrubb/NeuS", "max_stars_repo_head_hexsha": "6111c4157d5c2b15fc00aa2b1ddcfb73860a6270", "max_stars_repo_license... |
import os
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from matplotlib.image import AxesImage
from matplotlib.text import Text
from scipy.spatial.distance import cosine
from tensorflow import keras, Tensor
from typing import List, Tuple, Set
import tensorflow_datasets as tfds
from tensorflow.py... | {"hexsha": "126bb5e01f79e23005cff5c2063692c1e38d6c16", "size": 22727, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/transformer_tensorflow.py", "max_stars_repo_name": "konstantinschulz/asrael", "max_stars_repo_head_hexsha": "f169dacaa2883a02857f1b4a5050534408809eb8", "max_stars_repo_licenses": ["Unlicen... |
export basin_fractions, tipping_probabilities
"""
basin_fractions(basins::Array) → fs::Dict
Calculate the fraction of the basins of attraction encoded in `basins`.
The elements of `basins` are integers, enumerating the attractor that the entry of `basins`
converges to. Return a dictionary that maps attractor IDs t... | {"hexsha": "9054f9e74f16d4829fddda99be534128729ac289", "size": 3358, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/basins/tipping.jl", "max_stars_repo_name": "onkyo14taro/ChaosTools.jl", "max_stars_repo_head_hexsha": "2b7d157de60027f9fbf6efcfaeb8cdccf8f480f6", "max_stars_repo_licenses": ["MIT"], "max_stars_... |
import requests
import os
import numpy as np
import json
import sys
import time
import uproot
import numba
import hepaccelerate
import hepaccelerate.kernels as kernels
from hepaccelerate.utils import Results, Dataset, Histogram, choose_backend
from tests.kernel_test import load_dataset
USE_CUDA = int(os.environ.get("... | {"hexsha": "7936498f598ebcf3d6ab89053ad6152459e29c2e", "size": 6029, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/timing.py", "max_stars_repo_name": "irenedutta23/hepaccelerate", "max_stars_repo_head_hexsha": "c18fac25a0b88414836bc0a84a333f30ba02ac47", "max_stars_repo_licenses": ["BSD-3-Clause"], "ma... |
#include <boost/mpl/aux_/preprocessor/is_seq.hpp>
| {"hexsha": "4033fd472c8c200e346bcda90a4b1d0825de4e00", "size": 50, "ext": "hpp", "lang": "C++", "max_stars_repo_path": "src/boost_mpl_aux__preprocessor_is_seq.hpp", "max_stars_repo_name": "miathedev/BoostForArduino", "max_stars_repo_head_hexsha": "919621dcd0c157094bed4df752b583ba6ea6409e", "max_stars_repo_licenses": ["... |
% Autogenerated translation of about.md by Texpad
% To stop this file being overwritten during the typeset process, please move or remove this header
\documentclass[12pt]{book}
\usepackage{graphicx}
\usepackage[utf8]{inputenc}
\usepackage[a4paper,left=.5in,right=.5in,top=.3in,bottom=0.3in]{geometry}
\setlength\parinde... | {"hexsha": "4970cc02da0084fa24de0f93c16cf0770b8249c7", "size": 2219, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "_pages/about.tex", "max_stars_repo_name": "bmazoure/bmazoure.github.io", "max_stars_repo_head_hexsha": "02af7f10a4432d0966fca63d85d40587b9773594", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
SUBROUTINE AF_ATIM ( isdx, maxsc, istim, ietim, iret )
C************************************************************************
C* AF_ATIM *
C* *
C* This subroutine locates, decodes, and stores the time (HHMM) data *
C* from within an AIREP report. The report must have already been *
C* broken up int... | {"hexsha": "21fe63494e5dfdd9147d77ccf8b955087f5c434d", "size": 2393, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "gempak/source/bridge/af/afatim.f", "max_stars_repo_name": "oxelson/gempak", "max_stars_repo_head_hexsha": "e7c477814d7084c87d3313c94e192d13d8341fa1", "max_stars_repo_licenses": ["BSD-3-Clause"], "... |
# Function to return feature scaled points
import cv2
import numpy as np
from . import plot_image
def scale_features(obj, mask, points, boundary_line, device, debug=None):
"""scale_features: returns feature scaled points
This is a function to transform the coordinates of landmark points onto a common scale ... | {"hexsha": "e2723a925ee10488f3020151e0f5f50b34be31a3", "size": 4994, "ext": "py", "lang": "Python", "max_stars_repo_path": "plantcv/scale_features.py", "max_stars_repo_name": "Atitsuda/plantcv", "max_stars_repo_head_hexsha": "29932a0f0aa3573a74335425bccc394b2fb7b885", "max_stars_repo_licenses": ["MIT"], "max_stars_coun... |
#!/usr/bin/python
from src.logger.LOG import LOG
from src.TVEKDNN.CustomDNN import CustomDNN
import numpy as np
"""
.. module:: CustomDNN
:platform: Unix, Windows
:synopsis: A useful module indeed.
.. moduleauthor:: Thomas Vimal Easo K<thomasvml@gmail.com>
"""
class ShallowDNN(CustomDNN):
''' ShallowDNN is... | {"hexsha": "e2c37c49a7d875cc48f298fe2775ea21b83cf383", "size": 2371, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/TVEKDNN/ShallowDNN.py", "max_stars_repo_name": "tvek/DatasciencePythonInitBase", "max_stars_repo_head_hexsha": "e578b4a3026b55bc2935b200453e511f1731c75e", "max_stars_repo_licenses": ["MIT"], "... |
module Semacrolon
import Nope
%access public export
%default total
--data SemacrolonToken : Type where
-- STMacro : SemacrolonToken
-- STEq : SemacrolonToken
-- STMacroName : String -> SemacrolonToken
-- STTerm : Term String -> SemacrolonToken
data SemacrolonExpr : Type where
MacroAppl : String ... | {"hexsha": "f3f0ebd5c782cb0b281a11687d55747e5cb06828", "size": 1803, "ext": "idr", "lang": "Idris", "max_stars_repo_path": "Semacrolon.idr", "max_stars_repo_name": "PolyglotSymposium/nosh", "max_stars_repo_head_hexsha": "3b1adb5db66ab54256b08126c3c7e7d8ffc3a27f", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 1... |
import numpy as np
def ortho(vect2d):
"""Computes an orthogonal vector to the one given"""
return np.array((-vect2d[1], vect2d[0]))
def dist(pt_a, pt_b):
"""Euclidian distance between two (x, y) points"""
return ((pt_a[0]-pt_b[0])**2 + (pt_a[1]-pt_b[1])**2)**.5
class Dubins:
"""
C... | {"hexsha": "55f3c63f6b1a3be212b1efb8e8a1f195356fb6cc", "size": 23181, "ext": "py", "lang": "Python", "max_stars_repo_path": "code/dubins.py", "max_stars_repo_name": "FelicienC/RRT-Dubins", "max_stars_repo_head_hexsha": "db074c15ee51d1dda73c327a68b192dd9001054e", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 12... |
import pulp
import numpy as np
import nltk
#nltk.download('punkt')
#nltk.download('stopwords')
from nltk.data import load as LPickle
import sys, os.path as path
from nltk.tokenize import word_tokenize
from nltk.util import ngrams
sys.path.append(path.dirname(path.dirname(path.dirname(path.abspath(__file__)))))
from ... | {"hexsha": "ea9e98d910e20c1bf27195a9ebfb050fa48338e6", "size": 7000, "ext": "py", "lang": "Python", "max_stars_repo_path": "summarizer/algorithms/upper_bound_ilp.py", "max_stars_repo_name": "UKPLab/acl2017-interactive_summarizer", "max_stars_repo_head_hexsha": "0f5ab42e3b9a89015147d194916eaba00c56623d", "max_stars_repo... |
import os
import pandas as pd
import numpy as np
import re
class read:
def __init__(self,file):
self.file = file
def read_aeronet_ocv3(self, skiprows=8):
''' Read and format in pandas data.frame the standard AERONET-OC data '''
dateparse = lambda x: pd.datetime.strptime(x, "%d:%m:%Y ... | {"hexsha": "1e4d6d4d18b7a0551435b27f0037f456ad02809a", "size": 7245, "ext": "py", "lang": "Python", "max_stars_repo_path": "aeronet_visu/data_loading.py", "max_stars_repo_name": "Tristanovsk/aeronet_visu", "max_stars_repo_head_hexsha": "03905aa3f9aacae501f0af378afd885ca25cd981", "max_stars_repo_licenses": ["MIT"], "max... |
// Copyright 2011-2012 Renato Tegon Forti
// Copyright 2014 Renato Tegon Forti, Antony Polukhin.
// Copyright 2015-2020 Antony Polukhin.
//
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE_1_0.txt
// or copy at http://www.boost.org/LICENSE_1_0.txt)
// For more information... | {"hexsha": "4f9831770a3b1c6dad3d4de36673901aee0d3c3d", "size": 1509, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/dll/test/shared_library_search_symbol_test.cpp", "max_stars_repo_name": "armdevvel/boost", "max_stars_repo_head_hexsha": "30d0930951181ef5bc5aad2231ebac8575db0720", "max_stars_repo_licenses": [... |
import NuralNetwork as N
import numpy as np
import cv2 as cv2
import sys
import glob
import matplotlib.pyplot as plt
import h5py
import scipy
from PIL import Image
from scipy import ndimage
N.clearScreen()
X,Y= N.readImageData()
layers_dims = [X.shape[0], 20, 7, 5, 1] # 4-layer model
parameters = N.L_layer_mo... | {"hexsha": "0713c4018ef69739106d75a8b1dd8f28091dfbc5", "size": 421, "ext": "py", "lang": "Python", "max_stars_repo_path": "31_NuralNetwork_FaceClassifier_MultiHidden_Layer/main.py", "max_stars_repo_name": "ManMohan291/PyProgram", "max_stars_repo_head_hexsha": "edcaa927bd70676bd14355acad7262ae2d32b8e5", "max_stars_repo_... |
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
class LinearRegression:
def __init__(self, learning_rate=0.001, n_iters=1000):
self.lr = learning_rate
self.n_iters = n_iters
self.weights = None
self.bias = None
def fit(self, X, y):
... | {"hexsha": "579b87fbdf4847d15c688634f52da41bcaca8429", "size": 6625, "ext": "py", "lang": "Python", "max_stars_repo_path": "HOUSE_PRICES/house_prices.py", "max_stars_repo_name": "Mohit-007/KAGGLE-MACHINE-LEARNING", "max_stars_repo_head_hexsha": "f53ef40390fde09a0b783c6bc46fd6e6269c10fa", "max_stars_repo_licenses": ["MI... |
import sys, os
from sklearn.metrics import roc_auc_score, accuracy_score, precision_score, recall_score, matthews_corrcoef
import pandas as pd
import numpy as np
import json
EVALUATE_CAPITAL_ONLY=True
DO_SEQ_AVERAGING=False
#BINDING_CMAPS_PATH="/home/rsanchez/Tesis/rriPredMethod/data/bench5Data/newCodeData/computedF... | {"hexsha": "7c9a632a17d8f97a2f2d797a674df71c7ad96e16", "size": 6884, "ext": "py", "lang": "Python", "max_stars_repo_path": "evaluation/getPrecsAt_diffThr.py", "max_stars_repo_name": "rsanchezgarc/BIPSPI", "max_stars_repo_head_hexsha": "e155fee0836084ea02bc9919c58817d26a4a13e5", "max_stars_repo_licenses": ["Apache-2.0"]... |
import copy
import warnings
from distutils.version import LooseVersion
import dask
import dask.array as da
import scipy.sparse as sp
from dask.base import tokenize
from dask.delayed import Delayed, delayed
from sklearn.utils import safe_indexing
from sklearn.utils.validation import _is_arraylike, indexable
from ..uti... | {"hexsha": "fd573cd8cdb941e318caa888d4f271be4512361f", "size": 4321, "ext": "py", "lang": "Python", "max_stars_repo_path": "dask_ml/model_selection/utils.py", "max_stars_repo_name": "jjerphan/dask-ml", "max_stars_repo_head_hexsha": "cfa57e5789fc29bdcee5d846b5116c07f5fbe292", "max_stars_repo_licenses": ["BSD-3-Clause"],... |
# fmt: off
import torch
import numpy as np
import pandas as pd
import random
from farm.modeling.tokenization import Tokenizer
from farm.data_handler.data_silo import DataSilo
from farm.modeling.language_model import LanguageModel
from farm.modeling.prediction_head import TextClassificationHead, TokenClassificationHead
... | {"hexsha": "09e442185ad214e811f973f017a82d740bd265f3", "size": 10096, "ext": "py", "lang": "Python", "max_stars_repo_path": "examples/multitask_learning.py", "max_stars_repo_name": "imdiptanu/FARM", "max_stars_repo_head_hexsha": "44736869c3756fc730e07a10b8198e42b8af0940", "max_stars_repo_licenses": ["Apache-2.0"], "max... |
"""
Copyright 2020 RICHARD TJÖRNHAMMAR
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, sof... | {"hexsha": "92ceb9bff10d180327810f3a461ffa7e1b17c6b1", "size": 1033, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/rankor/contrasts.py", "max_stars_repo_name": "richardtjornhammar/rankor", "max_stars_repo_head_hexsha": "f04a83091a92130b9b73439b4ad65b5be3056cf9", "max_stars_repo_licenses": ["Apache-2.0"], "... |
#!/usr/bin/env python
"""
Copyright 2010-2018 University Of Southern California
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
http://www.apache.org/licenses/LICENSE-2.0
Unless required by appli... | {"hexsha": "5dfc070d2fc93ff18cf2121f8929e4c846d7014e", "size": 53527, "ext": "py", "lang": "Python", "max_stars_repo_path": "bbp/comps/sdsu_mogof.py", "max_stars_repo_name": "ZhangHCFJEA/bbp", "max_stars_repo_head_hexsha": "33bd999cf8d719c49f9a904872c62f02eb5850d1", "max_stars_repo_licenses": ["BSD-3-Clause"], "max_sta... |
// Copyright Ricardo Calheiros de Miranda Cosme 2018.
// Distributed under the Boost Software License, Version 1.0.
// (See accompanying file LICENSE or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <boost/core/lightweight_test.hpp>
#include <coruja/container/vector.hpp>
#include <coruja/object/object.h... | {"hexsha": "894d3fe82b0b6a94ae579f706bb1043cfc03543c", "size": 4207, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "test/observer_class.cpp", "max_stars_repo_name": "ethiago/coruja", "max_stars_repo_head_hexsha": "13c595a7b37271d04c51de1ffb45b965c95a18cf", "max_stars_repo_licenses": ["BSL-1.0"], "max_stars_count"... |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import rc
from matplotlib.pyplot import MultipleLocator
rc('mathtext', default='regular')
plt.rcParams['font.family'] = ['Times New Roman'] #
plt.rcParams['axes.unicode_minus'] = False
g = [1, 2, 4, 8, 16, 32, 64, 128, 256, 512]
x =... | {"hexsha": "5103c877b016594e17b9d2adc3a6ad660d39edbb", "size": 2696, "ext": "py", "lang": "Python", "max_stars_repo_path": "pictures/HEM_expand/exp1_matchingTime_memory_be.py", "max_stars_repo_name": "shiwanghua/HEM", "max_stars_repo_head_hexsha": "be164ba1d577ea5e99141c7cbae98db1ca85f9c7", "max_stars_repo_licenses": [... |
#!/usr/bin/env python
from datetime import timedelta
import gym
from gym import spaces
import numpy as np
import os
import random
import time
from stable_baselines3.common.callbacks import BaseCallback
from stable_baselines3.common.monitor import Monitor
from stable_baselines3 import DQN
from stable_baselines3.dqn im... | {"hexsha": "9d775cde34fa97c122c138116a2a1b30c9308af5", "size": 5304, "ext": "py", "lang": "Python", "max_stars_repo_path": "spire_env.py", "max_stars_repo_name": "lacker/snecko", "max_stars_repo_head_hexsha": "9b40cd4185a1797f2497f2316c08dcccdde518f8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "max_s... |
import numpy as np
from ..util.convert import ConvertibleType
from typing import Any, Optional, Iterable, Callable, Tuple, List, Dict
GLOBAL_GROUP = ''
class analyzer_property:
def __init__(
self,
client_name: Optional[str] = None,
*,
default,
detail: Opti... | {"hexsha": "1395051c4145ac5444084c328c6b3e20ca71ac06", "size": 4367, "ext": "py", "lang": "Python", "max_stars_repo_path": "_lib/analyzer/core.py", "max_stars_repo_name": "accup/Vyjit", "max_stars_repo_head_hexsha": "b7fc04625348eed04fc076950f7fabc99007eed8", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null,... |
#!/usr/bin/env python
import sys, os, time
import numpy as np
import collections
def buildGraphImpl(fullMeshCells, nth, nr, targetListOfCells, varGroups):
# the total number of cells we have
numCells = len(fullMeshCells)
# graph is an adjecency list (here we use a dictionary) for each dof:
# - key = the gl... | {"hexsha": "c8472473b1fcd403f76ef01629172880b67c5649", "size": 6233, "ext": "py", "lang": "Python", "max_stars_repo_path": "meshing/helpers/build_graph_impl.py", "max_stars_repo_name": "fnrizzi/ElasticShearWaves", "max_stars_repo_head_hexsha": "b09cde0711562412c6bc24de0d18ad3a972b7289", "max_stars_repo_licenses": ["BSD... |
/*
* Copyright (c) 2014 Nicholas Corgan (n.corgan@gmail.com)
*
* Distributed under the MIT License (MIT) (See accompanying file LICENSE.txt
* or copy at http://opensource.org/licenses/MIT)
*/
#include <iostream>
#include <boost/filesystem.hpp>
#include <boost/locale/encoding_utf.hpp>
#include <pkmn/enums.hpp>
... | {"hexsha": "98a1447e0984728e92d6783d1e632c5a3f5d8c9d", "size": 1556, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "lib/game_save_gen3impl.cpp", "max_stars_repo_name": "codemonkey85/LibPKMN", "max_stars_repo_head_hexsha": "96a1800a24bf3861da405cf56daa2d7afd6c850d", "max_stars_repo_licenses": ["MIT"], "max_stars_c... |
import pickle
import sys
import os
import time
import lasagne
import theano
import numpy as np
import theano.tensor as T
from lasagne import layers
from lasagne.updates import nesterov_momentum
from theano.sandbox.neighbours import neibs2images
from lasagne.nonlinearities import sigmoid, rectify, leaky_rectify, identi... | {"hexsha": "07df42c2f8c523f416fbb2a9ba29cadf6652aa24", "size": 14477, "ext": "py", "lang": "Python", "max_stars_repo_path": "u24_lymphocyte/prediction/lymphocyte/pred.py", "max_stars_repo_name": "ALSM-PhD/quip_classification", "max_stars_repo_head_hexsha": "7347bfaa5cf11ae2d7a528fbcc43322a12c795d3", "max_stars_repo_lic... |
from __future__ import division, print_function, absolute_import
import weakref
import numpy as np
from collections import OrderedDict
from ...framework.observer import Listener, Observable
from .primitive import MDPPrimitive
class StateDistribution(object):
"""Probability Distribution.
This class handles ... | {"hexsha": "f0edd140aa0797954d1837521a6e451b4bccd6f3", "size": 9255, "ext": "py", "lang": "Python", "max_stars_repo_path": "rlpy/agent/mdp/state.py", "max_stars_repo_name": "evenmarbles/rlpy", "max_stars_repo_head_hexsha": "3c3c39a316285ca725268e81aef030e5c764f797", "max_stars_repo_licenses": ["0BSD"], "max_stars_count... |
"""
DQN and DDQN
"""
import time
import numpy as np
from yarlp.utils.env_utils import GymEnv
from yarlp.agent.base_agent import Agent
from yarlp.model.networks import cnn
from dateutil.relativedelta import relativedelta as rd
from yarlp.model.model_factories import ddqn_model_factory
from yarlp.model.model_factories i... | {"hexsha": "830e7881a7b99eb9c6451a8161990aeb8bcda2ef", "size": 11562, "ext": "py", "lang": "Python", "max_stars_repo_path": "yarlp/agent/ddqn_agent.py", "max_stars_repo_name": "btaba/yarlp", "max_stars_repo_head_hexsha": "e6bc70afe32f8617f56180d60d6a100c83868119", "max_stars_repo_licenses": ["MIT"], "max_stars_count": ... |
// Copyright (C) 2019 T. Zachary Laine
//
// Distributed under the Boost Software License, Version 1.0. (See
// accompanying file LICENSE_1_0.txt or copy at
// http://www.boost.org/LICENSE_1_0.txt)
#include <boost/stl_interfaces/iterator_interface.hpp>
#include "ill_formed.hpp"
#include <boost/core/lightweight_test.h... | {"hexsha": "b9a0a73ca58a5800a2e9c7d239145dc69e201fac", "size": 28337, "ext": "cpp", "lang": "C++", "max_stars_repo_path": "libs/stl_interfaces/test/random_access.cpp", "max_stars_repo_name": "anarthal/boost-unix-mirror", "max_stars_repo_head_hexsha": "8c34eb2fe471d6c3113c680c1fbef29e7a8063a0", "max_stars_repo_licenses"... |
-10 ghijklmnop
-9 hijklmnop
-8 ijklmnop
-7 jklmnop
-6 klmnop
-5 lmnop
-4 mnop
-3 nop
-2 op
-1 p
| {"hexsha": "468f586bc6ce3bdb3a182db29f2cb257dbf85ed9", "size": 106, "ext": "r", "lang": "R", "max_stars_repo_path": "test/unittest/funcs/substr/tst.substr-multi-var-idx-neg-no-cnt.r", "max_stars_repo_name": "LaudateCorpus1/dtrace-utils", "max_stars_repo_head_hexsha": "8ef492246aef8c034151a75a158163f42c635ec3", "max_sta... |
From Coqprime Require Import PocklingtonRefl.
Local Open Scope positive_scope.
Lemma primo40:
prime 206209712180582360058470143->
prime 25812668917919247631855455535243715547043.
Proof.
intro H.
apply (Pocklington_refl
(Ell_certif
25812668917919247631855455535243715547043
125176785540123
... | {"author": "mukeshtiwari", "repo": "Formally_Verified_Verifiable_Group_Generator", "sha": "e80e8d43e81b5201d6ab82a8ebc07a5cef03476b", "save_path": "github-repos/coq/mukeshtiwari-Formally_Verified_Verifiable_Group_Generator", "path": "github-repos/coq/mukeshtiwari-Formally_Verified_Verifiable_Group_Generator/Formally_Ve... |
"""
CREATED: 3-JUN-2021
LAST EDIT: 11-JUN-2021
AUTHOR: DUANE RINEHART, MBA (duane.rinehart@gmail.com)
READS blotter.xlsx (SYMBOL, SHARES, UNITARY, EXTENDED, DATE, BROKER, EXIT_TARGET)
COMPARES holding period to SP500 performance ($, days) using PME (Public Market Equivalent) ref: https://docs.preqin.com/reports/Preqin... | {"hexsha": "ae26f1674b23ad60a0cdb5680f0d104ed5ab8192", "size": 5917, "ext": "py", "lang": "Python", "max_stars_repo_path": "stock_analysisPME.py", "max_stars_repo_name": "drinehart1/finance1", "max_stars_repo_head_hexsha": "5bdd2f6ca53838e84147652441bb76c2a08c778c", "max_stars_repo_licenses": ["MIT"], "max_stars_count"... |
from pyspark.sql.types import *
from optimus import Optimus
from optimus.helpers.json import json_enconding
from pyspark.ml.linalg import Vectors, VectorUDT, DenseVector
import numpy as np
nan = np.nan
import datetime
from pyspark.sql import functions as F
op = Optimus(master='local')
source_df=op.create.df([('names', ... | {"hexsha": "22ff57ed6fbece8e552d3a6b8746329b3b97b9e5", "size": 9561, "ext": "py", "lang": "Python", "max_stars_repo_path": "tests/test_df_cols_2.py", "max_stars_repo_name": "atwoodjw/Optimus", "max_stars_repo_head_hexsha": "938463cec41a6683d2077c9afc7d6ba05c3b993f", "max_stars_repo_licenses": ["Apache-2.0"], "max_stars... |
/-
Copyright (c) 2019 The Flypitch Project. All rights reserved.
Released under Apache 2.0 license as described in the file LICENSE.
Authors: Jesse Han, Floris van Doorn
-/
import .henkin
local attribute [instance, priority 0] classical.prop_decidable
open fol fol.Lhom
namespace fol
lemma satisfied_of_provable {L ... | {"author": "Jlh18", "repo": "ModelTheoryInLean8", "sha": "fbda7d869d4169b6e739bb74165e99ee03ca63d6", "save_path": "github-repos/lean/Jlh18-ModelTheoryInLean8", "path": "github-repos/lean/Jlh18-ModelTheoryInLean8/ModelTheoryInLean8-fbda7d869d4169b6e739bb74165e99ee03ca63d6/src/completeness.lean"} |
# Copyright (c) 2020-2022, NVIDIA CORPORATION.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agre... | {"hexsha": "5d44e979b8e5b31241cfec22d52bbff563253458", "size": 27137, "ext": "py", "lang": "Python", "max_stars_repo_path": "python/cugraph/cugraph/structure/number_map.py", "max_stars_repo_name": "AyodeAwe/cugraph", "max_stars_repo_head_hexsha": "e3273ee38a6dfd35d180b9b00071fd0e7a94e73e", "max_stars_repo_licenses": ["... |
[STATEMENT]
lemma
assumes "P dvd x"
shows "[x = 0] (mod P)"
[PROOF STATE]
proof (prove)
goal (1 subgoal):
1. [x = 0::'a] (mod P)
[PROOF STEP]
using assms cong_def
[PROOF STATE]
proof (prove)
using this:
P dvd x
[?b = ?c] (mod ?a) = (?b mod ?a = ?c mod ?a)
goal (1 subgoal):
1. [x = 0::'a] (mod P)
[PROOF STEP]
by f... | {"llama_tokens": 166, "file": "Multi_Party_Computation_ETP_RSA_OT", "length": 2} |
# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved.
#
# This source code is licensed under the MIT license found in the
# LICENSE file in the root directory of this source tree.
import random
import numpy as np
import nevergrad as ng
import nevergrad.common.typing as tp
from nevergrad.parametrizati... | {"hexsha": "1b09231b63a752ffa7b1a9783abe55df2bc5bff8", "size": 6069, "ext": "py", "lang": "Python", "max_stars_repo_path": "nevergrad/optimization/multiobjective/test_nsga2.py", "max_stars_repo_name": "juliendehos/nevergrad", "max_stars_repo_head_hexsha": "b31a66bdc883e29a6c8572e341b4b56cc4157a9d", "max_stars_repo_lice... |
function obj_write ( output_file_name, node_num, face_num, normal_num, ...
order_max, node_xyz, face_order, face_node, normal_vector, vertex_normal )
%*****************************************************************************80
%
%% OBJ_WRITE writes graphics information to an Alias OBJ file.
%
% Discussion:
%
% ... | {"author": "johannesgerer", "repo": "jburkardt-m", "sha": "1726deb4a34dd08a49c26359d44ef47253f006c1", "save_path": "github-repos/MATLAB/johannesgerer-jburkardt-m", "path": "github-repos/MATLAB/johannesgerer-jburkardt-m/jburkardt-m-1726deb4a34dd08a49c26359d44ef47253f006c1/obj_io/obj_write.m"} |
"""
# test_guppi.py
This testbench tests a guppi gpuspec reader
"""
import os
import glob
import numpy as np
import bifrost.pipeline as bfp
from bifrost.blocks import GuppiRawSourceBlock
from scipy.fftpack import fft as scipy_fft
if __name__ == "__main__":
# Setup pipeline
filenames = sorted(glob.glob... | {"hexsha": "2df37b0e3b1e098a7b692b424d4ebac511db2c10", "size": 509, "ext": "py", "lang": "Python", "max_stars_repo_path": "testbench/test_guppi_reader.py", "max_stars_repo_name": "MilesCranmer/bifrost", "max_stars_repo_head_hexsha": "951dd4a449850d22cfd74f4db13ecf806fe5cc30", "max_stars_repo_licenses": ["BSD-3-Clause"]... |
import nltk
import numpy as np
import random
import string
f=open('chatbot.txt','r',errors = 'ignore')
raw=f.read()
raw=raw.lower()# converts to lowercase
nltk.download()
sent_tokens = nltk.sent_tokenize(raw)# converts to list of sentences
word_tokens = nltk.word_tokenize(raw)# converts to list of words
| {"hexsha": "3d41ced0f52cb5205a262847f08b28b04cea669c", "size": 306, "ext": "py", "lang": "Python", "max_stars_repo_path": "School/Chat.py", "max_stars_repo_name": "Bamgm14/My-Random-Work", "max_stars_repo_head_hexsha": "b9678a3a84dd8ff00efd638890cff76eb6967c1b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": nu... |
''' Build models to detect Algorithmically Generated Domain Names (DGA).
We're trying to classify domains as being 'legit' or having a high probability
of being generated by a DGA (Dynamic Generation Algorithm). We have 'legit' in
quotes as we're using the domains in Alexa as the 'legit' set.
'''
import o... | {"hexsha": "df7e373485aabb7fc54a4eac365b136a11b57740", "size": 13941, "ext": "py", "lang": "Python", "max_stars_repo_path": "dga_detection/dga_model_gen.py", "max_stars_repo_name": "bkaimal/data_hacking", "max_stars_repo_head_hexsha": "dd89fb8d7a53b07134cf72f80c61f6e943e21de5", "max_stars_repo_licenses": ["MIT"], "max_... |
#!/bin/python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 12 12:18:00 2022
Author: Gianluca Bianco
"""
#################################################
# Libraries
#################################################
from termcolor import colored
from mathematics import e_parser
import numpy as np
import matplot... | {"hexsha": "4b85105addf3bb5e6821a7830351271fe51ddef0", "size": 2854, "ext": "py", "lang": "Python", "max_stars_repo_path": "src/arsenalgear/plotter.py", "max_stars_repo_name": "JustWhit3/arsenalgear", "max_stars_repo_head_hexsha": "a9402279fa40d46f310b387a8d4cd13883d07cb7", "max_stars_repo_licenses": ["MIT"], "max_star... |
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import numpy as np
import os
from PIL import Image
traindir = r'\train'
valdir = r'\validation'
testdir = r'\test'
PATH = os.path.join(r'C:\Users\shast\.keras\datasets', 'cats_and_dogs_filtered')
#os.makedirs(r'C:\Users\shast... | {"hexsha": "dac3a3bc49477d67e6d7a3d3363f77725120556f", "size": 2341, "ext": "py", "lang": "Python", "max_stars_repo_path": "mobile_net.py", "max_stars_repo_name": "parth-shastri/neural_nets_tensorflow", "max_stars_repo_head_hexsha": "aea973fba6042c4317511e230532457a6a84fd73", "max_stars_repo_licenses": ["Intel"], "max_... |
'''
Initialize and update configurations (basis states) in the Sz-basis
Calculate ratios of overlaps based on spin flips
'''
import numpy as np
SQRT_2 = np.sqrt(2)
SQRT_HALF = np.sqrt(0.5)
def is_odd(num):
return num & 0x1
def sz_director_basis(d):
if d.shape[0] == 3:
return np.array([SQRT_HALF*(-... | {"hexsha": "5f52f0a325ac9b192842476c8bac2375931be03c", "size": 10263, "ext": "py", "lang": "Python", "max_stars_repo_path": "wavefunction.py", "max_stars_repo_name": "butchertx/pyvmc", "max_stars_repo_head_hexsha": "1c10d95a2036160769488a51423a261352e20a01", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, ... |
Stadium 5 on G Street has stadiumstyle seating (and smaller theaters and screens than the Regal Holiday), as well as digital surround sound systems in all of its theaters.
Both Stadium and Regal Cinemas Davis Holiday 6 Holiday split what they show, so make sure you have the right theater when you show up. These two ... | {"hexsha": "7c7ccee1f4047333ca2d3bab3fcf163fdd927f52", "size": 7885, "ext": "f", "lang": "FORTRAN", "max_stars_repo_path": "lab/davisWiki/Regal_Cinemas_Davis_Stadium_5.f", "max_stars_repo_name": "voflo/Search", "max_stars_repo_head_hexsha": "55088b2fe6a9d6c90590f090542e0c0e3c188c7d", "max_stars_repo_licenses": ["MIT"],... |
import os
os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
import sys
sys.path.append(".")
import warnings
warnings.filterwarnings('ignore', category=DeprecationWarning)
warnings.filterwarnings('ignore', category=FutureWarning)
import numpy as np
from datasets import DataSet
import utils
import likelihoods
from sdt_rff_gpu im... | {"hexsha": "c4505abd5a9962a9f6813a87730764e53c883e64", "size": 1917, "ext": "py", "lang": "Python", "max_stars_repo_path": "experiments/sdt_rff_classification_gpu.py", "max_stars_repo_name": "abcdabcdabcda/GPDT", "max_stars_repo_head_hexsha": "4ea5bc7e4bc50b1a1c820c55026639ccb5f10cae", "max_stars_repo_licenses": ["Apac... |
from filterpy.kalman import KalmanFilter
import numpy as np
data = np.array([10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90,95,100])
noise = np.array([1,-1,0, -1, 1, 2, 0,-1, 1,-2,-1,-1,0, 1, 1,-2, 1,0, 0])
impluse = np.array([0,0,0,0,0,0,0,0,0,0,10,10,10,10,10,10,10,10,10])
print data + noise + impluse
def stat... | {"hexsha": "b18c2b41af1538552eafc0f89c85b8b3368c1f81", "size": 1070, "ext": "py", "lang": "Python", "max_stars_repo_path": "new/filter_demo.py", "max_stars_repo_name": "ZhouYzzz/CTT", "max_stars_repo_head_hexsha": "385b6c7ac2e6633f72b49df7e8a599f40c50188b", "max_stars_repo_licenses": ["MIT"], "max_stars_count": 3, "max... |
%Template by Mark Jervelund - 2015 - mjerv15@student.sdu.dk
\documentclass[a4paper,10pt,titlepage]{report}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage[english]{babel}
\usepackage{amssymb}
\usepackage{amsmath}
\usepackage{amsthm}
\usepackage{graphicx}
\usepackage{fancyhdr}
\usepackage{lastpage}
\u... | {"hexsha": "0ee7361dcfb4250ac91f8799fc3ea0e615fc7966", "size": 17431, "ext": "tex", "lang": "TeX", "max_stars_repo_path": "report/report 2/main.tex", "max_stars_repo_name": "Doommius/Compiler", "max_stars_repo_head_hexsha": "015e62e6c914cdfff3d77998ea937bd8adb8104b", "max_stars_repo_licenses": ["MIT"], "max_stars_count... |
from utility import *
from numpy import prod
def A200544(n):
if n == 0:
return 1
p = [[y for y in multiples(x)] for x in partitions(n)]
return sum([prod([choose(fib(x[i][1]+1)+x[i][0]-1,x[i][0]) for i in range(0,len(x))]) for x in p])
fib_memo = [0, 1]
def fib(n):
s = len(fib_memo)
if n < ... | {"hexsha": "9ead8fe3f2af7683e57fa320833be5e2f77dc066", "size": 456, "ext": "py", "lang": "Python", "max_stars_repo_path": "oeis/jenga.py", "max_stars_repo_name": "GuySrinivasan/oeis", "max_stars_repo_head_hexsha": "d6e9d11017b613ae64f38cbedb9019c72b594dd0", "max_stars_repo_licenses": ["MIT"], "max_stars_count": null, "... |
@testset "$TEST $G" begin
g = graph(:diamond, G)
@test nv(g) == 4 && ne(g) == 5
@test typeof(g) == G
g = graph(:bull, G)
@test nv(g) == 5 && ne(g) == 5
g = graph(:chvatal, G)
@test nv(g) == 12 && ne(g) == 24
g = graph(:cubical, G)
@test nv(g) == 8 && ne(g) == 12
g = graph(:desargues, G)
@test nv(g) == 20 && ne(g) ... | {"hexsha": "60f25b82fe71caa3d88a6082558ef953b1df72bf", "size": 1482, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "test/generators/smallgraphs.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/Erdos.jl-90d7349d-81aa-5495-813a-883243abfe31", "max_stars_repo_head_hexsha": "2eb248772a05eac35823a07373dd56... |
module StingerGraphs
# package code goes here
include("stinger_lib.jl")
include("stinger_graph.jl")
include("stinger_config.jl")
include("stinger_core.jl")
include("fields.jl")
include("traversal.jl")
include("algorithms/bfs.jl")
include("algorithms/parallelbfs.jl")
include("algorithms/kcore.jl")
include("generators/... | {"hexsha": "1144b4529c86e9399523bca5bb32bcb3cc5c01a1", "size": 349, "ext": "jl", "lang": "Julia", "max_stars_repo_path": "src/StingerGraphs.jl", "max_stars_repo_name": "UnofficialJuliaMirrorSnapshots/StingerGraphs.jl-6b32d12a-ade9-56b9-afe9-2f8a1d6b3202", "max_stars_repo_head_hexsha": "f27b6d61bc3eef8c91b20ac4232f60aed... |
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