| {"source_row": 0, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {997: [(192, 30.0)], 192: [(725, 5.0), (292, 5.0)], 292: [(88, 20.0)], 725: [(88, 20.0)], 88: [(4, 15.0)], 4: [(128, 0.0)], 128: [], 506: [(997, 10.0)]}\nsource = 506\ntarget = 128\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 80.0, "source_answer": 80.0} |
| {"source_row": 1, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {321: [(247, 5.0)], 676: [(247, 5.0)], 247: [(725, 10.0), (59, 5.0)], 725: [(875, 60.0)], 59: [(875, 60.0)], 875: [(263, 10.0)], 263: [(57, 0.0)], 57: [], 520: [(321, 5.0), (676, 5.0)]}\nsource = 520\ntarget = 57\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 2, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {322: [(587, 5.0), (537, 15.0)], 587: [(412, 5.0)], 537: [(412, 5.0)], 412: [(890, 60.0)], 890: [(849, 0.0)], 849: [], 9: [(322, 5.0)]}\nsource = 9\ntarget = 849\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 85.0, "source_answer": 85.0} |
| {"source_row": 3, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {694: [(234, 2.0)], 234: [(673, 1.0)], 673: [(274, 4.0), (516, 5.0)], 274: [(394, 5.0)], 516: [(394, 5.0)], 394: [(291, 525600.0)], 291: [(191, 0.0)], 191: [], 744: [(694, 1.0)]}\nsource = 744\ntarget = 191\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525614.0, "source_answer": 525614.0} |
| {"source_row": 4, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {46: [(18, 15.0)], 398: [(18, 15.0)], 18: [(397, 0.17)], 397: [(521, 0.08)], 521: [(3, 2.0)], 3: [(940, 20.0)], 940: [(53, 0.0)], 53: [], 581: [(46, 0.33), (398, 0.33)]}\nsource = 581\ntarget = 53\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.58, "source_answer": 37.58} |
| {"source_row": 5, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {896: [(43, 600.0)], 196: [(43, 600.0)], 43: [(500, 129600.0)], 500: [(179, 525600.0)], 179: [(622, 129600.0)], 622: [(220, 525600.0)], 220: [(936, 0.0)], 936: [], 381: [(896, 1200.0), (196, 300.0)]}\nsource = 381\ntarget = 936\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1312200.0, "source_answer": 1312200.0} |
| {"source_row": 6, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {873: [(431, 15.0), (722, 10.0)], 431: [(304, 1.0)], 722: [(304, 1.0)], 304: [(820, 3.0)], 820: [(797, 0.0)], 797: [], 956: [(873, 15.0)]}\nsource = 956\ntarget = 797\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 34.0, "source_answer": 34.0} |
| {"source_row": 7, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {913: [(586, 0.25)], 586: [(751, 15.0)], 751: [(420, 15.0), (61, 15.0)], 420: [(590, 0.0)], 61: [(590, 0.0)], 590: [], 775: [(913, 2.0)]}\nsource = 775\ntarget = 590\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.25, "source_answer": 32.25} |
| {"source_row": 8, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {290: [(308, 2.0)], 308: [(92, 3.0)], 92: [(56, 5.0)], 56: [(675, 2.0)], 888: [(979, 2.0)], 979: [(56, 5.0)], 675: [(908, 0.0)], 908: [], 376: [(290, 1.0), (888, 5.0)]}\nsource = 376\ntarget = 908\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14.0, "source_answer": 14.0} |
| {"source_row": 9, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {651: [(304, 2.0)], 304: [(521, 10.0), (591, 10.0)], 591: [(115, 5.0)], 521: [(115, 5.0)], 115: [(989, 5.0)], 989: [(602, 1.0)], 602: [(600, 0.0)], 600: [], 293: [(651, 20.0)]}\nsource = 293\ntarget = 600\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43.0, "source_answer": 43.0} |
| {"source_row": 10, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {194: [(801, 30.0)], 479: [(176, 120.0)], 176: [(717, 0.0)], 669: [(801, 30.0)], 801: [(479, 60.0)], 717: [], 147: [(194, 30.0), (669, 30.0)]}\nsource = 147\ntarget = 717\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0} |
| {"source_row": 11, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {353: [(872, 15.0)], 20: [(681, 2.0)], 681: [(980, 0.0)], 872: [(20, 10.0), (900, 1.0)], 900: [(681, 2.0)], 980: [], 728: [(353, 1.0)]}\nsource = 728\ntarget = 980\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 28.0, "source_answer": 28.0} |
| {"source_row": 12, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {505: [(917, 7200.0)], 917: [(328, 7200.0), (391, 7200.0)], 391: [(196, 7200.0)], 328: [(196, 7200.0)], 196: [(143, 7200.0)], 143: [(574, 0.0)], 574: [], 895: [(505, 7200.0)]}\nsource = 895\ntarget = 574\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 36000.0, "source_answer": 36000.0} |
| {"source_row": 13, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {664: [(486, 0.0)], 399: [(925, 15.0)], 925: [(293, 15.0)], 293: [(475, 10.0)], 475: [(486, 0.0)], 486: [], 160: [(664, 10.0), (399, 15.0)]}\nsource = 160\ntarget = 486\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0} |
| {"source_row": 14, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {461: [(840, 360.0)], 524: [(840, 360.0)], 840: [(816, 1.0)], 816: [(928, 5.0)], 928: [(929, 0.0)], 929: [], 70: [(461, 1440.0), (524, 60.0)]}\nsource = 70\ntarget = 929\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1806.0, "source_answer": 1806.0} |
| {"source_row": 15, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {53: [(909, 3.0)], 57: [(817, 1.0)], 817: [(909, 3.0)], 909: [(367, 1.0)], 367: [(268, 0.5)], 268: [(904, 0.5)], 904: [(63, 0.0)], 63: [], 834: [(53, 10.0), (57, 0.17)]}\nsource = 834\ntarget = 63\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 16, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {793: [(5, 10.0), (114, 20.0), (257, 10.0)], 5: [(754, 0.0)], 257: [(877, 10.0)], 114: [(877, 10.0)], 877: [(754, 0.0)], 754: [], 209: [(793, 10.0)]}\nsource = 209\ntarget = 754\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 17, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {261: [(709, 0.5)], 519: [(709, 0.5)], 709: [(774, 0.5)], 774: [(202, 5.0)], 202: [(596, 5.0)], 596: [(410, 0.0)], 410: [], 452: [(261, 0.25), (519, 0.25)]}\nsource = 452\ntarget = 410\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.25, "source_answer": 11.25} |
| {"source_row": 18, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {932: [(532, 0.75)], 733: [(41, 0.25), (459, 0.25)], 459: [(874, 0.0)], 41: [(874, 0.0)], 281: [(330, 0.58)], 330: [(874, 0.0)], 532: [(874, 0.0)], 874: [], 880: [(932, 0.42), (733, 0.42), (281, 0.42)]}\nsource = 880\ntarget = 874\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.17, "source_answer": 1.17} |
| {"source_row": 19, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {54: [(467, 2.0)], 467: [(855, 120.0), (349, 5.0)], 349: [(937, 259200.0)], 855: [(937, 259200.0)], 937: [(882, 120.0)], 882: [(711, 0.0)], 711: [], 97: [(54, 30.0)]}\nsource = 97\ntarget = 711\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 259472.0, "source_answer": 259472.0} |
| {"source_row": 20, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {261: [(160, 5.0), (820, 5.0)], 160: [(514, 10.0)], 820: [(514, 10.0)], 514: [(7, 240.0)], 7: [(954, 0.0)], 954: [], 767: [(261, 2.0)]}\nsource = 767\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 257.0, "source_answer": 257.0} |
| {"source_row": 21, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {406: [(966, 240.0)], 966: [(84, 60.0), (66, 60.0)], 66: [(884, 60.0)], 84: [(884, 60.0)], 884: [(817, 0.0)], 817: [], 150: [(406, 240.0)]}\nsource = 150\ntarget = 817\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 600.0, "source_answer": 600.0} |
| {"source_row": 22, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {819: [(368, 0.75)], 435: [(636, 0.58)], 636: [(638, 0.58)], 638: [(518, 0.33)], 518: [(879, 0.0)], 154: [(638, 0.58)], 368: [(518, 0.33)], 879: [], 255: [(819, 1.0), (435, 0.58), (154, 0.58)]}\nsource = 255\ntarget = 879\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.08, "source_answer": 2.08} |
| {"source_row": 23, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {739: [(828, 3.0)], 962: [(80, 5.0)], 80: [(636, 3.0)], 636: [(828, 3.0)], 828: [(208, 1.0)], 208: [(56, 0.0)], 56: [], 697: [(739, 45.0), (962, 2.0)]}\nsource = 697\ntarget = 56\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 49.0, "source_answer": 49.0} |
| {"source_row": 24, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {502: [(871, 1.0), (312, 10.0), (176, 1.0)], 871: [(290, 1.0)], 176: [(480, 5.0)], 312: [(230, 40.0)], 290: [(230, 40.0)], 480: [(290, 1.0)], 230: [(383, 0.0)], 383: [], 403: [(502, 20.0)]}\nsource = 403\ntarget = 383\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0} |
| {"source_row": 25, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {720: [(819, 2.0), (55, 2.0)], 819: [(516, 2.0)], 55: [(516, 2.0)], 516: [(955, 2.0)], 955: [(196, 0.0)], 196: [], 610: [(720, 2.0)]}\nsource = 610\ntarget = 196\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0} |
| {"source_row": 26, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {708: [(670, 0.08), (124, 0.17)], 670: [(347, 0.33)], 124: [(347, 0.33)], 347: [(891, 0.25)], 891: [(693, 25.0)], 693: [(493, 0.0)], 493: [], 123: [(708, 0.05)]}\nsource = 123\ntarget = 493\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.8, "source_answer": 25.8} |
| {"source_row": 27, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {823: [(556, 2.0)], 556: [(784, 0.58), (618, 2.0)], 618: [(280, 3.0)], 784: [(87, 2.0)], 280: [(87, 2.0)], 87: [(940, 35.0)], 940: [(90, 0.0)], 90: [], 906: [(823, 6832800.0)]}\nsource = 906\ntarget = 90\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6832844.0, "source_answer": 6832844.0} |
| {"source_row": 28, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {901: [(632, 1.0)], 632: [(255, 2.0)], 255: [(301, 15.0), (343, 5.0)], 301: [(341, 10.0)], 343: [(341, 10.0)], 341: [(748, 60.0)], 748: [(553, 0.0)], 553: [], 233: [(901, 2.0)]}\nsource = 233\ntarget = 553\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 29, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {489: [(999, 1440.0), (103, 1440.0)], 999: [(885, 180.0)], 103: [(885, 180.0)], 885: [(682, 1440.0)], 682: [(14, 240.0)], 14: [(15, 0.0)], 15: [], 749: [(489, 60.0)]}\nsource = 749\ntarget = 15\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3360.0, "source_answer": 3360.0} |
| {"source_row": 30, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {581: [(957, 0.5)], 957: [(443, 30.0)], 443: [(603, 20.0)], 603: [(65, 10.0)], 65: [(322, 900.0), (974, 900.0)], 322: [(798, 0.0)], 974: [(798, 0.0)], 798: [], 502: [(581, 0.08)]}\nsource = 502\ntarget = 798\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 960.58, "source_answer": 960.58} |
| {"source_row": 31, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {952: [(716, 60.0)], 716: [(747, 10.0)], 747: [(355, 60.0), (496, 10.0)], 355: [(374, 5.0)], 496: [(374, 5.0)], 374: [(665, 0.0)], 665: [], 569: [(952, 60.0)]}\nsource = 569\ntarget = 665\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 195.0, "source_answer": 195.0} |
| {"source_row": 32, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {324: [(384, 1.0)], 384: [(874, 60.0), (834, 60.0)], 834: [(503, 525600.0)], 874: [(503, 525600.0)], 503: [(744, 0.0)], 744: [], 333: [(324, 1.0)]}\nsource = 333\ntarget = 744\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 525662.0, "source_answer": 525662.0} |
| {"source_row": 33, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {590: [(325, 1.0)], 632: [(325, 1.0)], 325: [(451, 15.0)], 451: [(237, 10.0)], 237: [(913, 0.0)], 913: [], 341: [(590, 2.0), (632, 1.0)]}\nsource = 341\ntarget = 913\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 28.0, "source_answer": 28.0} |
| {"source_row": 34, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {674: [(972, 0.5), (916, 0.5)], 972: [(181, 1.0)], 916: [(181, 1.0)], 181: [(877, 0.33)], 877: [(703, 1.0)], 703: [(243, 0.0)], 243: [], 430: [(674, 0.33)]}\nsource = 430\ntarget = 243\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.16, "source_answer": 3.16} |
| {"source_row": 35, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {639: [(621, 5.0)], 339: [(621, 5.0)], 621: [(833, 0.0)], 833: [], 811: [(639, 30.0), (339, 10.0)]}\nsource = 811\ntarget = 833\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0} |
| {"source_row": 36, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {345: [(70, 1.0)], 70: [(460, 0.0)], 934: [(460, 0.0)], 460: [], 107: [(345, 5.0), (934, 3.0)]}\nsource = 107\ntarget = 460\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0} |
| {"source_row": 37, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {139: [(868, 1440.0)], 21: [(868, 1440.0)], 868: [(678, 0.0)], 678: [], 279: [(139, 1440.0), (21, 1440.0)]}\nsource = 279\ntarget = 678\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2880.0, "source_answer": 2880.0} |
| {"source_row": 38, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {291: [(331, 10.0)], 331: [(655, 0.0)], 740: [(655, 0.0)], 655: [], 720: [(291, 30.0), (740, 20.0)]}\nsource = 720\ntarget = 655\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 39, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {555: [(816, 20.0)], 631: [(816, 20.0)], 816: [(528, 0.0)], 528: [], 648: [(555, 10.0), (631, 15.0)]}\nsource = 648\ntarget = 528\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 35.0, "source_answer": 35.0} |
| {"source_row": 40, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {57: [(610, 0.0)], 117: [(610, 0.0)], 864: [(117, 10.0), (57, 10.0)], 610: [], 598: [(864, 20.0)]}\nsource = 598\ntarget = 610\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 41, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {881: [(306, 4320.0)], 726: [(306, 4320.0)], 306: [(644, 0.0)], 644: [], 878: [(881, 30.0), (726, 60.0)]}\nsource = 878\ntarget = 644\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4380.0, "source_answer": 4380.0} |
| {"source_row": 42, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {388: [(806, 0.0)], 245: [(388, 5.0)], 73: [(388, 5.0)], 806: [], 242: [(245, 10.0), (73, 15.0)]}\nsource = 242\ntarget = 806\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0} |
| {"source_row": 43, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {168: [(610, 0.02)], 702: [(610, 0.02)], 408: [(610, 0.02)], 610: [(172, 0.0)], 172: [], 880: [(168, 0.03), (702, 0.02), (408, 0.02)]}\nsource = 880\ntarget = 172\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.05, "source_answer": 0.05} |
| {"source_row": 44, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {523: [(958, 0.0)], 214: [(967, 10080.0)], 967: [(958, 0.0)], 958: [], 446: [(523, 10.0), (214, 30.0)]}\nsource = 446\ntarget = 958\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10110.0, "source_answer": 10110.0} |
| {"source_row": 45, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {696: [(772, 1.0)], 772: [(931, 1.0)], 931: [(548, 0.0)], 180: [(548, 0.0)], 548: [], 132: [(696, 1.0), (180, 1.0)]}\nsource = 132\ntarget = 548\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0} |
| {"source_row": 46, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {229: [(266, 5.0), (580, 3.0)], 266: [(812, 0.0)], 580: [(812, 0.0)], 812: [], 79: [(229, 2.0)]}\nsource = 79\ntarget = 812\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0} |
| {"source_row": 47, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {599: [(925, 5.0)], 19: [(925, 5.0)], 480: [(925, 5.0)], 925: [(890, 0.0)], 890: [], 515: [(599, 5.0), (19, 2.0), (480, 2.0)]}\nsource = 515\ntarget = 890\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0} |
| {"source_row": 48, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {845: [(51, 10.0)], 51: [(916, 5.0)], 916: [(831, 0.0)], 355: [(831, 0.0)], 831: [], 969: [(845, 5.0), (355, 30.0)]}\nsource = 969\ntarget = 831\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 49, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {12: [(910, 3.0)], 128: [(910, 3.0)], 20: [(910, 3.0)], 910: [(871, 0.0)], 871: [], 253: [(12, 5.0), (128, 3.0), (20, 10.0)]}\nsource = 253\ntarget = 871\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0} |
| {"source_row": 50, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {341: [(336, 10.0)], 336: [(283, 15.0)], 283: [(844, 0.0)], 745: [(844, 0.0)], 844: [], 674: [(341, 5.0), (745, 10.0)]}\nsource = 674\ntarget = 844\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 51, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {858: [(837, 120.0)], 837: [(361, 0.0)], 153: [(361, 0.0)], 361: [], 7: [(858, 60.0), (153, 300.0)]}\nsource = 7\ntarget = 361\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 300.0, "source_answer": 300.0} |
| {"source_row": 52, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {93: [(951, 30.0), (887, 15.0), (138, 20.0)], 887: [(681, 0.0)], 138: [(681, 0.0)], 951: [(681, 0.0)], 681: [], 220: [(93, 10.0)]}\nsource = 220\ntarget = 681\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 53, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {655: [(513, 20160.0)], 448: [(513, 20160.0)], 513: [(148, 2880.0)], 148: [(295, 0.0)], 295: [], 889: [(655, 1440.0), (448, 2880.0)]}\nsource = 889\ntarget = 295\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25920.0, "source_answer": 25920.0} |
| {"source_row": 54, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {417: [(93, 20.0)], 963: [(332, 0.0)], 93: [(332, 0.0)], 538: [(93, 20.0)], 332: [], 856: [(417, 10.0), (963, 15.0), (538, 30.0)]}\nsource = 856\ntarget = 332\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 55, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {564: [(380, 60.0), (479, 120.0), (494, 60.0)], 479: [(443, 0.0)], 494: [(443, 0.0)], 380: [(443, 0.0)], 443: [], 233: [(564, 60.0)]}\nsource = 233\ntarget = 443\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0} |
| {"source_row": 56, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {266: [(918, 1.0)], 918: [(287, 0.0)], 487: [(102, 1.0)], 102: [(287, 0.0)], 287: [], 479: [(266, 1.0), (487, 2.0)]}\nsource = 479\ntarget = 287\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0} |
| {"source_row": 57, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {640: [(577, 0.0)], 267: [(640, 10.0)], 750: [(577, 0.0)], 39: [(577, 0.0)], 577: [], 620: [(267, 10.0), (750, 15.0), (39, 5.0)]}\nsource = 620\ntarget = 577\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0} |
| {"source_row": 58, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {248: [(123, 0.0)], 942: [(248, 30.0)], 271: [(248, 30.0)], 123: [], 86: [(942, 10080.0), (271, 5.0)]}\nsource = 86\ntarget = 123\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10110.0, "source_answer": 10110.0} |
| {"source_row": 59, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {614: [(424, 20.0)], 267: [(378, 5.0)], 424: [(378, 5.0)], 378: [(23, 0.0)], 23: [], 828: [(614, 15.0), (267, 5.0)]}\nsource = 828\ntarget = 23\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 60, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {854: [(771, 15.0)], 362: [(771, 15.0)], 692: [(465, 2.0)], 465: [(771, 15.0)], 771: [(681, 0.0)], 681: [], 380: [(854, 5.0), (362, 10.0), (692, 5.0)]}\nsource = 380\ntarget = 681\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 61, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {887: [(527, 1.0)], 728: [(527, 1.0)], 106: [(527, 1.0)], 527: [(363, 1.0)], 363: [(672, 0.0)], 672: [], 506: [(887, 5.0), (728, 2.0), (106, 1.0)]}\nsource = 506\ntarget = 672\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0} |
| {"source_row": 62, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {880: [(492, 0.0)], 283: [(463, 20.0)], 463: [(492, 0.0)], 492: [], 576: [(880, 20160.0), (283, 40320.0)]}\nsource = 576\ntarget = 492\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40340.0, "source_answer": 40340.0} |
| {"source_row": 63, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {144: [(974, 4.0)], 628: [(166, 2.0)], 166: [(45, 1.0)], 45: [(974, 4.0)], 974: [(999, 4.0)], 999: [(300, 3.0)], 300: [(759, 0.0)], 759: [], 682: [(144, 10.0), (628, 1.0)]}\nsource = 682\ntarget = 759\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0} |
| {"source_row": 64, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {391: [(333, 3.0)], 901: [(333, 3.0)], 883: [(333, 3.0)], 333: [(538, 0.0)], 538: [], 957: [(391, 5.0), (901, 2.0), (883, 10.0)]}\nsource = 957\ntarget = 538\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0} |
| {"source_row": 65, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {46: [(107, 15.0), (470, 10.0)], 107: [(500, 0.0)], 470: [(500, 0.0)], 500: [], 933: [(46, 30.0)]}\nsource = 933\ntarget = 500\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0} |
| {"source_row": 66, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {114: [(526, 10.0), (404, 10.0), (769, 15.0)], 526: [(609, 0.0)], 769: [(609, 0.0)], 404: [(609, 0.0)], 521: [(609, 0.0)], 476: [(54, 15.0), (521, 5.0)], 54: [(609, 0.0)], 609: [], 272: [(114, 5.0), (476, 10.0)]}\nsource = 272\ntarget = 609\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 67, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {942: [(884, 0.0)], 830: [(253, 15.0)], 957: [(884, 0.0)], 25: [(884, 0.0)], 445: [(884, 0.0)], 253: [(884, 0.0)], 485: [(884, 0.0)], 884: [], 58: [(942, 30.0), (830, 20.0), (957, 60.0), (25, 45.0), (445, 25.0), (485, 60.0)]}\nsource = 58\ntarget = 884\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0} |
| {"source_row": 68, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {940: [(460, 5.0)], 972: [(460, 5.0)], 296: [(460, 5.0)], 192: [(460, 5.0)], 377: [(460, 5.0)], 460: [(771, 1.0)], 771: [(768, 0.0)], 768: [(617, 0.0)], 617: [], 658: [(940, 10.0), (972, 2.0), (296, 2.0), (192, 5.0), (377, 3.0)]}\nsource = 658\ntarget = 617\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 16.0, "source_answer": 16.0} |
| {"source_row": 69, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {242: [(565, 0.0)], 928: [(242, 30.0)], 221: [(242, 30.0)], 565: [], 967: [(928, 15.0), (221, 20.0)]}\nsource = 967\ntarget = 565\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 70, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {342: [(132, 2.0)], 132: [(211, 10.0)], 211: [(172, 20.0)], 172: [(667, 2.0)], 816: [(398, 5.0)], 667: [(398, 5.0)], 398: [(995, 1.0)], 995: [(110, 0.0)], 110: [], 303: [(342, 10.0), (816, 5.0)]}\nsource = 303\ntarget = 110\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 71, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {805: [(448, 5.0)], 426: [(790, 1.0)], 41: [(448, 5.0)], 448: [(790, 1.0)], 790: [(843, 4320.0)], 843: [(189, 60.0)], 189: [(624, 0.0)], 624: [], 16: [(805, 5.0), (426, 2.0), (41, 1.0)]}\nsource = 16\ntarget = 624\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4391.0, "source_answer": 4391.0} |
| {"source_row": 72, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {925: [(46, 10.0)], 46: [(371, 15.0)], 371: [(350, 2.0)], 350: [(901, 5.0), (475, 5.0)], 475: [(138, 20.0)], 138: [(161, 30.0)], 161: [(187, 0.0)], 901: [(187, 0.0)], 187: [], 366: [(925, 5.0)]}\nsource = 366\ntarget = 187\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 87.0, "source_answer": 87.0} |
| {"source_row": 73, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {918: [(173, 5.0)], 388: [(130, 5.0)], 130: [(250, 10.0)], 819: [(250, 10.0)], 250: [(173, 5.0)], 173: [(14, 15.0)], 14: [(479, 0.0)], 479: [], 663: [(918, 5.0), (388, 10.0), (819, 5.0)]}\nsource = 663\ntarget = 479\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0} |
| {"source_row": 74, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {337: [(524, 3.0)], 1: [(337, 2.0)], 524: [(303, 5.0), (583, 2.0), (651, 15.0), (692, 10.0), (792, 7.0)], 863: [(303, 5.0), (583, 2.0), (692, 10.0), (792, 7.0), (651, 15.0)], 692: [(512, 0.0)], 303: [(512, 0.0)], 792: [(512, 0.0)], 583: [(512, 0.0)], 651: [(512, 0.0)], 512: [], 829: [(1, 5.0), (863, 5.0)]}\nsource = 829\ntarget = 512\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 75, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {680: [(216, 5.0)], 609: [(216, 5.0)], 487: [(216, 5.0)], 751: [(216, 5.0)], 219: [(216, 5.0)], 38: [(216, 5.0)], 356: [(216, 5.0)], 542: [(216, 5.0)], 216: [(231, 0.0)], 231: [], 385: [(680, 5.0), (609, 10.0), (487, 15.0), (751, 5.0), (219, 10.0), (38, 20.0), (356, 5.0), (542, 15.0)]}\nsource = 385\ntarget = 231\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 76, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {531: [(796, 40.0)], 224: [(125, 2.0)], 386: [(125, 2.0)], 125: [(796, 40.0)], 796: [(684, 0.0)], 684: [], 475: [(531, 5.0), (224, 2.0), (386, 5.0)]}\nsource = 475\ntarget = 684\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 47.0, "source_answer": 47.0} |
| {"source_row": 77, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {940: [(281, 5.0)], 843: [(633, 3.0)], 281: [(843, 2.0)], 833: [(633, 3.0)], 663: [(633, 3.0)], 632: [(633, 3.0)], 383: [(633, 3.0)], 633: [(945, 1.0)], 945: [(461, 0.0)], 461: [], 865: [(940, 2.0), (833, 2.0), (663, 1.0), (632, 2.0), (383, 2.0)]}\nsource = 865\ntarget = 461\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0} |
| {"source_row": 78, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {96: [(255, 15.0)], 359: [(255, 15.0)], 255: [(722, 120.0)], 722: [(391, 0.0)], 391: [], 630: [(96, 15.0), (359, 60.0)]}\nsource = 630\ntarget = 391\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 195.0, "source_answer": 195.0} |
| {"source_row": 79, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {684: [(571, 2880.0)], 2: [(956, 0.0)], 414: [(571, 2880.0)], 3: [(571, 2880.0)], 571: [(758, 1440.0), (840, 1440.0)], 840: [(416, 20160.0)], 758: [(173, 2880.0)], 173: [(416, 20160.0)], 416: [(761, 120.0)], 761: [(2, 60480.0)], 956: [], 403: [(684, 2880.0), (414, 4320.0), (3, 2880.0)]}\nsource = 403\ntarget = 956\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 92280.0, "source_answer": 92280.0} |
| {"source_row": 80, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {934: [(502, 2.0)], 458: [(250, 0.0)], 502: [(463, 5.0)], 920: [(195, 5.0)], 195: [(493, 5.0)], 493: [(484, 7.0)], 455: [(412, 8.0)], 463: [(920, 3.0)], 484: [(455, 6.0)], 412: [(250, 0.0)], 250: [], 689: [(934, 5.0), (458, 2.0)]}\nsource = 689\ntarget = 250\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46.0, "source_answer": 46.0} |
| {"source_row": 81, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {978: [(837, 0.03)], 478: [(837, 0.03)], 837: [(9, 0.0)], 689: [(837, 0.03)], 470: [(837, 0.03)], 890: [(837, 0.03)], 857: [(837, 0.03)], 492: [(837, 0.03)], 830: [(837, 0.03)], 918: [(837, 0.03)], 192: [(837, 0.03)], 9: [], 566: [(978, 0.02), (689, 0.02), (470, 0.02), (890, 0.02), (857, 0.02), (492, 0.02), (830, 0.02), (918, 0.02), (192, 0.02), (478, 0.02)]}\nsource = 566\ntarget = 9\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.05, "source_answer": 0.05} |
| {"source_row": 82, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {880: [(334, 5.0)], 273: [(334, 5.0)], 551: [(84, 5.0)], 334: [(84, 5.0)], 84: [(738, 5.0)], 738: [(345, 2.0)], 345: [(372, 0.0)], 372: [], 123: [(880, 5.0), (273, 10.0), (551, 10.0)]}\nsource = 123\ntarget = 372\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0} |
| {"source_row": 83, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {958: [(36, 5.0)], 226: [(968, 20.0)], 36: [(133, 3.0)], 133: [(968, 20.0)], 968: [(626, 0.0)], 626: [(889, 0.0)], 889: [], 485: [(958, 15.0), (226, 15.0)]}\nsource = 485\ntarget = 889\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43.0, "source_answer": 43.0} |
| {"source_row": 84, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {367: [(840, 10.0)], 203: [(52, 10.0)], 755: [(52, 10.0)], 52: [(527, 0.0)], 840: [(526, 5.0), (687, 10.0), (203, 5.0), (255, 10.0), (610, 10.0), (867, 5.0), (406, 10.0), (755, 10.0), (631, 5.0)], 406: [(52, 10.0)], 867: [(52, 10.0)], 255: [(52, 10.0)], 631: [(52, 10.0)], 526: [(52, 10.0)], 610: [(52, 10.0)], 687: [(52, 10.0)], 527: [], 344: [(367, 30.0)]}\nsource = 344\ntarget = 527\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0} |
| {"source_row": 85, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {328: [(195, 300.0), (783, 60.0), (667, 120.0), (110, 300.0)], 129: [(449, 0.0)], 279: [(449, 0.0)], 326: [(449, 0.0)], 561: [(195, 300.0), (667, 120.0), (783, 60.0), (110, 300.0)], 638: [(195, 300.0), (667, 120.0), (783, 60.0), (110, 300.0)], 88: [(195, 300.0), (667, 120.0), (783, 60.0), (110, 300.0)], 783: [(279, 120.0), (326, 120.0), (129, 60.0), (179, 30.0)], 195: [(279, 120.0), (129, 60.0), (326, 120.0), (179, 30.0)], 110: [(129, 60.0), (326, 120.0), (179, 30.0), (279, 120.0)], 667: [(279, 120.0), (129, 60.0), (326, 120.0), (179, 30.0)], 179: [(449, 0.0)], 449: [], 498: [(328, 30.0), (561, 45.0), (638, 15.0), (88, 60.0)]}\nsource = 498\ntarget = 449\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 480.0, "source_answer": 480.0} |
| {"source_row": 86, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {542: [(834, 5.0)], 952: [(888, 1.0)], 888: [(181, 1.0)], 181: [(913, 1.0)], 913: [(87, 0.0)], 44: [(542, 5.0)], 647: [(542, 5.0)], 894: [(542, 5.0)], 834: [(419, 15.0)], 419: [(597, 3.0)], 597: [(961, 2.0)], 961: [(748, 2.0)], 748: [(952, 120.0)], 87: [], 394: [(44, 2.0), (647, 2.0), (894, 1.0)]}\nsource = 394\ntarget = 87\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 157.0, "source_answer": 157.0} |
| {"source_row": 87, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {371: [(946, 30.0)], 651: [(555, 2.0)], 555: [(929, 7.0)], 929: [(567, 1.0)], 567: [(764, 0.0)], 764: [(950, 0.0)], 946: [(923, 5.0)], 923: [(879, 2.0), (435, 1.0)], 879: [(63, 2.0)], 435: [(374, 1.0), (963, 5.0)], 374: [(63, 2.0)], 63: [(811, 2.0)], 963: [(811, 2.0)], 811: [(651, 10.0)], 950: [], 982: [(371, 5.0)]}\nsource = 982\ntarget = 950\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 68.0, "source_answer": 68.0} |
| {"source_row": 88, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {751: [(757, 20160.0), (447, 1440.0), (954, 20160.0), (773, 40320.0)], 954: [(408, 0.0)], 773: [(408, 0.0)], 757: [(408, 0.0)], 447: [(408, 0.0)], 408: [], 784: [(751, 1440.0)]}\nsource = 784\ntarget = 408\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41760.0, "source_answer": 41760.0} |
| {"source_row": 89, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {938: [(65, 0.0)], 549: [(434, 10.0), (246, 15.0)], 246: [(778, 5.0)], 434: [(778, 5.0)], 778: [(65, 0.0)], 65: [], 387: [(938, 5.0), (549, 30.0)]}\nsource = 387\ntarget = 65\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 90, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {574: [(735, 0.0)], 791: [(705, 30.0)], 705: [(735, 0.0)], 735: [], 361: [(574, 60.0), (791, 120.0)]}\nsource = 361\ntarget = 735\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0} |
| {"source_row": 91, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {636: [(205, 5.0)], 691: [(205, 5.0)], 205: [(331, 0.0)], 331: [], 910: [(636, 10.0), (691, 2.0)]}\nsource = 910\ntarget = 331\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 92, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {157: [(550, 15.0)], 550: [(842, 0.0)], 978: [(842, 0.0)], 418: [(842, 0.0)], 432: [(842, 0.0)], 842: [], 741: [(157, 30.0), (978, 60.0), (418, 60.0), (432, 120.0)]}\nsource = 741\ntarget = 842\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0} |
| {"source_row": 93, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {786: [(749, 30.0)], 847: [(749, 30.0)], 749: [(787, 45.0)], 787: [(78, 10.0)], 78: [(205, 0.0)], 205: [], 496: [(786, 20.0), (847, 20.0)]}\nsource = 496\ntarget = 205\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0} |
| {"source_row": 94, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {306: [(588, 30.0)], 829: [(588, 30.0)], 568: [(588, 30.0)], 652: [(356, 0.0)], 588: [(652, 480.0)], 356: [], 782: [(306, 10.0), (829, 20.0), (568, 30.0)]}\nsource = 782\ntarget = 356\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 540.0, "source_answer": 540.0} |
| {"source_row": 95, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {181: [(156, 0.0)], 790: [(640, 1.0)], 640: [(156, 0.0)], 156: [], 413: [(181, 2.0), (790, 5.0)]}\nsource = 413\ntarget = 156\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0} |
| {"source_row": 96, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {976: [(442, 10.0)], 56: [(70, 0.0)], 727: [(442, 10.0)], 442: [(56, 1440.0)], 70: [], 207: [(976, 15.0), (727, 5.0)]}\nsource = 207\ntarget = 70\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1465.0, "source_answer": 1465.0} |
| {"source_row": 97, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {385: [(898, 0.0)], 766: [(898, 0.0)], 17: [(898, 0.0)], 820: [(390, 60.0), (385, 30.0), (766, 45.0), (17, 15.0)], 390: [(898, 0.0)], 898: [], 118: [(820, 60.0)]}\nsource = 118\ntarget = 898\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 120.0, "source_answer": 120.0} |
| {"source_row": 98, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {593: [(977, 2.0)], 977: [(869, 3.0)], 869: [(996, 0.0)], 691: [(977, 2.0)], 996: [], 811: [(593, 5.0), (691, 1.0)]}\nsource = 811\ntarget = 996\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0} |
| {"source_row": 99, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {333: [(375, 0.0)], 329: [(333, 30.0)], 336: [(375, 0.0)], 375: [], 514: [(329, 15.0), (336, 15.0)]}\nsource = 514\ntarget = 375\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0} |
| {"source_row": 100, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {788: [(673, 5.0), (817, 10.0), (602, 10.0), (79, 5.0), (56, 5.0)], 602: [(374, 0.0)], 79: [(374, 0.0)], 673: [(374, 0.0)], 56: [(374, 0.0)], 817: [(374, 0.0)], 374: [], 636: [(788, 5.0)]}\nsource = 636\ntarget = 374\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 101, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {89: [(783, 5.0)], 150: [(783, 5.0)], 627: [(783, 5.0)], 901: [(783, 5.0)], 588: [(783, 5.0)], 783: [(640, 0.0)], 640: [], 86: [(89, 15.0), (150, 10.0), (627, 10.0), (901, 10.0), (588, 10.0)]}\nsource = 86\ntarget = 640\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0} |
| {"source_row": 102, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {343: [(363, 20.0), (253, 10.0), (831, 15.0)], 253: [(323, 5.0)], 831: [(323, 5.0)], 363: [(323, 5.0)], 323: [(695, 10.0)], 695: [(258, 0.0)], 258: [], 13: [(343, 5.0)]}\nsource = 13\ntarget = 258\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 103, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {230: [(556, 1.0)], 556: [(822, 0.5)], 822: [(777, 1.0)], 777: [(936, 15.0)], 936: [(749, 0.0)], 12: [(749, 0.0)], 749: [], 941: [(230, 5.0), (12, 10.0)]}\nsource = 941\ntarget = 749\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.5, "source_answer": 22.5} |
| {"source_row": 104, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {862: [(472, 0.0)], 343: [(472, 0.0)], 479: [(51, 2.0)], 51: [(472, 0.0)], 472: [], 958: [(862, 2.0), (343, 3.0), (479, 1.0)]}\nsource = 958\ntarget = 472\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0} |
| {"source_row": 105, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {876: [(778, 2880.0)], 142: [(621, 4.0)], 621: [(157, 60.0)], 157: [(30, 0.0)], 972: [(181, 10080.0)], 181: [(157, 60.0)], 778: [(157, 60.0)], 30: [], 830: [(876, 60.0), (142, 2.0), (972, 10.0)]}\nsource = 830\ntarget = 30\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10150.0, "source_answer": 10150.0} |
| {"source_row": 106, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {245: [(702, 129600.0)], 751: [(702, 129600.0)], 702: [(81, 10.0)], 81: [(309, 60.0)], 503: [(702, 129600.0)], 309: [(851, 0.0)], 851: [], 601: [(245, 1440.0), (751, 30.0), (503, 4320.0)]}\nsource = 601\ntarget = 851\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 133990.0, "source_answer": 133990.0} |
| {"source_row": 107, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {206: [(634, 0.0)], 827: [(9, 5.0)], 9: [(634, 0.0)], 483: [(969, 30.0)], 969: [(634, 0.0)], 201: [(457, 5.0)], 457: [(634, 0.0)], 634: [], 406: [(206, 2.0), (827, 15.0), (483, 15.0), (201, 10.0)]}\nsource = 406\ntarget = 634\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0} |
| {"source_row": 108, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {121: [(66, 30.0)], 10: [(920, 0.0)], 66: [(570, 30.0)], 570: [(54, 30.0)], 54: [(362, 30.0), (10, 5.0)], 362: [(920, 0.0)], 920: [], 217: [(121, 30.0)]}\nsource = 217\ntarget = 920\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 150.0, "source_answer": 150.0} |
| {"source_row": 109, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {13: [(899, 10.0), (80, 10.0)], 899: [(35, 10.0)], 80: [(35, 10.0)], 35: [(624, 10.0)], 624: [(795, 10.0)], 795: [(441, 10.0)], 441: [(308, 0.0)], 308: [], 57: [(13, 10.0)]}\nsource = 57\ntarget = 308\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0} |
| {"source_row": 110, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {394: [(47, 60.0)], 47: [(135, 60480.0), (83, 60.0)], 83: [(790, 60.0)], 135: [(790, 60.0)], 790: [(301, 0.0)], 301: [], 951: [(394, 0.5)]}\nsource = 951\ntarget = 301\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60600.5, "source_answer": 60600.5} |
| {"source_row": 111, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {900: [(166, 0.25)], 217: [(166, 0.25)], 166: [(269, 5.0)], 269: [(333, 15.0)], 333: [(275, 5.0)], 275: [(636, 0.0)], 636: [], 396: [(900, 30.0), (217, 30.0)]}\nsource = 396\ntarget = 636\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.25, "source_answer": 55.25} |
| {"source_row": 112, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {795: [(798, 1.0)], 798: [(925, 1.0)], 925: [(251, 1.0), (945, 1.0)], 251: [(274, 1.0)], 945: [(274, 1.0)], 274: [(591, 1.0)], 591: [(104, 0.0)], 104: [], 773: [(795, 1.0)]}\nsource = 773\ntarget = 104\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0} |
| {"source_row": 113, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {974: [(224, 30.0)], 814: [(224, 30.0)], 224: [(808, 10.0)], 808: [(136, 30.0)], 136: [(228, 10.0)], 228: [(654, 0.0)], 654: [], 595: [(974, 2.0), (814, 10.0)]}\nsource = 595\ntarget = 654\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 114, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {415: [(320, 120.0)], 416: [(320, 120.0)], 320: [(273, 1440.0)], 273: [(96, 172800.0)], 96: [(800, 10080.0), (936, 10080.0)], 800: [(898, 0.0)], 936: [(898, 0.0)], 898: [], 141: [(415, 60.0), (416, 120.0)]}\nsource = 141\ntarget = 898\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 184560.0, "source_answer": 184560.0} |
| {"source_row": 115, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {346: [(762, 1.0)], 762: [(180, 0.25)], 180: [(207, 1.0)], 207: [(27, 0.17), (41, 0.17), (277, 0.17)], 41: [(696, 0.0)], 27: [(696, 0.0)], 277: [(696, 0.0)], 696: [], 102: [(346, 0.25)]}\nsource = 102\ntarget = 696\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.67, "source_answer": 2.67} |
| {"source_row": 116, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {658: [(778, 15.0)], 827: [(778, 15.0)], 778: [(260, 1.0)], 260: [(528, 1.0)], 104: [(778, 15.0)], 528: [(980, 1.0)], 980: [(167, 0.0)], 167: [], 165: [(658, 10.0), (827, 1.0), (104, 15.0)]}\nsource = 165\ntarget = 167\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 33.0, "source_answer": 33.0} |
| {"source_row": 117, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {8: [(329, 0.5), (288, 180.0)], 329: [(653, 0.17)], 288: [(653, 0.17)], 653: [(512, 0.08)], 512: [(639, 129600.0)], 639: [(894, 0.0)], 894: [], 120: [(8, 60.0)]}\nsource = 120\ntarget = 894\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 129840.25, "source_answer": 129840.25} |
| {"source_row": 118, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {425: [(264, 120.0), (520, 120.0)], 264: [(229, 120.0)], 520: [(229, 120.0)], 229: [(917, 10.0)], 917: [(643, 0.0)], 643: [], 711: [(425, 60.0)]}\nsource = 711\ntarget = 643\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 310.0, "source_answer": 310.0} |
| {"source_row": 119, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {776: [(82, 10.0), (480, 10.0)], 82: [(362, 45.0)], 480: [(362, 45.0)], 362: [(789, 5.0)], 789: [(244, 5.0)], 244: [(652, 0.0)], 652: [], 853: [(776, 5.0)]}\nsource = 853\ntarget = 652\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 70.0, "source_answer": 70.0} |
| {"source_row": 120, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {66: [(489, 5.0), (100, 10.0)], 489: [(889, 5.0)], 100: [(889, 5.0)], 889: [(333, 10.0)], 333: [(454, 10.0)], 454: [(694, 10.0)], 694: [(845, 0.0)], 845: [], 610: [(66, 10.0)]}\nsource = 610\ntarget = 845\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 55.0, "source_answer": 55.0} |
| {"source_row": 121, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {832: [(850, 30.0), (977, 60.0)], 850: [(500, 10.0)], 977: [(500, 10.0)], 500: [(778, 30.0)], 778: [(91, 0.0)], 91: [], 521: [(832, 30.0)]}\nsource = 521\ntarget = 91\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 130.0, "source_answer": 130.0} |
| {"source_row": 122, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {302: [(610, 1.0), (689, 180.0)], 689: [(835, 1440.0)], 610: [(345, 1440.0)], 835: [(949, 0.0)], 345: [(949, 0.0)], 949: [], 49: [(302, 0.25)]}\nsource = 49\ntarget = 949\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1620.25, "source_answer": 1620.25} |
| {"source_row": 123, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {483: [(991, 10.0)], 469: [(991, 10.0)], 991: [(137, 3.0)], 137: [(795, 5.0)], 795: [(780, 0.0)], 780: [], 733: [(483, 0.5), (469, 0.5)]}\nsource = 733\ntarget = 780\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.5, "source_answer": 18.5} |
| {"source_row": 124, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {952: [(916, 2.0)], 916: [(691, 3.0)], 691: [(276, 4.0)], 276: [(529, 3.0), (74, 3.0)], 74: [(11, 0.0)], 529: [(11, 0.0)], 11: [], 374: [(952, 3.0)]}\nsource = 374\ntarget = 11\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 125, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {360: [(16, 0.0)], 844: [(932, 15.0)], 932: [(16, 0.0)], 16: [], 939: [(360, 5.0), (844, 10.0)]}\nsource = 939\ntarget = 16\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 126, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {690: [(46, 20160.0)], 298: [(46, 20160.0)], 142: [(789, 0.0)], 46: [(142, 43200.0)], 789: [], 386: [(690, 86400.0), (298, 129600.0)]}\nsource = 386\ntarget = 789\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 192960.0, "source_answer": 192960.0} |
| {"source_row": 127, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {54: [(287, 0.0)], 706: [(54, 5.0)], 567: [(54, 5.0)], 287: [], 955: [(706, 10.0), (567, 15.0)]}\nsource = 955\ntarget = 287\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0} |
| {"source_row": 128, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {601: [(130, 0.0)], 889: [(130, 0.0)], 246: [(130, 0.0)], 940: [(889, 1440.0), (601, 10080.0), (246, 20160.0)], 130: [], 830: [(940, 43200.0)]}\nsource = 830\ntarget = 130\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 63360.0, "source_answer": 63360.0} |
| {"source_row": 129, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {297: [(694, 15.0)], 693: [(694, 15.0)], 694: [(233, 2.0)], 233: [(890, 0.0)], 890: [], 362: [(297, 15.0), (693, 10.0)]}\nsource = 362\ntarget = 890\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0} |
| {"source_row": 130, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {465: [(551, 10.0)], 551: [(447, 2.0)], 447: [(298, 0.0)], 841: [(298, 0.0)], 298: [], 821: [(465, 5.0), (841, 1.0)]}\nsource = 821\ntarget = 298\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0} |
| {"source_row": 131, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {393: [(709, 0.5)], 709: [(50, 2.0)], 50: [(31, 0.0)], 146: [(50, 2.0)], 31: [], 946: [(393, 1.0), (146, 1.0)]}\nsource = 946\ntarget = 31\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.5, "source_answer": 3.5} |
| {"source_row": 132, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {671: [(403, 0.0)], 505: [(16, 20160.0)], 16: [(403, 0.0)], 403: [], 586: [(671, 20160.0), (505, 40320.0)]}\nsource = 586\ntarget = 403\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60480.0, "source_answer": 60480.0} |
| {"source_row": 133, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {376: [(433, 10.0)], 433: [(846, 240.0)], 846: [(683, 0.0)], 146: [(619, 240.0)], 619: [(433, 10.0)], 683: [], 757: [(376, 2.0), (146, 20.0)]}\nsource = 757\ntarget = 683\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 510.0, "source_answer": 510.0} |
| {"source_row": 134, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {581: [(208, 360.0)], 259: [(208, 360.0)], 746: [(208, 360.0)], 208: [(122, 1.0)], 122: [(418, 0.0)], 418: [], 897: [(581, 5.0), (259, 10.0), (746, 2.0)]}\nsource = 897\ntarget = 418\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 371.0, "source_answer": 371.0} |
| {"source_row": 135, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {438: [(733, 10.0)], 268: [(733, 10.0)], 733: [(499, 30.0)], 499: [(561, 5.0)], 561: [(915, 0.0)], 915: [], 164: [(438, 10.0), (268, 20.0)]}\nsource = 164\ntarget = 915\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 65.0, "source_answer": 65.0} |
| {"source_row": 136, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {72: [(960, 20.0), (146, 10.0), (667, 15.0), (875, 15.0)], 667: [(185, 0.0)], 875: [(185, 0.0)], 960: [(185, 0.0)], 146: [(185, 0.0)], 185: [], 701: [(72, 10.0)]}\nsource = 701\ntarget = 185\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 137, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {713: [(854, 0.0)], 8: [(713, 5.0)], 997: [(854, 0.0)], 854: [], 727: [(8, 10.0), (997, 15.0)]}\nsource = 727\ntarget = 854\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 138, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {245: [(266, 2.0)], 266: [(305, 7.0)], 656: [(305, 7.0)], 305: [(537, 0.0)], 517: [(537, 0.0)], 537: [], 176: [(245, 2.0), (656, 1.0), (517, 8.0)]}\nsource = 176\ntarget = 537\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 11.0, "source_answer": 11.0} |
| {"source_row": 139, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {416: [(948, 10.0)], 948: [(936, 0.0)], 53: [(936, 0.0)], 661: [(936, 0.0)], 628: [(936, 0.0)], 936: [], 205: [(416, 5.0), (53, 2.0), (661, 3.0), (628, 1.0)]}\nsource = 205\ntarget = 936\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 140, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {507: [(152, 30.0), (71, 10.0)], 152: [(208, 5.0)], 71: [(208, 5.0)], 208: [(755, 60.0)], 755: [(734, 0.0)], 734: [], 231: [(507, 10.0)]}\nsource = 231\ntarget = 734\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0} |
| {"source_row": 141, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {368: [(77, 1440.0)], 77: [(510, 0.0)], 113: [(510, 0.0)], 510: [], 436: [(368, 10080.0), (113, 43200.0)]}\nsource = 436\ntarget = 510\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43200.0, "source_answer": 43200.0} |
| {"source_row": 142, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {58: [(649, 3.0)], 649: [(552, 3.0)], 182: [(552, 3.0)], 552: [(809, 12.0)], 809: [(496, 0.0)], 496: [], 45: [(58, 5.0), (182, 2.0)]}\nsource = 45\ntarget = 496\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 23.0, "source_answer": 23.0} |
| {"source_row": 143, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {953: [(810, 5.0)], 496: [(967, 10.0), (810, 5.0)], 810: [(572, 0.0)], 967: [(502, 5.0)], 502: [(572, 0.0)], 572: [], 135: [(953, 5.0), (496, 10.0)]}\nsource = 135\ntarget = 572\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 144, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {314: [(390, 3.0)], 113: [(390, 3.0)], 923: [(390, 3.0)], 390: [(718, 0.0)], 718: [], 416: [(314, 5.0), (113, 2.0), (923, 30.0)]}\nsource = 416\ntarget = 718\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 33.0, "source_answer": 33.0} |
| {"source_row": 145, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {366: [(396, 180.0)], 396: [(349, 0.0)], 494: [(349, 0.0)], 609: [(349, 0.0)], 676: [(349, 0.0)], 349: [], 703: [(366, 60.0), (494, 10.0), (609, 30.0), (676, 15.0)]}\nsource = 703\ntarget = 349\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 240.0, "source_answer": 240.0} |
| {"source_row": 146, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {611: [(184, 0.0)], 768: [(611, 2.0)], 845: [(611, 2.0)], 762: [(611, 2.0)], 700: [(184, 0.0)], 184: [], 17: [(768, 0.5), (845, 0.5), (762, 0.5), (700, 2.0)]}\nsource = 17\ntarget = 184\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.5, "source_answer": 2.5} |
| {"source_row": 147, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {78: [(902, 15.0), (837, 3.0)], 274: [(902, 15.0)], 894: [(902, 15.0)], 800: [(902, 15.0)], 837: [(489, 0.0)], 902: [(489, 0.0)], 489: [], 676: [(78, 5.0), (274, 10.0), (894, 5.0), (800, 2.0)]}\nsource = 676\ntarget = 489\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 148, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {732: [(420, 0.0)], 899: [(658, 2.0)], 369: [(11, 2.0)], 11: [(504, 1.0), (658, 2.0)], 658: [(420, 0.0)], 504: [(420, 0.0)], 420: [], 479: [(732, 5.0), (899, 3.0), (369, 1.0)]}\nsource = 479\ntarget = 420\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0} |
| {"source_row": 149, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {817: [(280, 30.0)], 562: [(584, 0.0)], 807: [(584, 0.0)], 323: [(584, 0.0)], 781: [(584, 0.0)], 280: [(584, 0.0)], 584: [], 260: [(817, 60.0), (562, 30.0), (807, 15.0), (323, 20.0), (781, 60.0)]}\nsource = 260\ntarget = 584\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 150, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {845: [(840, 15.0)], 582: [(845, 5.0)], 840: [(499, 0.0)], 230: [(499, 0.0)], 499: [], 354: [(582, 10.0), (230, 5.0)]}\nsource = 354\ntarget = 499\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 151, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {74: [(343, 30.0)], 164: [(786, 2.0)], 328: [(786, 2.0)], 786: [(343, 30.0)], 343: [(222, 2.0)], 222: [(173, 0.0)], 173: [], 810: [(74, 10.0), (164, 5.0), (328, 15.0)]}\nsource = 810\ntarget = 173\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 49.0, "source_answer": 49.0} |
| {"source_row": 152, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {45: [(652, 0.0)], 94: [(730, 5.0)], 730: [(652, 0.0)], 652: [], 924: [(45, 30.0), (94, 10.0)]}\nsource = 924\ntarget = 652\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 153, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {944: [(69, 10.0)], 69: [(719, 10.0)], 246: [(719, 10.0)], 719: [(215, 1.0)], 149: [(908, 0.0)], 215: [(149, 1.0)], 908: [], 9: [(944, 5.0), (246, 2.0)]}\nsource = 9\ntarget = 908\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 27.0, "source_answer": 27.0} |
| {"source_row": 154, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {587: [(486, 2.0)], 342: [(486, 2.0)], 486: [(188, 2.0)], 188: [(672, 0.0)], 678: [(672, 0.0)], 23: [(672, 0.0)], 594: [(672, 0.0)], 672: [], 33: [(587, 2.0), (342, 2.0), (678, 3.0), (23, 2.0), (594, 3.0)]}\nsource = 33\ntarget = 672\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0} |
| {"source_row": 155, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {513: [(668, 0.08), (688, 0.08)], 668: [(85, 0.0)], 688: [(85, 0.0)], 921: [(833, 0.17)], 833: [(235, 0.08)], 235: [(661, 0.08)], 661: [(513, 0.17)], 85: [], 486: [(921, 0.17)]}\nsource = 486\ntarget = 85\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.75, "source_answer": 0.75} |
| {"source_row": 156, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {270: [(987, 30.0)], 737: [(656, 3.0)], 656: [(64, 5.0), (606, 5.0), (254, 10.0)], 987: [(737, 2.0)], 606: [(843, 0.0)], 64: [(843, 0.0)], 254: [(843, 0.0)], 843: [], 567: [(270, 5.0)]}\nsource = 567\ntarget = 843\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 157, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {694: [(503, 0.0)], 150: [(503, 0.0)], 638: [(437, 10.0)], 437: [(503, 0.0)], 503: [], 613: [(694, 60.0), (150, 30.0), (638, 34164000.0)]}\nsource = 613\ntarget = 503\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 34164010.0, "source_answer": 34164010.0} |
| {"source_row": 158, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {203: [(228, 3.0), (736, 10.0), (614, 8.0), (462, 5.0), (995, 3.0), (743, 3.0)], 462: [(907, 0.0)], 995: [(907, 0.0)], 736: [(907, 0.0)], 743: [(907, 0.0)], 614: [(907, 0.0)], 228: [(907, 0.0)], 907: [], 35: [(203, 30.0)]}\nsource = 35\ntarget = 907\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 159, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {573: [(706, 0.0)], 532: [(706, 0.0)], 263: [(706, 0.0)], 903: [(532, 60.0), (573, 30.0)], 706: [], 53: [(263, 60.0), (903, 20160.0)]}\nsource = 53\ntarget = 706\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20220.0, "source_answer": 20220.0} |
| {"source_row": 160, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {459: [(426, 5.0)], 20: [(426, 5.0)], 44: [(426, 5.0)], 280: [(426, 5.0)], 517: [(426, 5.0)], 344: [(426, 5.0)], 426: [(36, 0.0)], 36: [], 389: [(459, 5.0), (20, 2.0), (44, 3.0), (280, 3.0), (517, 3.0), (344, 1.0)]}\nsource = 389\ntarget = 36\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0} |
| {"source_row": 161, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {24: [(136, 20.0)], 682: [(136, 20.0)], 136: [(320, 0.0)], 320: [], 252: [(24, 30.0), (682, 15.0)]}\nsource = 252\ntarget = 320\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 162, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {439: [(678, 10.0), (545, 14400.0), (842, 10.0)], 545: [(137, 0.0)], 842: [(137, 0.0)], 678: [(137, 0.0)], 137: [], 845: [(439, 120.0)]}\nsource = 845\ntarget = 137\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 14520.0, "source_answer": 14520.0} |
| {"source_row": 163, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {12: [(692, 10.0), (224, 5.0)], 692: [(668, 2.0)], 224: [(668, 2.0)], 668: [(525, 30.0)], 525: [(747, 2.0)], 747: [(648, 1.0)], 648: [(17, 0.0)], 17: [], 377: [(12, 5.0)]}\nsource = 377\ntarget = 17\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 164, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {198: [(287, 60.0), (928, 15.0)], 287: [(960, 0.0)], 928: [(960, 0.0)], 960: [], 773: [(198, 30.0)]}\nsource = 773\ntarget = 960\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 165, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {845: [(610, 0.0)], 357: [(610, 0.0)], 454: [(610, 0.0)], 354: [(610, 0.0)], 497: [(620, 30.0)], 620: [(217, 30.0)], 217: [(411, 60.0)], 411: [(610, 0.0)], 610: [], 253: [(845, 120.0), (357, 60.0), (454, 180.0), (354, 30.0), (497, 60.0)]}\nsource = 253\ntarget = 610\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 180.0, "source_answer": 180.0} |
| {"source_row": 166, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {767: [(52, 5.0)], 52: [(697, 10.0)], 308: [(697, 10.0)], 697: [(533, 1.0)], 533: [(426, 0.0)], 426: [], 272: [(767, 5.0), (308, 2.0)]}\nsource = 272\ntarget = 426\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0} |
| {"source_row": 167, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {727: [(789, 10.0), (412, 15.0)], 789: [(463, 0.0)], 412: [(463, 0.0)], 347: [(789, 10.0), (412, 15.0)], 463: [], 524: [(727, 5.0), (347, 15.0)]}\nsource = 524\ntarget = 463\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 168, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {496: [(413, 1.0), (920, 2.0), (386, 1.0), (807, 1.0), (685, 1.0), (41, 2.0), (811, 1.0)], 41: [(494, 0.0)], 807: [(494, 0.0)], 386: [(494, 0.0)], 811: [(494, 0.0)], 685: [(494, 0.0)], 413: [(494, 0.0)], 920: [(494, 0.0)], 494: [], 826: [(496, 5.0)]}\nsource = 826\ntarget = 494\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 7.0, "source_answer": 7.0} |
| {"source_row": 169, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {40: [(736, 1.0)], 736: [(859, 0.0)], 686: [(859, 0.0)], 707: [(859, 0.0)], 66: [(859, 0.0)], 859: [], 359: [(40, 5.0), (686, 2.0), (707, 1.0), (66, 3.0)]}\nsource = 359\ntarget = 859\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 6.0, "source_answer": 6.0} |
| {"source_row": 170, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {917: [(446, 1.0), (927, 1.0)], 446: [(967, 2.0)], 927: [(967, 2.0)], 967: [(322, 1.0)], 322: [(488, 1.0)], 488: [(863, 1.0)], 863: [(148, 5.0)], 148: [(822, 0.0)], 822: [], 710: [(917, 2.0)]}\nsource = 710\ntarget = 822\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 13.0, "source_answer": 13.0} |
| {"source_row": 171, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {371: [(728, 2.0)], 994: [(728, 2.0)], 728: [(967, 60.0)], 967: [(141, 0.0)], 141: [], 506: [(371, 5.0), (994, 10.0)]}\nsource = 506\ntarget = 141\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 72.0, "source_answer": 72.0} |
| {"source_row": 172, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {792: [(523, 0.0)], 532: [(523, 0.0)], 187: [(928, 5.0), (341, 5.0)], 341: [(216, 10.0)], 928: [(216, 10.0)], 216: [(523, 0.0)], 120: [(354, 10.0)], 354: [(523, 0.0)], 761: [(523, 0.0)], 523: [], 449: [(792, 5.0), (532, 10.0), (187, 15.0), (120, 15.0), (761, 5.0)]}\nsource = 449\ntarget = 523\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 30.0, "source_answer": 30.0} |
| {"source_row": 173, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {191: [(649, 15.0)], 282: [(29, 15.0)], 29: [(289, 30.0)], 289: [(366, 10.0)], 366: [(649, 15.0)], 649: [(411, 20.0)], 411: [(177, 10.0)], 559: [(177, 10.0)], 177: [(831, 0.0)], 831: [], 84: [(191, 10.0), (282, 10.0), (559, 8.0)]}\nsource = 84\ntarget = 831\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 110.0, "source_answer": 110.0} |
| {"source_row": 174, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {800: [(218, 4.0)], 270: [(765, 0.0)], 934: [(218, 4.0)], 689: [(218, 4.0)], 495: [(218, 4.0)], 104: [(218, 4.0)], 218: [(216, 3.0)], 216: [(843, 3.0)], 843: [(190, 2.0)], 190: [(270, 5.0)], 765: [], 133: [(800, 5.0), (934, 3.0), (689, 2.0), (495, 3.0), (104, 3.0)]}\nsource = 133\ntarget = 765\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0} |
| {"source_row": 175, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {10: [(443, 1.0), (660, 1.0), (209, 2.0)], 245: [(854, 2.0)], 443: [(700, 2.0), (829, 2.0), (742, 1.0)], 209: [(700, 2.0), (829, 2.0), (742, 1.0)], 660: [(700, 2.0), (829, 2.0), (742, 1.0)], 829: [(635, 3.0)], 700: [(635, 3.0)], 742: [(635, 3.0)], 635: [(245, 1.0)], 854: [(954, 0.0)], 954: [], 492: [(10, 2.0)]}\nsource = 492\ntarget = 954\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0} |
| {"source_row": 176, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {141: [(274, 3.0)], 652: [(274, 3.0)], 970: [(274, 3.0)], 435: [(274, 3.0)], 399: [(274, 3.0)], 274: [(61, 10.0)], 61: [(846, 0.0)], 846: [], 644: [(141, 5.0), (652, 3.0), (970, 2.0), (435, 4.0), (399, 1.0)]}\nsource = 644\ntarget = 846\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 18.0, "source_answer": 18.0} |
| {"source_row": 177, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {748: [(747, 15.0)], 574: [(748, 5.0)], 747: [(730, 60.0)], 730: [(24, 5.0)], 24: [(621, 0.0)], 757: [(748, 5.0)], 935: [(748, 5.0)], 127: [(748, 5.0)], 151: [(748, 5.0)], 816: [(748, 5.0)], 621: [], 449: [(757, 15.0), (935, 30.0), (127, 45.0), (151, 40.0), (816, 35.0), (574, 50.0)]}\nsource = 449\ntarget = 621\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 135.0, "source_answer": 135.0} |
| {"source_row": 178, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {49: [(603, 2.0)], 603: [(160, 10.0)], 160: [(252, 5.0)], 252: [(885, 0.0)], 1: [(885, 0.0)], 885: [], 433: [(49, 5.0), (1, 2.0)]}\nsource = 433\ntarget = 885\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0} |
| {"source_row": 179, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {969: [(803, 25.0)], 960: [(803, 25.0)], 803: [(149, 0.0)], 808: [(281, 1.0)], 465: [(589, 1.0)], 589: [(892, 10.0)], 892: [(281, 1.0)], 338: [(247, 3.0)], 247: [(378, 2.0)], 378: [(960, 1.0)], 281: [(960, 1.0)], 149: [], 746: [(969, 1.0), (808, 1.0), (465, 2.0), (338, 2.0)]}\nsource = 746\ntarget = 149\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 40.0, "source_answer": 40.0} |
| {"source_row": 180, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {491: [(594, 2.0), (98, 5.0)], 716: [(296, 10.0)], 296: [(834, 0.0)], 64: [(594, 2.0)], 594: [(786, 10.0), (817, 5.0)], 703: [(98, 5.0)], 98: [(786, 10.0), (817, 5.0)], 786: [(693, 60.0)], 817: [(693, 60.0)], 693: [(563, 180.0)], 563: [(716, 10.0)], 834: [], 432: [(491, 5.0), (64, 5.0), (703, 7.0)]}\nsource = 432\ntarget = 834\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 282.0, "source_answer": 282.0} |
| {"source_row": 181, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {726: [(230, 5.0)], 401: [(400, 3.0)], 519: [(901, 0.0)], 230: [(171, 2.0)], 168: [(171, 2.0)], 171: [(494, 5.0)], 494: [(205, 2.0)], 205: [(763, 5.0)], 763: [(372, 2.0)], 372: [(401, 2.0)], 400: [(519, 1.0)], 901: [], 742: [(726, 5.0), (168, 3.0)]}\nsource = 742\ntarget = 901\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 32.0, "source_answer": 32.0} |
| {"source_row": 182, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {622: [(790, 15.0)], 146: [(737, 60.0)], 737: [(229, 0.0)], 47: [(790, 15.0)], 864: [(790, 15.0)], 790: [(57, 10.0)], 57: [(319, 10.0)], 319: [(435, 30.0)], 435: [(255, 15.0)], 255: [(877, 5.0)], 877: [(146, 10.0)], 229: [], 473: [(622, 10.0), (47, 10.0), (864, 5.0)]}\nsource = 473\ntarget = 229\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 165.0, "source_answer": 165.0} |
| {"source_row": 183, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {992: [(796, 0.0)], 410: [(796, 0.0)], 837: [(796, 0.0)], 644: [(796, 0.0)], 568: [(796, 0.0)], 612: [(796, 0.0)], 520: [(812, 2.0)], 54: [(812, 2.0)], 812: [(796, 0.0)], 796: [], 714: [(992, 1.0), (410, 1.0), (837, 1.0), (644, 1.0), (568, 1.0), (612, 1.0), (520, 1.0), (54, 1.0)]}\nsource = 714\ntarget = 796\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0} |
| {"source_row": 184, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {988: [(357, 10.0)], 800: [(532, 5.0), (988, 5.0)], 548: [(355, 10.0)], 355: [(134, 5.0)], 134: [(482, 10.0)], 99: [(429, 2.0)], 429: [(539, 10.0)], 539: [(419, 2.0)], 419: [(369, 0.0)], 357: [(142, 5.0)], 142: [(381, 5.0)], 381: [(250, 10.0)], 532: [(250, 10.0)], 250: [(99, 2.0)], 482: [(652, 10.0)], 652: [(800, 5.0), (922, 10.0)], 922: [(988, 5.0), (532, 5.0)], 369: [], 978: [(548, 15.0)]}\nsource = 978\ntarget = 369\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 111.0, "source_answer": 111.0} |
| {"source_row": 185, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {264: [(831, 0.5)], 831: [(62, 1.0)], 62: [(141, 1.0)], 141: [(23, 0.5), (283, 1.0)], 283: [(397, 0.75)], 23: [(397, 0.75)], 397: [(402, 0.0)], 402: [], 408: [(264, 0.5)]}\nsource = 408\ntarget = 402\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 4.75, "source_answer": 4.75} |
| {"source_row": 186, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {15: [(61, 0.0)], 814: [(894, 1.0)], 894: [(61, 0.0)], 61: [], 553: [(15, 5.0), (814, 2.0)]}\nsource = 553\ntarget = 61\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0} |
| {"source_row": 187, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {170: [(76, 0.0)], 408: [(966, 2.0)], 966: [(76, 0.0)], 76: [], 656: [(170, 5.0), (408, 10.0)]}\nsource = 656\ntarget = 76\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0} |
| {"source_row": 188, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {272: [(714, 60.0), (950, 15.0)], 714: [(301, 0.0)], 950: [(301, 0.0)], 301: [], 3: [(272, 30.0)]}\nsource = 3\ntarget = 301\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 189, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {267: [(754, 43200.0)], 40: [(698, 0.0)], 754: [(698, 0.0)], 698: [], 427: [(267, 5.0), (40, 20160.0)]}\nsource = 427\ntarget = 698\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 43205.0, "source_answer": 43205.0} |
| {"source_row": 190, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {481: [(570, 2628000.0)], 865: [(570, 2628000.0)], 570: [(763, 0.0)], 763: [], 302: [(481, 2628000.0), (865, 2102400.0)]}\nsource = 302\ntarget = 763\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5256000.0, "source_answer": 5256000.0} |
| {"source_row": 191, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {906: [(422, 3.0), (793, 3.0)], 422: [(339, 0.0)], 793: [(339, 0.0)], 339: [], 341: [(906, 5.0)]}\nsource = 341\ntarget = 339\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0} |
| {"source_row": 192, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {166: [(528, 0.0)], 457: [(476, 3.0)], 476: [(528, 0.0)], 528: [], 540: [(166, 10.0), (457, 5.0)]}\nsource = 540\ntarget = 528\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10.0, "source_answer": 10.0} |
| {"source_row": 193, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {42: [(617, 0.0)], 631: [(42, 5.0)], 329: [(42, 5.0)], 617: [], 763: [(631, 10.0), (329, 15.0)]}\nsource = 763\ntarget = 617\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 20.0, "source_answer": 20.0} |
| {"source_row": 194, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {248: [(973, 0.0)], 388: [(973, 0.0)], 677: [(388, 3.0), (248, 5.0)], 973: [], 73: [(677, 4.0)]}\nsource = 73\ntarget = 973\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 9.0, "source_answer": 9.0} |
| {"source_row": 195, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {830: [(853, 10.0)], 499: [(372, 15.0)], 853: [(372, 15.0)], 372: [(875, 0.0)], 875: [], 993: [(830, 5.0), (499, 30.0)]}\nsource = 993\ntarget = 875\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 45.0, "source_answer": 45.0} |
| {"source_row": 196, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {514: [(791, 15.0), (63, 30.0)], 63: [(487, 10080.0)], 487: [(204, 0.0)], 791: [(204, 0.0)], 204: [], 818: [(514, 15.0)]}\nsource = 818\ntarget = 204\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 10125.0, "source_answer": 10125.0} |
| {"source_row": 197, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {832: [(923, 3.0)], 923: [(933, 3.0), (615, 2.0)], 615: [(925, 0.0)], 933: [(925, 0.0)], 925: [], 28: [(832, 2.0)]}\nsource = 28\ntarget = 925\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 8.0, "source_answer": 8.0} |
| {"source_row": 198, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {715: [(631, 1.0)], 33: [(631, 1.0)], 631: [(270, 1.0)], 270: [(951, 0.0)], 951: [], 225: [(715, 1.0), (33, 1.0)]}\nsource = 225\ntarget = 951\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0} |
| {"source_row": 199, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {742: [(277, 2.0)], 85: [(277, 2.0)], 277: [(328, 0.0)], 328: [], 226: [(742, 5.0), (85, 10.0)]}\nsource = 226\ntarget = 328\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 12.0, "source_answer": 12.0} |
| {"source_row": 200, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {101: [(917, 0.0)], 797: [(101, 5.0)], 175: [(917, 0.0)], 592: [(917, 0.0)], 917: [], 487: [(797, 10.0), (175, 5.0), (592, 5.0)]}\nsource = 487\ntarget = 917\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 15.0, "source_answer": 15.0} |
| {"source_row": 201, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {494: [(705, 1.0)], 335: [(705, 1.0)], 705: [(155, 0.0)], 155: [], 483: [(494, 1.0), (335, 2.0)]}\nsource = 483\ntarget = 155\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.0, "source_answer": 3.0} |
| {"source_row": 202, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {863: [(678, 0.0)], 691: [(896, 2.0)], 896: [(678, 0.0)], 875: [(678, 0.0)], 678: [], 913: [(863, 5.0), (691, 15.0), (875, 10.0)]}\nsource = 913\ntarget = 678\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 17.0, "source_answer": 17.0} |
| {"source_row": 203, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {52: [(642, 0.0)], 152: [(873, 15.0)], 873: [(642, 0.0)], 147: [(642, 0.0)], 642: [], 903: [(52, 30.0), (152, 45.0), (147, 15.0)]}\nsource = 903\ntarget = 642\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 60.0, "source_answer": 60.0} |
| {"source_row": 204, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {954: [(977, 10.0), (471, 30.0)], 977: [(74, 0.0)], 471: [(74, 0.0)], 74: [], 769: [(954, 20.0)]}\nsource = 769\ntarget = 74\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 50.0, "source_answer": 50.0} |
| {"source_row": 205, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {586: [(153, 0.5)], 153: [(709, 5.0)], 709: [(322, 10.0), (790, 10.0)], 322: [(122, 5.0)], 790: [(122, 5.0)], 122: [(301, 0.0)], 301: [], 899: [(586, 0.5)]}\nsource = 899\ntarget = 301\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 21.0, "source_answer": 21.0} |
| {"source_row": 206, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {29: [(416, 0.08), (678, 0.08)], 416: [(986, 0.08)], 678: [(457, 0.08)], 986: [(239, 0.08)], 457: [(762, 0.08)], 239: [(811, 0.0)], 762: [(811, 0.0)], 811: [], 357: [(29, 0.5)]}\nsource = 357\ntarget = 811\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 0.74, "source_answer": 0.74} |
| {"source_row": 207, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {843: [(434, 0.5), (863, 0.17)], 434: [(327, 0.02)], 863: [(327, 0.02)], 327: [(427, 0.17)], 427: [(637, 0.17)], 637: [(511, 0.0)], 511: [], 486: [(843, 1.0)]}\nsource = 486\ntarget = 511\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 1.86, "source_answer": 1.86} |
| {"source_row": 208, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {963: [(365, 0.42), (439, 0.75), (422, 0.42)], 365: [(411, 1.0)], 422: [(411, 1.0)], 439: [(576, 0.17)], 411: [(871, 1.0)], 576: [(411, 1.0)], 871: [(853, 0.0)], 853: [], 29: [(963, 1.0)]}\nsource = 29\ntarget = 853\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 3.92, "source_answer": 3.92} |
| {"source_row": 209, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {938: [(232, 1.0)], 232: [(51, 15.0)], 51: [(551, 5.0), (40, 5.0)], 551: [(344, 15.0)], 40: [(344, 15.0)], 344: [(855, 0.0)], 855: [], 37: [(938, 15.0)]}\nsource = 37\ntarget = 855\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 51.0, "source_answer": 51.0} |
| {"source_row": 210, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {864: [(980, 5.0)], 424: [(980, 5.0)], 980: [(963, 1.0)], 963: [(361, 1.0)], 361: [(561, 0.0)], 561: [], 413: [(864, 5.0), (424, 15.0)]}\nsource = 413\ntarget = 561\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 22.0, "source_answer": 22.0} |
| {"source_row": 211, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {798: [(906, 15.0)], 906: [(590, 0.0)], 152: [(364, 30.0)], 364: [(217, 15.0)], 217: [(70, 30.0)], 70: [(906, 15.0)], 590: [], 402: [(798, 15.0), (152, 15.0)]}\nsource = 402\ntarget = 590\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 105.0, "source_answer": 105.0} |
| {"source_row": 212, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {135: [(293, 35.0)], 293: [(939, 30.0), (990, 10.0)], 990: [(681, 30.0)], 939: [(959, 0.0)], 681: [(959, 0.0)], 959: [], 149: [(135, 15.0)]}\nsource = 149\ntarget = 959\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 90.0, "source_answer": 90.0} |
| {"source_row": 213, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {344: [(240, 10.0), (311, 5.0)], 240: [(109, 5.0)], 311: [(109, 5.0)], 109: [(795, 5.0)], 795: [(960, 10.0)], 960: [(375, 2.0)], 375: [(104, 0.0)], 104: [], 417: [(344, 5.0)]}\nsource = 417\ntarget = 104\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 37.0, "source_answer": 37.0} |
| {"source_row": 214, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {481: [(476, 2.0), (268, 10.0), (77, 0.05)], 476: [(98, 0.03)], 98: [(973, 0.0)], 268: [(847, 1.0)], 77: [(658, 1.0)], 847: [(98, 0.03)], 658: [(973, 0.0)], 973: [], 323: [(481, 30.0)]}\nsource = 323\ntarget = 973\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 41.03, "source_answer": 41.03} |
| {"source_row": 215, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {565: [(218, 5.0)], 218: [(58, 5.0)], 58: [(745, 10.0)], 745: [(151, 30.0), (829, 60.0)], 829: [(65, 0.0)], 151: [(65, 0.0)], 65: [], 438: [(565, 20.0)]}\nsource = 438\ntarget = 65\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 100.0, "source_answer": 100.0} |
| {"source_row": 216, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {293: [(578, 1.0), (986, 0.5)], 578: [(201, 2.0)], 986: [(201, 2.0)], 201: [(102, 1.0)], 102: [(745, 0.0)], 745: [], 41: [(293, 1.0)]}\nsource = 41\ntarget = 745\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 5.0, "source_answer": 5.0} |
| {"source_row": 217, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {685: [(533, 5.0), (291, 5.0)], 533: [(986, 10.0)], 291: [(988, 3.0)], 986: [(840, 3.0)], 988: [(840, 3.0)], 840: [(134, 2.0)], 134: [(6, 0.0)], 6: [], 966: [(685, 5.0)]}\nsource = 966\ntarget = 6\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 25.0, "source_answer": 25.0} |
| {"source_row": 218, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {473: [(848, 10.0), (361, 15.0)], 848: [(201, 15.0)], 201: [(939, 1.0)], 939: [(180, 0.0)], 361: [(201, 15.0)], 180: [], 776: [(473, 15.0)]}\nsource = 776\ntarget = 180\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 46.0, "source_answer": 46.0} |
| {"source_row": 219, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {275: [(273, 120.0)], 273: [(69, 5.0)], 69: [(663, 5.0), (129, 5.0)], 663: [(615, 43200.0)], 129: [(615, 43200.0)], 615: [(162, 43200.0)], 162: [(849, 0.0)], 849: [], 44: [(275, 60.0)]}\nsource = 44\ntarget = 849\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 86590.0, "source_answer": 86590.0} |
| {"source_row": 220, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {469: [(475, 2880.0)], 475: [(538, 1440.0), (952, 28800.0), (372, 30.0), (616, 10.0), (256, 20.0)], 952: [(687, 0.0)], 538: [(687, 0.0)], 616: [(687, 0.0)], 372: [(687, 0.0)], 256: [(687, 0.0)], 687: [], 371: [(469, 432000.0)]}\nsource = 371\ntarget = 687\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 463680.0, "source_answer": 463680.0} |
| {"source_row": 221, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {581: [(466, 1.0)], 101: [(466, 1.0)], 466: [(106, 0.17)], 106: [(169, 0.17)], 567: [(169, 0.17)], 169: [(164, 0.0)], 164: [], 242: [(581, 1.0), (101, 0.5), (567, 0.5)]}\nsource = 242\ntarget = 164\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.34, "source_answer": 2.34} |
| {"source_row": 222, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {292: [(823, 15.0)], 835: [(823, 15.0)], 823: [(133, 1.0)], 133: [(990, 5.0)], 990: [(724, 0.0)], 724: [], 774: [(292, 5.0), (835, 10.0)]}\nsource = 774\ntarget = 724\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 31.0, "source_answer": 31.0} |
| {"source_row": 223, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {977: [(369, 1.0)], 227: [(369, 1.0)], 369: [(859, 0.58)], 859: [(386, 0.25)], 386: [(345, 0.0)], 345: [], 533: [(977, 1.0), (227, 1.0)]}\nsource = 533\ntarget = 345\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 2.83, "source_answer": 2.83} |
| {"source_row": 224, "question": "Below is a python function to search for the longest path from source node to target node in a directed acyclic graph (DAG) using the adjacency list representation.\nThe function takes a weighted adjacency list (a dictionary mapping each source node i to a list of (j, w) pairs, where j is a target node and w is the weight of the edge), along with a source and target node, and returns the longest path length from source to target.\n\n```python\nimport networkx as nx\n\ndef find_longest_path_from_source_to_target(weighted_adj_list, source, target):\n G = nx.DiGraph()\n for src, neighbors in weighted_adj_list.items():\n for tgt, weight in neighbors:\n G.add_edge(src, tgt, weight=weight)\n\n topo_order = list(nx.topological_sort(G))\n dist = {node: float('-inf') for node in G.nodes}\n pred = {node: None for node in G.nodes}\n dist[source] = 0\n\n for u in topo_order:\n for v in G.successors(u):\n weight = G[u][v]['weight']\n if dist[u] + weight > dist[v]:\n dist[v] = dist[u] + weight\n pred[v] = u\n\n if dist[target] == float('-inf'):\n return None, []\n\n path = []\n current = target\n while current is not None:\n path.append(current)\n current = pred[current]\n path.reverse()\n\n return dist[target]\n```\nSuppose your inputs are as follows:\n```python\nadj_list = {125: [(139, 300.0), (482, 300.0)], 139: [(403, 120.0)], 482: [(403, 120.0)], 403: [(22, 120.0)], 22: [(470, 0.0)], 470: [], 377: [(125, 10.0)]}\nsource = 377\ntarget = 470\n```\nThink step by step. Then, encode the output of the function in <answer></answer> (e.g. <answer>1</answer>).", "answer": 550.0, "source_answer": 550.0} |
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