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49295c4 | 1 2 3 4 5 6 7 | {"type": "session", "harness": "optiq-lab", "id": "8767800524", "name": "Using python, implement Dijkstra's algorithm on a randomly generated weighted directed graph with 100 nodes and report t", "model": "deepseek/deepseek-v4-flash-0731", "mode": "chat", "turns_used": 2, "tool_calls": 1, "tools_used": ["python"], "ok": true, "domain": "algorithms and complexity", "elapsed_s": 18.1, "max_turns": 8, "topic": "", "model_calls": 0, "sources": 0, "citations": 0, "report_chars": 0, "summary_chars": 0, "rounds": 0, "queries_per_round": 0, "sources_per_query": 0}
{"type": "message", "message": {"role": "system", "content": "You are OptiQ Lab, running on the user's own machine. You have three tools: web_search for looking things up, python for computation and data work, and terminal for shell one-liners.\n\nThe python and terminal tools run in a container with no network and ONLY the Python standard library. numpy, pandas, scipy, sklearn and matplotlib are not installed, so use math, statistics, json, csv, re, itertools, collections and random instead.\n\nEvery call gets a FRESH sandbox. Nothing persists between calls: files you write, variables you define and imports you make are all gone by the next one. So do the whole job in a single call and print the result. Do not write a file in one call expecting to read it in the next.\n\nIf a call fails, read the error and change approach rather than repeating it. Once you have the answer, state it plainly and stop calling tools."}}
{"type": "message", "message": {"role": "user", "content": "Using python, implement Dijkstra's algorithm on a randomly generated weighted directed graph with 100 nodes and report the shortest\u2011path distance from node 0 to node 99."}}
{"type": "message", "message": {"role": "assistant", "content": "", "toolCalls": [{"id": "call_4c1339b37eeb4f108d3ebc3c", "function": {"name": "python", "arguments": "{\"code\": \"import random\\nimport heapq\\n\\nN = 100\\nrandom.seed(42)\\n\\n# Generate a random weighted directed graph (edge prob ~0.12, weights 1..100)\\nwhile True:\\n adj = [[] for _ in range(N)]\\n for u in range(N):\\n for v in range(N):\\n if u != v and random.random() < 0.12:\\n w = random.randint(1, 100)\\n adj[u].append((v, w))\\n\\n # Quick reachability check from 0\\n seen = {0}\\n stack = [0]\\n while stack:\\n u = stack.pop()\\n for v, _ in adj[u]:\\n if v not in seen:\\n seen.add(v)\\n stack.append(v)\\n if 99 in seen:\\n break\\n\\n# Dijkstra's algorithm (binary heap)\\ndef dijkstra(start, adj):\\n dist = [float('inf')] * N\\n prev = [None] * N\\n dist[start] = 0\\n pq = [(0, start)]\\n while pq:\\n d, u = heapq.heappop(pq)\\n if d > dist[u]:\\n continue\\n for v, w in adj[u]:\\n nd = d + w\\n if nd < dist[v]:\\n dist[v] = nd\\n prev[v] = u\\n heapq.heappush(pq, (nd, v))\\n return dist, prev\\n\\ndist, prev = dijkstra(0, adj)\\n\\n# Reconstruct path\\npath = []\\nnode = 99\\nwhile node is not None:\\n path.append(node)\\n node = prev[node]\\npath.reverse()\\n\\nprint(f\\\"Shortest-path distance from node 0 to node 99: {dist[99]}\\\")\\nprint(f\\\"Path (length {len(path)} nodes): {path}\\\")\\nprint(f\\\"Number of edges in graph: {sum(len(adj[u]) for u in range(N))}\\\")\"}"}}]}}
{"type": "message", "message": {"role": "tool", "content": "sandbox: container rc=0\n--- stdout ---\nShortest-path distance from node 0 to node 99: 42\nPath (length 6 nodes): [0, 82, 19, 72, 65, 99]\nNumber of edges in graph: 1184\n\n--- stderr ---\n", "toolCallId": "call_4c1339b37eeb4f108d3ebc3c"}}
{"type": "message", "message": {"role": "assistant", "content": "Done. I implemented Dijkstra's algorithm with a binary heap (priority queue) in Python on a randomly generated weighted directed graph with 100 nodes (edge probability 0.12, weights 1\u2013100, seeded for reproducibility, regenerated until node 99 was reachable from node 0).\n\n**Result: the shortest-path distance from node 0 to node 99 is 42.**\n\nThe shortest path found was: 0 \u2192 82 \u2192 19 \u2192 72 \u2192 65 \u2192 99 (5 edges, total weight 42)."}}
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