77 lines
2.0 KiB
Python
77 lines
2.0 KiB
Python
import multiprocessing
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import os
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import requests
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def worker(outqueue, id):
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print(f"Worker {id} is preparing...")
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import numpy as np
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import networkx as nx
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with open("/dkebab/GBA1000.txt") as f:
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n = int(f.readline()) # pontok száma
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m = int(f.readline()) # élek száma
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A = [[0] * n for _ in range(n)] # táblázat, ahol az élek helyén 1 van, ammeg 0
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for _ in range(m):
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[u, v] = map(int, f.readline().split())
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A[u][v] = 1
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A[v][u] = 1
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input_graph = nx.Graph(np.array(A))
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def max_comp_size(G):
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return max([len(c) for c in nx.connected_components(G)])
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print(f"Worker {id} is working...")
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while True:
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del_list = []
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G = input_graph.copy()
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target = len(G.nodes) / 2
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while max_comp_size(G) > target:
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u = np.random.choice(G.nodes)
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if u not in del_list:
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del_list.append(u)
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G.remove_node(u)
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outqueue.put(del_list)
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def worker_watchdog(outqueue, id):
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while True:
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try:
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worker(outqueue, id)
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except:
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print(f"Worker {id} died! Restarting...")
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def main():
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target_url = os.environ['TARGET_URL']
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multiprocessing.set_start_method('spawn')
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outqueue = multiprocessing.Queue()
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processes = []
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for id in range(os.cpu_count()):
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p = multiprocessing.Process(target=worker_watchdog, args=(outqueue, id))
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p.start()
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processes.append(p)
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local_best = 1000
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while True:
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result = outqueue.get()
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result_score = len(result)
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if result_score < local_best:
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print(f"New local best found: {local_best} -> {result_score}")
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local_best = result_score
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try:
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r = requests.post(target_url, json=result)
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r.raise_for_status()
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except (ConnectionError, requests.HTTPError) as e:
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print(f"Error while posting result: {e}")
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if __name__ == '__main__':
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main()
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