122 lines
3.6 KiB
Python
122 lines
3.6 KiB
Python
import os
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import signal
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import json
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from model.anneal_model import SimAnneal
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from model.my_model import MyModel
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def log(msg):
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print('[*] {msg}'.format(msg=msg))
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def load_data(file):
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coords = []
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with open(file, "r") as infile:
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line = infile.readline()
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# Skip instance header
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while "NODE_COORD_SECTION" not in line:
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line = infile.readline()
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for line in infile.readlines():
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line = line.replace("\n", "")
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if line and 'EOF' not in line:
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line = [float(x) for x in line.lstrip().split(" ", 1)[1].split(" ") if x]
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coords.append(line)
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return coords
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def timeout_handler(signum, frame):
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print("Timeout")
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raise Exception("Timeout")
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def TSP(tsp_file, model):
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best_solution = []
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fitness_list = []
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coords = load_data(tsp_file)
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# Set timeout
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# signal.signal(signal.SIGALRM, timeout_handler)
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# signal.alarm(60)
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# Try your algorithm
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try:
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model.init(coords)
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best_solution, fitness_list = model.fit(max_it=10000)
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except Exception as exc:
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if(str(exc) == "Timeout"):
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log("Timeout -3")
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with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
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outfile.write("-3")
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else:
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print(exc)
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log("Unkown -4")
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with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
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outfile.write("-4")
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# Collect results
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if(len(fitness_list) > 0):
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log("Best: " + str(fitness_list[-1]))
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if (len(best_solution) == 0):
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log("No Answer -1")
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with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
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outfile.write("-1")
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elif (len(best_solution) != len(coords)):
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log("Invalid -2")
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with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
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outfile.write("-2")
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else:
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log("Writing the best solution to file" )
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with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.txt', "w") as outfile:
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outfile.write(str(model.fitness(best_solution)))
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outfile.write("\n")
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outfile.write(", ".join(str(item) for item in best_solution))
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# Write to JSON
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data = {}
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data['nodes'] = []
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for i in range(len(coords)):
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data['nodes'].append({
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'title': str(i),
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'id': i,
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'x': int(coords[i][0]),
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'y': int(coords[i][1])
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})
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data['edges'] = []
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for i in range(len(best_solution)):
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if i == len(best_solution)-1:
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data['edges'].append({
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'source': best_solution[i],
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'target': best_solution[0]
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})
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else:
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data['edges'].append({
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'source': best_solution[i],
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'target': best_solution[i+1]
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})
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with open('output/' + os.path.splitext(os.path.basename(tsp_file))[0] + '.json', 'w') as outfile:
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json.dump(data, outfile)
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if __name__ == "__main__":
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for root, _, files in os.walk('./data'):
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if(files):
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for f in files:
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# Get input file name
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tsp_file = str(root) + '/' + str(f)
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log(tsp_file)
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# Your Model
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# model = MyModel()
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# Simulated Annealing
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model = SimAnneal()
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TSP(tsp_file, model)
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print()
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