106 lines
2.9 KiB
Python
106 lines
2.9 KiB
Python
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import numpy as np
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import math
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import random
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from geneticalgorithm import geneticalgorithm as ga
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from astar2 import *
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class Mine():
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def __init__(self, row, column):
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self.row = row
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self.columnt = column
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class GeneticalWithLib():
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Miasta = [[0, 130, 180, 300],
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[130, 0, 320, 350],
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[180, 320, 0, 360],
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[300, 350, 360, 0]]
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#Miasta = [[0, 2, 6, 3, 7],
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# [2, 0, 6, 4, 8],
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# [6, 6, 0, 5, 8],
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# [3, 4, 5, 0, 9],
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# [7, 8, 8, 9, 0]]
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self.mine_points = set()
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def getCoordinates(self):
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temp_map = [list(item) for item in SweeperAgent.loadMap('genetic_maps/map1.txt')]
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a_row = 0
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a_column = 0
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for row in range(MAP_SIZE):
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for column, pos in enumerate(temp_map[row]):
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if pos == "m" :
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a_row = row
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a_column = column
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location = tuple([a_row, a_column])
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self.mine_points.add(location)
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def setDistance(self):
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pass
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def fillDistanceMatrix(self):
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pass
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def f(X):
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for i in range(len(Miasta[0])):
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if X[i] not in Miasta[i]:
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return np.sum(X)+12000
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for i in range(len(Miasta[0])):
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if i == 0:
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if (X[0]-X[len(Miasta[0])-1]) == 0:
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return np.sum(X)+12000
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else:
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if abs(X[i]-X[i-1]) == 0:
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return np.sum(X)+12000
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if i != len(Miasta[0])-1:
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if abs(X[i+1]-X[i-1]) == 0:
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return np.sum(X)+12000
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var = []
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for j in range(len(Miasta[0])):
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var.append(0)
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for i in range(len(Miasta[0])):
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var[Miasta[i].index(X[i])] += 1
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for s in var:
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if s != 1:
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return np.sum(X)+9000
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return np.sum(X)
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varbound = np.array([[130,300],
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[130,350],
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[180,360],
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[300,360]])
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#varbound = np.array([[2,7],
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# [2,8],
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# [5,8],
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# [4,9],
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# [7,9]])
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algorithm_param = {'max_num_iteration': 3000,\
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'population_size':100,\
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'mutation_probability':0.1,\
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'elit_ratio': 0.01,\
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'crossover_probability': 0.5,\
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'parents_portion': 0.3,\
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'crossover_type':'uniform',\
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'max_iteration_without_improv':None}
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model=ga(function=f,\
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dimension=len(Miasta[0]),\
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variable_type='int',\
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variable_boundaries=varbound,\
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algorithm_parameters=algorithm_param)
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model.run()
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#print(f([180, 350, 180, 350]))
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