tsp v2
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@ -1,10 +1,13 @@
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import numpy as np, random, operator, pandas as pd, matplotlib.pyplot as plt
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from path_search_algorthms import a_star
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from decision_tree import decisionTree
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# klasa tworząca miasta czy też śmietniki
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class City:
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def __init__(self, x, y):
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def __init__(self, x, y, dist):
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self.x = x
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self.y = y
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# self.dist = distance
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def distance(self, city):
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xDis = abs(self.x - city.x)
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9
main.py
9
main.py
@ -17,7 +17,7 @@ from path_search_algorthms import a_star, a_star_utils
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from decision_tree import decisionTree
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from NeuralNetwork import prediction
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from game_objects.trash import Trash
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from genetic_algorithm import TSP
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from game_objects import aiPlayer
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import itertools
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@ -155,7 +155,14 @@ class Game():
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# print(self.positive_actions[0])
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# self.t.startAiController(self.positive_actions[0])
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def init_TSP(self):
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city_list = self.positive_decision
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dist = a_star.path_dist
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for i in range(0,25):
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city_list.append(TSP.City(x=int(random.random() * 200), y=int(random.random() * 200)))
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tsp_list = TSP.geneticAlgorithm(population=city_list, popSize=100, eliteSize=20, mutationRate=0.01, generations=1000)
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def load_data(self):
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game_folder = path.dirname(__file__)
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