pre-alpha multithreading
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22
src/AI/GA.py
22
src/AI/GA.py
@ -1,12 +1,15 @@
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import random
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import numpy
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import copy
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from src.AI.Affinities import Affinities
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from src.AI.ThreadedSimulation import ThreadedSimulation
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from src.entities.Enums import Classifiers
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from src.entities.Player import Player
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from src.game.Map import Map
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def geneticAlgorithm(map, iter, solutions, mutationAmount=0.05):
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def geneticAlgorithm(map, iter, solutions, mutationAmount=0.05, multithread=False, threadCount=4):
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"""
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This algorithm will attempt to find the best affinities for player's goal choices.
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@ -22,14 +25,23 @@ def geneticAlgorithm(map, iter, solutions, mutationAmount=0.05):
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initialPopulation = numpy.random.uniform(low=0.0, high=1.0, size=(solutions, weightsCount))
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population = initialPopulation
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for i in range(iter):
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print("Running {} generation...".format(i))
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print("\nRunning {} generation...".format(i))
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fitness = []
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for player in population:
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fitness.append(doSimulation(player, map))
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if not multithread:
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for player in population:
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fitness.append(doSimulation(player, map))
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else:
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threads = []
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for p in range(len(population)):
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threads.append(ThreadedSimulation(p+1, p+1, population[p], map))
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threads[-1].start()
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for t in threads:
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t.join()
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fitness.append(t.getResult())
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parents = selectMatingPool(population, fitness, int(solutions / 2))
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print("Best fitness: {}".format(max(fitness)))
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offspring = mating(parents, solutions, mutationAmount)
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print("Best offspring: ", offspring[0])
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population = offspring
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27
src/AI/ThreadedSimulation.py
Normal file
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src/AI/ThreadedSimulation.py
Normal file
@ -0,0 +1,27 @@
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import threading
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import time
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exitFlag = 0
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class ThreadedSimulation(threading.Thread):
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def __init__(self, threadID, counter, player, map):
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threading.Thread.__init__(self)
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self.threadID = threadID
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self.counter = counter
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self.player = player
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self.result = None
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self.map = map
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def run(self):
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from src.AI.GA import doSimulation
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from src.game.Map import Map
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newMap = Map(self.map.filename, None)
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self.result = doSimulation(self.player, newMap)
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def getResult(self):
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return self.result
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