77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import numpy as np
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import random
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from brain import Neural_Network
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from params import MUTATION_RATE, SELECTION_ALG, KWAY_TOURNAMENT_PLAYERS
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def kway_selection(brains, exclude=None):
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tourn_pool = []
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best_play = None
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if exclude:
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brains = [x for x in brains if x != exclude]
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for x in range(KWAY_TOURNAMENT_PLAYERS):
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new_play = random.choice(brains)
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while new_play in tourn_pool:
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new_play = random.choice(brains)
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if not best_play or best_play.fitness < new_play.fitness:
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best_play = new_play
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return best_play
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def genetic_selection(brains):
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parents_pool = []
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half_pop = int(len(brains) / 2)
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if SELECTION_ALG == "kway":
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for x in range(half_pop):
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p1 = kway_selection(brains)
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p2 = kway_selection(brains, exclude=p1)
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parents_pool.append([p1, p2])
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elif SELECTION_ALG == "roulette":
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# does not seem very optimized... TBR
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# constitute a list where every brain is represented
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# proportionnally to its relative fitness
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wheel = []
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for b in brains:
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wheel += [b] * int(b.fitness)
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tot_fitness = len(wheel)
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# selection of pool/2 pair of parents to reproduce
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for _ in range(half_pop):
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idx1 = round(random.random() * tot_fitness - 1)
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idx2 = round(random.random() * tot_fitness - 1)
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parents_pool.append([wheel[idx1], wheel[idx2]])
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return parents_pool
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def cross_mutate_genes(p1_gene, p2_gene):
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child = []
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p1_gene = list(p1_gene)
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p2_gene = list(p1_gene)
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for idx, x in enumerate(p2_gene):
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if random.random() > 0.5:
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choice = p1_gene[idx]
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else:
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choice = p2_gene[idx]
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# Mutation
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if random.random() < MUTATION_RATE:
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choice[random.randint(0, len(choice) - 1)] = random.random()
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print("Mutation !")
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child.append(choice)
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return np.array(child)
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def genetic_reproduction(parents_pool):
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# every pair of parents will produce a mixed child
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new_pop = []
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for [p1, p2] in parents_pool:
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W1_kid = cross_mutate_genes(p1.W1, p2.W1)
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W2_kid = cross_mutate_genes(p1.W2, p2.W2)
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c_brain1 = Neural_Network(W1=W1_kid, W2=W2_kid)
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c_brain2 = Neural_Network(W1=W1_kid, W2=W2_kid)
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new_pop.append(c_brain1)
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new_pop.append(c_brain2)
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return new_pop
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