106 lines
4.1 KiB
Python
106 lines
4.1 KiB
Python
import random
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import numpy as np
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from GenTunic.gen_math import bit_to_grey, grey_to_bit, project_bit
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from ReinforcmentLearning.learning import multipleTries
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def create_population(size, GEN_SIZE):
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dtype = [("population", np.int32, (GEN_SIZE,)), ("probability", np.float64)]
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population_propability = np.zeros(size, dtype=dtype)
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for i in range(size):
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gen = np.random.randint(0, 2, GEN_SIZE)
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population_propability[i] = (gen, 0)
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return np.array(population_propability)
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def calc_population_fitness(population_propability, AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE):
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population_fitness_sum = 0
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for individual in population_propability:
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gen = individual["population"]
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alpha, epsilon, gamma = [project_bit(x) for x in np.split(gen, 3)]
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_, multiple_tries_win_prob = multipleTries(alpha, epsilon, gamma, AMOUNT_TRIES, AMOUNT_RUNS, REWARD_ON_WIN, REWARD_ON_LOSE)
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fitness = np.array(multiple_tries_win_prob).mean()
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individual["probability"] = fitness
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population_fitness_sum += fitness
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best_fitness_index = np.argmax(population_propability["probability"])
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best_fitness = population_propability[best_fitness_index]["probability"]
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population_propability["probability"] = population_propability["probability"] / population_fitness_sum
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return population_propability, (best_fitness_index, best_fitness)
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def turnament_selection(population_propability, amount_selections):
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selected_population = []
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best_fitness_index = np.argmax(population_propability["probability"])
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selected_population.append(population_propability[best_fitness_index])
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while len(selected_population) < amount_selections:
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pair_indecies = random.sample(range(len(population_propability)), 2)
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if population_propability[pair_indecies[0]]["probability"] > population_propability[pair_indecies[1]]["probability"]:
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selected_population.append(population_propability[pair_indecies[0]])
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else:
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selected_population.append(population_propability[pair_indecies[1]])
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return np.array(selected_population)
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def crossover(population_propability, selected_population, amount_crossover, GEN_SIZE):
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crossover_population = turnament_selection(population_propability, amount_crossover)
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select_one_parent = False
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if amount_crossover % 2 == 1:
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amount_crossover -= 1
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select_one_parent = True
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for i in range(0, amount_crossover, 2):
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crossover_point = np.random.randint(1, GEN_SIZE)
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mother_a = crossover_population[i]["population"][:crossover_point]
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mother_b = crossover_population[i]["population"][crossover_point:]
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father_a = crossover_population[i+1]["population"][:crossover_point]
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father_b = crossover_population[i+1]["population"][crossover_point:]
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child_one = np.empty(1, dtype=selected_population.dtype)
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child_one["population"] = np.concatenate((mother_a, father_b))
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child_one["probability"] = 0
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child_two = np.empty(1, dtype=selected_population.dtype)
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child_two["population"] = np.concatenate((mother_b, father_a))
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child_two["probability"] = 0
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selected_population = np.concatenate((selected_population, child_one))
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selected_population = np.concatenate((selected_population, child_two))
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is_last_iteration = (i >= amount_crossover - 2)
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if is_last_iteration and select_one_parent:
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selected_population = np.append(selected_population, crossover_population[i])
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return selected_population
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def mutation(population, MUTATION_RATE, GEN_SIZE):
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amount_mutation = len(population) * MUTATION_RATE
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mutation_indecies = np.random.choice(len(population), int(amount_mutation), replace=False)
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for individual_index in mutation_indecies:
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bit_index = np.random.randint(0, GEN_SIZE)
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bit_to_mutate = population[individual_index]["population"][bit_index]
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mutated_grey = bit_to_grey(bit_to_mutate) ^ 1
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population[individual_index]["population"][bit_index] = grey_to_bit(mutated_grey)
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return population |