forked from s464965/WMICraft
82 lines
2.5 KiB
Python
82 lines
2.5 KiB
Python
import random
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from typing import List
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import numpy as np
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import numpy.typing as npt
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from genome import Genome
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class Population:
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population: List[Genome] = [] # array to hold the current population
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mating_pool: List[Genome] = [] # array which we will use for our "mating pool"
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generations: int = 0 # number of generations
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finished: bool = False # are we finished evolving?
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mutation_rate: float
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perfect_score: int
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best_genome: Genome
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def __init__(self, mutation_rate, population_size, perfect_score=20):
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self.mutation_rate = mutation_rate
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self.perfect_score = perfect_score
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for i in range(0, population_size):
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new_genome = Genome()
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new_genome.calc_fitness()
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self.population.append(new_genome)
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# create a new generation
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def generate(self):
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max_fitness = 0
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for genome in self.population:
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if genome.fitness > max_fitness:
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max_fitness = genome.fitness
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print("Max fitness of generation " + str(self.generations) + " = " + str(max_fitness))
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# refill the population with children from the mating pool
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new_population = []
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for genome in self.population:
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partner_a = self.accept_reject(max_fitness)
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partner_b = self.accept_reject(max_fitness)
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child = partner_a.crossover(partner_b)
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child.mutate(self.mutation_rate)
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new_population.append(child)
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self.population = new_population
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self.generations += 1
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# select random with correct probability from population
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def accept_reject(self, max_fitness: int):
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safe_flag = 0
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while safe_flag < 10000:
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partner = random.choice(self.population)
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r = random.randint(0, max_fitness)
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if r < partner.fitness:
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return partner
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safe_flag += 1
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# compute the current "most fit" member of the population
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def evaluate(self):
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record = 0
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best_index = 0
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for index in range(len(self.population)):
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genome = self.population[index]
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if genome.fitness > record:
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record = genome.fitness
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best_index = index
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self.best_genome = self.population[best_index]
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if record >= self.perfect_score:
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self.finished = True
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return self.finished
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def calc_fitness(self):
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for genome in self.population:
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genome.calc_fitness()
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