trabmh/initialization.py at main · thj3a/trabmh · GitHub

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import numpy as np
import random
from individual import Individual
from search import Search
from utils import Utils
from mutation import Mutation
import time
import pdb
from copy import deepcopy

class Initialization:

    def __init__(self):
        pass

    @classmethod
    def binary_random(self, environment, population_size): # uniform random
        chromosomes = []

        for i in range(0, population_size):
            chromosome = np.zeros((environment.n, 1))
            chromosome[random.sample(range(0, environment.n), k = environment.s)] = 1
            chromosomes.append(chromosome)
        return chromosomes

    @classmethod
    def binary_biased(self, environment, population_size, min_diff):
        min_diff = float(min_diff)
        chromosomes = []

        while len(chromosomes) < population_size:
            chromosome = np.zeros((environment.n, 1))
            chromosome[random.sample(range(0, environment.n), k = environment.s)] = 1

            found_close = False
            for existing_chromosome in chromosomes:
                if np.mean(np.abs(existing_chromosome - chromosome)) < min_diff:
                    found_close = True
                    break
            if not found_close:
                chromosomes.append(chromosome)
        return chromosomes

    @classmethod
    def binary_biasedweighted(self, environment, population_size): # can it be called uniform?
        chromosomes = []
        chromosome = np.zeros((environment.n, 1))
        chromosome[random.sample(range(0, environment.n), k = environment.s)] = 1
        chromosomes.append(chromosome)
        while len(chromosomes) < population_size:
            sum = 1/(np.sum(chromosomes, axis=0)+1)
            p = sum.T[0]/sum.sum()
            chromosome = np.zeros((environment.n, 1))
            chromosome[np.random.choice(range(0, environment.n), p=p, size=environment.s, replace=False)] = 1
            chromosomes.append(chromosome)
        return chromosomes


    @classmethod
    def binary_heuristics(self, environment, population_size):
        chromosomes = Search.heuristic_solutions(environment)
        new_chromosomes = []
        remaining_chromosomes = population_size - len(chromosomes)

        for i in range(0, remaining_chromosomes):
            index = random.choice(range(0, len(chromosomes)))
            chosen_chromosome = chromosomes[index]
            new_chromosome = Mutation.mutate(chosen_chromosome, environment)
            new_chromosomes.append(new_chromosome)

        return chromosomes + new_chromosomes

    @classmethod
    def permutation_random(self, environment, population_size):
        chromosomes = self.binary_random(environment, population_size)
        return Utils.convert_chromosomes_from_binary_to_permutation(chromosomes)

    @classmethod
    def permutation_biased(self, environment, population_size, min_diff):
        chromosomes = self.binary_biased(environment, population_size, min_diff)
        return Utils.convert_chromosomes_from_binary_to_permutation(chromosomes)

    @classmethod
    def permutation_biasedweighted(self, environment, population_size):
        chromosomes = self.binary_biasedweighted(environment, population_size)
        return Utils.convert_chromosomes_from_binary_to_permutation(chromosomes)

    @classmethod
    def permutation_heuristics(self, environment, population_size):
        chromosomes = Search.heuristic_solutions(environment)
        chromosomes = Utils.convert_chromosomes_from_binary_to_permutation(chromosomes)
        new_chromosomes = []
        remaining_chromosomes = population_size - len(chromosomes)

        for i in range(0, remaining_chromosomes):
            index = random.choice(range(0, len(chromosomes)))
            chosen_chromosome = chromosomes[index]
            new_chromosome = Mutation.mutate(chosen_chromosome, environment)
            new_chromosomes.append(new_chromosome)

        return chromosomes + new_chromosomes

    @classmethod
    def initialize_population(self, environment, population_size):
        function_and_params = environment.initialization_method.split("_")
        function_name = function_and_params[0]
        params = function_and_params[1:] if len(function_and_params) > 0 else []
        function_name = environment.encoding + "_" + function_name

        if hasattr(self, function_name) and callable(getattr(self, function_name)):
            func = getattr(self, function_name)
            population_chromosomes = func(environment, population_size, *params)
            population = []
            best_sol = - np.inf
            best_individual = None

            for chromosome in population_chromosomes:
                individual = Individual(
                    chromosome,
                    environment
                )
                if individual.fitness > best_sol:
                    best_sol = individual.fitness
                    best_individual = individual
                population.append(individual)

            return population, best_sol, best_individual
        else:
            raise Exception("Method \"{}\" not found.".format(function_name))