import numpy

# Genetic Algorithm methods
x = "Hello world"


def cal_pop_fitness(equation_inputs, pop):
    # Calculating the fitness value of each solution in the current population.
    # The fitness function calulates the sum of products between each input and its corresponding weight.
    fitness = numpy.sum(pop * equation_inputs, axis=1)
    return fitness


def select_mating_pool(pop, fitness, num_parents):
    # Selecting the best individuals in the current generation as parents for producing the offspring of the next generation.
    parents = numpy.empty((num_parents, pop.shape[1]))
    for parent_num in range(num_parents):
        max_fitness_idx = numpy.where(fitness == numpy.max(fitness))
        max_fitness_idx = max_fitness_idx[0][0]
        parents[parent_num, :] = pop[max_fitness_idx, :]
        fitness[max_fitness_idx] = -99999999999
    return parents


def crossover(parents, offspring_size):
    offspring = numpy.empty(offspring_size)
    # The point at which crossover takes place between two parents. Usually, it is at the center.
    crossover_point = numpy.uint8(offspring_size[1] / 2)

    for k in range(offspring_size[0]):
        # Index of the first parent to mate.
        parent1_idx = k % parents.shape[0]
        # Index of the second parent to mate.
        parent2_idx = (k + 1) % parents.shape[0]
        # The new offspring will have its first half of its genes taken from the first parent.
        offspring[k, 0:crossover_point] = parents[parent1_idx, 0:crossover_point]
        # The new offspring will have its second half of its genes taken from the second parent.
        offspring[k, crossover_point:] = parents[parent2_idx, crossover_point:]
    return offspring


def mutation(offspring_crossover, num_mutations=1):
    mutations_counter = numpy.uint8(offspring_crossover.shape[1] / num_mutations)
    # Mutation changes a number of genes as defined by the num_mutations argument. The changes are random.
    for idx in range(offspring_crossover.shape[0]):
        gene_idx = mutations_counter - 1
        for mutation_num in range(num_mutations):
            # The random value to be added to the gene.
            random_value = numpy.random.uniform(-1.0, 1.0, 1)
            offspring_crossover[idx, gene_idx] = offspring_crossover[idx, gene_idx] + random_value
            gene_idx = gene_idx + mutations_counter
    return offspring_crossover