Stochastic Select.py

import random, math

def decimal(binary):

    inverse = [0] * len(binary)
    decimal = [0] * len(binary)
    value = 0

    for i in range(len(binary)):
        inverse[i] = binary[len(binary)-1-i]

    for i in range(len(inverse)):
        if inverse[i] == 1: decimal[i] = (2 ** i)
        if inverse[i] == 0: decimal[i] = 0

    for i in range(len(decimal)):
        value = value + decimal[i]

    return value

def fitness(x):
    
    y = x * x

    return y

def randomChromosome(genes):

    chromosome = [0] * genes
    chromosome = [random.randint(0, 1) for i in range(genes)]
    
    return chromosome

def stochasticSelect(chromosomes, genes):
  
    genome = [[] for i in range(chromosomes)]
    decimal_list = [0] * chromosomes
    fitness_list = [0] * chromosomes
    expected_list = [0] * chromosomes
    probability_list = [0] * chromosomes

    total = 0
    partsum = 0
    expected_total = 0

    fathers = []
    mothers = []
    mothers_index = []
    fathers_index = []

    for i in range(chromosomes):
        genome[i] = randomChromosome(genes)
        decimal_list[i] = decimal(genome[i])
        fitness_list[i] = fitness(decimal_list[i])
        total = total + fitness_list[i]

    if total == 0:
        print "Genoma incompleto"
        return 0

    average = round(float(total)/chromosomes, 2)

    for i in range(chromosomes):
        expected_list[i] = round(fitness_list[i]/float(average), 2)

        if int(math.floor(expected_list[i])) > 0:
            fathers.append(genome[i])
            fathers_index.append(i+1)

        expected_list[i] = round(abs(math.floor(expected_list[i])-expected_list[i]),2)
        expected_total = expected_total + expected_list[i]
    
    for i in range(chromosomes):
        probability_list[i] = round(expected_list[i]/float(expected_total), 2)
        
    for i in range(chromosomes-len(fathers)):

        rand = random.uniform(0,1) * expected_total

        j = 0
        
        while True:
            
            partsum = partsum + expected_list[j]
            j = j + 1
            
            if (partsum > rand) or (j == genes):
                break

        mothers.append(genome[j-1])
        mothers_index.append(j)

    print ""

    print "Genoma:", genome
    print "Valores:", decimal_list
    print "Fitness:", fitness_list
    print "Total:", total
    print "Promedio:", average

    print ""

    print "Valores esperados:", expected_list
    print "Total esperado:", expected_total
    print "Probabilidad:", probability_list
    
    print ""

    print "Padres:", fathers_index
    print "Madres:", mothers_index

    print ""