I am currently creating my genetic algorithm and want to print the number of generations at the very end of the program when it finishes. However I am unsure how to access the counter variable that is the number of generations when it is outside of the class and method. So for example, at the end it would be like

Generation 100, average fit 18966, best fit 18947
Your best chromosone at generation 100
'\x06pzÂ\x8cYÆr¯n0q\x07l¿M8\x93Þ\x19\x87"\x01\x85\x1er\x89[F_VyER\x9b\x0bm=)\x9a\x9a¿¥\x10F\x12A\x84\x0fZ^\x14\x99\x8a4®\x9f¿*\\\xa0yi\x19E\x8aÇ 6(_<¾£cO~\x9c\x99\x932\x06\x0f\x82\x7f¤\x808xǸñA\x13\x0e<%\x06ÿ@í\x91Pô\x98 ®\r\x1b}\x89y¦\x0cqAK\tp\x95\x99ÔNj=Wn\x16\x94\x0cu!¯ñ\x13Qü[e8_ÂóU\x10\x1av_ %Q_¡ù\x87=\x08~ciÎ_Ï[\x8f@AëT\x14©qG\x89#Z«L\x9b¢\x94WL\x1dV¶R03\x84æ^ßr\x1fÃÈ\x1d\x8e Læª&®x\x94?TAÒD\x14£i\x82J\x15=w~\x03\x0c\xa0¾5\x02f5T\x91ol¢bIÞfk¬¡27W16(}6\x92\x87\n®xm0\x1a\n<8(à}ñ\x88̾\x17g\x9bj6\x8fI&\x12\x9aÂ\x9a_F\x1a\r[\x1dK\x15<.±DjcIy`98d>\x197Z\x91£%tIJ\x820\x93|\x07\x8dnÚ QÂ!Pf\x1d\nåòf\x91\x1d@S¾|\x9ff[d>O=T$ݶI\x9e»QÛÂ\x1d"¿U=û´F÷\x83C}wA\xa0É\x8aD\x93x»\x85\x7f\x14^\x0eL'
done:
100 generations

How do I exactly access the 100 from the method in the class?

import random

class GeneticAlgorithm(object):
    def __init__(self, genetics):
        self.genetics = genetics
        pass

    def run(self):
        population = self.genetics.initial()
        while True:
            fits_pops = [(self.genetics.fitness(ch),  ch) for ch in population]
            if self.genetics.check_stop(fits_pops): break
            population = self.next(fits_pops)
            pass
        return population

    def next(self, fits):
        parents_generator = self.genetics.parents(fits)
        size = len(fits)
        nexts = []
        while len(nexts) < size:
            parents = next(parents_generator)
            cross = random.random() < self.genetics.probability_crossover()
            children = self.genetics.crossover(parents) if cross else parents
            for ch in children:
                mutate = random.random() < self.genetics.probability_mutation()
                nexts.append(self.genetics.mutation(ch) if mutate else ch)
                pass
            pass
        return nexts[0:size]
    pass

class GeneticFunctions(object):
    def probability_crossover(self):
        r"""returns rate of occur crossover(0.0-1.0)"""
        return 1.0

    def probability_mutation(self):
        r"""returns rate of occur mutation(0.0-1.0)"""
        return 0.0

    def initial(self):
        r"""returns list of initial population
        """
        return []

    def fitness(self, chromosome):
        r"""returns domain fitness value of chromosome
        """
        return len(chromosome)

    def check_stop(self, fits_populations):
        r"""stop run if returns True
        - fits_populations: list of (fitness_value, chromosome)
        """
        return False

    def parents(self, fits_populations):
        r"""generator of selected parents
        """
        gen = iter(sorted(fits_populations))
        while True:
            f1, ch1 = next(gen)
            f2, ch2 = next(gen)
            yield (ch1, ch2)
            pass
        return

    def crossover(self, parents):
        r"""breed children
        """
        return parents

    def mutation(self, chromosome):
        r"""mutate chromosome
        """
        return chromosome
    pass

if __name__ == "__main__":
    """
    example: Mapped guess prepared Text
    """
    class GuessText(GeneticFunctions):
        def __init__(self, target_text,
                     limit=100, size=100,
                     prob_crossover=0.9, prob_mutation=0.2):
            self.target = self.text2chromo(target_text)
            self.counter = 0

            self.limit = limit
            self.size = size
            self.prob_crossover = prob_crossover
            self.prob_mutation = prob_mutation
            pass

        # GeneticFunctions interface impls
        def probability_crossover(self):
            return self.prob_crossover

        def probability_mutation(self):
            return self.prob_mutation

        def initial(self):
            return [self.random_chromo() for j in range(self.size)]

        def fitness(self, chromo):
            # larger is better, matched == 0
            return -sum(abs(c - t) for c, t in zip(chromo, self.target))

        def check_stop(self, fits_populations):
            self.counter  = 1
            if self.counter % 100 == 0:
                best_match = list(sorted(fits_populations))[-1][1]
                fits = [f for f, ch in fits_populations]
                best = -(max(fits))
                ave = -(sum(fits) / len(fits))
                print(
                    "Generation =, average fit M, best fit M" %
                    (self.counter, ave, best,
                     ))
                print("Your best chromosone at generation =" % self.counter)
                print("%r" % self.chromo2text(best_match))
                pass
            return self.counter >= self.limit

        def parents(self, fits_populations):
            while True:
                father = self.tournament(fits_populations)
                mother = self.tournament(fits_populations)
                yield (father, mother)
                pass
            pass

        def crossover(self, parents):
            father, mother = parents
            index1 = random.randint(1, len(self.target) - 2)
            index2 = random.randint(1, len(self.target) - 2)
            if index1 > index2: index1, index2 = index2, index1
            child1 = father[:index1]   mother[index1:index2]   father[index2:]
            child2 = mother[:index1]   father[index1:index2]   mother[index2:]
            return (child1, child2)

        def mutation(self, chromosome):
            index = random.randint(0, len(self.target) - 1)
            vary = random.randint(-5, 5)
            mutated = list(chromosome)
            mutated[index]  = vary
            return mutated

        # internals
        def tournament(self, fits_populations):
            alicef, alice = self.select_random(fits_populations)
            bobf, bob = self.select_random(fits_populations)
            return alice if alicef > bobf else bob

        def select_random(self, fits_populations):
            return fits_populations[random.randint(0, len(fits_populations)-1)]

        def text2chromo(self, text):
            return [ord(ch) for ch in text]
        def chromo2text(self, chromo):
            return "".join(chr(max(1, min(ch, 255))) for ch in chromo)

        def random_chromo(self):
            return [random.randint(1, 255) for i in range(len(self.target))]
        pass

    GeneticAlgorithm(GuessText("""The smartest and fastest Pixel yet.
    
    Google Tensor: Our first custom-built processor.
    The first processor designed by Google and made for Pixel, Tensor makes the new Pixel phones our most powerful yet.

    The most advanced Pixel Camera ever.
    Capture brilliant color and vivid detail with Pixels best-in-class computational photography and new pro-level lenses.""")).run()
    print('done:')
    print("= " 'generations' % counter)
    pass

CodePudding user response：

Define the GuessText first. Then access the counter.

gt = GuessText("""The smartest and fastest Pixel yet.
    
    Google Tensor: Our first custom-built processor.
    The first processor designed by Google and made for Pixel, Tensor makes the new Pixel phones our most powerful yet.

    The most advanced Pixel Camera ever.
    Capture brilliant color and vivid detail with Pixels best-in-class computational photography and new pro-level lenses.""")

GeneticAlgorithm(gt).run()


print('done:')
print("= " 'generations' % gt.counter)