从 ASCII 获取特定值 table

Question

我目前正在创建一个遗传算法，并试图只从 ASCII table 中获取某些值，因此算法的运行时间可以更快一些。在下面的代码中，我获得了 9-127 之间的值，但我只需要来自 ASCII table 的值 9-10 和 32-127，我不确定如何准确地只获得这些特定值。下面的代码是在 python.

中完成的

import numpy as np

TARGET_PHRASE = """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."""       # target DNA
POP_SIZE = 4000                      # population size
CROSS_RATE = 0.8                    # mating probability (DNA crossover)
MUTATION_RATE = 0.00001                # mutation probability
N_GENERATIONS = 100000

DNA_SIZE = len(TARGET_PHRASE)
TARGET_ASCII = np.fromstring(TARGET_PHRASE, dtype=np.uint8)  # convert string to number
ASCII_BOUND = [9, 127]


class GA(object):
    def __init__(self, DNA_size, DNA_bound, cross_rate, mutation_rate, pop_size):
        self.DNA_size = DNA_size
        DNA_bound[1] += 1
        self.DNA_bound = DNA_bound
        self.cross_rate = cross_rate
        self.mutate_rate = mutation_rate
        self.pop_size = pop_size

        self.pop = np.random.randint(*DNA_bound, size=(pop_size, DNA_size)).astype(np.int8)  # int8 for convert to ASCII

    def translateDNA(self, DNA):                 # convert to readable string
        return DNA.tostring().decode('ascii')

    def get_fitness(self):                      # count how many character matches
        match_count = (self.pop == TARGET_ASCII).sum(axis=1)
        return match_count

    def select(self):
        fitness = self.get_fitness()     # add a small amount to avoid all zero fitness
        idx = np.random.choice(np.arange(self.pop_size), size=self.pop_size, replace=True, p=fitness/fitness.sum())
        return self.pop[idx]

    def crossover(self, parent, pop):
        if np.random.rand() < self.cross_rate:
            i_ = np.random.randint(0, self.pop_size, size=1)                        # select another individual from pop
            cross_points = np.random.randint(0, 2, self.DNA_size).astype(np.bool)   # choose crossover points
            parent[cross_points] = pop[i_, cross_points]                            # mating and produce one child
        return parent

    def mutate(self, child):
        for point in range(self.DNA_size):
            if np.random.rand() < self.mutate_rate:
                child[point] = np.random.randint(*self.DNA_bound)  # choose a random ASCII index
        return child

    def evolve(self):
        pop = self.select()
        pop_copy = pop.copy()
        for parent in pop:  # for every parent
            child = self.crossover(parent, pop_copy)
            child = self.mutate(child)
            parent[:] = child
        self.pop = pop

if __name__ == '__main__':
    ga = GA(DNA_size=DNA_SIZE, DNA_bound=ASCII_BOUND, cross_rate=CROSS_RATE,
            mutation_rate=MUTATION_RATE, pop_size=POP_SIZE)

    for generation in range(N_GENERATIONS):
        fitness = ga.get_fitness()
        best_DNA = ga.pop[np.argmax(fitness)]
        best_phrase = ga.translateDNA(best_DNA)
        print('Gen', generation, ': ', best_phrase)
        if best_phrase == TARGET_PHRASE:
            break
        ga.evolve()

Answer 1

您需要一种自定义方法来生成 9-10 和 32-127 范围内的随机样本，luke

def my_rand(pop_size, DNA_size):
    bold1=[9,10]
    bold2=list(range(32,127))
    bold=bold1+bold2
    pop = np.random.choice(bold,(pop_size,DNA_size)).astype(np.int8)
    return pop

然后调用此方法替换第29行，如

delete -- self.pop = np.random.randint(*DNA_bound, size=(pop_size, DNA_size)).astype(np.int8)  # int8 for convert to ASCII
call ---self.pop = my_rand(pop_size, DNA_size)

从 ASCII 获取特定值 table

Getting specific values from ASCII table

python

ascii

genetic-algorithm