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| import numpy as np
from numpy.random import default_rng
import random
import sys
answer = sys.argv[1]
low = 65
high = 123
dawkins = np.array(
[ord(x) for x in answer if x != ' ']
)
space_position = [x[0] for x in enumerate(answer) if x[1] == ' ']
def chromosomes():
return np.random.randint(
low=low, high=high, size=len(dawkins)
)
def init_population(r: int):
return np.array([
chromosomes() for _ in range(r)
])
def diff(x: np.ndarray):
return np.subtract(dawkins, x)
def std(dif: np.ndarray, r=4):
return round(np.std(dif), r)
def fitness(population: np.ndarray):
return np.array([std(diff(x)) for x in population])
def description(label: str, min_loss: float, master_string: str):
print('--------------------------------------------')
print(label, 'min loss = ', min_loss)
print(label, 'master = ', master_string)
print('--------------------------------------------')
def ranking_selection(population: np.ndarray, fargs: np.ndarray, n: int):
return population[fargs][:n]
def sex(s1: np.ndarray, s2: np.ndarray, mw: int = 99):
p = np.zeros(len(s1), int)
d1 = diff(s1)
d2 = diff(s2)
for i in range(len(s1)):
[m] = random.choices(range(0, 2), weights=[mw, 1])
if m == 0:
p[i] = s1[i] if abs(d1[i]) < abs(d2[i]) else s2[i]
else:
p[i] = np.random.randint(low, high)
return p
def crossover(selection: np.ndarray, r: int, mw: int = 99):
high = len(selection)
offspring = np.empty((0, len(selection[0])), int)
while r > 0:
rng = default_rng()
x, y = rng.choice(high, size=2, replace=False)
child = sex(selection[x], selection[y], mw)
offspring = np.vstack((offspring, child))
r -= 1
return offspring
def ascii_to_str(gene: np.ndarray):
a = list(map(chr, gene))
for s in space_position:
a.insert(s, ' ')
return ''.join(a)
gen = 1
n = 1000
m = 100
mw = 99
population = init_population(n)
while True:
fit = fitness(population)
fargs = np.argsort(fit)
master_index = fargs[0]
master_string = ascii_to_str(population[master_index])
description(f'Generation {gen}', fit[fargs[0]], master_string)
if np.array_equal(population[master_index], dawkins):
print(f'Generation {gen} found the answer: {answer}')
break
else:
selection = ranking_selection(population, fargs, m)
population = crossover(selection, n, mw)
gen += 1
|
