我目前正在創建我的遺傳演算法，并希望在程式完成時在程式的最后列印代數。但是，當它在類和方法之外時，我不確定如何訪問作為代數的計數器變數。所以例如，最后它會像
Generation 100, average fit 18966, best fit 18947
Your best chromosone at generation 100
'\x06pz?\x8cY?rˉn0q\x07l?M8\x93T\x19\x87"\x01\x85\x1er\x89[F_VyER\x9b\x0bm=)\x9a\x9a?￥\x10F\x12A\x84\x0fZ^\x14\x99\x8a4?\x9f?*\\\xa0yi\x19E\x8a? 6(_<?￡cO~\x9c\x99\x932\x06\x0f\x82\x7f¤\x808x???A\x13\x0e<%\x06?@í\x91P?\x98 ?\r\x1b}\x89y|\x0cqAK\tp\x95\x99?Nj=Wn\x16\x94\x0cu!ˉ?\x13Qü[e8_?óU\x10\x1av_ %Q_?ù\x87=\x08~ci?_?[\x8f@A?T\x14?qG\x89#Z?L\x9b￠\x94WL\x1dV?R03\x84?^?r\x1f?è\x1d\x8e L?a&?x\x94?TAòD\x14￡i\x82J\x15=w~\x03\x0c\xa0?5\x02f5T\x91ol￠bITfk??27W16(}6\x92\x87\n?xm0\x1a\n<8(à}?\x88ì?\x17g\x9bj6\x8fI&\x12\x9a?\x9a_F\x1a\r[\x1dK\x15<.±DjcIy`98d>\x197Z\x91￡%tIJ\x820\x93|\x07\x8dnú Q?!Pf\x1d\n?òf\x91\x1d@S?|\x9ff[d>O=T$Y?I\x9e?Q??\x1d"?U=?′F÷\x83C}wA\xa0é\x8aD\x93x?\x85\x7f\x14^\x0eL'
done:
100 generations
如何從類中的方法中準確訪問 100？
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
uj5u.com熱心網友回復：
首先定義 GuessText。然后訪問計數器。
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)
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