关于python：ValueError：位数必须大于零在处理上述异常时，发生了另一个异常

ValueError: number of bits must be greater than zero During handling of the above exception, another exception occurred

我正在尝试实现遗传算法，程序终止后第一代..不知道为什么..代码在下面..提出的xeception是..

File
"C:\Users\Eshan\AppData\Local\Programs\Python\Python36\lib
andom.py",
line 255, in choice
i = self._randbelow(len(seq)) File"C:\Users\Eshan\AppData\Local\Programs\Python\Python36\lib
andom.py",
line 232, in _randbelow
r = getrandbits(k) # 0 <= r < 2**k ValueError: number of bits must be greater than zero

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File
"C:/Users/Eshan/Desktop/model/simple_ga.py", line 87, in
ga() File"C:/Users/Eshan/Desktop/model/simple_ga.py", line 32, in ga
agents = crossover(agents) File"C:/Users/Eshan/Desktop/model/simple_ga.py", line 62, in crossover
parent1 = random.choice(agents) File"C:\Users\Eshan\AppData\Local\Programs\Python\Python36\lib
andom.py",
line 257, in choice
raise IndexError('Cannot choose from an empty sequence') IndexError: Cannot choose from an empty sequence

Process finished with exit code 1

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from fuzzywuzzy import fuzz
import random
import string

class Agent:
def __init__(self, length):

self.string = ''.join(random.choice(string.ascii_letters) for _ in range(length))
self.fitness = -1

def __str__(self):
return 'String: ' + str(self.string) + ' Fitness: ' + str(self.fitness)

in_str = 'string-python'
in_str_len = len(in_str)
population = 20
generations = 1000

def ga():

agents = init_agents(population, in_str_len)

for generation in range(generations):

print('Generation: ' + str(generation))

agents = selection(agents)
agents = fitness(agents)
agents = crossover(agents)
agents = mutation(agents)

if any(agent.fitness >= 90 for agent in agents):
print('Threshod met!')
exit(0)

def init_agents(population, length):
return [Agent(length) for _ in range(population)]

def fitness(agents):
for agent in agents:
agent.fitness = fuzz.ratio(agent.string, in_str)

return agents

def selection(agents):

agents = sorted(agents, key=lambda agent: agent.fitness, reverse=True)
print('
'.join(map(str, agents)))
agents = agents[:int(0.2 * len(agents))]
return agents

def crossover(agents):
offspring = []
for _ in range(int((population - len(agents)) / 2)):
parent1 = random.choice(agents)
parent2 = random.choice(agents)
child1 = Agent(in_str_len)
child2 = Agent(in_str_len)
split = random.randint(0, in_str_len)
child1.string = parent1.string[0:split] + parent2.string[split:in_str_len]
child2.string = parent2.string[0:split] + parent1.string[split:in_str_len]
offspring.append(child1)
offspring.append(child2)

return agents

def mutation(agents):

for agent in agents:
for idx, param in enumerate(agent.string):
if random.uniform(0.0, 1.0) <= 0.1:
agent.string = agent.string[0:idx] + random.choice(string.ascii_letters) + agent.string[idx+1:in_str_len]

return agents

if __name__ == '__main__':
in_str = 'string-python'
in_str_len = len(in_str)
ga()