python整洁示例已损坏

我正在尝试运行下面的代码，从中可以将遗传算法应用于神经网络。这是一个非常有趣的概念，只是我无法运行示例代码

"""
2-input XOR example -- this is most likely the simplest possible example.
"""

from __future__ import print_function
import os
import neat
import visualize

# 2-input XOR inputs and expected outputs.
xor_inputs = [(0.0, 0.0), (0.0, 1.0), (1.0, 0.0), (1.0, 1.0)]
xor_outputs = [   (0.0,),     (1.0,),     (1.0,),     (0.0,)]


def eval_genomes(genomes, config):
    for genome_id, genome in genomes:
        genome.fitness = 4.0
        net = neat.nn.FeedForwardNetwork.create(genome, config)
        for xi, xo in zip(xor_inputs, xor_outputs):
            output = net.activate(xi)
            genome.fitness -= (output[0] - xo[0]) ** 2


def run(config_file):
    # Load configuration.
    config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,
                         neat.DefaultSpeciesSet, neat.DefaultStagnation,
                         config_file)

    # Create the population, which is the top-level object for a NEAT run.
    p = neat.Population(config)

    # Add a stdout reporter to show progress in the terminal.
    p.add_reporter(neat.StdOutReporter(True))
    stats = neat.StatisticsReporter()
    p.add_reporter(stats)
    p.add_reporter(neat.Checkpointer(5))

    # Run for up to 300 generations.
    winner = p.run(eval_genomes, 300)

    # Display the winning genome.
    print('\nBest genome:\n{!s}'.format(winner))

    # Show output of the most fit genome against training data.
    print('\nOutput:')
    winner_net = neat.nn.FeedForwardNetwork.create(winner, config)
    for xi, xo in zip(xor_inputs, xor_outputs):
        output = winner_net.activate(xi)
        print("input {!r}, expected output {!r}, got {!r}".format(xi, xo, output))

    node_names = {-1:'A', -2: 'B', 0:'A XOR B'}
    visualize.draw_net(config, winner, True, node_names=node_names)
    visualize.plot_stats(stats, ylog=False, view=True)
    visualize.plot_species(stats, view=True)

    p = neat.Checkpointer.restore_checkpoint('neat-checkpoint-4')
    p.run(eval_genomes, 10)


if __name__ == '__main__':
    # Determine path to configuration file. This path manipulation is
    # here so that the script will run successfully regardless of the
    # current working directory.
    local_dir = os.path.dirname(__file__)
    config_path = os.path.join(local_dir, 'config-feedforward')
    run(config_path)

这是配置文件

#--- parameters for the XOR-2 experiment ---#

[NEAT]
fitness_criterion     = max
fitness_threshold     = 3.9
pop_size              = 150
reset_on_extinction   = False

[DefaultGenome]
# node activation options
activation_default      = sigmoid
activation_mutate_rate  = 0.0
activation_options      = sigmoid

# node aggregation options
aggregation_default     = sum
aggregation_mutate_rate = 0.0
aggregation_options     = sum

# node bias options
bias_init_mean          = 0.0
bias_init_stdev         = 1.0
bias_max_value          = 30.0
bias_min_value          = -30.0
bias_mutate_power       = 0.5
bias_mutate_rate        = 0.7
bias_replace_rate       = 0.1

# genome compatibility options
compatibility_disjoint_coefficient = 1.0
compatibility_weight_coefficient   = 0.5

# connection add/remove rates
conn_add_prob           = 0.5
conn_delete_prob        = 0.5

# connection enable options
enabled_default         = True
enabled_mutate_rate     = 0.01

feed_forward            = True
initial_connection      = full

# node add/remove rates
node_add_prob           = 0.2
node_delete_prob        = 0.2

# network parameters
num_hidden              = 0
num_inputs              = 2
num_outputs             = 1

# node response options
response_init_mean      = 1.0
response_init_stdev     = 0.0
response_max_value      = 30.0
response_min_value      = -30.0
response_mutate_power   = 0.0
response_mutate_rate    = 0.0
response_replace_rate   = 0.0

# connection weight options
weight_init_mean        = 0.0
weight_init_stdev       = 1.0
weight_max_value        = 30
weight_min_value        = -30
weight_mutate_power     = 0.5
weight_mutate_rate      = 0.8
weight_replace_rate     = 0.1

[DefaultSpeciesSet]
compatibility_threshold = 3.0

[DefaultStagnation]
species_fitness_func = max
max_stagnation       = 20
species_elitism      = 2

[DefaultReproduction]
elitism            = 2
survival_threshold = 0.2

最后，这里是我安装的python neat版本

pip show neat-python
Name: neat-python
Version: 0.92
Summary: A NEAT (NeuroEvolution of Augmenting Topologies) implementation
Home-page: https://github.com/CodeReclaimers/neat-python 

有人能帮我解决以下错误吗？因为到目前为止，我尝试的是从github安装和从pip安装，这两种方法在示例代码中都给出了相同的错误。我也尝试了变体代码，但我得到了相同的错误

 ****** Running generation 0 ****** 

Traceback (most recent call last):
  File "driver.py", line 34, in <module>
    winner = p.run(eval_genomes)
  File "/home/j/anaconda3/lib/python3.8/site-packages/neat_python-0.92-py3.8.egg/neat/population.py", line 88, in run
  File "driver.py", line 18, in eval_genomes
    output = net.activate(xi)
  File "/home/j/anaconda3/lib/python3.8/site-packages/neat_python-0.92-py3.8.egg/neat/nn/feed_forward.py", line 13, in activate
RuntimeError: Expected 3 inputs, got 2

****正在运行第0代*******
回溯（最近一次呼叫最后一次）：
文件“driver.py”，第34行，在
winner=p.run（评估基因组）
文件“/home/j/anaconda3/lib/python3.8/site packages/neat_python-0.92-py3.8.egg/neat/population.py”，第88行，运行中
文件“driver.py”，第18行，在eval_基因组中
输出=净激活（xi）
文件“/home/j/anaconda3/lib/python3.8/site packages/neat_python-0.92-py3.8.egg/neat/nn/feed_forward.py”，第13行，激活
运行时错误：预期3个输入，得到2个

看起来该项目已经5年没有活动了。我不想让你气馁，但你能为此投入多少精力？@alexander.Hayes我有很多空闲时间。