我想了解如何實(shí)現(xiàn)簡(jiǎn)潔的 python，以便在每次預(yù)測(cè)后重新訓(xùn)練，因此每次預(yù)測(cè)后訓(xùn)練集的大小都會(huì)增加。我正在嘗試通過(guò)配置文件設(shè)置整潔的 python，以便在每次預(yù)測(cè)測(cè)試/未見集后重新訓(xùn)練。例如，如果 XOR“進(jìn)化最小”示例，根據(jù)我的理解，它可以進(jìn)行調(diào)整，以便它對(duì)部分?jǐn)?shù)據(jù)進(jìn)行訓(xùn)練（達(dá)到特定的適應(yīng)度水平，獲得最佳基因組），然后它會(huì)根據(jù)設(shè)置的其他數(shù)據(jù)進(jìn)行預(yù)測(cè)一邊作為測(cè)試集。請(qǐng)參閱下面的代碼以了解我的意思：from __future__ import print_functionimport neatimport visualize# 2-input XOR inputs and expected outputs. Training setxor_inputs = [(0.0, 0.0, 0.0), (0.0, 1.0, 0.0), (1.0, 1.0, 1.0), (0.0, 0.0, 1.0), (1.0, 1.0, 0.0)]xor_outputs = [(1.0,), (1.0,), (1.0,), (0.0,), (0.0,)]# Test setxor_inputs2 = [(1.0, 0.0, 1.0), (1.0, 1.0, 0.0), (1.0, 0.0, 0.0)]xor_outputs2 = [(1.0,), (0.0,), (0.0,)]def eval_genomes(genomes, config): for genome_id, genome in genomes:    genome.fitness = 5    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# Load configuration.config = neat.Config(neat.DefaultGenome, neat.DefaultReproduction,                 neat.DefaultSpeciesSet, neat.DefaultStagnation,                 'config-feedforward')# 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)# Run until a solution is found.winner = p.run(eval_genomes) # 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)count = 0#To make predictions using the best genomefor xi, xo in zip(xor_inputs2, xor_outputs2):  prediction = winner_net.activate(xi)  print("  input {!r}, expected output {!r}, got {!r}".format(  xi, xo[0], round(prediction[0])))  #to get prediction accuracy  if int(xo[0]) == int(round(prediction[0])):    count = count + 1accuracy = count / len(xor_outputs2)print('\nAccuracy: ', accuracy)