请写出ga-lstm的python代码

GA-LSTM 是一种基于遗传算法的长短期记忆网络，其代码实现相对较为复杂，以下是一个简单的示例：

import numpy as np
import random
from keras.models import Sequential
from keras.layers import Dense, LSTM, Bidirectional
from keras.callbacks import EarlyStopping
from sklearn.metrics import mean_squared_error

class GA_LSTM:
    def __init__(self, population_size, n_bits, n_epochs, n_layers, n_neurons):
        self.population_size = population_size
        self.n_bits = n_bits
        self.n_epochs = n_epochs
        self.n_layers = n_layers
        self.n_neurons = n_neurons

    def create_population(self):
        population = []
        for i in range(self.population_size):
            network = []
            for j in range(self.n_layers):
                layer = []
                for k in range(self.n_neurons):
                    weights = np.random.uniform(low=-1.0, high=1.0, size=self.n_bits)
                    layer.append(weights)
                network.append(layer)
            population.append(network)
        return population

    def decode_network(self, chromosome):
        model = Sequential()
        for i in range(self.n_layers):
            if i == 0:
                model.add(Bidirectional(LSTM(units=self.n_neurons, activation='tanh', input_shape=(1, self.n_bits))))
            else:
                model.add(Bidirectional(LSTM(units=self.n_neurons, activation='tanh')))
            model.add(Dense(units=self.n_bits, activation='linear'))
            model.layers[-1].set_weights(np.array(chromosome[i]))
        model.compile(loss='mean_squared_error', optimizer='adam')
        return model

    def fitness_function(self, model, X_train, y_train, X_test, y_test):
        early_stop = EarlyStopping(monitor='val_loss', patience=5, verbose=1)
        model.fit(X_train, y_train, epochs=self.n_epochs, validation_data=(X_test, y_test), callbacks=[early_stop])
        y_pred = model.predict(X_test)
        mse = mean_squared_error(y_test, y_pred)
        return 1 / (1 + mse)

    def selection(self, population, fitness):
        total_fitness = sum(fitness)
        probabilities = [f / total_fitness for f in fitness]
        selected_indices = np.random.choice(len(population), size=self.population_size, replace=True, p=probabilities)
        selected_population = [population[i] for i in selected_indices]
        return selected_population

    def crossover(self, population):
        new_population = []
        for i in range(self.population_size):
            parent1 = random.choice(population)
            parent2 = random.choice(population)
            child = []
            for j in range(self.n_layers):
                layer = []
                for k in range(self.n_neurons):
                    if np.random.uniform() < 0.5:
                        layer.append(parent1[j][k])
                    else:
                        layer.append(parent2[j][k])
                child.append(layer)
            new_population.append(child)
        return new_population

    def mutation(self, population):
        mutation_rate = 1 / (self.n_layers * self.n_neurons * self.n_bits)
        for i in range(self.population_size):
            for j in range(self.n_layers):
                for k in range(self.n_neurons):
                    for l in range(self.n_bits):
                        if np.random.uniform() < mutation_rate:
                            population[i][j][k][l] = np.random.uniform(low=-1.0, high=1.0)
        return population

    def train(self, X_train, y_train, X_test, y_test):
        population = self.create_population()
        for i in range(self.n_epochs):
            fitness = []
            for j in range(self.population_size):
                model = self.decode_network(population[j])
                fitness.append(self.fitness_function(model, X_train, y_train, X_test, y_test))
            print('Generation', i + 1, '- Best fitness:', max(fitness))
            population = self.selection(population, fitness)
            population = self.crossover(population)
            population = self.mutation(population)
        best_individual = population[fitness.index(max(fitness))]
        best_model = self.decode_network(best_individual)
        return best_model

上述代码使用 Keras 创建 GA-LSTM 神经网络，并使用遗传算法进行训练。其中，`create_population()` 方法用于创建初始种群，`decode_network()` 方法用于将染色体解码为神经网络并进行编译，`fitness_function()` 方法用于计算染色体的适应度，`selection()` 方法用于进行选择操作，`crossover()` 方法用于进行交叉操作，`mutation()` 方法用于进行变异操作。最后，`train()` 方法用于训练 GA-LSTM 网络，并返回最佳模型。