ga-lstm代码

以下是一个简单的 GA-LSTM 模型的 Python 代码示例：

import numpy as np
import random

class GALSTM():
    def __init__(self, input_size, output_size, hidden_size, population_size, mutation_rate):
        self.input_size = input_size
        self.output_size = output_size
        self.hidden_size = hidden_size
        self.population_size = population_size
        self.mutation_rate = mutation_rate
        self.population = []
        self.fitness_scores = []

        # Initialize population
        for i in range(population_size):
            self.population.append(self.generate_individual())

    def generate_individual(self):
        # Generate weights for the GA-LSTM individual
        # Each individual is a numpy array with shape (input_size + hidden_size + 1, 4 * hidden_size + output_size)
        individual = np.zeros((self.input_size + self.hidden_size + 1, 4 * self.hidden_size + self.output_size))
        for i in range(self.input_size + self.hidden_size + 1):
            for j in range(4 * self.hidden_size + self.output_size):
                individual[i][j] = random.uniform(-1, 1)
        return individual

    def sigmoid(self, x):
        return 1 / (1 + np.exp(-x))

    def forward(self, x, h, c, individual):
        # Perform forward pass through the GA-LSTM individual
        # x is the input vector, h is the previous hidden state, c is the previous cell state
        # individual is the numpy array representing the weights of the individual
        input_concat = np.concatenate((x, h, np.array([1])))
        gates = np.dot(input_concat, individual)
        input_gate = self.sigmoid(gates[:self.hidden_size])
        forget_gate = self.sigmoid(gates[self.hidden_size:2*self.hidden_size])
        output_gate = self.sigmoid(gates[2*self.hidden_size:3*self.hidden_size])
        cell_update = np.tanh(gates[3*self.hidden_size:4*self.hidden_size])
        c_new = c * forget_gate + input_gate * cell_update
        h_new = output_gate * np.tanh(c_new)

        return h_new, c_new

    def predict(self, x, individual):
        # Use the GA-LSTM individual to make a prediction for the input x
        h = np.zeros(self.hidden_size)
        c = np.zeros(self.hidden_size)
        for i in range(len(x)):
            h, c = self.forward(x[i], h, c, individual)
        output = np.dot(np.concatenate((h, np.array([1]))), individual)[-self.output_size:]
        return output

    def evaluate_fitness(self, x_train, y_train, individual):
        # Evaluate the fitness of the GA-LSTM individual using mean squared error
        mse = 0
        for i in range(len(x_train)):
            y_pred = self.predict(x_train[i], individual)
            mse += np.mean((y_pred - y_train[i])**2)
        fitness = 1 / (mse + 1e-6)
        return fitness

    def select_parents(self):
        # Select two parents from the population using tournament selection
        parent1, parent2 = None, None
        for i in range(2):
            tournament_indices = np.random.choice(self.population_size, size=5, replace=False)
            tournament_individuals = [self.population[i] for i in tournament_indices]
            tournament_fitness_scores = [self.fitness_scores[i] for i in tournament_indices]
            winner = tournament_individuals[np.argmax(tournament_fitness_scores)]
            if i == 0:
                parent1 = winner
            else:
                parent2 = winner
        return parent1, parent2

    def crossover(self, parent1, parent2):
        # Perform crossover between two parents to create a new child
        child = np.zeros(parent1.shape)
        for i in range(parent1.shape[0]):
            for j in range(parent1.shape[1]):
                if random.random() < 0.5:
                    child[i][j] = parent1[i][j]
                else:
                    child[i][j] = parent2[i][j]
        return child

    def mutate(self, child):
        # Perform mutation on a child
        for i in range(child.shape[0]):
            for j in range(child.shape[1]):
                if random.random() < self.mutation_rate:
                    child[i][j] += random.uniform(-1, 1)
        return child

    def evolve(self, x_train, y_train):
        # Evolve the population using genetic algorithm
        self.fitness_scores = [self.evaluate_fitness(x_train, y_train, individual) for individual in self.population]
        sorted_indices = np.argsort(self.fitness_scores)[::-1]
        self.population = [self.population[i] for i in sorted_indices]
        elite_individual = self.population[0]
        new_population = [elite_individual]
        for i in range(1, self.population_size):
            parent1, parent2 = self.select_parents()
            child = self.crossover(parent1, parent2)
            child = self.mutate(child)
            new_population.append(child)
        self.population = new_population

    def fit(self, x_train, y_train, num_generations):
        # Train the GA-LSTM model on the training data for a given number of generations
        for i in range(num_generations):
            self.evolve(x_train, y_train)
            fitness_scores = [self.evaluate_fitness(x_train, y_train, individual) for individual in self.population]
            print("Generation", i+1, "- Best Fitness:", max(fitness_scores))
        return self.population[0]

这个 GA-LSTM 类包括以下方法：

- `__init__(self, input_size, output_size, hidden_size, population_size, mutation_rate)`：初始化 GA-LSTM 模型，其中`input_size`是输入向量的维数，`output_size`是输出向量的维数，`hidden_size`是隐藏层向量的维数，`population_size`是种群大小，`mutation_rate`是突变率。
- `generate_individual(self)`：生成 GA-LSTM 个体的权重矩阵。
- `sigmoid(self, x)`：计算 sigmoid 函数的值。
- `forward(self, x, h, c, individual)`：执行 GA-LSTM 个体的前向传播。
- `predict(self, x, individual)`：使用 GA-LSTM 个体对输入 x 进行预测。
- `evaluate_fitness(self, x_train, y_train, individual)`：计算 GA-LSTM 个体的适应度分数。
- `select_parents(self)`：使用锦标赛选择法从种群中选择两个父代。
- `crossover(self, parent1, parent2)`：在两个父代之间执行交叉操作以创建一个新的孩子。
- `mutate(self, child)`：对一个孩子执行突变操作。
- `evolve(self, x_train, y_train)`：使用遗传算法进化种群。
- `fit(self, x_train, y_train, num_generations)`：在训练数据上训练 GA-LSTM 模型。