不使用pyswarms库

好的，以下是一个使用纯Python实现PSO算法优化LSTM神经网络参数的代码示例：

import numpy as np
import random
from keras.models import Sequential
from keras.layers import LSTM, Dense

# Define the LSTM model
def create_model(units, batch_size, learning_rate):
    model = Sequential()
    model.add(LSTM(units=units, batch_input_shape=(batch_size, X_train.shape[1], X_train.shape[2]), stateful=True))
    model.add(Dense(1))
    optimizer = keras.optimizers.Adam(lr=learning_rate)
    model.compile(loss='mean_squared_error', optimizer=optimizer)
    return model

# Define the fitness function to be optimized
def fitness_function(params):
    units = params[0]
    batch_size = params[1]
    learning_rate = params[2]
    model = create_model(units=units, batch_size=batch_size, learning_rate=learning_rate)
    history = model.fit(X_train, y_train, epochs=10, batch_size=batch_size, verbose=0, shuffle=False)
    loss = history.history['loss'][-1]
    return loss

# Define the PSO optimizer
def PSO(fitness_function, n_particles, n_iterations, bounds):
    # Initialize the particles with random positions and velocities
    particles_position = np.zeros((n_particles, len(bounds)))
    particles_velocity = np.zeros((n_particles, len(bounds)))
    for i in range(n_particles):
        for j in range(len(bounds)):
            particles_position[i][j] = random.uniform(bounds[j][0], bounds[j][1])
            particles_velocity[i][j] = random.uniform(-1, 1)
            
    # Initialize the global best position and fitness
    global_best_position = np.zeros(len(bounds))
    global_best_fitness = np.inf
    
    # Iterate over the specified number of iterations
    for iteration in range(n_iterations):
        # Evaluate the fitness of each particle and update the personal best position and fitness
        for i in range(n_particles):
            fitness = fitness_function(particles_position[i])
            if fitness < personal_best_fitness[i]:
                personal_best_position[i] = particles_position[i]
                personal_best_fitness[i] = fitness
            # Update the global best position and fitness
            if fitness < global_best_fitness:
                global_best_position = particles_position[i]
                global_best_fitness = fitness
                
        # Update the velocity and position of each particle
        for i in range(n_particles):
            for j in range(len(bounds)):
                r1 = random.random()
                r2 = random.random()
                particles_velocity[i][j] = w * particles_velocity[i][j] + c1 * r1 * (personal_best_position[i][j] - particles_position[i][j]) + c2 * r2 * (global_best_position[j] - particles_position[i][j])
                particles_position[i][j] = particles_position[i][j] + particles_velocity[i][j]

        print('Iteration:', iteration, 'Best loss:', global_best_fitness, 'Best params:', global_best_position)
    
    return global_best_position, global_best_fitness

# Set the search space for the PSO algorithm
bounds = [(10, 100), (16, 64), (0.0001, 0.1)]

# Define the PSO parameters
n_particles = 10
n_iterations = 50
w = 0.9
c1 = 0.5
c2 = 0.3

# Run the PSO optimizer to find the optimal parameters
best_params, best_fitness = PSO(fitness_function, n_particles, n_iterations, bounds)

# Train the LSTM model with the optimal parameters
model = create_model(units=int(best_params[0]), batch_size=int(best_params[1]), learning_rate=best_params[2])
history = model.fit(X_train, y_train, epochs=100, batch_size=int(best_params[1]), validation_data=(X_test, y_test), verbose=0, shuffle=False)

# Evaluate the LSTM model
loss = model.evaluate(X_test, y_test, batch_size=int(best_params[1]), verbose=0)
print('Test loss:', loss)

在这个示例中，我们定义了一个名为PSO的函数，它接收适应度函数、粒子数量、迭代次数和参数搜索范围作为输入，并返回最优参数和最小损失值。在PSO函数中，我们初始化了粒子的位置和速度，并迭代地更新它们的位置和速度，使用适应度函数计算每个粒子的适应度，并更新个人最优位置和全局最优位置。最后，我们使用找到的最优参数重新训练LSTM模型，并评估其在测试集上的表现。