编写pytorch代码，定义LSTMAttention模型，定义个FA_CPSO优化算法，读取特征训练集X_train和标签训练集y_train，训练模型，利用萤火虫随机扰动的参数和混沌映射系数调整粒子群参数，调用优化算法去优化模型的损失函数，将最优的参数设置给模型，然后在读取特征测试集X_test和标签测试集y_test，再测试集上测试模型，继续优化模型，将优化后的参数设置给模型，并输出测试损失，绘制测试集的预测值和实际值，计算测试集的均方根误差，加载预测集，绘制预测集的实际值和预测值

好的，我明白了。下面是代码实现：

import torch
import torch.nn as nn
import numpy as np
import random

# 定义LSTMAttention模型
class LSTMAttention(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTMAttention, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size, batch_first=True)
        self.linear = nn.Linear(hidden_size, output_size)
        self.softmax = nn.Softmax(dim=1)

    def forward(self, inputs):
        output, _ = self.lstm(inputs)
        attention_weights = self.softmax(self.linear(output))
        context_vector = torch.sum(attention_weights * output, dim=1)
        output = self.linear(context_vector)
        return output

# 定义FA_CPSO优化算法
class Particle:
    def __init__(self, position, velocity, fitness, personal_best_position, personal_best_fitness):
        self.position = position
        self.velocity = velocity
        self.fitness = fitness
        self.personal_best_position = personal_best_position
        self.personal_best_fitness = personal_best_fitness

class Swarm:
    def __init__(self, num_particles, num_params, X_train, y_train, X_test, y_test):
        self.num_particles = num_particles
        self.num_params = num_params
        self.X_train = X_train
        self.y_train = y_train
        self.X_test = X_test
        self.y_test = y_test
        self.particles = []
        self.global_best_position = None
        self.global_best_fitness = float('inf')
        self.w = 0.729
        self.c1 = 1.49445
        self.c2 = 1.49445

        # 初始化粒子群
        for i in range(self.num_particles):
            position = np.random.uniform(low=-1.0, high=1.0, size=self.num_params)
            velocity = np.zeros(self.num_params)
            fitness = self.evaluate(position)
            personal_best_position = np.copy(position)
            personal_best_fitness = fitness
            particle = Particle(position, velocity, fitness, personal_best_position, personal_best_fitness)
            self.particles.append(particle)

    # 计算模型的损失函数
    def evaluate(self, position):
        model = LSTMAttention(input_size=1, hidden_size=32, output_size=1)
        optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
        criterion = nn.MSELoss()
        num_epochs = 100

        # 调整模型参数
        for epoch in range(num_epochs):
            inputs = torch.from_numpy(self.X_train).unsqueeze(2).float()
            labels = torch.from_numpy(self.y_train).float()
            optimizer.zero_grad()
            outputs = model(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
        
        # 使用模型预测测试集并计算损失函数
        inputs = torch.from_numpy(self.X_test).unsqueeze(2).float()
        labels = torch.from_numpy(self.y_test).float()
        outputs = model(inputs)
        test_loss = criterion(outputs, labels)
        return test_loss.item()

    # 更新粒子群的位置和速度
    def update(self):
        for particle in self.particles:
            r1 = random.random()
            r2 = random.random()

            # 更新速度
            particle.velocity = self.w * particle.velocity \
                                 + self.c1 * r1 * (particle.personal_best_position - particle.position) \
                                 + self.c2 * r2 * (self.global_best_position - particle.position)

            # 更新位置
            particle.position = particle.position + particle.velocity

            # 更新个体最优解
            fitness = self.evaluate(particle.position)
            if fitness < particle.personal_best_fitness:
                particle.personal_best_position = np.copy(particle.position)
                particle.personal_best_fitness = fitness

            # 更新全局最优解
            if fitness < self.global_best_fitness:
                self.global_best_position = np.copy(particle.position)
                self.global_best_fitness = fitness

    # 扰动粒子群的参数
    def perturb(self, chaos_map):
        for particle in self.particles:
            for i in range(len(particle.position)):
                particle.position[i] = particle.position[i] + chaos_map[i]

    # 运行粒子群优化算法
    def run(self, num_iterations):
        chaos_map = self.generate_chaos_map()
        for i in range(num_iterations):
            self.update()
            self.perturb(chaos_map)
            print('Iteration:', i, ', Best fitness:', self.global_best_fitness)

    # 产生萤火虫随机扰动的参数
    def generate_chaos_map(self):
        x = 0.1
        y = 0.1
        z = 0.1
        a = 10
        b = 28
        c = 8/3
        chaos_map = []
        for i in range(self.num_params):
            x_new = y - x
            y_new = x * (b - z) - y
            z_new = x * y - c * z
            x = x_new
            y = y_new
            z = z_new
            chaos_map.append(a * abs(z))
        return chaos_map

# 读取特征训练集X_train和标签训练集y_train
X_train = np.load('X_train.npy')
y_train = np.load('y_train.npy')

# 读取特征测试集X_test和标签测试集y_test
X_test = np.load('X_test.npy')
y_test = np.load('y_test.npy')

# 定义粒子群
swarm = Swarm(num_particles=10, num_params=32, X_train=X_train, y_train=y_train, X_test=X_test, y_test=y_test)

# 运行粒子群优化算法
swarm.run(num_iterations=50)

# 将最优的参数设置给模型
model = LSTMAttention(input_size=1, hidden_size=32, output_size=1)
model.load_state_dict(torch.load('best_model.pt'))

# 在测试集上测试模型
inputs = torch.from_numpy(X_test).unsqueeze(2).float()
labels = torch.from_numpy(y_test).float()
outputs = model(inputs)
test_loss = nn.MSELoss()(outputs, labels)
print('Test loss:', test_loss.item())

# 绘制测试集的预测值和实际值
import matplotlib.pyplot as plt
plt.plot(outputs.detach().numpy(), label='Predicted')
plt.plot(labels.numpy(), label='Actual')
plt.legend()
plt.show()

# 计算测试集的均方根误差
from sklearn.metrics import mean_squared_error
rmse = mean_squared_error(labels.numpy(), outputs.detach().numpy(), squared=False)
print('Test RMSE:', rmse)

# 加载预测集
X_pred = np.load('X_pred.npy')

# 预测并绘制预测集的实际值和预测值
inputs = torch.from_numpy(X_pred).unsqueeze(2).float()
outputs = model(inputs)
plt.plot(outputs.detach().numpy(), label='Predicted')
plt.legend()
plt.show()

需要注意的是，这只是一种实现方式，具体的实现方法可能因为数据的不同而有所不同。因此，您需要根据自己的数据和需求进行适当的修改。