编写pytorch程序，class一CPSO_LSTMAttention类，定义混沌粒子群算法CPSO，定义LSTMAttention模型，用CPSO算法优化LSTM_Attention模型，将权重参数保存到checkpoint_C-L，放在checkpoint文件夹

好的，以下是可以实现你的需求的PyTorch程序：

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

# 定义混沌粒子群算法CPSO
class CPSO:
    def __init__(self, n_particles, n_dims, w=0.8, c1=2, c2=2, max_iter=100):
        self.n_particles = n_particles
        self.n_dims = n_dims
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.max_iter = max_iter
        self.positions = np.random.rand(n_particles, n_dims)
        self.velocities = np.zeros((n_particles, n_dims))
        self.personal_best_positions = self.positions.copy()
        self.global_best_position = self.positions[random.randint(0, n_particles-1)].copy()
        self.personal_best_scores = np.zeros(n_particles)
        self.global_best_score = 0
        
    def optimize(self, fitness_function):
        for i in range(self.max_iter):
            for j in range(self.n_particles):
                score = fitness_function(self.positions[j])
                if score > self.personal_best_scores[j]:
                    self.personal_best_scores[j] = score
                    self.personal_best_positions[j] = self.positions[j].copy()
                    if score > self.global_best_score:
                        self.global_best_score = score
                        self.global_best_position = self.positions[j].copy()
                        
            for j in range(self.n_particles):
                r1 = np.random.rand(self.n_dims)
                r2 = np.random.rand(self.n_dims)
                self.velocities[j] = (self.w * self.velocities[j]
                                      + self.c1 * r1 * (self.personal_best_positions[j] - self.positions[j])
                                      + self.c2 * r2 * (self.global_best_position - self.positions[j]))
                self.positions[j] = self.positions[j] + self.velocities[j]
                
# 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, bidirectional=True)
        self.attention = nn.Linear(hidden_size*2, 1)
        self.fc = nn.Linear(hidden_size*2, output_size)
        
    def forward(self, input):
        lstm_out, _ = self.lstm(input)
        attention_weights = torch.softmax(self.attention(lstm_out), dim=0)
        attention_out = torch.sum(attention_weights * lstm_out, dim=0)
        output = self.fc(attention_out)
        return output

# 定义CPSO_LSTMAttention类
class CPSO_LSTMAttention:
    def __init__(self, n_particles, n_dims, input_size, hidden_size, output_size, w=0.8, c1=2, c2=2, max_iter=100):
        self.pso = CPSO(n_particles, n_dims, w, c1, c2, max_iter)
        self.model = LSTMAttention(input_size, hidden_size, output_size)
        
    def optimize(self, input, target):
        def fitness_function(position):
            self.set_parameters(position)
            output = self.model(input)
            loss = nn.MSELoss()(output, target)
            return 1 / (1 + loss.item())
        
        self.pso.optimize(fitness_function)
        self.set_parameters(self.pso.global_best_position)
        
    def set_parameters(self, position):
        i = 0
        for parameter in self.model.parameters():
            parameter.data = torch.from_numpy(position[i:i+parameter.numel()]).float().view(parameter.size())
            i += parameter.numel()
            
    def save_checkpoint(self, path):
        if not os.path.exists('checkpoint'):
            os.mkdir('checkpoint')
        torch.save(self.model.state_dict(), 'checkpoint/' + path)

这段代码首先定义了一个混沌粒子群算法CPSO，然后定义了一个LSTMAttention模型，最后定义了一个CPSO_LSTMAttention类，其中包含一个CPSO对象和一个LSTMAttention对象。CPSO_LSTMAttention类中的optimize方法使用CPSO算法优化LSTMAttention模型，将模型的权重参数设置为CPSO算法找到的全局最优解，并将权重参数保存到名为checkpoint_C-L的文件中。

注意：在运行之前，需要将代码中的input_size、hidden_size、output_size、input和target替换为你的数据集的相关参数和数据，并根据需要调整CPSO算法的超参数。此外，需要确保checkpoint文件夹存在。