编写pytorch代码，定义LSTMAttention模型，定义一个CPSO优化算法，在算法下面初始化粒子数、惯性权重、学习因子、加速常数。生成初始粒子群，利用训练集的预测误差当作每个粒子的适应度值，根据粒子的适应度值，选择最优的粒子作为当前的全局最优解，以及每个粒子自身的局部最优解，根据全局最优解和局部最优解，更新每个粒子的速度和位置，使用更新后的参数来更新LSTM模型的权重和偏置，计算更新后的LSTM模型在训练集上的误差，并更新全局最优解和局部最优解，根据收敛条件是否满足，决定是否继续迭代，如果迭代次数达到预设值或误差已经足够小，则停止迭代，并返回最优的LSTM模型参数

以下是一个基于PyTorch和CPSO优化算法的LSTMAttention模型的示例代码：

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

# 定义LSTMAttention模型
class LSTMAttention(nn.Module):
    def __init__(self, input_size, hidden_size, num_layers, output_size):
        super(LSTMAttention, self).__init__()
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
        self.attention = nn.Linear(hidden_size, 1)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device=x.device)
        out, _ = self.lstm(x, (h0, c0))
        alpha = torch.softmax(self.attention(out), dim=1)
        out = torch.sum(alpha * out, dim=1)
        out = self.fc(out)
        return out

# 定义CPSO优化算法
class CPSO:
    def __init__(self, num_particles, max_iterations, w, c1, c2, input_size, hidden_size, num_layers, output_size):
        self.num_particles = num_particles
        self.max_iterations = max_iterations
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.input_size = input_size
        self.hidden_size = hidden_size
        self.num_layers = num_layers
        self.output_size = output_size
        self.best_particle = None
        self.best_particle_fitness = np.inf
        self.particles = []
        for i in range(num_particles):
            particle = {
                'position': np.random.uniform(-1, 1, (input_size + hidden_size * num_layers + output_size,)),
                'velocity': np.zeros((input_size + hidden_size * num_layers + output_size,)),
                'personal_best_position': None,
                'personal_best_fitness': np.inf
            }
            self.particles.append(particle)

    def optimize(self, x_train, y_train):
        model = LSTMAttention(self.input_size, self.hidden_size, self.num_layers, self.output_size)
        criterion = nn.MSELoss()
        optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
        for i in range(self.max_iterations):
            for j in range(self.num_particles):
                particle = self.particles[j]
                # 更新速度和位置
                particle['velocity'] = self.w * particle['velocity'] \
                                       + self.c1 * np.random.rand() * (particle['personal_best_position'] - particle['position']) \
                                       + self.c2 * np.random.rand() * (self.best_particle['position'] - particle['position'])
                particle['position'] = particle['position'] + particle['velocity']
                # 更新模型参数
                input_weight = particle['position'][:self.input_size * self.hidden_size].reshape(self.input_size, self.hidden_size)
                hidden_weight = particle['position'][self.input_size * self.hidden_size:(self.input_size + self.hidden_size * self.hidden_size * (self.num_layers - 1))].reshape(self.num_layers - 1, self.hidden_size, self.hidden_size)
                output_weight = particle['position'][-self.hidden_size * self.output_size:].reshape(self.hidden_size, self.output_size)
                model.lstm.weight_ih_l0.data = torch.from_numpy(input_weight).float()
                model.lstm.weight_hh_l0.data = torch.from_numpy(hidden_weight[0]).float()
                for k in range(self.num_layers - 2):
                    setattr(model.lstm, f'weight_hh_l{k+1}', torch.from_numpy(hidden_weight[k+1]).float())
                model.fc.weight.data = torch.from_numpy(output_weight).float()
                # 计算模型在训练集上的误差
                outputs = model(x_train)
                loss = criterion(outputs, y_train)
                fitness = loss.item()
                # 更新个体最优解和全局最优解
                if fitness < particle['personal_best_fitness']:
                    particle['personal_best_position'] = particle['position']
                    particle['personal_best_fitness'] = fitness
                if fitness < self.best_particle_fitness:
                    self.best_particle = particle
                    self.best_particle_fitness = fitness
            # 判断是否满足收敛条件
            if self.best_particle_fitness < 0.01:
                break
        # 返回最优的LSTM模型参数
        input_weight = self.best_particle['position'][:self.input_size * self.hidden_size].reshape(self.input_size, self.hidden_size)
        hidden_weight = self.best_particle['position'][self.input_size * self.hidden_size:(self.input_size + self.hidden_size * self.hidden_size * (self.num_layers - 1))].reshape(self.num_layers - 1, self.hidden_size, self.hidden_size)
        output_weight = self.best_particle['position'][-self.hidden_size * self.output_size:].reshape(self.hidden_size, self.output_size)
        model.lstm.weight_ih_l0.data = torch.from_numpy(input_weight).float()
        model.lstm.weight_hh_l0.data = torch.from_numpy(hidden_weight[0]).float()
        for k in range(self.num_layers - 2):
            setattr(model.lstm, f'weight_hh_l{k+1}', torch.from_numpy(hidden_weight[k+1]).float())
        model.fc.weight.data = torch.from_numpy(output_weight).float()
        return model

在上面的代码中，首先定义了LSTMAttention模型，包括一个LSTM层、一个Attention层和一个全连接层，用于对序列数据进行分类任务。接着，定义了CPSO优化算法，包括粒子数、最大迭代次数、惯性权重、学习因子、加速常数等参数，以及粒子的速度、位置、个体最优解、个体最优解的适应度值、全局最优解和全局最优解的适应度值等变量。在优化过程中，通过更新速度和位置，以及更新模型参数，计算模型在训练集上的误差，并更新个体最优解和全局最优解。最后，根据收敛条件是否满足，决定是否继续迭代，如果达到预设的最大迭代次数或误差已经足够小，则停止迭代，并返回最优的LSTM模型参数。