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。利用训练集计算模型的预测误差，将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

好的，以下是代码示例：

首先定义LSTMAttention模型，代码如下：

import torch
import torch.nn as nn

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.attn = nn.Linear(hidden_size, hidden_size)

    def forward(self, x):
        h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size).to(device)
        out, _ = self.lstm(x, (h0, c0))
        attn_weights = torch.softmax(self.attn(out[:, -1]), dim=1)
        context = torch.bmm(attn_weights.unsqueeze(1), out).squeeze(1)
        out = self.fc(context)
        return out

然后定义CPSO优化算法，代码如下：

import random
import numpy as np

class CPSO:
    def __init__(self, n_particles, w, c1, c2, max_iter, tol):
        self.n_particles = n_particles
        self.w = w
        self.c1 = c1
        self.c2 = c2
        self.max_iter = max_iter
        self.tol = tol
        self.global_best_pos = None
        self.global_best_val = np.inf
        self.particles = []
        self.velocities = []
        for i in range(n_particles):
            particle = []
            velocity = []
            for j in range(n_params):
                particle.append(random.uniform(-1, 1))
                velocity.append(random.uniform(-1, 1))
            self.particles.append(particle)
            self.velocities.append(velocity)

    def optimize(self, X_train, y_train, model):
        for i in range(self.max_iter):
            for j in range(self.n_particles):
                params = self.particles[j]
                velocity = self.velocities[j]
                model.set_params(params)
                y_pred = model.predict(X_train)
                val = mse(y_train, y_pred)
                if val < self.global_best_val:
                    self.global_best_val = val
                    self.global_best_pos = params
                if val < self.particle_best_vals[j]:
                    self.particle_best_vals[j] = val
                    self.particle_best_pos[j] = params
                v_new = self.w * velocity \
                      + self.c1 * random.uniform(0, 1) * (np.array(self.particle_best_pos[j]) - np.array(params)) \
                      + self.c2 * random.uniform(0, 1) * (np.array(self.global_best_pos) - np.array(params))
                x_new = params + v_new
                self.velocities[j] = v_new
                self.particles[j] = x_new.tolist()
                model.set_params(x_new.tolist())
            if self.global_best_val < self.tol:
                break
        model.set_params(self.global_best_pos)
        y_pred = model.predict(X_train)
        return model, self.global_best_val, y_pred

最后是主函数部分，代码如下：

import torch.optim as optim
import torch.nn.functional as F

def train(model, optimizer, X_train, y_train):
    model.train()
    optimizer.zero_grad()
    output = model(X_train)
    loss = F.mse_loss(output, y_train)
    loss.backward()
    optimizer.step()
    return loss.item()

def evaluate(model, X_val, y_val):
    model.eval()
    with torch.no_grad():
        output = model(X_val)
        loss = F.mse_loss(output, y_val)
    return loss.item()

n_particles = 10
w = 0.7
c1 = 1.5
c2 = 1.5
max_iter = 100
tol = 1e-4
n_params = sum(p.numel() for p in model.parameters())

c_pso = CPSO(n_particles, w, c1, c2, max_iter, tol)
params, train_loss, y_pred = c_pso.optimize(X_train, y_train, model)

model = LSTMAttention(input_size, hidden_size, num_layers, output_size).to(device)
optimizer = optim.Adam(model.parameters())

for i in range(100):
    train_loss = train(model, optimizer, X_train, y_train)
    val_loss = evaluate(model, X_val, y_val)
    print(f"Epoch {i+1}, Train Loss: {train_loss:.4f}, Val Loss: {val_loss:.4f}")

    # update global and local best
    if val_loss < global_best_val:
        global_best_val = val_loss
        global_best_params = model.get_params()
    if val_loss < particle_best_vals[j]:
        particle_best_vals[j] = val_loss
        particle_best_params[j] = model.get_params()

    # update velocity and position
    v_new = w * velocity \
          + c1 * random.uniform(0, 1) * (np.array(particle_best_params[j]) - np.array(params)) \
          + c2 * random.uniform(0, 1) * (np.array(global_best_params) - np.array(params))
    x_new = params + v_new
    velocity = v_new
    params = x_new.tolist()
    model.set_params(params)

在主函数中，我们首先定义了CPSO的参数，并使用CPSO算法优化模型参数。然后我们定义了LSTMAttention模型和优化器，并在训练集上训练模型。在训练过程中，我们更新全局最优解和局部最优解，并更新每个粒子的速度和位置。最后，我们根据收敛条件判断是否停止迭代，并返回最优的LSTM模型参数。