编写一段pytorch程序，定义一个稀疏堆叠自编码器SSAE，将带有时间列的多特征的数据集training_set.csv、test_set.csv和pred_set.csv加载到SSAE中，将数据集转换为pytorch张量，对数据进行特征提取和归一化、标准化以后，将处理好的数据对应放入new_training_set、new_test_set和new_pred_set三个文件夹中，最后一列是标签列

好的，以下是使用PyTorch编写稀疏堆叠自编码器SSAE的代码，可以将带有时间列的多特征数据集进行特征提取和归一化、标准化处理，将处理好的数据保存到new_training_set、new_test_set和new_pred_set中：

import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
from sklearn.preprocessing import StandardScaler, MinMaxScaler

# 定义稀疏堆叠自编码器SSAE模型
class SSAE(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(SSAE, self).__init__()
        self.encoder = nn.Sequential(
            nn.Linear(input_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, output_dim),
            nn.Sigmoid(),
        )
        self.decoder = nn.Sequential(
            nn.Linear(output_dim, hidden_dim),
            nn.ReLU(),
            nn.Linear(hidden_dim, input_dim),
            nn.Sigmoid(),
        )

    def forward(self, x):
        encoded = self.encoder(x)
        decoded = self.decoder(encoded)
        return encoded, decoded

# 加载数据集
train_data = pd.read_csv("training_set.csv")
test_data = pd.read_csv("test_set.csv")
pred_data = pd.read_csv("pred_set.csv")

# 将数据集转换为PyTorch张量
train_tensor = torch.tensor(train_data.values.astype(float))
test_tensor = torch.tensor(test_data.values.astype(float))
pred_tensor = torch.tensor(pred_data.values.astype(float))

# 特征提取和归一化、标准化
scaler = StandardScaler()
train_tensor[:, :-1] = torch.tensor(scaler.fit_transform(train_tensor[:, :-1]))
test_tensor[:, :-1] = torch.tensor(scaler.transform(test_tensor[:, :-1]))
pred_tensor = torch.tensor(scaler.transform(pred_tensor))

# 训练模型
input_dim = train_tensor.shape[1] - 1
hidden_dim = 10
output_dim = 5
model = SSAE(input_dim, hidden_dim, output_dim)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.01)

num_epochs = 100
batch_size = 16
for epoch in range(num_epochs):
    running_loss = 0.0
    for i in range(0, len(train_tensor), batch_size):
        batch_data = train_tensor[i:i+batch_size, :-1]
        batch_label = train_tensor[i:i+batch_size, -1]
        optimizer.zero_grad()
        encoded, decoded = model(batch_data)
        loss = criterion(decoded, batch_data)
        loss.backward()
        optimizer.step()
        running_loss += loss.item() * batch_data.shape[0]
    print(f"Epoch {epoch+1} loss: {running_loss / len(train_tensor)}")

# 对数据进行编码
train_encoded, _ = model(torch.tensor(train_data.values.astype(float))[:, :-1])
test_encoded, _ = model(torch.tensor(test_data.values.astype(float))[:, :-1])
pred_encoded, _ = model(torch.tensor(pred_data.values.astype(float))[:, :-1])

# 将数据保存到new_training_set、new_test_set和new_pred_set中
train_encoded = pd.concat([pd.DataFrame(train_encoded.detach().numpy()), train_data.iloc[:, -1]], axis=1)
test_encoded = pd.concat([pd.DataFrame(test_encoded.detach().numpy()), test_data.iloc[:, -1]], axis=1)
pred_encoded = pd.DataFrame(pred_encoded.detach().numpy())
train_encoded.to_csv("new_training_set.csv", index=False)
test_encoded.to_csv("new_test_set.csv", index=False)
pred_encoded.to_csv("new_pred_set.csv", index=False)

以上代码可以将训练集、测试集和预测集中的数据进行特征提取和归一化、标准化处理，并使用稀疏堆叠自编码器SSAE模型对数据进行编码，最后将处理好的数据保存到new_training_set、new_test_set和new_pred_set三个文件夹中。