编写pytorch代码，data = pd.read_csv('2-train.csv', header=0) ，通过滑动窗口划分法划分为训练集、验证集和测试集,窗口为30，步长为15，比例为0.7：0.2：0.1，保存到训练集、验证集和测试集

好的，我可以帮你编写这段代码。代码如下：

import pandas as pd
import numpy as np
import torch.utils.data as Data

# 读取数据集
data = pd.read_csv('2-train.csv', header=0)

# 滑动窗口划分法
window_size = 30
step_size = 15
train_ratio, val_ratio, test_ratio = 0.7, 0.2, 0.1

# 计算划分后的数据集大小
data_len = len(data)
train_len = int((data_len - window_size) * train_ratio) // step_size
val_len = int((data_len - window_size) * val_ratio) // step_size
test_len = int((data_len - window_size) * test_ratio) // step_size

# 定义保存数据的数组
train_data = np.zeros((train_len, window_size))
val_data = np.zeros((val_len, window_size))
test_data = np.zeros((test_len, window_size))

# 滑动窗口划分
for i in range(train_len):
    start = i * step_size
    train_data[i] = data[start:start + window_size]

for i in range(train_len, train_len + val_len):
    start = i * step_size
    val_data[i - train_len] = data[start:start + window_size]

for i in range(train_len + val_len, train_len + val_len + test_len):
    start = i * step_size
    test_data[i - train_len - val_len] = data[start:start + window_size]

# 将数据集转换为张量
train_data = torch.Tensor(train_data)
val_data = torch.Tensor(val_data)
test_data = torch.Tensor(test_data)

# 将数据集打包成 DataLoader
train_dataset = Data.TensorDataset(train_data)
val_dataset = Data.TensorDataset(val_data)
test_dataset = Data.TensorDataset(test_data)

train_dataloader = Data.DataLoader(train_dataset, batch_size=32, shuffle=True)
val_dataloader = Data.DataLoader(val_dataset, batch_size=32, shuffle=True)
test_dataloader = Data.DataLoader(test_dataset, batch_size=32, shuffle=True)

这段代码将数据集划分为训练集、验证集和测试集，并使用 PyTorch 中的 DataLoader 打包成可供神经网络训练的数据集。其中，滑动窗口的大小为30，步长为15，比例为0.7:0.2:0.1。