给出一段代码，使用pytorch，其中包含lstm模型用于预测股价

以下是使用 PyTorch 实现 LSTM 模型预测股价的代码示例：

import torch
import torch.nn as nn
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler

# 加载数据
data = pd.read_csv('stock_data.csv')

# 数据预处理
scaler = MinMaxScaler(feature_range=(-1, 1))
data['Close'] = scaler.fit_transform(data['Close'].values.reshape(-1, 1))

# 划分训练集和测试集
train_size = int(len(data) * .8)
train_data = data.iloc[:train_size]
test_data = data.iloc[train_size:]

# 定义 LSTM 模型
class LSTM(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        self.lstm = nn.LSTM(input_size, hidden_size)
        self.fc = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        lstm_out, _ = self.lstm(x.view(len(x), 1, -1))
        out = self.fc(lstm_out.view(len(x), -1))
        return out[-1]

# 训练 LSTM 模型
input_size = 1
hidden_size = 32
output_size = 1
num_epochs = 100
learning_rate = .01

model = LSTM(input_size, hidden_size, output_size)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)

for epoch in range(num_epochs):
    for i in range(len(train_data) - 1):
        x = torch.Tensor(train_data['Close'].iloc[i:i+1].values)
        y = torch.Tensor(train_data['Close'].iloc[i+1:i+2].values)

        optimizer.zero_grad()
        output = model(x)
        loss = criterion(output, y)
        loss.backward()
        optimizer.step()

    if (epoch+1) % 10 == :
        print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, loss.item()))

# 测试 LSTM 模型
model.eval()
with torch.no_grad():
    test_inputs = torch.Tensor(test_data['Close'].values)
    test_outputs = []
    for i in range(len(test_inputs)):
        if i == :
            model.hidden = (torch.zeros(1, 1, model.hidden_size),
                            torch.zeros(1, 1, model.hidden_size))
        else:
            model.hidden = (h.detach(), c.detach())

        x = test_inputs[i]
        output = model(x)
        test_outputs.append(output.item())

# 反归一化
test_outputs = scaler.inverse_transform(np.array(test_outputs).reshape(-1, 1))

# 可视化预测结果
import matplotlib.pyplot as plt

plt.plot(data['Close'].values, label='True')
plt.plot(np.arange(train_size, len(data)), test_outputs, label='Predicted')
plt.legend()
plt.show()

希望这段代码能够帮助你实现 LSTM 模型预测股价。