生成一段用于时序数据预测的transformer代码，其中包含一小段用于训练和测试的数据

以下是一个带有训练和测试数据的完整示例，用于时序数据预测的Transformer模型代码：

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

# 生成训练和测试数据
data = np.sin(np.arange(0, 100*np.pi, 0.1))
train_data = data[:int(len(data)*0.8)]
test_data = data[int(len(data)*0.8):]

# 定义Transformer模型
class TransformerModel(nn.Module):
    def __init__(self, n_features, n_hidden, n_layers, n_heads, dropout=0.1):
        super(TransformerModel, self).__init__()
        self.positional_encoder = PositionalEncoder(n_hidden)
        self.encoder_layers = nn.TransformerEncoderLayer(n_hidden, n_heads, dropout=dropout)
        self.encoder = nn.TransformerEncoder(self.encoder_layers, n_layers)
        self.decoder = nn.Linear(n_hidden, 1)

    def forward(self, x):
        x = self.positional_encoder(x)
        x = self.encoder(x)
        x = self.decoder(x[:, -1, :])
        return x.squeeze()

class PositionalEncoder(nn.Module):
    def __init__(self, n_features, max_seq_len=1000):
        super(PositionalEncoder, self).__init__()
        self.n_features = n_features
        self.max_seq_len = max_seq_len
        self.encoding = self.create_encoding()

    def create_encoding(self):
        encoding = torch.zeros(self.max_seq_len, self.n_features)
        pos = torch.arange(0, self.max_seq_len).unsqueeze(1)
        div_term = torch.exp(torch.arange(0, self.n_features, 2) * -(np.log(10000.0) / self.n_features))
        encoding[:, 0::2] = torch.sin(pos * div_term)
        encoding[:, 1::2] = torch.cos(pos * div_term)
        encoding = encoding.unsqueeze(0).transpose(0, 1)
        return encoding

    def forward(self, x):
        seq_len = x.shape[1]
        x = x + self.encoding[:seq_len, :]
        return x

# 训练模型
model = TransformerModel(n_features=1, n_hidden=64, n_layers=2, n_heads=4, dropout=0.1)
criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

sequence_length = 10
for epoch in range(100):
    for i in range(len(train_data) - sequence_length):
        x = train_data[i:i+sequence_length]
        y = train_data[i+sequence_length]
        x_tensor, y_tensor = torch.FloatTensor(x), torch.FloatTensor(y)
        optimizer.zero_grad()
        output = model(x_tensor.unsqueeze(0))
        loss = criterion(output, y_tensor)
        loss.backward()
        optimizer.step()

# 预测
with torch.no_grad():
    preds = []
    for i in range(len(test_data) - sequence_length):
        x = test_data[i:i+sequence_length]
        x_tensor = torch.FloatTensor(x)
        output = model(x_tensor.unsqueeze(0))
        preds.append(output.item())

# 打印预测结果
print(preds)

在这个示例中，我们生成了一个正弦波形的时间序列数据，并将其分为训练和测试数据。我们定义了一个Transformer模型，并使用均方误差作为损失函数，使用Adam优化器进行参数优化。在训练过程中，我们将序列长度设置为10，即用前10个数据点来预测下一个数据点。在预测阶段，我们使用训练好的模型对测试数据进行预测，并打印出预测结果。