首先，我们需要导入相关的库，包括PyTorch、NumPy、Pandas等：

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

接着，我们需要定义Informer网络的模型结构。

Informer网络的结构比较复杂，主要包括编码器、解码器和自注意力机制等部分。我们可以使用PyTorch提供的模块来实现这些部分，具体代码如下：

定义编码器

class Encoder(nn.Module): def init(self, input_size, hidden_size, num_layers, dropout): super(Encoder, self).init() self.input_size = input_size self.hidden_size = hidden_size self.num_layers = num_layers self.dropout = dropout

    # LSTM层
    self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout)

def forward(self, x):
    output, hidden = self.rnn(x)
    return output, hidden

定义解码器

class Decoder(nn.Module): def init(self, input_size, hidden_size, output_size, num_layers, dropout): super(Decoder, self).init() self.input_size = input_size self.hidden_size = hidden_size self.output_size = output_size self.num_layers = num_layers self.dropout = dropout

    # LSTM层和全连接层
    self.rnn = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, dropout=dropout)
    self.fc = nn.Linear(hidden_size, output_size)

def forward(self, x, hidden):
    output, hidden = self.rnn(x, hidden)
    output = self.fc(output)
    return output, hidden

定义多头注意力机制

class MultiheadAttention(nn.Module): def init(self, d_model, n_head, dropout): super(MultiheadAttention, self).init() self.d_model = d_model self.n_head = n_head

    # 三个全连接层和一个全连接层
    self.q_linear = nn.Linear(d_model, d_model)
    self.v_linear = nn.Linear(d_model, d_model)
    self.k_linear = nn.Linear(d_model, d_model)
    self.dropout = nn.Dropout(dropout)
    self.fc = nn.Linear(d_model, d_model)

def forward(self, q, k, v, mask=None):
    bs, len_q, d_model = q.size()
    bs, len_k, d_model = k.size()
    bs, len_v, d_model = v.size()
    
    # 将q、k、v分别通过全连接层进行变换，并将结果分成n_head份
    q = self.q_linear(q).view(bs, len_q, self.n_head, int(d_model/self.n_head)).transpose(1, 2)
    k = self.k_linear(k).view(bs, len_k, self.n_head, int(d_model/self.n_head)).transpose(1, 2)
    v = self.v_linear(v).view(bs, len_v, self.n_head, int(d_model/self.n_head)).transpose(1, 2)
    
    # 计算注意力分数并进行softmax操作
    scores = torch.matmul(q, k.transpose(-2, -1)) / np.sqrt(self.d_model/self.n_head)
    if mask is not None:
        mask = mask.unsqueeze(1).repeat(1, self.n_head, 1, 1)
        scores = scores.masked_fill(mask == 0, -1e9)
    scores = nn.functional.softmax(scores, dim=-1)
    scores = self.dropout(scores)
    
    # 将分数和v相乘得到输出
    output = torch.matmul(scores, v).transpose(1, 2).contiguous().view(bs, len_q, -1)
    output = self.fc(output)
    return output

定义编码器层

class EncoderLayer(nn.Module): def init(self, d_model, n_head, pf_dim, dropout): super(EncoderLayer, self).init()

    # 多头注意力机制、LayerNorm和Dropout层
    self.self_attn = MultiheadAttention(d_model, n_head, dropout)
    self.norm1 = nn.LayerNorm(d_model)
    self.dropout1 = nn.Dropout(dropout)
    
    # 全连接层和LayerNorm层
    self.fc = nn.Sequential(
        nn.Linear(d_model, pf_dim),
        nn.ReLU(),
        nn.Linear(pf_dim, d_model)
    )
    self.norm2 = nn.LayerNorm(d_model)
    self.dropout2 = nn.Dropout(dropout)

def forward(self, src, src_mask=None):
    src2 = self.self_attn(src, src, src, src_mask)
    src = src + self.dropout1(src2)
    src = self.norm1(src)
    
    src2 = self.fc(src)
    src = src + self.dropout2(src2)
    src = self.norm2(src)
    return src

定义解码器层

class DecoderLayer(nn.Module): def init(self, d_model, n_head, pf_dim, dropout): super(DecoderLayer, self).init()

    # 多头注意力机制、LayerNorm和Dropout层
    self.self_attn = MultiheadAttention(d_model, n_head, dropout)
    self.norm1 = nn.LayerNorm(d_model)
    self.dropout1 = nn.Dropout(dropout)
    
    # 多头注意力机制、LayerNorm和Dropout层
    self.src_attn = MultiheadAttention(d_model, n_head, dropout)
    self.norm2 = nn.LayerNorm(d_model)
    self.dropout2 = nn.Dropout(dropout)
    
    # 全连接层和LayerNorm层
    self.fc = nn.Sequential(
        nn.Linear(d_model, pf_dim),
        nn.ReLU(),
        nn.Linear(pf_dim, d_model)
    )
    self.norm3 = nn.LayerNorm(d_model)
    self.dropout3 = nn.Dropout(dropout)

def forward(self, tgt, src, tgt_mask=None, src_mask=None):
    tgt2 = self.self_attn(tgt, tgt, tgt, tgt_mask)
    tgt = tgt + self.dropout1(tgt2)
    tgt = self.norm1(tgt)
    
    tgt2 = self.src_attn(tgt, src, src, src_mask)
    tgt