247 lines
9.3 KiB
Python
247 lines
9.3 KiB
Python
import numpy as np
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# import pywt
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.fft
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from layers.Embed import DataEmbedding
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from layers.MSGBlock import GraphBlock, simpleVIT, Attention_Block, Predict
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def FFT_for_Period(x, k=2):
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# [B, T, C]
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xf = torch.fft.rfft(x, dim=1)
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frequency_list = abs(xf).mean(0).mean(-1)
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frequency_list[0] = 0
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_, top_list = torch.topk(frequency_list, k)
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top_list = top_list.detach().cpu().numpy()
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period = x.shape[1] // top_list
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return period, abs(xf).mean(-1)[:, top_list]
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class ScaleGraphBlock(nn.Module):
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def __init__(self, configs):
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super(ScaleGraphBlock, self).__init__()
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self.seq_len = configs.seq_len
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self.pred_len = configs.pred_len
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self.k = configs.top_k
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self.att0 = Attention_Block(configs.d_model, configs.d_ff,
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n_heads=configs.n_heads, dropout=configs.dropout, activation="gelu")
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self.norm = nn.LayerNorm(configs.d_model)
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self.gelu = nn.GELU()
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self.gconv = nn.ModuleList()
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for i in range(self.k):
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self.gconv.append(
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GraphBlock(configs.c_out , configs.d_model , configs.conv_channel, configs.skip_channel,
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configs.gcn_depth , configs.dropout, configs.propalpha ,configs.seq_len,
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configs.node_dim))
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def forward(self, x):
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B, T, N = x.size()
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scale_list, scale_weight = FFT_for_Period(x, self.k)
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res = []
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for i in range(self.k):
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scale = scale_list[i]
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#Gconv
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x = self.gconv[i](x)
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# paddng
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if (self.seq_len) % scale != 0:
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length = (((self.seq_len) // scale) + 1) * scale
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padding = torch.zeros([x.shape[0], (length - (self.seq_len)), x.shape[2]]).to(x.device)
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out = torch.cat([x, padding], dim=1)
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else:
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length = self.seq_len
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out = x
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out = out.reshape(B, length // scale, scale, N)
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#for Mul-attetion
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out = out.reshape(-1 , scale , N)
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out = self.norm(self.att0(out))
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out = self.gelu(out)
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out = out.reshape(B, -1 , scale , N).reshape(B ,-1 ,N)
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# #for simpleVIT
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# out = self.att(out.permute(0, 3, 1, 2).contiguous()) #return
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# out = out.permute(0, 2, 3, 1).reshape(B, -1 ,N)
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out = out[:, :self.seq_len, :]
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res.append(out)
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res = torch.stack(res, dim=-1)
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# adaptive aggregation
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scale_weight = F.softmax(scale_weight, dim=1)
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scale_weight = scale_weight.unsqueeze(1).unsqueeze(1).repeat(1, T, N, 1)
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res = torch.sum(res * scale_weight, -1)
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# residual connection
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res = res + x
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return res
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class Model(nn.Module):
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def __init__(self, configs):
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super(Model, self).__init__()
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self.configs = configs
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self.task_name = configs.task_name
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self.seq_len = configs.seq_len
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self.label_len = configs.label_len
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self.pred_len = configs.pred_len
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self.device = "cuda" if torch.cuda.is_available() else "cpu"
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# for graph
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# self.num_nodes = configs.c_out
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# self.subgraph_size = configs.subgraph_size
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# self.node_dim = configs.node_dim
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# to return adj (node , node)
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# self.graph = constructor_graph()
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self.model = nn.ModuleList([ScaleGraphBlock(configs) for _ in range(configs.e_layers)])
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self.enc_embedding = DataEmbedding(configs.enc_in, configs.d_model,
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configs.embed, configs.freq, configs.dropout)
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self.layer = configs.e_layers
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self.layer_norm = nn.LayerNorm(configs.d_model)
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self.predict_linear = nn.Linear(
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self.seq_len, self.pred_len + self.seq_len)
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self.projection = nn.Linear(configs.d_model, configs.c_out, bias=True)
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self.seq2pred = Predict(configs.individual, configs.c_out,
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configs.seq_len, configs.pred_len, configs.dropout)
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if self.task_name == 'imputation' or self.task_name == 'anomaly_detection':
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self.projection = nn.Linear(configs.d_model, configs.c_out, bias=True)
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if self.task_name == 'classification':
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self.act = F.gelu
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self.dropout = nn.Dropout(configs.dropout)
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self.projection = nn.Linear(
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configs.d_model * configs.seq_len, configs.num_class)
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def forecast(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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# embedding
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enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C]
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# adp = self.graph(torch.arange(self.num_nodes).to(self.device))
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for i in range(self.layer):
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enc_out = self.layer_norm(self.model[i](enc_out))
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# porject back
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dec_out = self.projection(enc_out)
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dec_out = self.seq2pred(dec_out.transpose(1, 2)).transpose(1, 2)
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(
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1, self.pred_len, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(
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1, self.pred_len, 1))
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return dec_out[:, -self.pred_len:, :]
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def imputation(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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_, L, N = x_enc.shape
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# embedding
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enc_out = self.enc_embedding(x_enc, x_mark_enc) # [B,T,C]
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# adp = self.graph(torch.arange(self.num_nodes).to(self.device))
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for i in range(self.layer):
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enc_out = self.layer_norm(self.model[i](enc_out))
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# porject back
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dec_out = self.projection(enc_out)
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# dec_out = self.seq2pred(dec_out.transpose(1, 2)).transpose(1, 2)
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# print(dec_out.shape)
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(
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1, L, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(
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1, L, 1))
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return dec_out
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def anomaly_detection(self, x_enc):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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_, L, N = x_enc.shape
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# embedding
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enc_out = self.enc_embedding(x_enc, None) # [B,T,C]
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# adp = self.graph(torch.arange(self.num_nodes).to(self.device))
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for i in range(self.layer):
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enc_out = self.layer_norm(self.model[i](enc_out))
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# porject back
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dec_out = self.projection(enc_out)
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# dec_out = self.seq2pred(dec_out.transpose(1, 2)).transpose(1, 2)
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# print(dec_out.shape)
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# De-Normalization from Non-stationary Transformer
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dec_out = dec_out * \
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(stdev[:, 0, :].unsqueeze(1).repeat(
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1, L, 1))
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dec_out = dec_out + \
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(means[:, 0, :].unsqueeze(1).repeat(
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1, L, 1))
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return dec_out
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def classification(self, x_enc, x_mark_enc):
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# Normalization from Non-stationary Transformer
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means = x_enc.mean(1, keepdim=True).detach()
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x_enc = x_enc - means
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stdev = torch.sqrt(
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torch.var(x_enc, dim=1, keepdim=True, unbiased=False) + 1e-5)
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x_enc /= stdev
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# embedding
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enc_out = self.enc_embedding(x_enc, None) # [B,T,C]
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# adp = self.graph(torch.arange(self.num_nodes).to(self.device))
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for i in range(self.layer):
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enc_out = self.layer_norm(self.model[i](enc_out))
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output = self.act(enc_out)
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output = self.dropout(output)
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# zero-out padding embeddings
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output = output * x_mark_enc.unsqueeze(-1)
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# (batch_size, seq_length * d_model)
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output = output.reshape(output.shape[0], -1)
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output = self.projection(output) # (batch_size, num_classes)
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return output
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def forward(self, x_enc, x_mark_enc, x_dec, x_mark_dec, mask=None):
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if self.task_name == 'long_term_forecast' or self.task_name == 'short_term_forecast':
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dec_out = self.forecast(x_enc, x_mark_enc, x_dec, x_mark_dec)
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return dec_out[:, -self.pred_len:, :] # [B, L, D]
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if self.task_name == 'imputation':
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dec_out = self.imputation(
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x_enc, x_mark_enc, x_dec, x_mark_dec, mask)
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return dec_out # [B, L, D]
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if self.task_name == 'anomaly_detection':
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dec_out = self.anomaly_detection(x_enc)
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return dec_out # [B, L, D]
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if self.task_name == 'classification':
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dec_out = self.classification(x_enc, x_mark_enc)
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return dec_out # [B, N]
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return None
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