| 147 | return self.embed(x) |
| 148 | |
| 149 | class DataEmbedding(nn.Module): |
| 150 | def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1): |
| 151 | super(DataEmbedding, self).__init__() |
| 152 | |
| 153 | self.value_embedding = TokenEmbedding(c_in=c_in, d_model=d_model) |
| 154 | self.position_embedding = PositionalEmbedding(d_model=d_model) |
| 155 | self.temporal_embedding = TemporalEmbedding(d_model=d_model, embed_type=embed_type, |
| 156 | freq=freq) if embed_type != 'timeF' else TimeFeatureEmbedding( |
| 157 | d_model=d_model, embed_type=embed_type, freq=freq) |
| 158 | self.dropout = nn.Dropout(p=dropout) |
| 159 | |
| 160 | def forward(self, x, x_mark): |
| 161 | x = self.value_embedding(x) + self.temporal_embedding(x_mark) + self.position_embedding(x) |
| 162 | return self.dropout(x) |
| 163 | |
| 164 | class DataEmbedding_mine(nn.Module): |
| 165 | def __init__(self, c_in, d_model, embed_type='fixed', freq='h', dropout=0.1, is_decoder=False): |