(self, d_model, embed_type='timeF', freq='h')
| 130 | |
| 131 | class TimeFeatureEmbedding_new(nn.Module): |
| 132 | def __init__(self, d_model, embed_type='timeF', freq='h'): |
| 133 | super(TimeFeatureEmbedding_new, self).__init__() |
| 134 | |
| 135 | freq_map = {'h': 4, 't': 5, 's': 6, 'm': 1, 'a': 1, 'w': 2, 'd': 3, 'b': 3} |
| 136 | d_inp = freq_map[freq] |
| 137 | self.embed = nn.Linear(d_inp+1, d_model, bias=False) |
| 138 | self.concats = dict() |
| 139 | |
| 140 | def forward(self, x, scale=1): |
| 141 | if (scale, x.shape[0], x.shape[1]) not in self.concats: |