Update modeling_super_linear.py

modeling_super_linear.py

@@ -98,16 +98,8 @@ class moving_avg(nn.Module):
         super(moving_avg, self).__init__()
         self.kernel_size = kernel_size
         self.avg = nn.AvgPool1d(kernel_size=kernel_size, stride=stride, padding=0)
-    """
-    def forward(self, x):
-        # padding on the both ends of time series
-        front = x[:, 0:1, :].repeat(1, (self.kernel_size - 1) // 2, 1)
-        end = x[:, -1:, :].repeat(1, (self.kernel_size - 1) // 2, 1)
-        x = torch.cat([front, x, end], dim=1)
-        x = self.avg(x.permute(0, 2, 1))
-        x = x.permute(0, 2, 1)
-        return x
-    """
+
+    
     def forward(self, x):
         # x: [Batch, Input length]
         # padding on the both ends of time series
@@ -236,7 +228,7 @@ class RLinear(nn.Module):
     def forward(self, x):
         # x: [Batch, Input length,Channel]
         x_shape = x.shape
-        if x.shape[1] < self.seq_len:
+        ''''if x.shape[1] < self.seq_len:
             #if self.zero_shot_Linear is None:
                 #print(F"new Lookkback : {x.shape[1]}")
             
@@ -244,7 +236,7 @@ class RLinear(nn.Module):
             x = self.revin_layer(x, 'norm')
             x = F.linear(x, self.zero_shot_Linear)
             x = self.revin_layer(x, 'denorm')
-            return x
+            return x'''
 
                 
         if len(x_shape) == 2: