def evaluate_lazy_tensor(self, lazy_tensor):
     constant = lazy_tensor.expanded_constant
     column = lazy_tensor.base_lazy_tensor.column
     return torch.cat([
         sym_toeplitz(column[0]).unsqueeze(0),
         sym_toeplitz(column[1]).unsqueeze(0)
     ]) * constant.view(2, 1, 1)
Example #2
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 def evaluate_lazy_tensor(self, lazy_tensor):
     return torch.cat(
         [
             toeplitz.sym_toeplitz(lazy_tensor.column[0]).unsqueeze(0),
             toeplitz.sym_toeplitz(lazy_tensor.column[1]).unsqueeze(0),
         ]
     )
Example #3
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 def evaluate_lazy_tensor(self, lazy_tensor):
     return torch.cat([
         toeplitz.sym_toeplitz(lazy_tensor.column[0, 0]).unsqueeze(0),
         toeplitz.sym_toeplitz(lazy_tensor.column[0, 1]).unsqueeze(0),
         toeplitz.sym_toeplitz(lazy_tensor.column[1, 0]).unsqueeze(0),
         toeplitz.sym_toeplitz(lazy_tensor.column[1, 1]).unsqueeze(0),
         toeplitz.sym_toeplitz(lazy_tensor.column[2, 0]).unsqueeze(0),
         toeplitz.sym_toeplitz(lazy_tensor.column[2, 1]).unsqueeze(0),
     ]).view(3, 2, 4, 4)
 def evaluate_lazy_tensor(self, lazy_tensor):
     constant = lazy_tensor.expanded_constant
     column = lazy_tensor.base_lazy_tensor.column
     return sym_toeplitz(column) * constant
Example #5
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 def evaluate_lazy_tensor(self, lazy_tensor):
     return toeplitz.sym_toeplitz(lazy_tensor.column)