More simplification, fix sign error

high_order_layers_torch/Basis.py

@@ -443,9 +443,9 @@ def interpolate(self, x: Tensor, w: Tensor) -> Tensor:
         return out_prod
 
 
-class Basis:  # Should this be a module? No stored data.
-    def __init__(self, n: int, basis: Callable[[Tensor, int], Tensor]):
-        super().__init__()
+class Basis:
+    # TODO: Is this the same as above? No! It is not!
+    def __init__(self, n: int, basis: Callable[[Tensor, int], float]):
         self.n = n
         self.basis = basis
 
@@ -461,28 +461,15 @@ def interpolate(self, x: Tensor, w: Tensor) -> Tensor:
         Returns:
             - result: size[batch, output]
         """
-        batch_size, input_size = x.shape
-        _, _, output_size, basis_size = w.shape
-
-        # Create a tensor to hold the basis functions, shape: [batch, input, n]
-        basis_functions = torch.empty((batch_size, input_size, self.n), device=x.device)
-
-        # Calculate all basis functions for each j in parallel
+        mat = []
         for j in range(self.n):
-            basis_functions[:, :, j] = self.basis(x, j)
-
-        # Reshape basis functions to match the required shape for einsum
-        # basis_functions: [batch, input, n] -> [n, batch, input]
-        basis_functions = basis_functions.permute(2, 0, 1)
+            basis_j = self.basis(x, j)
+            mat.append(basis_j)
+        mat = torch.stack(mat)
 
-        # Perform the einsum operation to calculate the result
-        # einsum equation explanation:
-        # - 'ijk' corresponds to basis_functions with shape [n, batch, input]
-        # - 'jkli' corresponds to w with shape [batch, input, output, basis]
-        # - 'jl' corresponds to the output with shape [batch, output]
-        result = torch.einsum("ijk,jkli->jl", basis_functions, w)
+        out_sum = torch.einsum("ijk,jkli->jl", mat, w)
 
-        return result
+        return out_sum
 
 
 class BasisProd:

high_order_layers_torch/LagrangePolynomial.py

@@ -20,7 +20,7 @@ def chebyshevLobatto(n: int):
     if n == 1:
         return torch.tensor([0.0])
 
-    return torch.cos(torch.pi * torch.arange(n) / (n - 1))
+    return -torch.cos(torch.pi * torch.arange(n) / (n - 1))
 
 
 class FourierBasis: