pwtools.rbf.core.Rbf.deriv¶
- Rbf.deriv(points)[source]¶
Analytic first partial derivatives.
Analytic reference implementation of
jax
grad
for 1d input orvmap(grad)
for 2d input.>>> x.shape (n,) >>> grad(f)(x).shape (n,)
>>> X.shape (m,n) >>> vmap(grad(self))(X).shape (m,n)
- Parameters:
points (2d array (L,N) or (N,))
- Returns:
Each row holds the gradient vector \(\partial f/\partial\mathbf x_i\) where \(\mathbf x_i = \texttt{points[i,:] = [xi_0, ..., xi_N-1]}\). For all points points (L,N) we get the matrix:
[[df/dx0_0, df/dx0_1, ..., df/dx0_N-1], [...], [df/dxL-1_0, df/dxL-1_1, ..., df/dxL-1_N-1]]
- Return type:
2d array (L,N) or (N,)
See also