Icm
ICMKernel
Bases: MultiOutputKernel
Intrinsic Coregionalization Model kernel.
Combines a single shared input kernel with a coregionalization matrix to produce a Kronecker-structured covariance: K = B (x) K_input.
Parameters:
-
base_kernel(AbstractKernel) โThe shared input-space kernel.
-
coregionalization_matrix(CoregionalizationMatrix) โThe output-space coregionalization.
cross_covariance
Cross-covariance for multi-output kernels.
Returns shape [NP, MP] where P is num_outputs โ overrides the single-output [N, M] annotation.
gram
Compute the gram matrix of the kernel.
Parameters:
-
x(Num[Array, 'N D']) โthe input matrix of shape
(N, D).
Returns:
-
LinearOperatorโThe gram matrix of the kernel of shape
(N, N).
diagonal
Compute the diagonal of the gram matrix of the kernel.
Parameters:
-
x(Num[Array, 'N D']) โthe input matrix of shape
(N, D).
Returns:
-
LinearOperatorโThe diagonal of the gram matrix of the kernel of shape
(N,).
slice_input
Slice out the relevant columns of the input matrix.
Select the relevant columns of the supplied matrix to be used within the kernel's evaluation.
Parameters:
-
x(Float[Array, '... D']) โthe matrix or vector that is to be sliced.
Returns:
-
Float[Array, '... Q']โThe sliced form of the input matrix.
__add__
Add two kernels together. Args: other (AbstractKernel): The kernel to be added to the current kernel.
Returns:
-
AbstractKernel(AbstractKernel) โA new kernel that is the sum of the two kernels.
__mul__
Multiply two kernels together.
Parameters:
-
other(AbstractKernel) โThe kernel to be multiplied with the current kernel.
Returns:
-
AbstractKernel(AbstractKernel) โA new kernel that is the product of the two kernels.