gpflow.kernels.base

Kernels form a core component of GPflow models and allow prior information to be encoded about a latent function of interest. The effect of choosing different kernels, and how it is possible to combine multiple kernels is shown in the “Using kernels in GPflow” notebook.

Broadcasting over leading dimensions: kernel.K(X1, X2) returns the kernel evaluated on every pair in X1 and X2. E.g. if X1 has shape [S1, N1, D] and X2 has shape [S2, N2, D], kernel.K(X1, X2) will return a tensor of shape [S1, N1, S2, N2]. Similarly, kernel.K(X1, X1) returns a tensor of shape [S1, N1, S1, N1]. In contrast, the return shape of kernel.K(X1) is [S1, N1, N1]. (Without leading dimensions, the behaviour of kernel.K(X, None) is identical to kernel.K(X, X).)

Classes

gpflow.kernels.base.ReducingCombination

class gpflow.kernels.base.ReducingCombination(kernels, name=None)

Bases: Combination

Parameters:

• kernels (Sequence[Kernel]) –

• name (Optional[str]) –