Subspace#

class tinygp.transforms.Subspace(axis: Union[Sequence[int], int], kernel: Kernel)[source]#

Bases: Kernel

A kernel transform that selects a subset of the input dimensions

For example, the following kernel only depends on the coordinates in the second (1-th) dimension:

>>> import numpy as np
>>> from tinygp import kernels, transforms
>>> kernel = transforms.Subspace(1, kernels.Matern32())
>>> np.testing.assert_allclose(
...     kernel.evaluate(np.array([0.5, 0.1]), np.array([-0.4, 0.7])),
...     kernel.evaluate(np.array([100.5, 0.1]), np.array([-70.4, 0.7])),
... )
Parameters:

  • axis – (Axis, optional): An integer or tuple of integers specifying the axes to select.

  • kernel (Kernel) – The kernel to use in the transformed space.

evaluate(X1: tinygp.helpers.JAXArray, X2: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray[source]#

Evaluate the kernel at a pair of input coordinates

This should be overridden be subclasses to return the kernel-specific value. Two things to note:

  1. Users shouldn’t generally call Kernel.evaluate(). Instead, always “call” the kernel instance directly; for example, you can evaluate the Matern-3/2 kernel using Matern32(1.5)(x1, x2), for arrays of input coordinates x1 and x2.

  2. When implementing a custom kernel, this method should treat X1 and X2 as single datapoints. In other words, these inputs will typically either be scalars of have shape n_dim, where n_dim is the number of input dimensions, rather than n_data or (n_data, n_dim), and you should let the Kernel vmap magic handle all the broadcasting for you.

evaluate_diag(X: tinygp.helpers.JAXArray) → tinygp.helpers.JAXArray#

Evaluate the kernel on its diagonal

The default implementation simply calls Kernel.evaluate() with X as both arguments, but subclasses can use this to make diagonal calcuations more efficient.