Conditioned#
- class tinygp.kernels.Conditioned(X: JAXArray, solver: Solver, kernel: Kernel)[source]#
Bases:
Kernel
A kernel used when conditioning a process on data
- Parameters:
X – The coordinates of the data.
scale_tril – The lower Cholesky factor of the base process’ kernel matrix.
kernel – The predictive kerenl; this will generally be the kernel from the kernel used by the original process.
- 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:
Users shouldn’t generally call
Kernel.evaluate()
. Instead, always “call” the kernel instance directly; for example, you can evaluate the Matern-3/2 kernel usingMatern32(1.5)(x1, x2)
, for arrays of input coordinatesx1
andx2
.When implementing a custom kernel, this method should treat
X1
andX2
as single datapoints. In other words, these inputs will typically either be scalars of have shapen_dim
, wheren_dim
is the number of input dimensions, rather thann_data
or(n_data, n_dim)
, and you should let theKernel
vmap
magic handle all the broadcasting for you.
- evaluate_diag(X: tinygp.helpers.JAXArray) tinygp.helpers.JAXArray [source]#
Evaluate the kernel on its diagonal
The default implementation simply calls
Kernel.evaluate()
withX
as both arguments, but subclasses can use this to make diagonal calcuations more efficient.