class probnum.kernels.ExpQuad(input_dim, lengthscale=1.0)

Covariance function defined by $$k(x_0, x_1) = \exp \big(-\frac{\lVert x_0 - x_1 \rVert^2}{2l^2}\big)$$. This kernel is also known as the squared exponential or radial basis function kernel.

Parameters

RatQuad

Matern

Matern kernel.

Examples

>>> import numpy as np
>>> K(np.linspace(0, 1, 3)[:, None])
array([[1.00000000e+00, 3.72665317e-06, 1.92874985e-22],
[3.72665317e-06, 1.00000000e+00, 3.72665317e-06],
[1.92874985e-22, 3.72665317e-06, 1.00000000e+00]])


Attributes Summary

 input_dim Dimension of arguments of the covariance function. output_dim Dimension of the evaluated covariance function.

Methods Summary

 __call__(x0[, x1]) Evaluate the kernel.

Attributes Documentation

input_dim

Dimension of arguments of the covariance function.

The dimension of inputs to the covariance function $$k : \mathbb{R}^{ d_{in}} \times \mathbb{R}^{d_{in}} \rightarrow \mathbb{R}^{d_{out} \times d_{out}}$$.

Return type

int

output_dim

Dimension of the evaluated covariance function.

The resulting evaluated kernel $$k(x_0, x_1) \in \mathbb{R}^{d_{out} \times d_{out}}$$ has shape=(output_dim, output_dim).

Return type

int

Methods Documentation

__call__(x0, x1=None)[source]

Evaluate the kernel.

Computes the covariance function at x0 and x1. If the inputs have more than one dimension the covariance function is evaluated pairwise for all observations determined by the first dimension of x0 and x1. If only x0 is given the kernel matrix $$K=k(X_0, X_0)$$ is computed.

Parameters
Returns

shape=(), (output_dim, output_dim) or (n0, n1) or (n0, n1, output_dim, output_dim) – Kernel evaluated at x0 and x1 or kernel matrix containing pairwise evaluations for all observations in x0 (and x1).

Return type

cov