Kernel

classsklearn.gaussian_process.kernels.Kernel[source]

Base class for all kernels.

Added in version 0.18.

Examples

>>> from sklearn.gaussian_process.kernels import Kernel, RBF
>>> import numpy as np
>>> class CustomKernel(Kernel):
...     def __init__(self, length_scale=1.0):
...         self.length_scale = length_scale
...     def __call__(self, X, Y=None):
...         if Y is None:
...             Y = X
...         return np.inner(X, X if Y is None else Y) ** 2
...     def diag(self, X):
...         return np.ones(X.shape[0])
...     def is_stationary(self):
...         return True
>>> kernel = CustomKernel(length_scale=2.0)
>>> X = np.array([[1, 2], [3, 4]])
>>> print(kernel(X))
[[ 25 121]
 [121 625]]

abstract__call__(X, Y=None, eval_gradient=False)[source]

Evaluate the kernel.

propertybounds

Returns the log-transformed bounds on the theta.

Returns:

boundsndarray of shape (n_dims, 2)

The log-transformed bounds on the kernel’s hyperparameters theta

clone_with_theta(theta)[source]

Returns a clone of self with given hyperparameters theta.

Parameters:

thetandarray of shape (n_dims,)

The hyperparameters

abstractdiag(X)[source]

Returns the diagonal of the kernel k(X, X).

The result of this method is identical to np.diag(self(X)); however, it can be evaluated more efficiently since only the diagonal is evaluated.

Parameters:

Xarray-like of shape (n_samples,)

Left argument of the returned kernel k(X, Y)

Returns:

K_diagndarray of shape (n_samples_X,)

Diagonal of kernel k(X, X)

get_params(deep=True)[source]

Get parameters of this kernel.

Parameters:

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:

paramsdict

Parameter names mapped to their values.

propertyhyperparameters

Returns a list of all hyperparameter specifications.

abstractis_stationary()[source]

Returns whether the kernel is stationary.

propertyn_dims

Returns the number of non-fixed hyperparameters of the kernel.

propertyrequires_vector_input

Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility.

set_params(**params)[source]

Set the parameters of this kernel.

The method works on simple kernels as well as on nested kernels. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Returns:

self

propertytheta

Returns the (flattened, log-transformed) non-fixed hyperparameters.

Note that theta are typically the log-transformed values of the kernel’s hyperparameters as this representation of the search space is more amenable for hyperparameter search, as hyperparameters like length-scales naturally live on a log-scale.

Returns:

thetandarray of shape (n_dims,)

The non-fixed, log-transformed hyperparameters of the kernel

Gallery examples

Gaussian processes on discrete data structures