`sklearn.gaussian_process.kernels`.Kernel#

class sklearn.gaussian_process.kernels.Kernel[source]#

Base class for all kernels.

New in version 0.18.

Attributes:

bounds: Returns the log-transformed bounds on the theta.
hyperparameters: Returns a list of all hyperparameter specifications.
n_dims: Returns the number of non-fixed hyperparameters of the kernel.
requires_vector_input: Returns whether the kernel is defined on fixed-length feature vectors or generic objects.
theta: Returns the (flattened, log-transformed) non-fixed hyperparameters.

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]]

Methods

`__call__`(X[, Y, eval_gradient])	Evaluate the kernel.
`clone_with_theta`(theta)	Returns a clone of self with given hyperparameters theta.
`diag`(X)	Returns the diagonal of the kernel k(X, X).
`get_params`([deep])	Get parameters of this kernel.
`is_stationary`()	Returns whether the kernel is stationary.
`set_params`(**params)	Set the parameters of this kernel.

abstract __call__(X, Y=None, eval_gradient=False)[source]#: Evaluate the kernel.

property bounds#

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

abstract diag(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.

property hyperparameters#: Returns a list of all hyperparameter specifications.

abstract is_stationary()[source]#: Returns whether the kernel is stationary.

property n_dims#: Returns the number of non-fixed hyperparameters of the kernel.

property requires_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

property theta#

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

Examples using `sklearn.gaussian_process.kernels.Kernel`#

Gaussian processes on discrete data structures

sklearn.gaussian_process.kernels.Kernel#

Examples using sklearn.gaussian_process.kernels.Kernel#

`sklearn.gaussian_process.kernels`.Kernel#

Examples using `sklearn.gaussian_process.kernels.Kernel`#