WhiteKernel (original) (raw)
class sklearn.gaussian_process.kernels.WhiteKernel(noise_level=1.0, noise_level_bounds=(1e-05, 100000.0))[source]#
White kernel.
The main use-case of this kernel is as part of a sum-kernel where it explains the noise of the signal as independently and identically normally-distributed. The parameter noise_level equals the variance of this noise.
\[k(x_1, x_2) = noise\_level \text{ if } x_i == x_j \text{ else } 0\]
Read more in the User Guide.
Added in version 0.18.
Parameters:
noise_levelfloat, default=1.0
Parameter controlling the noise level (variance)
noise_level_boundspair of floats >= 0 or “fixed”, default=(1e-5, 1e5)
The lower and upper bound on ‘noise_level’. If set to “fixed”, ‘noise_level’ cannot be changed during hyperparameter tuning.
Examples
from sklearn.datasets import make_friedman2 from sklearn.gaussian_process import GaussianProcessRegressor from sklearn.gaussian_process.kernels import DotProduct, WhiteKernel X, y = make_friedman2(n_samples=500, noise=0, random_state=0) kernel = DotProduct() + WhiteKernel(noise_level=0.5) gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) gpr.score(X, y) 0.3680... gpr.predict(X[:2,:], return_std=True) (array([653.0..., 592.1... ]), array([316.6..., 316.6...]))
__call__(X, Y=None, eval_gradient=False)[source]#
Return the kernel k(X, Y) and optionally its gradient.
Parameters:
Xarray-like of shape (n_samples_X, n_features) or list of object
Left argument of the returned kernel k(X, Y)
Yarray-like of shape (n_samples_X, n_features) or list of object, default=None
Right argument of the returned kernel k(X, Y). If None, k(X, X) is evaluated instead.
eval_gradientbool, default=False
Determines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is None.
Returns:
Kndarray of shape (n_samples_X, n_samples_Y)
Kernel k(X, Y)
K_gradientndarray of shape (n_samples_X, n_samples_X, n_dims), optional
The gradient of the kernel k(X, X) with respect to the log of the hyperparameter of the kernel. Only returned when eval_gradient is True.
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
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_X, n_features) or list of object
Argument to the kernel.
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.
Returns whether the kernel is stationary.
property n_dims#
Returns the number of non-fixed hyperparameters of the kernel.
property requires_vector_input#
Whether the kernel works only on fixed-length feature vectors.
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