sklearn.gaussian_process.kernels
.CompoundKernel¶
- class sklearn.gaussian_process.kernels.CompoundKernel(kernels)[source]¶
Kernel which is composed of a set of other kernels.
New in version 0.18.
- Parameters
- kernelslist of Kernels
The other kernels
- 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 discrete structures.
theta
Returns the (flattened, log-transformed) non-fixed hyperparameters.
Examples
>>> from sklearn.gaussian_process.kernels import WhiteKernel >>> from sklearn.gaussian_process.kernels import RBF >>> from sklearn.gaussian_process.kernels import CompoundKernel >>> kernel = CompoundKernel( ... [WhiteKernel(noise_level=3.0), RBF(length_scale=2.0)]) >>> print(kernel.bounds) [[-11.51292546 11.51292546] [-11.51292546 11.51292546]] >>> print(kernel.n_dims) 2 >>> print(kernel.theta) [1.09861229 0.69314718]
Methods
__call__
(X[, Y, eval_gradient])Return the kernel k(X, Y) and optionally its gradient.
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.
Returns whether the kernel is stationary.
set_params
(**params)Set the parameters of this kernel.
- __call__(X, Y=None, eval_gradient=False)[source]¶
Return the kernel k(X, Y) and optionally its gradient.
Note that this compound kernel returns the results of all simple kernel stacked along an additional axis.
- Parameters
- Xarray-like of shape (n_samples_X, n_features) or list of object, default=None
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.
- Returns
- Kndarray of shape (n_samples_X, n_samples_Y, n_kernels)
Kernel k(X, Y)
- K_gradientndarray of shape (n_samples_X, n_samples_X, n_dims, n_kernels), 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
- boundsarray 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
- 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_X, n_features) or list of object
Argument to the kernel.
- Returns
- K_diagndarray of shape (n_samples_X, n_kernels)
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.
- property n_dims¶
Returns the number of non-fixed hyperparameters of the kernel.
- property requires_vector_input¶
Returns whether the kernel is defined on discrete structures.
- 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