sklearn.gaussian_process.kernels.Kernel¶
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class
sklearn.gaussian_process.kernels.Kernel[source]¶ Base class for all kernels.
New in version 0.18.
Methods
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. -
__init__()¶ x.__init__(...) initializes x; see help(type(x)) for signature
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bounds¶ Returns the log-transformed bounds on the theta.
Returns: bounds : array, shape (n_dims, 2)
The log-transformed bounds on the kernel’s hyperparameters theta
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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: X : array, shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Returns: K_diag : array, shape (n_samples_X,)
Diagonal of kernel k(X, X)
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get_params(deep=True)[source]¶ Get parameters of this kernel.
Parameters: deep: boolean, optional :
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params : mapping of string to any
Parameter names mapped to their values.
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hyperparameters¶ Returns a list of all hyperparameter specifications.
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n_dims¶ Returns the number of non-fixed hyperparameters of the kernel.
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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 :
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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: theta : array, shape (n_dims,)
The non-fixed, log-transformed hyperparameters of the kernel
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