sklearn.gaussian_process.kernels.Product¶
- class sklearn.gaussian_process.kernels.Product(k1, k2)[source]¶
Product-kernel k1 * k2 of two kernels k1 and k2.
The resulting kernel is defined as k_prod(X, Y) = k1(X, Y) * k2(X, Y)
New in version 0.18.
Parameters: k1 : Kernel object
The first base-kernel of the product-kernel
k2 : Kernel object
The second base-kernel of the product-kernel
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. - __call__(X, Y=None, eval_gradient=False)[source]¶
Return the kernel k(X, Y) and optionally its gradient.
Parameters: X : array, shape (n_samples_X, n_features)
Left argument of the returned kernel k(X, Y)
Y : array, shape (n_samples_Y, n_features), (optional, default=None)
Right argument of the returned kernel k(X, Y). If None, k(X, X) if evaluated instead.
eval_gradient : bool (optional, default=False)
Determines whether the gradient with respect to the kernel hyperparameter is determined.
Returns: K : array, shape (n_samples_X, n_samples_Y)
Kernel k(X, Y)
K_gradient : array (opt.), shape (n_samples_X, n_samples_X, n_dims)
The gradient of the kernel k(X, X) with respect to the hyperparameter of the kernel. Only returned when eval_gradient is True.
- 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
- 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)
- 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.
- hyperparameters¶
Returns a list of all hyperparameter.
- n_dims¶
Returns the number of non-fixed hyperparameters of the kernel.
- 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 :
- 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