sklearn.gaussian_process.kernels
.Sum¶
-
class
sklearn.gaussian_process.kernels.
Sum
(k1, k2)[source]¶ The
Sum
kernel takes two kernels \(k_1\) and \(k_2\) and combines them via\[k_{sum}(X, Y) = k_1(X, Y) + k_2(X, Y)\]Note that the
__add__
magic method is overridden, soSum(RBF(), RBF())
is equivalent to using the + operator withRBF() + RBF()
.Read more in the User Guide.
New in version 0.18.
- Parameters
- k1Kernel
The first base-kernel of the sum-kernel
- k2Kernel
The second base-kernel of the sum-kernel
- Attributes
bounds
Returns the log-transformed bounds on the theta.
hyperparameters
Returns a list of all hyperparameter.
n_dims
Returns the number of non-fixed hyperparameters of the kernel.
requires_vector_input
Returns whether the kernel is stationary.
theta
Returns the (flattened, log-transformed) non-fixed hyperparameters.
Examples
>>> from sklearn.datasets import make_friedman2 >>> from sklearn.gaussian_process import GaussianProcessRegressor >>> from sklearn.gaussian_process.kernels import RBF, Sum, ConstantKernel >>> X, y = make_friedman2(n_samples=500, noise=0, random_state=0) >>> kernel = Sum(ConstantKernel(2), RBF()) >>> gpr = GaussianProcessRegressor(kernel=kernel, ... random_state=0).fit(X, y) >>> gpr.score(X, y) 1.0 >>> kernel 1.41**2 + RBF(length_scale=1)
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.
- 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.
- 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
-
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,)
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.
-
property
n_dims
¶ Returns the number of non-fixed hyperparameters of the kernel.
-
property
requires_vector_input
¶ Returns whether the kernel is stationary.
-
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