sklearn.gaussian_process.kernels.Kernel
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class sklearn.gaussian_process.kernels.Kernel
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Base class for all kernels.
New in version 0.18.
- Attributes
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bounds
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Returns the log-transformed bounds on the theta.
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hyperparameters
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Returns a list of all hyperparameter specifications.
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n_dims
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Returns the number of non-fixed hyperparameters of the kernel.
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requires_vector_input
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Returns whether the kernel is defined on fixed-length feature vectors or generic objects.
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theta
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Returns the (flattened, log-transformed) non-fixed hyperparameters.
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Methods
__call__
(X[, Y, eval_gradient])Evaluate the kernel.
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.
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abstract __call__(X, Y=None, eval_gradient=False)
[source] -
Evaluate the kernel.
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property bounds
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Returns the log-transformed bounds on the theta.
- Returns
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boundsndarray of shape (n_dims, 2)
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The log-transformed bounds on the kernel’s hyperparameters theta
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clone_with_theta(theta)
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Returns a clone of self with given hyperparameters theta.
- Parameters
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thetandarray of shape (n_dims,)
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The hyperparameters
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abstract diag(X)
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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
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Xarray-like of shape (n_samples,)
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Left argument of the returned kernel k(X, Y)
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- Returns
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K_diagndarray of shape (n_samples_X,)
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Diagonal of kernel k(X, X)
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get_params(deep=True)
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Get parameters of this kernel.
- Parameters
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deepbool, default=True
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If True, will return the parameters for this estimator and contained subobjects that are estimators.
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- Returns
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paramsdict
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Parameter names mapped to their values.
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property hyperparameters
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Returns a list of all hyperparameter specifications.
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abstract is_stationary()
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Returns whether the kernel is stationary.
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property n_dims
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Returns the number of non-fixed hyperparameters of the kernel.
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property requires_vector_input
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Returns whether the kernel is defined on fixed-length feature vectors or generic objects. Defaults to True for backward compatibility.
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set_params(**params)
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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
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- self
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property theta
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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
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thetandarray of shape (n_dims,)
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The non-fixed, log-transformed hyperparameters of the kernel
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Examples using sklearn.gaussian_process.kernels.Kernel
© 2007–2020 The scikit-learn developers
Licensed under the 3-clause BSD License.
https://scikit-learn.org/0.24/modules/generated/sklearn.gaussian_process.kernels.Kernel.html