sklearn.gaussian_process.kernels.Kernel — scikit-learn 0.20.4 documentation (original) (raw)
class sklearn.gaussian_process.kernels. Kernel[source]¶
Base class for all kernels.
New in version 0.18.
| 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. theta Returns the (flattened, log-transformed) non-fixed hyperparameters. |
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Methods
| __call__(X[, Y, eval_gradient]) | Evaluate the kernel. |
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| 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__($self, /, *args, **kwargs)¶
Initialize self. See help(type(self)) for accurate signature.
__call__(X, Y=None, eval_gradient=False)[source]¶
Evaluate the kernel.
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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clone_with_theta(theta)[source]¶
Returns a clone of self with given hyperparameters theta.
| Parameters: | theta : array, shape (n_dims,) The hyperparameters |
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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: | X : array, shape (n_samples_X, n_features) Left argument of the returned kernel k(X, Y) |
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| 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. |
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| Returns: | params : mapping of string to any Parameter names mapped to their values. |
hyperparameters¶
Returns a list of all hyperparameter specifications.
Returns whether the kernel is stationary.
n_dims¶
Returns the number of non-fixed hyperparameters of the kernel.
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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