class sklearn.gaussian_process.kernels.RationalQuadratic(length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-05, 100000.0), alpha_bounds=(1e-05, 100000.0))
[source]
Rational Quadratic kernel.
The RationalQuadratic kernel can be seen as a scale mixture (an infinite sum) of RBF kernels with different characteristic length-scales. It is parameterized by a length-scale parameter length_scale>0 and a scale mixture parameter alpha>0. Only the isotropic variant where length_scale is a scalar is supported at the moment. The kernel given by:
k(x_i, x_j) = (1 + d(x_i, x_j)^2 / (2*alpha * length_scale^2))^-alpha
New in version 0.18.
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__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. |
is_stationary () | Returns whether the kernel is stationary. |
set_params (**params) | Set the parameters of this kernel. |
__init__(length_scale=1.0, alpha=1.0, length_scale_bounds=(1e-05, 100000.0), alpha_bounds=(1e-05, 100000.0))
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__call__(X, Y=None, eval_gradient=False)
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Return the kernel k(X, Y) and optionally its gradient.
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bounds
Returns the log-transformed bounds on the theta.
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clone_with_theta(theta)
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Returns a clone of self with given hyperparameters theta.
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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.
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get_params(deep=True)
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Get parameters of this kernel.
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hyperparameters
Returns a list of all hyperparameter specifications.
is_stationary()
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Returns whether the kernel is stationary.
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.
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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.
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sklearn.gaussian_process.kernels.RationalQuadratic
© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.gaussian_process.kernels.RationalQuadratic.html