Class: RBF

Radial basis function kernel (aka squared-exponential kernel).

The RBF kernel is a stationary kernel. It is also known as the “squared exponential” kernel. It is parameterized by a length scale parameter \(l>0\), which can either be a scalar (isotropic variant of the kernel) or a vector with the same number of dimensions as the inputs X (anisotropic variant of the kernel). The kernel is given by:

Python Reference

Constructors

new RBF()

new RBF(opts?): RBF

Parameters

ParameterTypeDescription
opts?object-
opts.length_scale?number | ArrayLikeThe length scale of the kernel. If a float, an isotropic kernel is used. If an array, an anisotropic kernel is used where each dimension of l defines the length-scale of the respective feature dimension.
opts.length_scale_bounds?"fixed"The lower and upper bound on ‘length_scale’. If set to “fixed”, ‘length_scale’ cannot be changed during hyperparameter tuning.

Returns RBF

Defined in generated/gaussian_process/kernels/RBF.ts:23

Properties

PropertyTypeDefault valueDefined in
_isDisposedbooleanfalsegenerated/gaussian_process/kernels/RBF.ts:21
_isInitializedbooleanfalsegenerated/gaussian_process/kernels/RBF.ts:20
_pyPythonBridgeundefinedgenerated/gaussian_process/kernels/RBF.ts:19
idstringundefinedgenerated/gaussian_process/kernels/RBF.ts:16
optsanyundefinedgenerated/gaussian_process/kernels/RBF.ts:17

Accessors

py

Get Signature

get py(): PythonBridge

Returns PythonBridge

Set Signature

set py(pythonBridge): void

Parameters

ParameterType
pythonBridgePythonBridge

Returns void

Defined in generated/gaussian_process/kernels/RBF.ts:40

Methods

__call__()

__call__(opts): Promise<ArrayLike[]>

Return the kernel k(X, Y) and optionally its gradient.

Parameters

ParameterTypeDescription
optsobject-
opts.eval_gradient?booleanDetermines whether the gradient with respect to the log of the kernel hyperparameter is computed. Only supported when Y is undefined.
opts.X?ArrayLike[]Left argument of the returned kernel k(X, Y)
opts.Y?ArrayLike[]Right argument of the returned kernel k(X, Y). If undefined, k(X, X) if evaluated instead.

Returns Promise<ArrayLike[]>

Defined in generated/gaussian_process/kernels/RBF.ts:108


clone_with_theta()

clone_with_theta(opts): Promise<any>

Returns a clone of self with given hyperparameters theta.

Parameters

ParameterTypeDescription
optsobject-
opts.theta?ArrayLikeThe hyperparameters

Returns Promise<any>

Defined in generated/gaussian_process/kernels/RBF.ts:152


diag()

diag(opts): Promise<ArrayLike>

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

ParameterTypeDescription
optsobject-
opts.X?ArrayLike[]Left argument of the returned kernel k(X, Y)

Returns Promise<ArrayLike>

Defined in generated/gaussian_process/kernels/RBF.ts:186


dispose()

dispose(): Promise<void>

Disposes of the underlying Python resources.

Once dispose() is called, the instance is no longer usable.

Returns Promise<void>

Defined in generated/gaussian_process/kernels/RBF.ts:91


init()

init(py): Promise<void>

Initializes the underlying Python resources.

This instance is not usable until the Promise returned by init() resolves.

Parameters

ParameterType
pyPythonBridge

Returns Promise<void>

Defined in generated/gaussian_process/kernels/RBF.ts:53


is_stationary()

is_stationary(opts): Promise<any>

Returns whether the kernel is stationary.

Parameters

ParameterType
optsobject

Returns Promise<any>

Defined in generated/gaussian_process/kernels/RBF.ts:217