Class: ExpSineSquared
Exp-Sine-Squared kernel (aka periodic kernel).
The ExpSineSquared kernel allows one to model functions which repeat themselves exactly. It is parameterized by a length scale parameter \(l>0\) and a periodicity parameter \(p>0\). Only the isotropic variant where \(l\) is a scalar is supported at the moment. The kernel is given by:
Constructors
new ExpSineSquared()
new ExpSineSquared(
opts?):ExpSineSquared
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.length_scale? | any | The length scale of the kernel. |
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. |
opts.periodicity? | any | The periodicity of the kernel. |
opts.periodicity_bounds? | "fixed" | The lower and upper bound on ‘periodicity’. If set to “fixed”, ‘periodicity’ cannot be changed during hyperparameter tuning. |
Returns ExpSineSquared
Defined in generated/gaussian_process/kernels/ExpSineSquared.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/gaussian_process/kernels/ExpSineSquared.ts:21 |
_isInitialized | boolean | false | generated/gaussian_process/kernels/ExpSineSquared.ts:20 |
_py | PythonBridge | undefined | generated/gaussian_process/kernels/ExpSineSquared.ts:19 |
id | string | undefined | generated/gaussian_process/kernels/ExpSineSquared.ts:16 |
opts | any | undefined | generated/gaussian_process/kernels/ExpSineSquared.ts:17 |
Accessors
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/gaussian_process/kernels/ExpSineSquared.ts:52
Methods
__call__()
__call__(
opts):Promise<ArrayLike[]>
Return the kernel k(X, Y) and optionally its gradient.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.eval_gradient? | boolean | Determines 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/ExpSineSquared.ts:121
clone_with_theta()
clone_with_theta(
opts):Promise<any>
Returns a clone of self with given hyperparameters theta.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.theta? | ArrayLike | The hyperparameters |
Returns Promise<any>
Defined in generated/gaussian_process/kernels/ExpSineSquared.ts:165
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
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Left argument of the returned kernel k(X, Y) |
Returns Promise<ArrayLike>
Defined in generated/gaussian_process/kernels/ExpSineSquared.ts:201
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/ExpSineSquared.ts:104
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/gaussian_process/kernels/ExpSineSquared.ts:65
is_stationary()
is_stationary(
opts):Promise<any>
Returns whether the kernel is stationary.
Parameters
| Parameter | Type |
|---|---|
opts | object |
Returns Promise<any>
Defined in generated/gaussian_process/kernels/ExpSineSquared.ts:233