Class: DotProduct
Dot-Product kernel.
The DotProduct kernel is non-stationary and can be obtained from linear regression by putting \(N(0, 1)\) priors on the coefficients of \(x_d (d = 1, … , D)\) and a prior of \(N(0, \sigma_0^2)\) on the bias. The DotProduct kernel is invariant to a rotation of the coordinates about the origin, but not translations. It is parameterized by a parameter sigma_0 \(\sigma\) which controls the inhomogenity of the kernel. For \(\sigma_0^2 =0\), the kernel is called the homogeneous linear kernel, otherwise it is inhomogeneous. The kernel is given by
Constructors
new DotProduct()
new DotProduct(
opts?):DotProduct
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.sigma_0? | any | Parameter controlling the inhomogenity of the kernel. If sigma_0=0, the kernel is homogeneous. |
opts.sigma_0_bounds? | "fixed" | The lower and upper bound on ‘sigma_0’. If set to “fixed”, ‘sigma_0’ cannot be changed during hyperparameter tuning. |
Returns DotProduct
Defined in generated/gaussian_process/kernels/DotProduct.ts:23
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/gaussian_process/kernels/DotProduct.ts:21 |
_isInitialized | boolean | false | generated/gaussian_process/kernels/DotProduct.ts:20 |
_py | PythonBridge | undefined | generated/gaussian_process/kernels/DotProduct.ts:19 |
id | string | undefined | generated/gaussian_process/kernels/DotProduct.ts:16 |
opts | any | undefined | generated/gaussian_process/kernels/DotProduct.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/DotProduct.ts:40
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/DotProduct.ts:109
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/DotProduct.ts:153
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/DotProduct.ts:187
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/DotProduct.ts:92
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/DotProduct.ts:53
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/DotProduct.ts:219