RBF

Defined in: ds/src/ml/kernels/rbf.js:15

Abstract base class for GP kernels

Extends

• Kernel

Constructors

Constructor

new RBF(lengthScaleOrOpts?, variance?): RBF

Defined in: ds/src/ml/kernels/rbf.js:28

Parameters

lengthScaleOrOpts?

any = 1.0

Length scale or options object

variance?

number = 1.0

Signal variance (default: 1.0)

Returns

RBF

Examples

// Positional arguments (scikit-learn style)
new RBF(1.0, 1.0)

// Object arguments
new RBF({ lengthScale: 1.0, amplitude: 1.0 })

Overrides

Kernel.constructor

Properties

lengthScale

lengthScale: any

Defined in: ds/src/ml/kernels/rbf.js:33

---

variance

variance: any

Defined in: ds/src/ml/kernels/rbf.js:34

Methods

call()

call(X1, X2?): Matrix

Defined in: ds/src/ml/kernels/base.js:30

Compute covariance matrix between sets of points

Parameters

X1

any

First set of points (n1 x d)

X2?

any = null

Second set of points (n2 x d). If omitted, computes K(X1, X1)

Returns

Matrix

Covariance matrix (n1 x n2)

Inherited from

Kernel.call

---

clone()

clone(): Kernel

Defined in: ds/src/ml/kernels/base.js:76

Clone the kernel with the same parameters

Returns

Kernel

New kernel instance

Inherited from

Kernel.clone

---

compute()

compute(x1, x2): number

Defined in: ds/src/ml/kernels/rbf.js:42

Compute covariance between two points

Parameters

x1

any

First point

x2

any

Second point

Returns

number

Covariance value

Overrides

Kernel.compute

---

getParams()

getParams(): object

Defined in: ds/src/ml/kernels/rbf.js:55

Get kernel hyperparameters

Returns

object

Hyperparameters

lengthScale

lengthScale: any

variance

variance: any

Overrides

Kernel.getParams

---

setParams()

setParams(params): void

Defined in: ds/src/ml/kernels/rbf.js:62

Set kernel hyperparameters

Parameters

params

New parameters

amplitude

any

lengthScale

any

variance

any

Returns

void

Overrides

Kernel.setParams