RBF
Defined in: ml/kernels/rbf.js:15
Abstract base class for GP kernels
Extends
Constructors
Constructor
new RBF(
lengthScaleOrOpts?,variance?):RBF
Defined in: ml/kernels/rbf.js:36
Parameters
lengthScaleOrOpts?
any = 1.0
a length scale, one
per input dimension (ARD), or an options object with lengthScale,
variance, and optionally blocks (ARD by block, see Matern),
lengthScaleBounds and varianceBounds ([low, high], honoured by
hyperparameter optimization)
variance?
number = 1.0
Returns
RBF
Overrides
Properties
blocks
blocks:
any
Defined in: ml/kernels/rbf.js:48
lengthScale
lengthScale:
any
Defined in: ml/kernels/rbf.js:45
lengthScaleBounds
lengthScaleBounds:
any
Defined in: ml/kernels/rbf.js:49
variance
variance:
any
Defined in: ml/kernels/rbf.js:46
varianceBounds
varianceBounds:
any
Defined in: ml/kernels/rbf.js:50
Methods
call()
call(
X1,X2?):Matrix
Defined in: 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
clone()
clone():
Kernel
Defined in: ml/kernels/base.js:87
Clone the kernel with the same parameters
Returns
New kernel instance
Inherited from
compute()
compute(
x1,x2):number
Defined in: ml/kernels/rbf.js:59
Compute covariance between two points
Parameters
x1
any
First point
x2
any
Second point
Returns
number
Covariance value
Overrides
getParams()
getParams():
object
Defined in: ml/kernels/rbf.js:73
Get kernel hyperparameters
Returns
object
Hyperparameters
lengthScale
lengthScale:
any
variance
variance:
any
Overrides
setParams()
setParams(
params):void
Defined in: ml/kernels/rbf.js:81
Set kernel hyperparameters
Parameters
params
New parameters
amplitude
any
lengthScale
any
variance
any
Returns
void