SquaredExp

SquaredExp

Squared exponential (a.k.a. Gaussian) kernel: k(x,y) = A·exp(−‖x−y‖² / (2·ls²)), A = |ampl|. Parameters: [ls, ampl] (defaults ls = 1, ampl = 1). nbParameters = 2. Scalable.

Constructor

new SquaredExp(lsopt, amplopt)

Source:
Parameters:
Name Type Attributes Default Description
ls number <optional>
 1
ampl number <optional>
 1

Classes

SquaredExp

Members

ampl :number

Description:

  • amplitude

Source:

amplitude

Type:
  • number

ls :number

Description:

  • length scale

Source:

length scale

Type:
  • number

Methods

gradient(x1, x2) → {Array.<number>}

Description:

  • gradient = [grad_ls, grad_ampl] with d² = ‖x−y‖², A = |ampl|, e = exp(−d²/(2·ls²)): grad_ls = (d² · A · e) / ls³ grad_ampl = sign(ampl) · e

Source:
Parameters:
Name Type Description
x1 Array.<number>
x2 Array.<number>
Returns:
Type
Array.<number>

heuristicFit(inputs, outputs)

Description:

  • heuristic: ls = mean inter-sample distance, ampl = output variance.

Source:
Parameters:
Name Type Description
inputs Array.<Array.<number>>
outputs Array.<number>

kernel(x1, x2) → {number}

Source:
Parameters:
Name Type Description
x1 Array.<number>
x2 Array.<number>
Returns:
Type
number

rescale(scale)

Source:
Parameters:
Name Type Description
scale number

setParameters(parameters)

Source:
Parameters:
Name Type Description
parameters Array.<number>