Class BOGD

constructor

• new BOGD(
      b?: number,
      eta?: number,
      lambda?: number,
      gamma?: number,
      sampling?: "uniform" | "nonuniform",
      kernel?:
          | "gaussian"
          | "polynomial"
          | { name: "gaussian"; s?: number }
          | { d?: number; name: "polynomial" }
          | ((arg0: number[], arg1: number[]) => number),
      loss?: "zero_one" | "hinge",
  ): BOGD

  Parameters

  • Optionalb: number

    Maximum budget size

  • Optionaleta: number

    Stepsize

  • Optionallambda: number

    Regularization parameter

  • Optionalgamma: number

    Maximum coefficient

  • Optionalsampling: "uniform" | "nonuniform"

    Sampling approach

  • Optionalkernel:
        | "gaussian"
        | "polynomial"
        | { name: "gaussian"; s?: number }
        | { d?: number; name: "polynomial" }
        | ((arg0: number[], arg1: number[]) => number)

    Kernel name

  • Optionalloss: "zero_one" | "hinge"

    Loss type name

  Returns BOGD

_alpha

_b

_eta

_gamma

_gloss

_kernel

_lambda

_sampling

_sv

fit

• fit(x: number[][], y: (-1 | 1)[]): void

  Fit model.

  Parameters

  • x: number[][]

    Training data

  • y: (-1 | 1)[]

    Target values

  Returns void

predict

• predict(data: number[][]): (-1 | 1)[]

  Returns predicted values.

  Parameters

  • data: number[][]

    Sample data

  Returns (-1 | 1)[]

  Predicted values