Class ILK

constructor

• new ILK(
      eta?: number,
      lambda?: number,
      c?: number,
      kernel?:
          | "gaussian"
          | "polynomial"
          | { name: "gaussian"; s?: number }
          | { d?: number; name: "polynomial" }
          | ((arg0: number[], arg1: number[]) => number),
      loss?: "hinge" | "square" | "logistic",
  ): ILK

  Parameters

  • Optionaleta: number

    Learning rate
  • Optionallambda: number

    Regularization constant
  • Optionalc: number

    Penalty imposed on point prediction violations.
  • Optionalkernel:
        | "gaussian"
        | "polynomial"
        | { name: "gaussian"; s?: number }
        | { d?: number; name: "polynomial" }
        | ((arg0: number[], arg1: number[]) => number)

    Kernel name
  • Optionalloss: "hinge" | "square" | "logistic"

    Loss type name

  Returns ILK

_a

_c

_eta

_kernel

_lambda

_loss

_rho

_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

update

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

  Update model parameters with one data.

  Parameters

  • x: number[]

    Training data
  • y: -1 | 1

    Target value

  Returns void