Class KNNDensityEstimation

k-nearest neighbor density estimation

Hierarchy

  • KNNBase
    • KNNDensityEstimation

Constructors

constructor

Properties

_c _d _k _metric _p

Methods

_add _logGamma _near_points add fit predict

Constructors

constructor

  • new KNNDensityEstimation(
        k?: number,
        metric?:
            | "euclid"
            | "manhattan"
            | "chebyshev"
            | "minkowski"
            | (arg0: number[], arg1: number[]) => number,
    ): KNNDensityEstimation

  • Parameters

    • Optionalk: number

      Number of neighborhoods

    • Optionalmetric:
          | "euclid"
          | "manhattan"
          | "chebyshev"
          | "minkowski"
          | (arg0: number[], arg1: number[]) => number

      Metric name

    Returns KNNDensityEstimation

Properties

_c

_c: any[]

_d

_d: (a: any, b: any) => any

_k

_k: number

_metric

_metric:
    | "euclid"
    | "manhattan"
    | "chebyshev"
    | "minkowski"
    | (arg0: number[], arg1: number[]) => number

_p

_p: any[]

Methods

_add

  • _add(point: number[], category?: any): void

  • Add a data.

    Parameters

    • point: number[]

      Training data

    • Optionalcategory: any

      Target value

    Returns void

_logGamma

  • _logGamma(z: any): number

  • Parameters

    • z: any

    Returns number

_near_points

  • _near_points(data: any): any[]

  • Parameters

    • data: any

    Returns any[]

add

  • add(point: number[]): void

  • Add a data.

    Parameters

    • point: number[]

      Training data

    Returns void

fit

  • fit(datas: number[][]): void

  • Add datas.

    Parameters

    • datas: number[][]

      Training data

    Returns void

predict

  • predict(datas: number[][]): number[]

  • Returns predicted values.

    Parameters

    • datas: number[][]

      Sample data

    Returns number[]

    Predicted values

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