Class WeightedKNN

Weighted K-Nearest Neighbor

Index

Constructors

Properties

_c _d _k _metric _w _weight _x _y

Methods

Constructors

constructor

new WeightedKNN(
    k: number,
    metric?:
        | "euclid"
        | "manhattan"
        | "chebyshev"
        | "minkowski"
        | ((arg0: number[], arg1: number[]) => number),
    weight?:
        | "gaussian"
        | "epanechnikov"
        | "rectangular"
        | "triangular"
        | "triweight"
        | "quartic"
        | "cosine"
        | "inversion",
): WeightedKNN
Parameters
- k: number
  Number of neighbors
- Optionalmetric:
      | "euclid"
      | "manhattan"
      | "chebyshev"
      | "minkowski"
      | ((arg0: number[], arg1: number[]) => number)
  Metric name
- Optionalweight:
      | "gaussian"
      | "epanechnikov"
      | "rectangular"
      | "triangular"
      | "triweight"
      | "quartic"
      | "cosine"
      | "inversion"
  Weighting scheme name
Returns WeightedKNN
- Defined in lib/model/weighted_knn.d.ts:10

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)

_w

_w:
    | ((d: any) => number)
    | ((d: any) => 0 | 0.5)
    | ((d: any) => number)
    | ((d: any) => number)
    | ((d: any) => number)
    | ((d: any) => number)
    | ((d: any) => number)
    | ((d: any) => number)

_weight

_weight:
    | "gaussian"
    | "epanechnikov"
    | "rectangular"
    | "triangular"
    | "triweight"
    | "quartic"
    | "cosine"
    | "inversion"

_x

_x: number[][]

_y

_y: any[]

Methods

fit

fit(x: number[][], y: any[]): void
Fit model.
Parameters
- x: number[][]
  Training data
- y: any[]
  Target values
Returns void
- Defined in lib/model/weighted_knn.d.ts:21

predict

predict(data: number[][]): any[]
Returns predicted categories.
Parameters
- data: number[][]
  Sample data
Returns any[]
Predicted values
- Defined in lib/model/weighted_knn.d.ts:30