Class SemiSupervisedKNN

Semi-supervised k-nearest neighbor

Hierarchy

KNNBase
- SemiSupervisedKNN

Index

Constructors

Properties

_c _d _k _metric _orgk _p

Methods

_add _near_points add fit predict

Constructors

constructor

new SemiSupervisedKNN(
    k?: number,
    metric?:
        | "euclid"
        | "manhattan"
        | "chebyshev"
        | "minkowski"
        | ((arg0: number[], arg1: number[]) => number),
): SemiSupervisedKNN
Parameters
- Optionalk: number
  Number of neighborhoods
- Optionalmetric:
      | "euclid"
      | "manhattan"
      | "chebyshev"
      | "minkowski"
      | ((arg0: number[], arg1: number[]) => number)
  Metric name
Returns SemiSupervisedKNN
Inherited from KNNBase.constructor
- Defined in lib/model/knearestneighbor.d.ts:121

Properties

_c

_c: any[]

_d

_d: ((arg0: number[], arg1: number[]) => number) | ((a: any, b: any) => any)

_k

_k: number

_metric

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

_orgk

_orgk: 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
Inherited from KNNBase._add
- Defined in lib/model/knearestneighbor.d.ts:133

_near_points

_near_points(data: any): any[]
Parameters
- data: any
Returns any[]
Inherited from KNNBase._near_points
- Defined in lib/model/knearestneighbor.d.ts:127

add

add(point: number[], category: any): void
Add a data.
Parameters
- point: number[]
  Training data
- category: any
  Target value
Returns void
- Defined in lib/model/knearestneighbor.d.ts:100

fit

fit(datas: number[][], targets: any[]): void
Add datas.
Parameters
- datas: number[][]
  Training data
- targets: any[]
  Target values
Returns void
- Defined in lib/model/knearestneighbor.d.ts:106

predict

predict(): any[]
Returns predicted values.

Returns any[]
Predicted values
- Defined in lib/model/knearestneighbor.d.ts:111