Class SemiSupervisedGMM

Semi-Supervised gaussian mixture model

Hierarchy (View Summary)

GMM
- SemiSupervisedGMM

Index

Constructors

Properties

_classes _d _k _m _p _s

Accessors

Methods

_gaussian _init add clear fit init predict probability

Constructors

constructor

new SemiSupervisedGMM(): SemiSupervisedGMM
Returns SemiSupervisedGMM
Inherited from GMM.constructor

Properties

_classes

_classes: any[]

_d

_d: any

_k

_k: number

_m

_m: any[]

_p

_p: any[]

_s

_s: any[]

Accessors

categories

get categories(): any[]
Categories

Returns any[]
- Defined in lib/model/gmm.d.ts:46

Methods

_gaussian

_gaussian(x: any, m: any, s: any): number
Parameters
- x: any
- m: any
- s: any
Returns number
Inherited from GMM._gaussian
- Defined in lib/model/gmm.d.ts:31

_init

_init(datas: any): void
Parameters
- datas: any
Returns void
Inherited from GMM._init
- Defined in lib/model/gmm.d.ts:10

add

add(): void
Add a new cluster.

Returns void
Inherited from GMM.add
- Defined in lib/model/gmm.d.ts:14

clear

clear(): void
Clear all clusters.

Returns void
Inherited from GMM.clear
- Defined in lib/model/gmm.d.ts:18

fit

fit(datas: number[][], y: any[]): void
Fit model.
Parameters
- datas: number[][]
  Training data
- y: any[]
  Target values
Returns void
Overrides GMM.fit
- Defined in lib/model/gmm.d.ts:59

init

init(datas: number[][], labels: any[]): void
Initialize model.
Parameters
- datas: number[][]
  Training data
- labels: any[]
  Target values
Returns void
- Defined in lib/model/gmm.d.ts:52

predict

predict(data: number[][]): any[]
Returns predicted categories.
Parameters
- data: number[][]
  Sample data
Returns any[]
Predicted values
Overrides GMM.predict
- Defined in lib/model/gmm.d.ts:65

probability

probability(data: number[][]): number[][]
Returns probabilities.
Parameters
- data: number[][]
  Sample data
Returns number[][]
Predicted values
Inherited from GMM.probability
- Defined in lib/model/gmm.d.ts:24