Class ContinuousHMM

Continuous hidden Markov model

Hierarchy

HMMBase
- ContinuousHMM

Index

Constructors

Properties

_a _c _d _k _m _n _p _s

Methods

_backward _bt _btk _forward _gamma _update _xi bestPath fit generate probability

Constructors

constructor

new ContinuousHMM(n: number): ContinuousHMM
Parameters
- n: number
  Number of states
Returns ContinuousHMM
Inherited from HMMBase.constructor
- Defined in lib/model/hmm.d.ts:72

Properties

_a

_c

_c: Matrix<T>

_d

_d: number

_k

_k: number

_m

_m: any[]

_n

_n: number

_p

_p: Matrix<T>

_s

_s: any[]

Methods

_backward

_backward(x: any, c?: any, prob?: boolean): Matrix[]
Parameters
- x: any
- Optionalc: any
- Optionalprob: boolean
Returns Matrix[]
Inherited from HMMBase._backward
- Defined in lib/model/hmm.d.ts:78

_bt

_bt(o: any, t: any): Matrix<T>
Parameters
- o: any
- t: any
Returns Matrix<T>
Overrides HMMBase._bt
- Defined in lib/model/hmm.d.ts:38

_btk

_btk(o: any, t: any, k: any): Matrix<T>
Parameters
- o: any
- t: any
- k: any
Returns Matrix<T>
- Defined in lib/model/hmm.d.ts:37

_forward

_forward(x: any, scaled?: boolean): (Matrix<T> | Matrix<T>[])[]
Parameters
- x: any
- Optionalscaled: boolean
Returns (Matrix<T> | Matrix<T>[])[]
Inherited from HMMBase._forward
- Defined in lib/model/hmm.d.ts:77

_gamma

_gamma(alpha: any, beta: any): Matrix[]
Parameters
- alpha: any
- beta: any
Returns Matrix[]
Inherited from HMMBase._gamma
- Defined in lib/model/hmm.d.ts:79

_update

_update(gamma: any, xi: any): void
Parameters
- gamma: any
- xi: any
Returns void
Inherited from HMMBase._update
- Defined in lib/model/hmm.d.ts:81

_xi

_xi(x: any, alpha: any, beta: any, c: any): any[][]
Parameters
- x: any
- alpha: any
- beta: any
- c: any
Returns any[][]
Inherited from HMMBase._xi
- Defined in lib/model/hmm.d.ts:80

bestPath

bestPath(data: number[][] | number[][][]): number[][]
Returns best path of the datas.
Parameters
- data: number[][] | number[][][]
  Sample data
Returns number[][]
Predicted path
Overrides HMMBase.bestPath
- Defined in lib/model/hmm.d.ts:56

fit

fit(x: number[][] | number[][][], scaled?: boolean): void
Fit model.
Parameters
- x: number[][] | number[][][]
  Training data
- Optionalscaled: boolean
  Do scaled calculation or not
Returns void
- Defined in lib/model/hmm.d.ts:44

generate

generate(n?: number, length?: number): number[][][]
Returns generated values.
Parameters
- Optionaln: number
  Number of generated data
- Optionallength: number
  Path length
Returns number[][][]
Generated values
- Defined in lib/model/hmm.d.ts:63

probability

probability(datas: number[][] | number[][][]): number[]
Returns probability of the datas.
Parameters
- datas: number[][] | number[][][]
  Sample data
Returns number[]
Predicted values
Overrides HMMBase.probability
- Defined in lib/model/hmm.d.ts:50