HMM

java.lang.Object
- aima.core.probability.hmm.impl.HMM

All Implemented Interfaces:

HiddenMarkovModel
```
public class HMM
extends java.lang.Object
implements HiddenMarkovModel
```
Default implementation of the HiddenMarkovModel interface.

Author:

Ciaran O'Reilly, Ravi Mohan

Constructor Summary

Constructors
Constructor and Description
`HMM(RandomVariable stateVariable, Matrix transitionModel, java.util.Map<java.lang.Object,Matrix> sensorModel, Matrix prior)` Instantiate a Hidden Markov Model.

Method Summary

All Methods Instance Methods Concrete Methods
Modifier and Type	Method and Description
`Matrix`	`convert(CategoricalDistribution fromCD)` Convert a Categorical Distribution into a column vector in Matrix form.
`java.util.List<CategoricalDistribution>`	`convert(java.util.List<Matrix> matrixs)` Convert a list of column vectors in Matrix form into a corresponding list of Categorical Distributions.
`CategoricalDistribution`	`convert(Matrix fromMessage)` Convert a column vector in Matrix form to a Categorical Distribution.
`Matrix`	`createUnitMessage()` Return a new column vector in matrix form with all values set to 1.0.
`Matrix`	`getEvidence(java.util.List<AssignmentProposition> evidence)` Get the specific evidence matrix based on assigned evidence value.
`Matrix`	`getPrior()` Return the prior distribution represented as a column vector in Matrix form.
`java.util.Map<java.lang.Object,Matrix>`	`getSensorModel()` Return the sensor model in matrix form: P(e_t \| X_t = i) for each state i. For mathematical convenience we place each of these values into an S * S diagonal matrix.
`RandomVariable`	`getStateVariable()` Return the single discrete random variable used to describe the process state.
`Matrix`	`getTransitionModel()` Return the transition model: P(X_t \| X_t-1) is represented by an S * S matrix T where T_ij = P(X_t = j \| X_t-1 = i).
`Matrix`	`normalize(Matrix m)` Create a normalized column vector in matrix form of the passed in column vector.

Methods inherited from class java.lang.Object
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Constructor Detail
  - HMM
```
public HMM(RandomVariable stateVariable,
           Matrix transitionModel,
           java.util.Map<java.lang.Object,Matrix> sensorModel,
           Matrix prior)
```
    Instantiate a Hidden Markov Model.
    
    Parameters:
    
    stateVariable - the single discrete random variable used to describe the process states 1,...,S.
    
    transitionModel - the transition model:
    P(X_t | X_t-1)
    is represented by an S * S matrix T where
    T_ij = P(X_t = j | X_t-1 = i).
    
    sensorModel - the sensor model in matrix form:
    P(e_t | X_t = i) for each state i. For mathematical convenience we place each of these values into an S * S diagonal matrix.
    
    prior - the prior distribution represented as a column vector in Matrix form.
- Method Detail
  - getStateVariable
```
public RandomVariable getStateVariable()
```
    Description copied from interface: HiddenMarkovModel
    
    Return the single discrete random variable used to describe the process state.
    
    Specified by:
    
    getStateVariable in interface HiddenMarkovModel
    
    Returns:
    
    the single discrete random variable used to describe the process state.
  - getTransitionModel
```
public Matrix getTransitionModel()
```
    Description copied from interface: HiddenMarkovModel
    
    Return the transition model:
    P(X_t | X_t-1)
    is represented by an S * S matrix T where
    T_ij = P(X_t = j | X_t-1 = i).
    
    Specified by:
    
    getTransitionModel in interface HiddenMarkovModel
    
    Returns:
    
    the transition model in Matrix form.
  - getSensorModel
```
public java.util.Map<java.lang.Object,Matrix> getSensorModel()
```
    Description copied from interface: HiddenMarkovModel
    
    Return the sensor model in matrix form:
    P(e_t | X_t = i) for each state i.
    For mathematical convenience we place each of these values into an S * S diagonal matrix.
    
    Specified by:
    
    getSensorModel in interface HiddenMarkovModel
    
    Returns:
    
    the sensor model in matrix form.
  - getPrior
```
public Matrix getPrior()
```
    Description copied from interface: HiddenMarkovModel
    
    Return the prior distribution represented as a column vector in Matrix form.
    
    Specified by:
    
    getPrior in interface HiddenMarkovModel
    
    Returns:
    
    the prior distribution represented as a column vector in Matrix form.
  - getEvidence
```
public Matrix getEvidence(java.util.List<AssignmentProposition> evidence)
```
    Description copied from interface: HiddenMarkovModel
    
    Get the specific evidence matrix based on assigned evidence value.
    
    Specified by:
    
    getEvidence in interface HiddenMarkovModel
    
    Parameters:
    
    evidence - the evidence assignment e.
    
    Returns:
    
    the Matrix representation of this evidence assignment from the sensor model.
  - createUnitMessage
```
public Matrix createUnitMessage()
```
    Description copied from interface: HiddenMarkovModel
    
    Return a new column vector in matrix form with all values set to 1.0.
    
    Specified by:
    
    createUnitMessage in interface HiddenMarkovModel
    
    Returns:
    
    a new column vector in matrix form with all values set to 1.0.
  - convert
```
public Matrix convert(CategoricalDistribution fromCD)
```
    Description copied from interface: HiddenMarkovModel
    
    Convert a Categorical Distribution into a column vector in Matrix form.
    
    Specified by:
    
    convert in interface HiddenMarkovModel
    
    Parameters:
    
    fromCD - the categorical distribution to be converted.
    
    Returns:
    
    a column vector in Matrix form of the passed in categorical distribution.
  - convert
```
public CategoricalDistribution convert(Matrix fromMessage)
```
    Description copied from interface: HiddenMarkovModel
    
    Convert a column vector in Matrix form to a Categorical Distribution.
    
    Specified by:
    
    convert in interface HiddenMarkovModel
    
    Parameters:
    
    fromMessage - the column vector in Matrix form to be converted.
    
    Returns:
    
    a categorical distribution representation of the passed in column vector.
  - convert
```
public java.util.List<CategoricalDistribution> convert(java.util.List<Matrix> matrixs)
```
    Description copied from interface: HiddenMarkovModel
    
    Convert a list of column vectors in Matrix form into a corresponding list of Categorical Distributions.
    
    Specified by:
    
    convert in interface HiddenMarkovModel
    
    Parameters:
    
    matrixs - the column vectors in matrix form to be converted.
    
    Returns:
    
    a corresponding list of Categorical Distribution representation of the passed in column vectors.
  - normalize
```
public Matrix normalize(Matrix m)
```
    Description copied from interface: HiddenMarkovModel
    
    Create a normalized column vector in matrix form of the passed in column vector.
    
    Specified by:
    
    normalize in interface HiddenMarkovModel
    
    Parameters:
    
    m - a column vector representation in matrix form.
    
    Returns:
    
    a normalized column vector of the passed in column vector.

Class HMM

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Constructor Detail

HMM

Method Detail

getStateVariable

getTransitionModel

getSensorModel

getPrior

getEvidence

createUnitMessage

convert

convert

convert

normalize