calhoun.analysis.crf.solver
Class Viterbi

java.lang.Object
  calhoun.analysis.crf.solver.Viterbi

All Implemented Interfaces:

CRFInference

public class Viterbi
extends java.lang.Object
implements CRFInference

Nested Class Summary

Nested classes/interfaces inherited from interface calhoun.analysis.crf.CRFInference

CRFInference.InferenceResult

Constructor Summary

Viterbi()

Method Summary

int[]	getBackPointers()
double[]	getBestScore()
boolean	isAllPaths() true if all paths (valid and invalid) are to be evaluated during the viterbi search.
CRFInference.InferenceResult	predict(ModelManager fm, InputSequence<?> seq, double[] lambda) Return the labelling that maximizes the conditional probability P(y|x).
void	setAllPaths(boolean allPaths) sets whether all paths (valid and invalid) are to be evaluated during the viterbi search.
void	setBackPointers(int[] backPointers)
void	setBestScore(double[] bestScore)

Methods inherited from class java.lang.Object

equals, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Detail

Viterbi

public Viterbi()

Method Detail

isAllPaths

public boolean isAllPaths()

true if all paths (valid and invalid) are to be evaluated during the viterbi search. Defaults to false.

Returns:

true if all paths are to be examined

setAllPaths

public void setAllPaths(boolean allPaths)

sets whether all paths (valid and invalid) are to be evaluated during the viterbi search. Defaults to false.

Parameters:

allPaths - allPath true if all paths are to be examined

predict

public CRFInference.InferenceResult predict(ModelManager fm,
                                            InputSequence<?> seq,
                                            double[] lambda)

Description copied from interface: CRFInference

Return the labelling that maximizes the conditional probability P(y|x).

Specified by:

predict in interface CRFInference

Parameters:

fm - model to use for training

seq - input sequence to label

lambda - array of feature weights. Usually these will be derived from a training pass.

Returns:

inference result containing the hidden states which are predicted and the score outputs.

getBackPointers

public int[] getBackPointers()

setBackPointers

public void setBackPointers(int[] backPointers)

getBestScore

public double[] getBestScore()

setBestScore

public void setBestScore(double[] bestScore)