MixGaussian Class Reference

The MixGaussian class handles mixture sets of gaussians. More...

Inheritance diagram for MixGaussian:


Public Member Functions
	MixGaussian (int dim=ASR_DEFAULT_VECTORSIZE)
	MixGaussian (FILE *inFile, int dim, bool noReTrainig=false)
	MixGaussian (MixGaussian *org)
	MixGaussian (MixGaussian model1, MixGaussian model2)
	MixGaussian (MixGaussian model1, MixGaussian model2, double rate)
virtual	~MixGaussian ()
int	dim () const
virtual void	mergeMixGaussians (MixGaussian model1, MixGaussian model2)
virtual int	getNumberOfGaussians (void)
void	setVariance (const Vector *v)
virtual double	getP (Vector *observation)
virtual double	getLogP (Vector *observation)
virtual double	getLookaheadLogP (double *vectorList, int time, bool doSecondHalf)
void	getSumHist (Vector **histogram)
virtual void	train (MixGaussian *trainMG)
virtual void	train (bool useKMeans, double weight, Vector observation, MixGaussian doSat=NULL)
void	mapAdaptMeans ()
void	adapt_setInitialNode (Adapt_AM_TreeNode *node)
void	adapt_setHelperMatrices ()
void	adapt_adapt ()
void	adapt_unAdapt ()
void	initializeFastCalc (bool onlyOnce)
virtual int	trainFinish (bool neverSplit=false, double minVar=GAUSSIAN_VARIANCE_MINIMUM)
virtual double	finishSAT (double minVar=GAUSSIAN_VARIANCE_MINIMUM)
virtual bool	splitAllGaussians (bool alwaysSplit=false)
virtual bool	splitBestGaussian (bool alwaysSplit=false)
virtual void	shiftBestGaussian (int shiftFactor)
virtual void	copyGaussians (MixGaussian *destMixGaussian, int maxNmbr)
virtual void	addGaussian (Gaussian *gaussian, double weight)
virtual void	addGaussian (Vector *vector)
virtual void	normalizeWeights (int maxGaussians=-1)
virtual void	storeData (FILE *outFile)
void	storeSAT (FILE *outFile)
void	appendSAT (FILE *outFile)
void	adapt_setNode ()
void	adapt_clear ()
void	writeAccumulators (FILE file, FILE fileF=NULL, FILE *fileST=NULL, bool doBinary=false)
void	addAccumulators (FILE *file)
void	trainMMI (FILE fileEnum, FILE fileDenom)
void	adapt_setVarTrans ()
void	adapt_adaptVar ()
double	getCoSim (MixGaussian mg1, MixGaussian mg2)
double	getKLDistance (MixGaussian *mg2)
double	getNormDistance ()
void	moveModelGaussians (MixGaussian *model, double factor)
void	startCount ()
void	count (Vector *observation)
void	stopCount ()
int	getBestCount ()
void	addCountedGaussians (MixGaussian *source, int nmbr)
void	fillDistanceArray (int *distA)
void	printModel (FILE fileMean, FILE fileVariance, FILE *fileWeight)
int	printInfo (Vector *v)
void	printMixGaussian (void)
Public Attributes
double *	fastP_CPandWeight_meanAndVar
Protected Attributes
Gaussian **	myGaussians
double *	myGaussianWeights
double	trainP
int	numberOfGaussians
bool	noSplitting
double *	myGaussiansCounts
int *	myGaussiansSorted
double *	fastP_res
void *	lastObservation
int	vectorDimension
double	lookaheadRes [GMMLOOKAHEAD]
int	lookaheadPointer

Detailed Description

The MixGaussian class handles mixture sets of gaussians.

Gaussian objects offer functionality for training single gaussian parameter values and determining chances given specific observations. The MixGaussian class makes it possible to train and use mixtures of gaussians. The user may increase the number of gaussians in the mixture using splitBestGaussian(). This method splits the Gaussian that has been trained with the most observation samples.

Constructor & Destructor Documentation

MixGaussian::MixGaussian ( int dim = ASR_DEFAULT_VECTORSIZE )

The constructor initialises the object. It creates a single gaussian (mixture of one) and makes a weight table of size one with weight one.

References fastP_CPandWeight_meanAndVar, fastP_res, initializeFastCalc(), myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, noSplitting, numberOfGaussians, and vectorDimension.

Referenced by MultiMixGaussian::MultiMixGaussian().

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MixGaussian::MixGaussian	(	FILE *	inFile,
		int	dim,
		bool	noReTraining = `false`
	)

This constructor reads all its data from file. (number of gaussians, the weight table and the gaussian parameters)

References fastP_CPandWeight_meanAndVar, fastP_res, WriteFileLittleBigEndian::freadEndianSafe(), initializeFastCalc(), myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, noSplitting, numberOfGaussians, and vectorDimension.

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MixGaussian::MixGaussian ( MixGaussian * org )

Copy the settings from the org MixGaussian.

References Gaussian::dim(), fastP_CPandWeight_meanAndVar, fastP_res, initializeFastCalc(), myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, noSplitting, numberOfGaussians, and vectorDimension.

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MixGaussian::MixGaussian	(	MixGaussian *	model1,
		MixGaussian *	model2
	)

This constructor will merge the two input gaussians.

References Gaussian::dim(), fastP_CPandWeight_meanAndVar, fastP_res, initializeFastCalc(), mergeMixGaussians(), myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, noSplitting, and vectorDimension.

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MixGaussian::MixGaussian	(	MixGaussian *	model1,
		MixGaussian *	model2,
		double	rate
	)

This constructor will merge the two input gaussians.

References Gaussian::dim(), dim(), fastP_CPandWeight_meanAndVar, fastP_res, initializeFastCalc(), myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, noSplitting, numberOfGaussians, and vectorDimension.

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MixGaussian::~MixGaussian ( ) [virtual]

The destructor deletes all gaussians from the mix and also deletes the weights table.

References fastP_CPandWeight_meanAndVar, fastP_res, myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, and numberOfGaussians.

Member Function Documentation

void MixGaussian::adapt_adapt ( )

Once all adaptation matrices are calculated, this method will adapt all Gaussians in the mix according to those matrices.

References Gaussian::adapt_adapt(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_adapt().

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void MixGaussian::adapt_adaptVar ( )

Todo:: Docs

References Gaussian::adapt_adaptVar(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_adaptVar().

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void MixGaussian::adapt_clear ( )

This method will clear all adaptation settings, so that a new adaptation iteration can be started.

References Gaussian::adapt_clear(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_clear().

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void MixGaussian::adapt_setHelperMatrices ( )

This method will call the Gaussian::adapt_setHelperMatrices() method of all Gaussian objects in the mix (of this MixGaussian object).

References Gaussian::adapt_setHelperMatrices(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_setHelperMatrices().

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void MixGaussian::adapt_setInitialNode ( Adapt_AM_TreeNode * node )

The SMAPLR adaptation starts with creating one single cluster (node) at which all gaussians in the system will need to register. The method adapt_setInitialNode() will call the Gaussian::adapt_setInitialNode() of all Gaussian objects that are in the mix (of this MixGaussian object)

References Gaussian::adapt_setInitialNode(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_setInitialNode().

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void MixGaussian::adapt_setNode ( )

The MixGaussian has registered at least once to an adaptation node when this method is called. The first time adapt_setInitialNode() shall be used. This method will call the Gaussian::adapt_setNode() method of all Gaussian objects in the mix (of this MixGaussian object).

References Gaussian::adapt_setNode(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_setNode().

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void MixGaussian::adapt_setVarTrans ( )

Todo:: Docs

References Gaussian::adapt_setVarTrans(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_setVarTrans().

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void MixGaussian::adapt_unAdapt ( )

The mean Vectors of the Gaussian objects in the mix can be restored to the values before adaptation by calling adapt_unAdapt().

References Gaussian::adapt_unAdapt(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::adapt_unAdapt().

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void MixGaussian::addAccumulators ( FILE * file )

This method will read accumulator data from file

References Gaussian::addAccumulators(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::addAccumulators().

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void MixGaussian::addCountedGaussians	(	MixGaussian *	source,
		int	nmbr
	)

This method is added for the Train_Speaker_Clustering class. The amount of data assigned to each Gaussian of the mix is counted by count(). Using this method, the nmbr best gaussians from source are added to the mix.

References addGaussian(), getNumberOfGaussians(), myGaussians, myGaussiansSorted, and myGaussianWeights.

Referenced by TrainPhoneModel::addCountedGaussians().

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void MixGaussian::addGaussian ( Vector * v ) [virtual]

Todo:: docs

Reimplemented in MultiMixGaussian.

References myGaussians, myGaussianWeights, numberOfGaussians, and Gaussian::setMean().

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void MixGaussian::addGaussian	(	Gaussian *	gaussian,
		double	weight
	)			`[virtual]`

This is a helper method for copyGaussians(). It adds a gaussian directly to the mix. The gaussian itself and the weight are provided as input parameters.

References myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by addCountedGaussians(), TrainPhoneModel::addGaussian(), MultiMixGaussian::addGaussian(), and copyGaussians().

void MixGaussian::appendSAT ( FILE * outFile )

This method loads training data from file.

References Gaussian::appendSAT(), myGaussians, and numberOfGaussians.

Referenced by TrainPhoneModel::appendSAT().

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void MixGaussian::copyGaussians	(	MixGaussian *	destMixGaussian,
		int	maxNmbr
	)			`[virtual]`

This method copies the maxNmbr gaussians with the biggest weights into the destination gaussian mix. This method is used by the SpeechDetection class to make speech/non-speech models.

Reimplemented in MultiMixGaussian.

References addGaussian(), myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by PhoneModel::copyGaussians(), and MultiMixGaussian::copyGaussians().

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void MixGaussian::count ( Vector * observation )

Given the input parameter observation, this method counts the weights of the single Gaussian objects. These counts are later used by the Train_Speaker_Cluster class.

References Gaussian::getP(), myGaussians, myGaussiansCounts, myGaussianWeights, and numberOfGaussians.

Referenced by TrainPhoneModel::count().

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int MixGaussian::dim ( ) const [inline]

References vectorDimension.

Referenced by getLogP(), getLookaheadLogP(), getP(), initializeFastCalc(), mergeMixGaussians(), MixGaussian(), PhoneModel::PhoneModel(), setVariance(), and TrainPhoneModel::TrainPhoneModel().

void MixGaussian::fillDistanceArray ( int * distA )

Fills the distance array: an array of size numberOfGaussians. The two most distant gaussians are in the first and last spot. The others are sorted in between.

References Vector::distance(), Gaussian::meanVector, myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by TrainPhoneModel::fillDistanceArray().

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double MixGaussian::finishSAT ( double minVar = GAUSSIAN_VARIANCE_MINIMUM ) [virtual]

Todo:: Docs

References myGaussians, numberOfGaussians, and Gaussian::trainFinish().

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int MixGaussian::getBestCount ( )

This method is added for the Train_Speaker_Clustering class. The amount of data assigned to each Gaussian of the mix is counted by count(). When done, this method will return the best Gaussian...

References myGaussiansCounts, and numberOfGaussians.

Referenced by TrainPhoneModel::getDominantGaussian().

double MixGaussian::getCoSim	(	MixGaussian *	mg1,
		MixGaussian *	mg2
	)

References Gaussian::getCoSim(), myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by TrainPhoneModel::getCoSim().

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double MixGaussian::getKLDistance ( MixGaussian * mg2 )

References Gaussian::getKLDistance(), myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by TrainPhoneModel::getKLDistance().

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double MixGaussian::getLogP ( Vector * observation ) [virtual]

GetLogP() returns the chance of an observation beeing produced by this mixture of gaussians (in log)

Reimplemented in MultiMixGaussian.

References dim(), fastP_CPandWeight_meanAndVar, fastP_res, Vector::getVectorArray(), lastObservation, FastMath::log(), and numberOfGaussians.

Referenced by MultiMixGaussian::getLogP(), PhoneModel::getLogPDFProbability(), TrainPhoneModel::getSilP(), PhoneModel::processVector(), and TrainPhoneModel::viterbi().

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double MixGaussian::getLookaheadLogP	(	double *	vectorList,
		int	timeStamp,
		bool	doSecondHalf
	)			`[virtual]`

GetLogP() returns the chance of an observation beeing produced by this mixture of gaussians (in log)

Reimplemented in MultiMixGaussian.

References dim(), fastP_CPandWeight_meanAndVar, fastP_res, FastMath::log(), lookaheadPointer, lookaheadRes, and numberOfGaussians.

Referenced by PhoneModel::getLookaheadLogP(), MultiMixGaussian::getLookaheadLogP(), and thread_pdfCalculation().

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double MixGaussian::getNormDistance ( )

References Gaussian::getNormDistance(), myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by TrainPhoneModel::getNormDistance().

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int MixGaussian::getNumberOfGaussians ( void ) [virtual]

Returns the number of gaussians in the mix.

Reimplemented in MultiMixGaussian.

References numberOfGaussians.

Referenced by addCountedGaussians(), PhoneModel::getNumberOfGaussians(), MultiMixGaussian::getNumberOfGaussians(), TrainPhoneModel::maxNrOfGaussians(), thread_pdfCalculation(), TrainPhoneModel::train(), and TrainPhoneModel::TrainPhoneModel().

double MixGaussian::getP ( Vector * observation ) [virtual]

GetP() returns the chance of an observation beeing produced by this mixture of gaussians.

Reimplemented in MultiMixGaussian.

References dim(), fastP_CPandWeight_meanAndVar, fastP_res, Vector::getKey(), Vector::getVectorArray(), lastObservation, and numberOfGaussians.

Referenced by TrainPhoneModel::baumWelch(), TrainPhoneModel::getClusterP(), PhoneModel::getPDFProbability(), and train().

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void MixGaussian::getSumHist ( Vector ** histogram )

Todo:: Docs

References Vector::addElements(), Vector::addValue(), Vector::divideElements(), Gaussian::getP(), Vector::getValue(), Vector::len(), Vector::multiplyElements(), myGaussians, myGaussianWeights, numberOfGaussians, Vector::setAllValues(), Gaussian::setMean(), Vector::setValue(), Gaussian::setVariance(), Vector::sqrtElements(), and Vector::substractElements().

Referenced by PhoneModel::getSilSumHist().

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void MixGaussian::initializeFastCalc ( bool onlyOnce )

Todo:: Docs

References dim(), fastP_CPandWeight_meanAndVar, fastP_res, Gaussian::getCP(), Gaussian::getMean(), Vector::getValue(), Gaussian::getVariance(), lastObservation, FastMath::log(), lookaheadPointer, myGaussians, myGaussiansCounts, myGaussiansSorted, myGaussianWeights, and numberOfGaussians.

Referenced by MixGaussian(), splitAllGaussians(), splitBestGaussian(), trainFinish(), and trainMMI().

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void MixGaussian::mapAdaptMeans ( )

Todo:: docs

References Gaussian::mapAdaptMean(), myGaussians, and numberOfGaussians.

Referenced by PhoneModel::mapAdaptMeans().

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void MixGaussian::mergeMixGaussians	(	MixGaussian *	model1,
		MixGaussian *	model2
	)			`[virtual]`

Merge the two input gaussians.

Reimplemented in MultiMixGaussian.

References dim(), myGaussians, myGaussianWeights, noSplitting, and numberOfGaussians.

Referenced by MultiMixGaussian::mergeMixGaussians(), and MixGaussian().

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void MixGaussian::moveModelGaussians	(	MixGaussian *	model,
		double	factor
	)

References Gaussian::moveModel(), myGaussians, and numberOfGaussians.

Referenced by TrainPhoneModel::moveModelGaussians().

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void MixGaussian::normalizeWeights ( int maxGaussians = -1 ) [virtual]

With this method, the sum of the weights from the gaussian mix is forced to be one and if the mix contains more than maxGaussians, the gaussians with the smallest weight are pruned.

Reimplemented in MultiMixGaussian.

References myGaussians, myGaussianWeights, and numberOfGaussians.

Referenced by MultiMixGaussian::normalizeWeights(), TrainPhoneModel::setMaxGaussians(), and TrainPhoneModel::TrainPhoneModel().

int MixGaussian::printInfo ( Vector * v )

References myGaussians, numberOfGaussians, and Gaussian::printInfo().

Referenced by PhoneModel::printInfo().

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void MixGaussian::printMixGaussian ( void )

Prints the mixture of gaussians to the standard output for debugging purposes.

References myGaussians, myGaussianWeights, numberOfGaussians, and Gaussian::printGaussian().

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void MixGaussian::printModel	(	FILE *	fileMean,
		FILE *	fileVariance,
		FILE *	fileWeight
	)

References myGaussians, myGaussianWeights, numberOfGaussians, and Gaussian::printModel().

Referenced by PhoneModel::printModel().

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void MixGaussian::setVariance ( const Vector * v )

Set the variance for all of the gaussians in this mixture

References dim(), Vector::len(), myGaussians, and numberOfGaussians.

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void MixGaussian::shiftBestGaussian ( int shiftFactor ) [virtual]

This method searches the "best" gaussian, and shifts its mean vector.

The best gaussian is the gaussian with the largest weight. The weight is chosen, because indirectly, the weight represents the amount of training samples defining the gaussian. The mean of the new gaussian is 0.2*shiftFactor times shifted (shiftFactor may be negative).

Reimplemented in MultiMixGaussian.

References myGaussians, myGaussianWeights, numberOfGaussians, and Gaussian::shiftMean().

Referenced by MultiMixGaussian::shiftBestGaussian(), and TrainPhoneModel::TrainPhoneModel().

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bool MixGaussian::splitAllGaussians ( bool alwaysSplit = false ) [virtual]

Todo:: docs

References initializeFastCalc(), myGaussians, myGaussianWeights, noSplitting, numberOfGaussians, and Gaussian::shiftMean().

Referenced by TrainPhoneModel::train().

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bool MixGaussian::splitBestGaussian ( bool alwaysSplit = false ) [virtual]

This method searches the "best" gaussian, and splits it into two new gaussians as follows:

The best gaussian is the gaussian with the largest weight. The weight is chosen, because indirectly, the weight represents the amount of training samples defining the gaussian. The mean of the new gaussian is 0.2 times the variance bigger than the old mean. The mean of the existing gaussian is made 0.2 times the variance smaller. The variance is not changed. Note that the mean is shifted in all dimensions. One could argue if this is a good initialisation step. The weights of the two new gaussians is set half the weight of the original gaussian. Also, it could be argued if this is good practice.

Reimplemented in MultiMixGaussian.

References initializeFastCalc(), myGaussians, myGaussianWeights, noSplitting, numberOfGaussians, and Gaussian::shiftMean().

Referenced by MultiMixGaussian::splitBestGaussian(), and TrainPhoneModel::train().

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void MixGaussian::startCount ( )

This method is added for the Train_Speaker_Clustering class. The amount of data assigned to each Gaussian of the mix is counted by count(). Before counting is started, startCount should be called. This method will delete earlier allocated count-data and initialises the counting parameters.

References myGaussiansCounts, myGaussiansSorted, and numberOfGaussians.

Referenced by TrainPhoneModel::startCount().

void MixGaussian::stopCount ( )

This method is added for the Train_Speaker_Clustering class. The amount of data assigned to each Gaussian of the mix is counted by count(). When done, stopCount() will sort the counted gaussians so that the best X can be chosen.

References myGaussiansCounts, and numberOfGaussians.

Referenced by TrainPhoneModel::stopCount().

void MixGaussian::storeData ( FILE * outFile ) [virtual]

This method writes all its data to file. (number of gaussians, the weight table and the gaussian parameters)

Reimplemented in MultiMixGaussian.

References myGaussians, myGaussianWeights, numberOfGaussians, and Gaussian::storeData().

Referenced by MultiMixGaussian::storeData(), and PhoneModel::writeModel().

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void MixGaussian::storeSAT ( FILE * outFile )

This method writes training data to file.

References myGaussians, numberOfGaussians, and Gaussian::storeSAT().

Referenced by TrainPhoneModel::writeSAT().

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void MixGaussian::train	(	bool	useKMeans,
		double	weight,
		Vector *	observation,
		MixGaussian *	doSat = `NULL`
	)			`[virtual]`

Given a single sample-observation (only one Vector), the mixed gaussian is trained. If K-means is used, the observation is used to train only the best fitting Gaussian. K-means is (currently) only used during initialisation of a TrainPhoneModel. If K-means is not used, all gaussians in the mix are updated with the observation, using the probability that the observation would map on the perticular gaussian. See Gaussian::train() for more information on training single gaussians. The weight is passed to each of the gaussians (used by Baum-Welch).

After feeding all observations to this method, trainFinish() shall be called to finalize the training procedure.

Reimplemented in MultiMixGaussian.

References fastP_res, Vector::getKey(), getP(), lastObservation, myGaussians, numberOfGaussians, and Gaussian::train().

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void MixGaussian::train ( MixGaussian * trainMG ) [virtual]

Add all acumulator data to the data of this MixGaussian. The number of gaussians of both models should be the same!

Reimplemented in MultiMixGaussian.

References myGaussians, numberOfGaussians, and Gaussian::train().

Referenced by PhoneModel::adapt_addAcumulatorData(), PhoneModel::adapt_setAcumulators(), TrainPhoneModel::baumWelch(), TrainPhoneModel::train(), MultiMixGaussian::train(), and TrainPhoneModel::viterbi().

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int MixGaussian::trainFinish	(	bool	neverSplit = `false`,
		double	minVar = `GAUSSIAN_VARIANCE_MINIMUM`
	)			`[virtual]`

This method checks if all gaussians from the mix are updated by enough (MIN_TRAIN_SAMPLES_PER_GAUSSIAN) training observations. If not, the gaussian is pruned. After this, the mixture weights are updated according to the number of observations used for each gaussian, and the single gaussian training procedures are finalized by calling Gaussian::trainFinish(). This method will return the amount of used training data.

Reimplemented in MultiMixGaussian.

References Gaussian::getTrainingDenominator(), initializeFastCalc(), myGaussians, myGaussianWeights, numberOfGaussians, and Gaussian::trainFinish().

Referenced by TrainPhoneModel::baumWelch(), TrainPhoneModel::finishSAT(), TrainPhoneModel::train(), MultiMixGaussian::trainFinish(), and TrainPhoneModel::viterbi().

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