bool ParticleClassifier::train_(TimeSeriesClassificationData &trainingData){
clear();
numClasses = trainingData.getNumClasses();
numInputDimensions = trainingData.getNumDimensions();
ranges = trainingData.getRanges();
//Scale the training data if needed
if( useScaling ){
trainingData.scale(0, 1);
}
//Train the particle filter
particleFilter.train( numParticles, trainingData, sensorNoise, transitionSigma, phaseSigma, velocitySigma );
classLabels.resize(numClasses);
classLikelihoods.resize(numClasses,0);
classDistances.resize(numClasses,0);
for(unsigned int i=0; i<numClasses; i++){
classLabels[i] = trainingData.getClassTracker()[i].classLabel;
}
trained = true;
return trained;
}
bool TimeSeriesClassificationData::merge(const TimeSeriesClassificationData &labelledData){
if( labelledData.getNumDimensions() != numDimensions ){
errorLog << "merge(TimeSeriesClassificationData &labelledData) - The number of dimensions in the labelledData (" << labelledData.getNumDimensions() << ") does not match the number of dimensions of this dataset (" << numDimensions << ")" << std::endl;
return false;
}
//The dataset has changed so flag that any previous cross validation setup will now not work
crossValidationSetup = false;
crossValidationIndexs.clear();
//Add the data from the labelledData to this instance
for(UINT i=0; i<labelledData.getNumSamples(); i++){
addSample(labelledData[i].getClassLabel(), labelledData[i].getData());
}
//Set the class names from the dataset
Vector< ClassTracker > classTracker = labelledData.getClassTracker();
for(UINT i=0; i<classTracker.size(); i++){
setClassNameForCorrespondingClassLabel(classTracker[i].className, classTracker[i].classLabel);
}
return true;
}
bool HMM::train_continuous(TimeSeriesClassificationData &trainingData){
clear();
if( trainingData.getNumSamples() == 0 ){
errorLog << "train_continuous(TimeSeriesClassificationData &trainingData) - There are no training samples to train the CHMM classifer!" << endl;
return false;
}
//Reset the CHMM
numInputDimensions = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
classLabels.resize( numClasses );
for(UINT k=0; k<numClasses; k++){
classLabels[k] = trainingData.getClassTracker()[k].classLabel;
}
//Scale the training data if needed
ranges = trainingData.getRanges();
if( useScaling ){
trainingData.scale(0, 1);
}
//Setup the models, there will be 1 model for each training sample
const UINT numTrainingSamples = trainingData.getNumSamples();
continuousModels.resize( numTrainingSamples );
//Train each of the models
for(UINT k=0; k<numTrainingSamples; k++){
//Init the model
continuousModels[k].setDownsampleFactor( downsampleFactor );
continuousModels[k].setModelType( modelType );
continuousModels[k].setDelta( delta );
continuousModels[k].setSigma( sigma );
continuousModels[k].setAutoEstimateSigma( autoEstimateSigma );
continuousModels[k].enableScaling( false ); //Scaling should always off for the models as we do any scaling in the CHMM
//Train the model
if( !continuousModels[k].train_( trainingData[k] ) ){
errorLog << "train_continuous(TimeSeriesClassificationData &trainingData) - Failed to train CHMM for sample " << k << endl;
return false;
}
}
if( committeeSize > trainingData.getNumSamples() ){
committeeSize = trainingData.getNumSamples();
warningLog << "train_continuous(TimeSeriesClassificationData &trainingData) - The committeeSize is larger than the number of training sample. Setting committeeSize to number of training samples: " << trainingData.getNumSamples() << endl;
}
//Flag that the model has been trained
trained = true;
//Compute any null rejection thresholds if needed
if( useNullRejection ){
//Compute the rejection thresholds
nullRejectionThresholds.resize(numClasses);
}
return true;
}
bool HMM::train_discrete(TimeSeriesClassificationData &trainingData){
clear();
if( trainingData.getNumSamples() == 0 ){
errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - There are no training samples to train the HMM classifer!" << endl;
return false;
}
if( trainingData.getNumDimensions() != 1 ){
errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - The number of dimensions in the training data must be 1. If your training data is not 1 dimensional then you must quantize the training data using one of the GRT quantization algorithms" << endl;
return false;
}
//Reset the HMM
numInputDimensions = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
discreteModels.resize( numClasses );
classLabels.resize( numClasses );
//Init the models
for(UINT k=0; k<numClasses; k++){
discreteModels[k].resetModel(numStates,numSymbols,modelType,delta);
discreteModels[k].setMaxNumEpochs( maxNumEpochs );
discreteModels[k].setMinChange( minChange );
}
//Train each of the models
for(UINT k=0; k<numClasses; k++){
//Get the class ID of this gesture
UINT classID = trainingData.getClassTracker()[k].classLabel;
classLabels[k] = classID;
//Convert this classes training data into a list of observation sequences
TimeSeriesClassificationData classData = trainingData.getClassData( classID );
vector< vector< UINT > > observationSequences;
if( !convertDataToObservationSequence( classData, observationSequences ) ){
return false;
}
//Train the model
if( !discreteModels[k].train( observationSequences ) ){
errorLog << "train_discrete(TimeSeriesClassificationData &trainingData) - Failed to train HMM for class " << classID << endl;
return false;
}
}
//Compute the rejection thresholds
nullRejectionThresholds.resize(numClasses);
for(UINT k=0; k<numClasses; k++){
//Get the class ID of this gesture
UINT classID = trainingData.getClassTracker()[k].classLabel;
classLabels[k] = classID;
//Convert this classes training data into a list of observation sequences
TimeSeriesClassificationData classData = trainingData.getClassData( classID );
vector< vector< UINT > > observationSequences;
if( !convertDataToObservationSequence( classData, observationSequences ) ){
return false;
}
//Test the model
double loglikelihood = 0;
double avgLoglikelihood = 0;
for(UINT i=0; i<observationSequences.size(); i++){
loglikelihood = discreteModels[k].predict( observationSequences[i] );
avgLoglikelihood += fabs( loglikelihood );
}
nullRejectionThresholds[k] = -( avgLoglikelihood / double( observationSequences.size() ) );
}
//Flag that the model has been trained
trained = true;
return true;
}
