bool LabelledTimeSeriesClassificationData::merge(const LabelledTimeSeriesClassificationData &labelledData){
if( labelledData.getNumDimensions() != numDimensions ){
errorLog << "merge(LabelledTimeSeriesClassificationData &labelledData) - The number of dimensions in the labelledData (" << labelledData.getNumDimensions() << ") does not match the number of dimensions of this dataset (" << numDimensions << ")" << 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(LabelledTimeSeriesClassificationData trainingData){
if( trainingData.getNumSamples() == 0 ){
errorLog << "train(LabelledTimeSeriesClassificationData trainingData) - There are no training samples to train the HMM classifer!" << endl;
return false;
}
if( trainingData.getNumDimensions() != 1 ){
errorLog << "train(LabelledTimeSeriesClassificationData 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
trained = false;
useScaling = false;
numFeatures = trainingData.getNumDimensions();
numClasses = trainingData.getNumClasses();
models.clear();
classLabels.clear();
models.resize( numClasses );
classLabels.resize( numClasses );
//Init the models
for(UINT k=0; k<numClasses; k++){
models[k].resetModel(numStates,numSymbols,modelType,delta);
models[k].maxNumIter = maxNumIter;
models[k].minImprovement = minImprovement;
}
//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
LabelledTimeSeriesClassificationData classData = trainingData.getClassData( classID );
vector< vector< UINT > > observationSequences;
if( !convertDataToObservationSequence( classData, observationSequences ) ){
return false;
}
//Train the model
if( !models[k].train( observationSequences ) ){
errorLog << "train(LabelledTimeSeriesClassificationData &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
LabelledTimeSeriesClassificationData 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 = models[k].predict( observationSequences[i] );
avgLoglikelihood += fabs( loglikelihood );
cout << "Class: " << classID << " PredictedLogLikelihood: " << -loglikelihood << endl;
}
nullRejectionThresholds[k] = -( avgLoglikelihood / double( observationSequences.size() ) );
cout << "Class: " << classID << " NullRejectionThreshold: " << nullRejectionThresholds[k] << endl;
}
for(UINT k=0; k<numClasses; k++){
models[k].printAB();
}
trained = true;
return true;
}
