C++ TimeSeriesClassificationData类代码示例

TimeSeriesClassificationData TimeSeriesClassificationData::getTrainingFoldData(const UINT foldIndex) const {

    TimeSeriesClassificationData trainingData;

    if( !crossValidationSetup ){
        errorLog << "getTrainingFoldData(UINT foldIndex) - Cross Validation has not been setup! You need to call the spiltDataIntoKFolds(UINT K,bool useStratifiedSampling) function first before calling this function!" << std::endl;
        return trainingData;
    }

    if( foldIndex >= kFoldValue ) return trainingData;

    trainingData.setNumDimensions( numDimensions );

    //Add the data to the training set, this will consist of all the data that is NOT in the foldIndex
    UINT index = 0;
    for(UINT k=0; k<kFoldValue; k++){
        if( k != foldIndex ){
            for(UINT i=0; i<crossValidationIndexs[k].size(); i++){

                index = crossValidationIndexs[k][i];
                trainingData.addSample( data[ index ].getClassLabel(), data[ index ].getData() );
            }
        }
    }

    return trainingData;
}

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 HMM::convertDataToObservationSequence( TimeSeriesClassificationData &classData, vector< vector< UINT > > &observationSequences ){
        
    observationSequences.resize( classData.getNumSamples() );
    
    for(UINT i=0; i<classData.getNumSamples(); i++){
        MatrixDouble &timeseries = classData[i].getData();
        observationSequences[i].resize( timeseries.getNumRows() );
        for(UINT j=0; j<timeseries.getNumRows(); j++){
            if( timeseries[j][0] >= numSymbols ){
                errorLog << "train(TimeSeriesClassificationData &trainingData) - Found an observation sequence with a value outside of the symbol range! Value: " << timeseries[j][0] << endl;
                return false;
            }
            observationSequences[i][j] = (UINT)timeseries[j][0];
        }
    }
    
    return true;
}

TimeSeriesClassificationData TimeSeriesClassificationDataStream::getTimeSeriesClassificationData( const bool includeNullGestures ) const {
    
    TimeSeriesClassificationData tsData;
    
    tsData.setNumDimensions( getNumDimensions() );
    tsData.setAllowNullGestureClass( includeNullGestures );
    
    bool addSample = false;
    const UINT numTimeseries = (UINT)timeSeriesPositionTracker.size();
    for(UINT i=0; i<numTimeseries; i++){
        addSample = includeNullGestures ? true : timeSeriesPositionTracker[i].getClassLabel() != GRT_DEFAULT_NULL_CLASS_LABEL;
        if( addSample ){
            tsData.addSample(timeSeriesPositionTracker[i].getClassLabel(), getTimeSeriesData( timeSeriesPositionTracker[i] ) );
        }
    }
    
    return tsData;
}

TimeSeriesClassificationData TimeSeriesClassificationData::getTestFoldData(const UINT foldIndex) const {
    TimeSeriesClassificationData testData;

    if( !crossValidationSetup ) return testData;

    if( foldIndex >= kFoldValue ) return testData;

    //Add the data to the training
    testData.setNumDimensions( numDimensions );

    UINT index = 0;
    for(UINT i=0; i<crossValidationIndexs[ foldIndex ].size(); i++){

        index = crossValidationIndexs[ foldIndex ][i];
        testData.addSample( data[ index ].getClassLabel(), data[ index ].getData() );
    }

    return testData;
}

int main (int argc, const char * argv[])
{
    TimeSeriesClassificationData trainingData;      //This will store our training data

    GestureRecognitionPipeline pipeline;               //This is a wrapper for our classifier and any pre/post processing modules

	string dirPath = "/home/vlad/AndroidStudioProjects/DataCapture/dataSetGenerator/build";


	if (!trainingData.loadDatasetFromFile(dirPath + "/acc-training-set-segmented.data")) {
		printf("Cannot open training set\n");
		return 0;
	}

	printf("Successfully opened training data set ...\n");

    HMM hmm;
    hmm.setHMMType( HMM_CONTINUOUS );
    hmm.setDownsampleFactor( 5 );
    hmm.setAutoEstimateSigma( true );
    hmm.setSigma( 20.0 );

    hmm.setModelType( HMM_LEFTRIGHT );
    hmm.setDelta( 1 );
//    LowPassFilter lpf(0.1, 1, 3);
//    pipeline.setPreProcessingModule(lpf);

    pipeline.setClassifier( hmm );

    pipeline.train(trainingData, 20);

    //You can then get then get the accuracy of how well the pipeline performed during the k-fold cross validation testing
    double accuracy = pipeline.getCrossValidationAccuracy();

    printf("Accuracy: %f\n", accuracy);

}

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 KMeansFeatures::train_(TimeSeriesClassificationData &trainingData){
    MatrixDouble data = trainingData.getDataAsMatrixDouble();
    return train_( data );
}

int main (int argc, const char * argv[])
{
	//Create a new DTW instance, using the default parameters
	DTW dtw;
    
	//Load some training data to train the classifier - the DTW uses TimeSeriesClassificationData
	TimeSeriesClassificationData trainingData;
    
	if( !trainingData.load("DTWTrainingData.grt") ){
		cout << "Failed to load training data!\n";
		return EXIT_FAILURE;
	}
    
	//Use 20% of the training dataset to create a test dataset
	TimeSeriesClassificationData testData = trainingData.partition( 80 );

	//Trim the training data for any sections of non-movement at the start or end of the recordings
	dtw.enableTrimTrainingData(true,0.1,90);
    
	//Train the classifier
	if( !dtw.train( trainingData ) ){
		cout << "Failed to train classifier!\n";
		return EXIT_FAILURE;
	}	
    
	//Save the DTW model to a file
	if( !dtw.save("DTWModel.grt") ){
		cout << "Failed to save the classifier model!\n";
		return EXIT_FAILURE;
	}
    
	//Load the DTW model from a file
	if( !dtw.load("DTWModel.grt") ){
		cout << "Failed to load the classifier model!\n";
		return EXIT_FAILURE;
	}
    
	//Use the test dataset to test the DTW model
	double accuracy = 0;
	for(UINT i=0; i<testData.getNumSamples(); i++){
		//Get the i'th test sample - this is a timeseries
		UINT classLabel = testData[i].getClassLabel();
		MatrixDouble timeseries = testData[i].getData();
        
		//Perform a prediction using the classifier
		if( !dtw.predict( timeseries ) ){
			cout << "Failed to perform prediction for test sampel: " << i <<"\n";
			return EXIT_FAILURE;
		}
        
		//Get the predicted class label
		UINT predictedClassLabel = dtw.getPredictedClassLabel();
		double maximumLikelihood = dtw.getMaximumLikelihood();
		VectorDouble classLikelihoods = dtw.getClassLikelihoods();
		VectorDouble classDistances = dtw.getClassDistances();
        
		//Update the accuracy
		if( classLabel == predictedClassLabel ) accuracy++;
        
        cout << "TestSample: " << i <<  "\tClassLabel: " << classLabel << "\tPredictedClassLabel: " << predictedClassLabel << "\tMaximumLikelihood: " << maximumLikelihood << endl;
	}
    
	cout << "Test Accuracy: " << accuracy/double(testData.getNumSamples())*100.0 << "%" << endl;
    
	return EXIT_SUCCESS;
}

bool RBMQuantizer::train_(TimeSeriesClassificationData &trainingData){
    MatrixFloat data = trainingData.getDataAsMatrixFloat();
    return train_( data );
}

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;
}

int main() {
    vector<string> gestures(0,"");
    GetFilesInDirectory(gestures, "rawdata");
    CreateDirectory("processed", NULL);
    sort(gestures.begin(), gestures.end());
    data = vector<vector<vector<double > > >(gestures.size(), vector<vector<double > >(0,vector<double>(0,0)));
    for(size_t i = 0; i < gestures.size(); i++) {
        ifstream fin(gestures[i]);
        int n; fin >> n;
       // cerr << gestures[i] << endl;
       // cerr << n << endl;
        data[i] = vector<vector<double> >(n, vector<double>(NUMPARAM, 0));
        for(int j = 0; j < n; j++) {
            for(int k = 0; k < NUMPARAM; k++) {
                fin >> data[i][j][k];
            }
        }
        fin.close();
    }


    //Create a new instance of the TimeSeriesClassificationDataStream
    TimeSeriesClassificationData trainingData;

    // ax, ay, az
    trainingData.setNumDimensions(3);
    trainingData.setDatasetName("processed\\GestureTrainingData.txt");
    ofstream labelfile("processed\\GestureTrainingDataLabels.txt");
    UINT currLabel = 1;
    Random random;
    map<string, int> gesturenames;
    for(size_t overall = 0; overall < gestures.size(); overall++) {

        string nam = gestures[overall].substr(8,gestures[overall].find_first_of('_')-8);
        if(gesturenames.count(nam)) currLabel = gesturenames[nam];
        else {
            currLabel = gesturenames.size()+1;
            gesturenames[nam] = currLabel;
            labelfile << currLabel << " " << nam << endl;
        }
        MatrixDouble trainingSample;
        VectorDouble currVec( trainingData.getNumDimensions() );
        for(size_t k = 1; k < data[overall].size(); k++) {
            for(UINT j=0; j<currVec.size(); j++){
                currVec[j] = data[overall][k][j];
            }
            trainingSample.push_back(currVec);
        }
        trainingData.addSample(currLabel, trainingSample);

    }
    for(size_t i = 0; i < gestures.size(); i++) {
        MatrixDouble trainingSample;
        VectorDouble currVec(trainingData.getNumDimensions());
        for(UINT j = 0; j < currVec.size(); j++) {
            currVec[j] = random.getRandomNumberUniform(-1.0, 1.0);
        }
        for(size_t k = 0; k < 100; k++) {
            trainingSample.push_back(currVec);
        }
        trainingData.addSample(0, trainingSample);
    }

    //After recording your training data you can then save it to a file
    if( !trainingData.save( "processed\\TrainingData.grt" ) ){
        cout << "ERROR: Failed to save dataset to file!\n";
        return EXIT_FAILURE;
    }

    //This can then be loaded later
    if( !trainingData.load( "processed\\TrainingData.grt" ) ){
        cout << "ERROR: Failed to load dataset from file!\n";
        return EXIT_FAILURE;
    }

    //This is how you can get some stats from the training data
    string datasetName = trainingData.getDatasetName();
    string infoText = trainingData.getInfoText();
    UINT numSamples = trainingData.getNumSamples();
    UINT numDimensions = trainingData.getNumDimensions();
    UINT numClasses = trainingData.getNumClasses();

    cout << "Dataset Name: " << datasetName << endl;
    cout << "InfoText: " << infoText << endl;
    cout << "NumberOfSamples: " << numSamples << endl;
    cout << "NumberOfDimensions: " << numDimensions << endl;
    cout << "NumberOfClasses: " << numClasses << endl;

    //You can also get the minimum and maximum ranges of the data
    vector< MinMax > ranges = trainingData.getRanges();

    cout << "The ranges of the dataset are: \n";
    for(UINT j=0; j<ranges.size(); j++){
        cout << "Dimension: " << j << " Min: " << ranges[j].minValue << " Max: " << ranges[j].maxValue << endl;
    }

    DTW dtw;

    if( !dtw.train( trainingData ) ){
        cerr << "Failed to train classifier!\n";
//.........这里部分代码省略.........

int main (int argc, const char * argv[])
{
    //Create a new instance of the TimeSeriesClassificationData
    TimeSeriesClassificationData trainingData;
    
    //Set the dimensionality of the data (you need to do this before you can add any samples)
    trainingData.setNumDimensions( 3 );
    
    //You can also give the dataset a name (the name should have no spaces)
    trainingData.setDatasetName("DummyData");
    
    //You can also add some info text about the data
    trainingData.setInfoText("This data contains some dummy timeseries data");
    
    //Here you would record a time series, when you have finished recording the time series then add the training sample to the training data
    UINT gestureLabel = 1;
    MatrixDouble trainingSample;
    
    //For now we will just add 10 x 20 random walk data timeseries
    Random random;
    for(UINT k=0; k<10; k++){//For the number of classes
        gestureLabel = k+1;
        
        //Get the init random walk position for this gesture
        VectorDouble startPos( trainingData.getNumDimensions() );
        for(UINT j=0; j<startPos.size(); j++){
            startPos[j] = random.getRandomNumberUniform(-1.0,1.0);
        }
                
        //Generate the 20 time series
        for(UINT x=0; x<20; x++){
            
            //Clear any previous timeseries
            trainingSample.clear();
            
            //Generate the random walk
            UINT randomWalkLength = random.getRandomNumberInt(90, 110);
            VectorDouble sample = startPos;
            for(UINT i=0; i<randomWalkLength; i++){
                for(UINT j=0; j<startPos.size(); j++){
                    sample[j] += random.getRandomNumberUniform(-0.1,0.1);
                }
                
                //Add the sample to the training sample
                trainingSample.push_back( sample );
            }
            
            //Add the training sample to the dataset
            trainingData.addSample( gestureLabel, trainingSample );
            
        }
    }
    
    //After recording your training data you can then save it to a file
    if( !trainingData.saveDatasetToFile( "TrainingData.txt" ) ){
	    cout << "Failed to save dataset to file!\n";
	    return EXIT_FAILURE;
	}
    
    //This can then be loaded later
    if( !trainingData.loadDatasetFromFile( "TrainingData.txt" ) ){
		cout << "Failed to load dataset from file!\n";
		return EXIT_FAILURE;
	}
    
    //This is how you can get some stats from the training data
    string datasetName = trainingData.getDatasetName();
    string infoText = trainingData.getInfoText();
    UINT numSamples = trainingData.getNumSamples();
    UINT numDimensions = trainingData.getNumDimensions();
    UINT numClasses = trainingData.getNumClasses();
    
    cout << "Dataset Name: " << datasetName << endl;
    cout << "InfoText: " << infoText << endl;
    cout << "NumberOfSamples: " << numSamples << endl;
    cout << "NumberOfDimensions: " << numDimensions << endl;
    cout << "NumberOfClasses: " << numClasses << endl;
    
    //You can also get the minimum and maximum ranges of the data
    vector< MinMax > ranges = trainingData.getRanges();
    
    cout << "The ranges of the dataset are: \n";
    for(UINT j=0; j<ranges.size(); j++){
        cout << "Dimension: " << j << " Min: " << ranges[j].minValue << " Max: " << ranges[j].maxValue << endl;
    }
    
    //If you want to partition the dataset into a training dataset and a test dataset then you can use the partition function
    //A value of 80 means that 80% of the original data will remain in the training dataset and 20% will be returned as the test dataset
    TimeSeriesClassificationData testData = trainingData.partition( 80 );
    
    //If you have multiple datasets that you want to merge together then use the merge function
    if( !trainingData.merge( testData ) ){
		cout << "Failed to merge datasets!\n";
		return EXIT_FAILURE;
	}
    
    //If you want to run K-Fold cross validation using the dataset then you should first spilt the dataset into K-Folds
    //A value of 10 splits the dataset into 10 folds and the true parameter signals that stratified sampling should be used
    if( !trainingData.spiltDataIntoKFolds( 10, true ) ){
		cout << "Failed to spiltDataIntoKFolds!\n";
//.........这里部分代码省略.........

int main(int argc, const char * argv[]){
    
    //Load the training data
    TimeSeriesClassificationData trainingData;
    
    if( !trainingData.loadDatasetFromFile("HMMTrainingData.grt") ){
        cout << "ERROR: Failed to load training data!\n";
        return false;
    }
    
    //Remove 20% of the training data to use as test data
    TimeSeriesClassificationData testData = trainingData.partition( 80 );
    
    //The input to the HMM must be a quantized discrete value
    //We therefore use a KMeansQuantizer to covert the N-dimensional continuous data into 1-dimensional discrete data
    const UINT NUM_SYMBOLS = 10;
    KMeansQuantizer quantizer( NUM_SYMBOLS );
    
    //Train the quantizer using the training data
    if( !quantizer.train( trainingData ) ){
        cout << "ERROR: Failed to train quantizer!\n";
        return false;
    }
    
    //Quantize the training data
    TimeSeriesClassificationData quantizedTrainingData( 1 );
    
    for(UINT i=0; i<trainingData.getNumSamples(); i++){
        
        UINT classLabel = trainingData[i].getClassLabel();
        MatrixDouble quantizedSample;
        
        for(UINT j=0; j<trainingData[i].getLength(); j++){
            quantizer.quantize( trainingData[i].getData().getRowVector(j) );
            
            quantizedSample.push_back( quantizer.getFeatureVector() );
        }
        
        if( !quantizedTrainingData.addSample(classLabel, quantizedSample) ){
            cout << "ERROR: Failed to quantize training data!\n";
            return false;
        }
        
    }
    
    //Create a new HMM instance
    HMM hmm;
    
    //Set the number of states in each model
    hmm.setNumStates( 4 );
    
    //Set the number of symbols in each model, this must match the number of symbols in the quantizer
    hmm.setNumSymbols( NUM_SYMBOLS );
    
    //Set the HMM model type to LEFTRIGHT with a delta of 1
    hmm.setModelType( HiddenMarkovModel::LEFTRIGHT );
    hmm.setDelta( 1 );
    
    //Set the training parameters
    hmm.setMinImprovement( 1.0e-5 );
    hmm.setMaxNumIterations( 100 );
    hmm.setNumRandomTrainingIterations( 20 );
    
    //Train the HMM model
    if( !hmm.train( quantizedTrainingData ) ){
        cout << "ERROR: Failed to train the HMM model!\n";
        return false;
    }
    
    //Save the HMM model to a file
    if( !hmm.save( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to save the model to a file!\n";
        return false;
    }
    
    //Load the HMM model from a file
    if( !hmm.load( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to load the model from a file!\n";
        return false;
    }
    
    //Quantize the test data
    TimeSeriesClassificationData quantizedTestData( 1 );
    
    for(UINT i=0; i<testData.getNumSamples(); i++){
        
        UINT classLabel = testData[i].getClassLabel();
        MatrixDouble quantizedSample;
        
        for(UINT j=0; j<testData[i].getLength(); j++){
            quantizer.quantize( testData[i].getData().getRowVector(j) );
            
            quantizedSample.push_back( quantizer.getFeatureVector() );
        }
        
        if( !quantizedTestData.addSample(classLabel, quantizedSample) ){
            cout << "ERROR: Failed to quantize training data!\n";
            return false;
        }
    }
//.........这里部分代码省略.........

int main(int argc, const char * argv[]){
    
    //Load the training data
    TimeSeriesClassificationData trainingData;
    
    if( !trainingData.load("HMMTrainingData.grt") ){
        cout << "ERROR: Failed to load training data!\n";
        return false;
    }
    
    //Remove 20% of the training data to use as test data
    TimeSeriesClassificationData testData = trainingData.partition( 80 );
    
    //Create a new HMM instance
    HMM hmm;
    
    //Set the HMM as a Continuous HMM
    hmm.setHMMType( HMM_CONTINUOUS );
    
    //Set the downsample factor, a higher downsample factor will speed up the prediction time, but might reduce the classification accuracy
    hmm.setDownsampleFactor( 5 );
    
    //Set the committee size, this sets the (top) number of models that will be used to make a prediction
    hmm.setCommitteeSize( 10 );
    
    //Tell the hmm algorithm that we want it to estimate sigma from the training data
    hmm.setAutoEstimateSigma( true );
    
    //Set the minimum value for sigma, you might need to adjust this based on the range of your data
    //If you set setAutoEstimateSigma to false, then all sigma values will use the value below
    hmm.setSigma( 20.0 );
    
    //Set the HMM model type to LEFTRIGHT with a delta of 1, this means the HMM can only move from the left-most state to the right-most state
    //in steps of 1
    hmm.setModelType( HMM_LEFTRIGHT );
    hmm.setDelta( 1 );
    
    //Train the HMM model
    if( !hmm.train( trainingData ) ){
        cout << "ERROR: Failed to train the HMM model!\n";
        return false;
    }
    
    //Save the HMM model to a file
    if( !hmm.save( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to save the model to a file!\n";
        return false;
    }
    
    //Load the HMM model from a file
    if( !hmm.load( "HMMModel.grt" ) ){
        cout << "ERROR: Failed to load the model from a file!\n";
        return false;
    }

    //Compute the accuracy of the HMM models using the test data
    double numCorrect = 0;
    double numTests = 0;
    for(UINT i=0; i<testData.getNumSamples(); i++){
        
        UINT classLabel = testData[i].getClassLabel();
        hmm.predict( testData[i].getData() );
        
        if( classLabel == hmm.getPredictedClassLabel() ) numCorrect++;
        numTests++;
        
        VectorFloat classLikelihoods = hmm.getClassLikelihoods();
        VectorFloat classDistances = hmm.getClassDistances();
        
        cout << "ClassLabel: " << classLabel;
        cout << " PredictedClassLabel: " << hmm.getPredictedClassLabel();
        cout << " MaxLikelihood: " << hmm.getMaximumLikelihood();
        
        cout << "  ClassLikelihoods: ";
        for(UINT k=0; k<classLikelihoods.size(); k++){
            cout << classLikelihoods[k] << "\t";
        }
        
        cout << "ClassDistances: ";
        for(UINT k=0; k<classDistances.size(); k++){
            cout << classDistances[k] << "\t";
        }
        cout << endl;
    }
    
    cout << "Test Accuracy: " << numCorrect/numTests*100.0 << endl;
    
    return true;
}