bool KMeans::train_(UnlabelledData &trainingData){

    //Convert the training data into one matrix
	UINT M = trainingData.getNumSamples();
    UINT N = trainingData.getNumDimensions();
    MatrixFloat data(M,N);
    for(UINT i=0; i<M; i++){
        for(UINT j=0; j<N; j++){
            data[i][j] = trainingData[i][j];
        }
    }
	
	return train_(data);
}

UnlabelledData ClassificationData::reformatAsUnlabelledData() const{

    UnlabelledData unlabelledData;

    if( totalNumSamples == 0 ){
        return unlabelledData;
    }

    unlabelledData.setNumDimensions( numDimensions );

    for(UINT i=0; i<totalNumSamples; i++){
        unlabelledData.addSample( data[i].getSample() );
    }

    return unlabelledData;
}

UnlabelledData TimeSeriesClassificationData::reformatAsUnlabelledData() const {

    UnlabelledData unlabelledData;

    if( totalNumSamples == 0 ){
        return unlabelledData;
    }

    unlabelledData.setNumDimensions( numDimensions );

    for(UINT i=0; i<totalNumSamples; i++){
        for(UINT x=0; x<data[i].getLength(); x++){
            unlabelledData.addSample( data[i].getData().getRow( x ) );
        }
    }

    return unlabelledData;
}

bool HierarchicalClustering::train_(UnlabelledData &trainingData){
    
    if( trainingData.getNumSamples() == 0 ){
		return false;
	}

    //Convert the training data into one matrix
	M = trainingData.getNumSamples();
    N = trainingData.getNumDimensions();

    MatrixFloat data(M,N);
    for(UINT i=0; i<M; i++){
        for(UINT j=0; j<N; j++){
            data[i][j] = trainingData[i][j];
        }
    }
	
	return train( data );
}

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

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

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

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

bool KMeansQuantizer::train_(UnlabelledData &trainingData){
	MatrixDouble data = trainingData.getDataAsMatrixDouble();
    return train( data );
}