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 ); }