PKMeansClustering
VTKExamples/Cxx/InfoVis/PKMeansClustering
Code¶
PKMeansClustering.cxx
#include <vtkVersion.h> #include <vtkSmartPointer.h> #include <vtkProperty.h> #include <vtkPointData.h> #include <vtkPolyData.h> #include <vtkXMLPolyDataWriter.h> #include <vtkIntArray.h> #include <vtkMultiBlockDataSet.h> #include <vtkPoints.h> #include <vtkTable.h> #include <vtkDoubleArray.h> #include <vtkPKMeansStatistics.h> //display #include <vtkPolyDataMapper.h> #include <vtkActor.h> #include <vtkRenderWindow.h> #include <vtkRenderer.h> #include <vtkRenderWindowInteractor.h> #include <vtkVertexGlyphFilter.h> #include <vtkInteractorStyleTrackballCamera.h> #include <sstream> int main(int, char*[]) { //create 2 clusters, one near (0,0,0) and the other near (3,3,3) vtkSmartPointer<vtkPoints> points = vtkSmartPointer<vtkPoints>::New(); points->InsertNextPoint ( 0.0, 0.0, 0.0 ); points->InsertNextPoint ( 3.0, 3.0, 3.0 ); points->InsertNextPoint ( 0.1, 0.1, 0.1 ); points->InsertNextPoint ( 3.1, 3.1, 3.1 ); points->InsertNextPoint ( 0.2, 0.2, 0.2 ); points->InsertNextPoint ( 3.2, 3.2, 3.2 ); // Get the points into the format needed for KMeans vtkSmartPointer<vtkTable> inputData = vtkSmartPointer<vtkTable>::New(); for ( int c = 0; c < 3; ++c ) { std::stringstream colName; colName << "coord " << c; vtkSmartPointer<vtkDoubleArray> doubleArray = vtkSmartPointer<vtkDoubleArray>::New(); doubleArray->SetNumberOfComponents(1); doubleArray->SetName( colName.str().c_str() ); doubleArray->SetNumberOfTuples(points->GetNumberOfPoints()); for ( int r = 0; r < points->GetNumberOfPoints(); ++ r ) { double p[3]; points->GetPoint(r, p); doubleArray->SetValue( r, p[c] ); } inputData->AddColumn( doubleArray ); } vtkSmartPointer<vtkPKMeansStatistics> pKMeansStatistics = vtkSmartPointer<vtkPKMeansStatistics>::New(); //vtkSmartPointer<vtkKMeansStatistics> pKMeansStatistics = vtkSmartPointer<vtkKMeansStatistics>::New(); //pks->SetMaxNumIterations( 10 ); #if VTK_MAJOR_VERSION <= 5 pKMeansStatistics->SetInput( vtkStatisticsAlgorithm::INPUT_DATA, inputData ); #else pKMeansStatistics->SetInputData( vtkStatisticsAlgorithm::INPUT_DATA, inputData ); #endif pKMeansStatistics->SetColumnStatus( inputData->GetColumnName( 0 ) , 1 ); pKMeansStatistics->SetColumnStatus( inputData->GetColumnName( 1 ) , 1 ); pKMeansStatistics->SetColumnStatus( inputData->GetColumnName( 2 ) , 1 ); pKMeansStatistics->RequestSelectedColumns(); pKMeansStatistics->SetAssessOption( true ); pKMeansStatistics->SetDefaultNumberOfClusters( 2 ); pKMeansStatistics->Update() ; // Display the results pKMeansStatistics->GetOutput()->Dump(); vtkSmartPointer<vtkIntArray> clusterArray = vtkSmartPointer<vtkIntArray>::New(); clusterArray->SetNumberOfComponents(1); clusterArray->SetName( "ClusterId" ); for(unsigned int r = 0; r < pKMeansStatistics->GetOutput()->GetNumberOfRows(); r++) { vtkVariant v = pKMeansStatistics->GetOutput()->GetValue(r, pKMeansStatistics->GetOutput()->GetNumberOfColumns() - 1); std::cout << "Point " << r << " is in cluster " << v.ToInt() << std::endl; clusterArray->InsertNextValue(v.ToInt()); } vtkSmartPointer<vtkPolyData> polydata = vtkSmartPointer<vtkPolyData>::New(); polydata->SetPoints(points); polydata->GetPointData()->SetScalars(clusterArray); // Output the cluster centers vtkMultiBlockDataSet* outputMetaDS = vtkMultiBlockDataSet::SafeDownCast( pKMeansStatistics->GetOutputDataObject( vtkStatisticsAlgorithm::OUTPUT_MODEL ) ); vtkSmartPointer<vtkTable> outputMeta = vtkTable::SafeDownCast( outputMetaDS->GetBlock( 0 ) ); //vtkSmartPointer<vtkTable> outputMeta = vtkTable::SafeDownCast( outputMetaDS->GetBlock( 1 ) ); vtkDoubleArray* coord0 = vtkDoubleArray::SafeDownCast(outputMeta->GetColumnByName("coord 0")); vtkDoubleArray* coord1 = vtkDoubleArray::SafeDownCast(outputMeta->GetColumnByName("coord 1")); vtkDoubleArray* coord2 = vtkDoubleArray::SafeDownCast(outputMeta->GetColumnByName("coord 2")); for(unsigned int i = 0; i < coord0->GetNumberOfTuples(); ++i) { std::cout << coord0->GetValue(i) << " " << coord1->GetValue(i) << " " << coord2->GetValue(i) << std::endl; } return EXIT_SUCCESS; }
CMakeLists.txt¶
cmake_minimum_required(VERSION 2.8) PROJECT(PKMeansClustering) find_package(VTK REQUIRED) include(${VTK_USE_FILE}) add_executable(PKMeansClustering MACOSX_BUNDLE PKMeansClustering.cxx) target_link_libraries(PKMeansClustering ${VTK_LIBRARIES})
Download and Build PKMeansClustering¶
Danger
The generation of tar files has not been ported to the new VTKExamples. Some tarballs may be missing or out-of-date.
Click here to download PKMeansClustering and its CMakeLists.txt file. Once the tarball PKMeansClustering.tar has been downloaded and extracted,
cd PKMeansClustering/build
If VTK is installed:
cmake ..
If VTK is not installed but compiled on your system, you will need to specify the path to your VTK build:
cmake -DVTK_DIR:PATH=/home/me/vtk_build ..
Build the project:
make
and run it:
./PKMeansClustering
WINDOWS USERS PLEASE NOTE: Be sure to add the VTK bin directory to your path. This will resolve the VTK dll's at run time.