# PKMeansClustering

VTKExamples/Cxx/InfoVis/PKMeansClustering

### Code

PKMeansClustering.cxx

    #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 );
      pKMeansStatistics->SetInputData( vtkStatisticsAlgorithm::INPUT_DATA, inputData );
      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

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

Be sure to add the VTK bin directory to your path. This will resolve the VTK dll's at run time.