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| ==Description== | | {{warning|1=The media wiki content on this page is no longer maintained. The examples presented on the https://itk.org/Wiki/* pages likely require ITK version 4.13 or earlier releases. In many cases, the examples on this page no longer conform to the best practices for modern ITK versions.}} |
| Cluster a collection of measurements using the KMeans algorithm. The name "KdTreeBased" indicates that this is an efficient implementation which uses a KdTree.
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| ==KdTree.cxx==
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| <source lang="cpp">
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| #include "itkVector.h"
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| #include "itkListSample.h"
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| #include "itkWeightedCentroidKdTreeGenerator.h"
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| #include "itkEuclideanDistanceMetric.h"
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| int main()
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| {
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| typedef itk::Vector< float, 2 > MeasurementVectorType;
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| typedef itk::Statistics::ListSample< MeasurementVectorType > SampleType;
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| SampleType::Pointer sample = SampleType::New();
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| sample->SetMeasurementVectorSize( 2 );
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| MeasurementVectorType mv;
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| for (unsigned int i = 0 ; i < 100 ; ++i )
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| {
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| mv[0] = static_cast<float>(i);
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| mv[1] = static_cast<float>(i);
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| sample->PushBack( mv );
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| }
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| typedef itk::Statistics::KdTreeGenerator< SampleType > TreeGeneratorType;
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| TreeGeneratorType::Pointer treeGenerator = TreeGeneratorType::New();
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| treeGenerator->SetSample( sample );
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| treeGenerator->SetBucketSize( 16 );
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| treeGenerator->Update();
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| typedef TreeGeneratorType::KdTreeType TreeType;
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| typedef TreeType::NearestNeighbors NeighborsType;
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| typedef TreeType::KdTreeNodeType NodeType;
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| TreeType::Pointer tree = treeGenerator->GetOutput();
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| MeasurementVectorType queryPoint;
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| queryPoint[0] = 10.0;
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| queryPoint[1] = 7.0;
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| // K-Neighbor search
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| std::cout << "K-Neighbor search:" << std::endl;
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| unsigned int numberOfNeighbors = 3;
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| TreeType::InstanceIdentifierVectorType neighbors;
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| tree->Search( queryPoint, numberOfNeighbors, neighbors ) ;
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| for ( unsigned int i = 0 ; i < neighbors.size() ; ++i )
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| {
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| std::cout << tree->GetMeasurementVector( neighbors[i] ) << std::endl;
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| }
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|
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| // Radius search
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| std::cout << "Radius search:" << std::endl;
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| double radius = 4.0;
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| tree->Search( queryPoint, radius, neighbors ) ; | |
| std::cout << "There are " << neighbors.size() << " neighbors." << std::endl;
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| for ( unsigned int i = 0 ; i < neighbors.size() ; ++i )
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| {
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| std::cout << tree->GetMeasurementVector( neighbors[i] ) << std::endl;
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| }
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| return EXIT_SUCCESS;
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| }
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| </source>
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| {{ITKVTKCMakeLists|KdTreeGenerator|}}
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