Classes
class	itk::Statistics::CovarianceCalculator< TSample >
	Calculates the covariance matrix of the target sample data. More...
class	itk::Statistics::DenseFrequencyContainer< TFrequencyValue >
	his class is a container for frequencies of bins in an histogram. More...
class	itk::Statistics::DensityFunction< TMeasurementVector >
	DensityFunction class defines common interfaces for density functions. More...
class	itk::Statistics::DistanceMetric< TVector >
	this class declares common interfaces for distance functions. More...
class	itk::Statistics::DistanceToCentroidMembershipFunction< TVector >
	DistanceToCentroidMembershipFunction class represents DistanceToCentroid Density Function. More...
class	itk::Statistics::EuclideanDistance< TVector >
	Euclidean distance function. More...
class	itk::Statistics::ExpectationMaximizationMixtureModelEstimator< TSample >
	This class generates the parameter estimates for a mixture model using expectation maximization strategy. More...
class	itk::Statistics::GaussianDensityFunction< TMeasurementVector >
	GaussianDensityFunction class represents Gaussian Density Function. More...
class	itk::Statistics::GaussianGoodnessOfFitComponent< TInputSample >
	is a GoodnessOfFitComponent for Gaussian distribution. More...
class	itk::Statistics::GaussianMixtureModelComponent< TSample >
	is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More...
class	itk::Statistics::GoodnessOfFitComponentBase< TInputSample >
	provides component (module) type specific functionalities for GoodnessOfFitMixtureModelCostFunction. More...
class	itk::Statistics::GoodnessOfFitFunctionBase< TInputHistogram >
	base class for classes calculates different types of goodness-of-fit statistics More...
class	itk::Statistics::GoodnessOfFitMixtureModelCostFunction< TInputSample >
	calculates the goodness-of-fit statstics for multivarate mixture model More...
class	itk::Statistics::Histogram< TMeasurement, VMeasurementVectorSize, TFrequencyContainer >
	This class stores measurement vectors in the context of n-dimensional histogram. More...
class	itk::Statistics::Histogram< TMeasurement, VMeasurementVectorSize, TFrequencyContainer >::Iterator
class	itk::Statistics::Histogram< TMeasurement, VMeasurementVectorSize, TFrequencyContainer >::ConstIterator
class	itk::Statistics::HypersphereKernelMeanShiftModeSeeker< TSample >
	Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More...
class	itk::Statistics::ImageToHistogramGenerator< TImageType >
class	itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector >
	This class provides ListSampleBase interfaces to ITK Image. More...
class	itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector >::Iterator
class	itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector >::ConstIterator
struct	itk::Statistics::ImageJointDomainTraits< TImage >
	This class provides the type defintion for the measurement vector in the joint domain (range domain -- pixel values + spatial domain -- pixel's physical coordinates). More...
class	itk::Statistics::JointDomainImageToListAdaptor< TImage >
	This adaptor returns measurement vectors composed of an image pixel's range domain value (pixel value) and spatial domain value (pixel's physical coordiantes). More...
struct	itk::Statistics::KdTreeNode< TSample >
	This class defines the interface of its derived classes. More...
struct	itk::Statistics::KdTreeNonterminalNode< TSample >
	This is a subclass of the KdTreeNode. More...
struct	itk::Statistics::KdTreeWeightedCentroidNonterminalNode< TSample >
	This is a subclass of the KdTreeNode. More...
struct	itk::Statistics::KdTreeTerminalNode< TSample >
	This class is the node that doesn't have any child node. The IsTerminal method returns true for this class. This class stores the instance identifiers belonging to this node, while the nonterminal nodes do not store them. The AddInstanceIdentifier and GetInstanceIdentifier are storing and retrieving the instance identifiers belonging to this node. More...
class	itk::Statistics::KdTree< TSample >
	This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More...
class	itk::Statistics::KdTree< TSample >::NearestNeighbors
	data structure for storing k-nearest neighbor search result (k number of Neighbors) More...
class	itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >
	fast k-means algorithm implementation using k-d tree structure More...
class	itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector
struct	itk::Statistics::KdTreeBasedKmeansEstimator< TKdTree >::CandidateVector::Candidate
class	itk::Statistics::KdTreeGenerator< TSample >
	This class generates a KdTree object without centroid information. More...
class	itk::Statistics::ListSample< TMeasurementVector >
	This class is the native implementation of the ListSampleBase. More...
class	itk::Statistics::ListSample< TMeasurementVector >::Iterator
class	itk::Statistics::ListSample< TMeasurementVector >::ConstIterator
class	itk::Statistics::ListSampleBase< TMeasurementVector >
	This class is the base class for containers that have a list of measurement vectors. More...
class	itk::Statistics::ListSampleToHistogramFilter< TListSample, THistogram >
	Imports data from ListSample object to Histogram object. More...
class	itk::Statistics::ListSampleToHistogramGenerator< TListSample, THistogramMeasurement, TFrequencyContainer >
	Generates a Histogram using the data from the ListSample object. More...
class	itk::Statistics::LogLikelihoodGoodnessOfFitFunction< TInputHistogram >
	calculates loglikelihood ratio statistics More...
class	itk::Statistics::MahalanobisDistanceMembershipFunction< TVector >
	MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density Function. More...
class	itk::Statistics::MeanCalculator< TSample >
	calculates sample mean More...
class	itk::Statistics::MeanShiftModeCacheMethod< TMeasurementVector >
	This class stores mappings between a query point and its resulting mode point. More...
struct	itk::Statistics::MeanShiftModeCacheMethod< TMeasurementVector >::LessMeasurementVector
class	itk::Statistics::MeanShiftModeSeekerBase< TSample >
	Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. More...
class	itk::Statistics::MembershipFunctionBase< TVector >
	MembershipFunctionBase class declares common interfaces for membership functions. More...
class	itk::Statistics::MembershipSample< TSample >
	Container for storing the instance-identifiers of other sample with their associated class labels. More...
class	itk::Statistics::MembershipSample< TSample >::ConstIterator
class	itk::Statistics::MembershipSampleGenerator< TInputSample, TClassMaskSample >
	MembershipSampleGenerator generates a MembershipSample object using a class mask sample. More...
class	itk::Statistics::MixtureModelComponentBase< TSample >
	base class for distribution modules that supports analytical way to update the distribution parameters More...
class	itk::Statistics::NeighborhoodSampler< TSample >
	generates a Subsample that is sampled from the input sample using a spherical kernel. More...
class	itk::Statistics::NormalVariateGenerator
	Normal random variate generator. More...
class	itk::Statistics::PointSetToListAdaptor< TPointSet >
	This class provides ListSampleBase interfaces to ITK PointSet. More...
class	itk::Statistics::PointSetToListAdaptor< TPointSet >::Iterator
class	itk::Statistics::PointSetToListAdaptor< TPointSet >::ConstIterator
class	itk::Statistics::RandomVariateGeneratorBase
	this class defines common interfaces for random variate generators More...
class	itk::Statistics::Sample< TMeasurementVector >
	Sample defines common iterfaces for each subclasses. More...
class	itk::Statistics::SampleAlgorithmBase< TInputSample >
	calculates sample mean More...
class	itk::Statistics::SampleClassifier< TSample >
	Integration point for MembershipCalculator, DecisionRule, and target sample data. More...
class	itk::Statistics::SampleClassifierWithMask< TSample, TMaskSample >
	Integration point for MembershipCalculator, DecisionRule, and target sample data. This class is functionally identical to the SampleClassifier, except that users can perform only part of the input sample that belongs to the subset of classes. More...
class	itk::Statistics::SampleMeanShiftBlurringFilter< TSample >
	This filter blurs the input sample data using mean shift algorithm. More...
class	itk::Statistics::SampleMeanShiftClusteringFilter< TSample >
	This filter create a cluster map from an input sample. More...
class	itk::Statistics::SampleSelectiveMeanShiftBlurringFilter< TSample >
	This filter blurs the input sample data using mean shift algorithm selectively. More...
class	itk::Statistics::SampleToHistogramProjectionFilter< TInputSample, THistogramMeasurement >
	projects measurement vectors on to an axis to generate an 1D histogram. More...
class	itk::Statistics::ScalarImageToHistogramGenerator< TImageType >
class	itk::Statistics::ScalarImageToListAdaptor< TImage >
	This class provides ListSampleBase interfaces to ITK Image. More...
class	itk::Statistics::SelectiveSubsampleGenerator< TInputSample, TClassMaskSample >
	SelectiveSubsampleGenerator generates a Subsample object that includes measurement vectors that belong to the classes that are specified by the SetSelectedClassLabels method. More...
class	itk::Statistics::SparseFrequencyContainer< TFrequencyValue >
	his class is a container for an histogram. More...
class	itk::Statistics::Subsample< TSample >
class	itk::Statistics::Subsample< TSample >::Iterator
class	itk::Statistics::Subsample< TSample >::ConstIterator
class	itk::Statistics::WeightedCentroidKdTreeGenerator< TSample >
	This class generates a KdTree object with centroid information. More...
class	itk::Statistics::WeightedCovarianceCalculator< TSample >
	Calculates the covariance matrix of the target sample data where each measurement vector has an associated weight value. More...
class	itk::Statistics::WeightedMeanCalculator< TSample >
	calculates sample mean where each measurement vector has associated weight value More...
Functions
template<class TSize> TSize	FloorLog (TSize size)
template<class TValue> TValue	MedianOfThree (const TValue a, const TValue b, const TValue c)
template<class TSample> void	FindSampleBound (const TSample *sample, typename TSample::ConstIterator begin, typename TSample::ConstIterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max)
template<class TSubsample> void	FindSampleBoundAndMean (const TSubsample *sample, int beginIndex, int endIndex, typename TSubsample::MeasurementVectorType &min, typename TSubsample::MeasurementVectorType &max, typename TSubsample::MeasurementVectorType &mean)
template<class TSubsample> int	Partition (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, const typename TSubsample::MeasurementType partitionValue)
template<class TSubsample> TSubsample::MeasurementType	QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth, typename TSubsample::MeasurementType medianGuess)
template<class TSubsample> TSubsample::MeasurementType	QuickSelect (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth)
template<class TSubsample> void	InsertSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)
template<class TSubsample> void	DownHeap (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int node)
template<class TSubsample> void	HeapSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)
template<class TSubsample> void	IntrospectiveSortLoop (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int depthLimit, int sizeThreshold)
template<class TSubsample> void	IntrospectiveSort (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int sizeThreshold)

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Function Documentation

template<class TSubsample> void DownHeap (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex, int  node )

template<class TSample> void FindSampleBound (  const TSample *  sample, typename TSample::ConstIterator  begin, typename TSample::ConstIterator  end, typename TSample::MeasurementVectorType &  min, typename TSample::MeasurementVectorType &  max )

template<class TSubsample> void FindSampleBoundAndMean (  const TSubsample *  sample, int  beginIndex, int  endIndex, typename TSubsample::MeasurementVectorType &  min, typename TSubsample::MeasurementVectorType &  max, typename TSubsample::MeasurementVectorType &  mean )

template<class TSize> TSize FloorLog (  TSize  size  )

template<class TSubsample> void HeapSort (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex )

template<class TSubsample> void InsertSort (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex )

template<class TSubsample> void IntrospectiveSort (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex, int  sizeThreshold )

template<class TSubsample> void IntrospectiveSortLoop (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex, int  depthLimit, int  sizeThreshold )

template<class TValue> TValue MedianOfThree (  const TValue  a, const TValue  b, const TValue  c )

template<class TSubsample> int Partition (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex, const typename TSubsample::MeasurementType  partitionValue )

template<class TSubsample> TSubsample::MeasurementType QuickSelect (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex, int  kth )

template<class TSubsample> TSubsample::MeasurementType QuickSelect (  TSubsample *  sample, unsigned int  activeDimension, int  beginIndex, int  endIndex, int  kth, typename TSubsample::MeasurementType  medianGuess )

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