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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::HypersphereKernelMeanShiftModeSeeker< TSample >
 Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More...
class  itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector >
 This class provides ListSampleBase interfaces to ITK Image. More...
class  itk::Statistics::ImageToListAdaptor< TImage, TMeasurementVector >::Iterator
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::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 >::Iterator
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::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::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::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 (TSample *sample, typename TSample::Iterator begin, typename TSample::Iterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max)
template<class TSubsample> void FindSampleBoundAndMean (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)


Function Documentation

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

template<class TSample>
void FindSampleBound TSample *  sample,
typename TSample::Iterator  begin,
typename TSample::Iterator  end,
typename TSample::MeasurementVectorType &  min,
typename TSample::MeasurementVectorType &  max
 

template<class TSubsample>
void FindSampleBoundAndMean 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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