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itk::Statistics Namespace Reference


Compounds

class  CovarianceCalculator
 Calculates the covariance matrix of the target sample data. More...

class  DenseFrequencyContainer
 his class is a container for frequencies of bins in an histogram. More...

class  DensityFunction
 DensityFunction class defines common interfaces for density functions. More...

class  DistanceMetric
 this class declares common interfaces for distance functions. More...

class  DistanceToCentroidMembershipFunction
 DistanceToCentroidMembershipFunction class represents DistanceToCentroid Density Function. More...

class  EuclideanDistance
 Euclidean distance function. More...

class  ExpectationMaximizationMixtureModelEstimator
 This class generates the parameter estimates for a mixture model using expectation maximization strategy. More...

class  GaussianDensityFunction
 GaussianDensityFunction class represents Gaussian Density Function. More...

class  GaussianGoodnessOfFitComponent
 is a GoodnessOfFitComponent for Gaussian distribution. More...

class  GaussianMixtureModelComponent
 is a component (derived from MixtureModelComponentBase) for Gaussian class. This class is used in ExpectationMaximizationMixtureModelEstimator. More...

class  GoodnessOfFitComponentBase
 provides component (module) type specific functionalities for GoodnessOfFitMixtureModelCostFunction. More...

class  GoodnessOfFitFunctionBase
 base class for classes calculates different types of goodness-of-fit statistics More...

class  GoodnessOfFitMixtureModelCostFunction
 calculates the goodness-of-fit statstics for multivarate mixture model More...

class  Histogram
 This class stores measurement vectors in the context of n-dimensional histogram. More...

class  Histogram.ConstIterator
class  Histogram.Iterator
class  HypersphereKernelMeanShiftModeSeeker
 Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More...

struct  ImageJointDomainTraits
 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  ImageToHistogramGenerator
class  ImageToListAdaptor
 This class provides ListSampleBase interfaces to ITK Image. More...

class  ImageToListAdaptor.ConstIterator
class  ImageToListAdaptor.Iterator
class  JointDomainImageToListAdaptor
 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...

class  KdTree
 This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. More...

class  KdTree.NearestNeighbors
 data structure for storing k-nearest neighbor search result (k number of Neighbors) More...

class  KdTreeBasedKmeansEstimator
 fast k-means algorithm implementation using k-d tree structure More...

class  KdTreeBasedKmeansEstimator.CandidateVector
struct  KdTreeBasedKmeansEstimator.CandidateVector.Candidate
class  KdTreeGenerator
 This class generates a KdTree object without centroid information. More...

struct  KdTreeNode
 This class defines the interface of its derived classes. More...

struct  KdTreeNonterminalNode
 This is a subclass of the KdTreeNode. More...

struct  KdTreeTerminalNode
 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...

struct  KdTreeWeightedCentroidNonterminalNode
 This is a subclass of the KdTreeNode. More...

class  ListSample
 This class is the native implementation of the ListSampleBase. More...

class  ListSample.ConstIterator
class  ListSample.Iterator
class  ListSampleBase
 This class is the base class for containers that have a list of measurement vectors. More...

class  ListSampleToHistogramFilter
 Imports data from ListSample object to Histogram object. More...

class  ListSampleToHistogramGenerator
 Generates a Histogram using the data from the ListSample object. More...

class  LogLikelihoodGoodnessOfFitFunction
 calculates loglikelihood ratio statistics More...

class  MahalanobisDistanceMembershipFunction
 MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density Function. More...

class  MeanCalculator
 calculates sample mean More...

class  MeanShiftModeCacheMethod
 This class stores mappings between a query point and its resulting mode point. More...

struct  MeanShiftModeCacheMethod.LessMeasurementVector
class  MeanShiftModeSeekerBase
 Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. More...

class  MembershipFunctionBase
 MembershipFunctionBase class declares common interfaces for membership functions. More...

class  MembershipSample
 Container for storing the instance-identifiers of other sample with their associated class labels. More...

class  MembershipSample.ConstIterator
class  MembershipSampleGenerator
 MembershipSampleGenerator generates a MembershipSample object using a class mask sample. More...

class  MixtureModelComponentBase
 base class for distribution modules that supports analytical way to update the distribution parameters More...

class  NeighborhoodSampler
 generates a Subsample that is sampled from the input sample using a spherical kernel. More...

class  NormalVariateGenerator
 Normal random variate generator. More...

class  PointSetToListAdaptor
 This class provides ListSampleBase interfaces to ITK PointSet. More...

class  PointSetToListAdaptor.ConstIterator
class  PointSetToListAdaptor.Iterator
class  RandomVariateGeneratorBase
 this class defines common interfaces for random variate generators More...

class  Sample
 Sample defines common iterfaces for each subclasses. More...

class  SampleAlgorithmBase
 calculates sample mean More...

class  SampleClassifier
 Integration point for MembershipCalculator, DecisionRule, and target sample data. More...

class  SampleClassifierWithMask
 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  SampleMeanShiftBlurringFilter
 This filter blurs the input sample data using mean shift algorithm. More...

class  SampleMeanShiftClusteringFilter
 This filter create a cluster map from an input sample. More...

class  SampleSelectiveMeanShiftBlurringFilter
 This filter blurs the input sample data using mean shift algorithm selectively. More...

class  SampleToHistogramProjectionFilter
 projects measurement vectors on to an axis to generate an 1D histogram. More...

class  ScalarImageToHistogramGenerator
class  ScalarImageToListAdaptor
 This class provides ListSampleBase interfaces to ITK Image. More...

class  SelectiveSubsampleGenerator
 SelectiveSubsampleGenerator generates a Subsample object that includes measurement vectors that belong to the classes that are specified by the SetSelectedClassLabels method. More...

class  SparseFrequencyContainer
 his class is a container for an histogram. More...

class  Subsample
class  Subsample.ConstIterator
class  Subsample.Iterator
class  WeightedCentroidKdTreeGenerator
 This class generates a KdTree object with centroid information. More...

class  WeightedCovarianceCalculator
 Calculates the covariance matrix of the target sample data where each measurement vector has an associated weight value. More...

class  WeightedMeanCalculator
 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)


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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