itk::Statistics Namespace Reference

Classes
class	BackPropagationLayer
class	BatchSupervisedTrainingFunction
class	ChiSquareDistribution
	ChiSquareDistribution class defines the interface for a univariate Chi-Square distribution (pdfs, cdfs, etc.). More...
class	CompletelyConnectedWeightSet
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
	class represents DistanceToCentroid Density Function. More...
class	ErrorBackPropagationLearningFunctionBase
class	ErrorBackPropagationLearningWithMomentum
class	ErrorFunctionBase
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	GaussianDistribution
	GaussianDistribution class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.). 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	GaussianRadialBasisFunction
class	GaussianTransferFunction
struct	GetAdaptorMeasurementVectorLength
struct	GetHistogramDimension
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	GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator
	This class computes texture feature coefficients from a grey level co-occurrence matrix. More...
class	HardLimitTransferFunction
class	Histogram
	This class stores measurement vectors in the context of n-dimensional histogram. More...
class	HypersphereKernelMeanShiftModeSeeker
	Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. More...
class	IdentityTransferFunction
class	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	ImageToCooccurrenceListAdaptor
	Converts pixel data into a list of pairs in order to compute a cooccurrence Histogram. More...
class	ImageToHistogramGenerator
	This class generates an histogram from an image. More...
class	ImageToListAdaptor
	This class provides ListSampleBase interfaces to ITK Image. More...
class	ImageToListGenerator
	The class takes an image as input and generates a list sample as output. More...
class	InputFunctionBase
class	IterativeSupervisedTrainingFunction
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	KdTreeBasedKmeansEstimator
	fast k-means algorithm implementation using k-d tree structure More...
class	KdTreeGenerator
	This class generates a KdTree object without centroid information. More...
class	KdTreeNode
	This class defines the interface of its derived classes. More...
class	KdTreeNonterminalNode
	This is a subclass of the KdTreeNode. More...
class	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...
class	KdTreeWeightedCentroidNonterminalNode
	This is a subclass of the KdTreeNode. More...
class	LayerBase
class	LearningFunctionBase
class	ListSample
	This class is the native implementation of the ListSampleBase. More...
class	ListSampleBase
	This class is the base class for Samples that store measurements in a list. 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	LogSigmoidTransferFunction
class	MahalanobisDistanceMembershipFunction
	MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density Function. More...
class	MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator
	This class computes a grey-level co-occurence matrix (histogram) from a given image and mask. GLCM's are used for image texture description. More...
class	MeanCalculator
	calculates sample mean More...
class	MeanShiftModeCacheMethod
	This class stores mappings between a query point and its resulting mode point. More...
class	MeanShiftModeSeekerBase
	Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. More...
class	MeanSquaredErrorFunction
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	MembershipSampleGenerator
	MembershipSampleGenerator generates a MembershipSample object using a class mask sample. More...
class	MersenneTwisterRandomVariateGenerator
	MersenneTwisterRandom random variate generator. More...
class	MixtureModelComponentBase
	base class for distribution modules that supports analytical way to update the distribution parameters More...
class	MultilayerNeuralNetworkBase
class	MultiquadricRadialBasisFunction
class	NeighborhoodSampler
	generates a Subsample that is sampled from the input sample using a spherical kernel. More...
class	NeuralNetworkObject
class	NNetDistanceMetricBase
class	NormalVariateGenerator
	Normal random variate generator. More...
class	OneHiddenLayerBackPropagationNeuralNetwork
class	PointSetToListAdaptor
	This class provides ListSampleBase interfaces to ITK PointSet. More...
class	ProbabilityDistribution
	ProbabilityDistribution class defines common interface for statistical distributions (pdfs, cdfs, etc.). More...
class	ProductInputFunction
class	QuickPropLearningRule
class	RadialBasisFunctionBase
class	RandomVariateGeneratorBase
	this class defines common interfaces for random variate generators More...
class	RBFBackPropagationLearningFunction
class	RBFLayer
class	RBFNetwork
class	Sample
	A collection of measurements for statistical analysis. More...
class	SampleAlgorithmBase
	This class is a base class for algorithms that operate on Sample data. The class is templated over the SampleType, which it takes as input using the SetInputSample() method. Derived classes that operate or calculate statistics on this input sample data and can access it using the GetInputSample() method. 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	ScalarImageTextureCalculator
	This class computes texture descriptions from an image. More...
class	ScalarImageToGreyLevelCooccurrenceMatrixGenerator
	This class computes a grey-level co-occurence matrix (histogram) from a given image. GLCM's are used for image texture description. 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	SigmoidTransferFunction
class	SignedHardLimitTransferFunction
class	SparseFrequencyContainer
	his class is a container for an histogram. More...
class	SquaredDifferenceErrorFunction
class	Subsample
	This class stores a subset of instance identifiers from another sample object. You can create a subsample out of another sample object or another subsample object. The class is useful when storing or extracting a portion of a sample object. Note that when the elements of a subsample are sorted, the instance identifiers of the subsample are sorted without changing the original source sample. Most Statistics algorithms (that derive from StatisticsAlgorithmBase accept Subsample objects as inputs). More...
class	SumInputFunction
class	SymmetricSigmoidTransferFunction
class	TanHTransferFunction
class	TanSigmoidTransferFunction
class	TDistribution
	TDistribution class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.). More...
class	TrainingFunctionBase
class	TransferFunctionBase
class	TwoHiddenLayerBackPropagationNeuralNetwork
class	VariableDimensionHistogram
	This class is similar to the Histogram class. It however allows you to specify the histogram dimension at run time. (and is therefore not templated over the size of a measurement vector). Users who know that the length of a measurement vector will be fixed, for instance joint statistics on pixel values of 2 images, (where the dimension will be 2), etc should use the Histogram class instead. More...
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...
class	WeightSetBase
Enumerations
enum	TextureFeatureName {   Energy,   Entropy,   Correlation,   InverseDifferenceMoment,   Inertia,   ClusterShade,   ClusterProminence,   HaralickCorrelation }
Functions
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>
TSubsample::MeasurementType	NthElement (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int nth)
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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Enumeration Type Documentation

enum itk::Statistics::TextureFeatureName

Texture feature types

Enumerator:

Energy
Entropy
Correlation
InverseDifferenceMoment
Inertia
ClusterShade
ClusterProminence
HaralickCorrelation

Definition at line 98 of file itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h.

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Function Documentation

template<class TSubsample>

void itk::Statistics::DownHeap	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		int	node
	)

template<class TSample>

void itk::Statistics::FindSampleBound	(	const TSample *	sample,
		typename TSample::ConstIterator	begin,
		typename TSample::ConstIterator	end,
		typename TSample::MeasurementVectorType &	min,
		typename TSample::MeasurementVectorType &	max
	)

template<class TSubsample>

void itk::Statistics::FindSampleBoundAndMean	(	const TSubsample *	sample,
		int	beginIndex,
		int	endIndex,
		typename TSubsample::MeasurementVectorType &	min,
		typename TSubsample::MeasurementVectorType &	max,
		typename TSubsample::MeasurementVectorType &	mean
	)

template<class TSize>

TSize itk::Statistics::FloorLog

(

TSize

size

)

template<class TSubsample>

void itk::Statistics::HeapSort	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex
	)

template<class TSubsample>

void itk::Statistics::InsertSort	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex
	)

template<class TSubsample>

void itk::Statistics::IntrospectiveSort	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		int	sizeThreshold
	)

template<class TSubsample>

void itk::Statistics::IntrospectiveSortLoop	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		int	depthLimit,
		int	sizeThreshold
	)

template<class TValue>

TValue itk::Statistics::MedianOfThree	(	const TValue	a,
		const TValue	b,
		const TValue	c
	)

template<class TSubsample>

TSubsample::MeasurementType itk::Statistics::NthElement	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		int	nth
	)

NthElement is an algorithm for finding the n-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. This algorithm was based on the procedure used in the STL nth_element method.

template<class TSubsample>

int itk::Statistics::Partition	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		const typename TSubsample::MeasurementType	partitionValue
	)

The Partition algorithm performs partial sorting in a sample. Given a partitionValue, the algorithm moves to the beginning of the sample all MeasurementVectors whose component activeDimension is smaller than the partitionValue. In this way, the sample is partially sorted in two groups. First the group with activeDimension component smaller than the partitionValue, then the group of MeasurementVectors with activeDimension component larger than the partitionValue. The Partition algorithm takes as input a sample, and a range in that sample defined by [beginIndex,endIndex]. Only the activeDimension components of the MeasurementVectors in the sample will be considered by the algorithm. The Algorithm return an index in the range of [beginIndex,endIndex] pointing to the element with activeDimension component closest to the partitionValue.

template<class TSubsample>

TSubsample::MeasurementType itk::Statistics::QuickSelect	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		int	kth
	)

QuickSelect is an algorithm for finding the k-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. http://en.wikipedia.org/wiki/Selection_algorithm.

template<class TSubsample>

TSubsample::MeasurementType itk::Statistics::QuickSelect	(	TSubsample *	sample,
		unsigned int	activeDimension,
		int	beginIndex,
		int	endIndex,
		int	kth,
		typename TSubsample::MeasurementType	medianGuess
	)

QuickSelect is an algorithm for finding the k-th largest element of a list. In this case, only of the components of the measurement vectors is considered. This component is defined by the argument activeDimension. The search is rectricted to the range between the index begin and end, also passed as arguments. In this version, a guess value for the median index is provided in the argument medianGuess. The algoritm returns the value of the activeDimension component in the MeasurementVector located in the kth position. http://en.wikipedia.org/wiki/Selection_algorithm

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