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<h1>itk::Statistics Namespace Reference</h1><table border="0" cellpadding="0" cellspacing="0">
<tr><td></td></tr>
<tr><td colspan="2"><br><h2>Classes</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1BackPropagationLayer.html">BackPropagationLayer</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1BatchSupervisedTrainingFunction.html">BatchSupervisedTrainingFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ChiSquareDistribution.html">ChiSquareDistribution</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1ChiSquareDistribution.html" title="ChiSquareDistribution class defines the interface for a univariate Chi-Square distribution...">ChiSquareDistribution</a> class defines the interface for a univariate Chi-Square distribution (pdfs, cdfs, etc.). <a href="classitk_1_1Statistics_1_1ChiSquareDistribution.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1CompletelyConnectedWeightSet.html">CompletelyConnectedWeightSet</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1CovarianceCalculator.html">CovarianceCalculator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Calculates the covariance matrix of the target sample data. <a href="classitk_1_1Statistics_1_1CovarianceCalculator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1DenseFrequencyContainer.html">DenseFrequencyContainer</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">his class is a container for frequencies of bins in an histogram. <a href="classitk_1_1Statistics_1_1DenseFrequencyContainer.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1DensityFunction.html">DensityFunction</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1DensityFunction.html" title="DensityFunction class defines common interfaces for density functions.">DensityFunction</a> class defines common interfaces for density functions. <a href="classitk_1_1Statistics_1_1DensityFunction.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1DistanceMetric.html">DistanceMetric</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">this class declares common interfaces for distance functions. <a href="classitk_1_1Statistics_1_1DistanceMetric.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1DistanceToCentroidMembershipFunction.html">DistanceToCentroidMembershipFunction</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">class represents DistanceToCentroid Density <a class="el" href="namespaceitk_1_1Function.html">Function</a>. <a href="classitk_1_1Statistics_1_1DistanceToCentroidMembershipFunction.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ErrorBackPropagationLearningFunctionBase.html">ErrorBackPropagationLearningFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ErrorBackPropagationLearningWithMomentum.html">ErrorBackPropagationLearningWithMomentum</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ErrorFunctionBase.html">ErrorFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1EuclideanDistance.html">EuclideanDistance</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Euclidean distance function. <a href="classitk_1_1Statistics_1_1EuclideanDistance.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ExpectationMaximizationMixtureModelEstimator.html">ExpectationMaximizationMixtureModelEstimator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class generates the parameter estimates for a mixture model using expectation maximization strategy. <a href="classitk_1_1Statistics_1_1ExpectationMaximizationMixtureModelEstimator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GaussianDensityFunction.html">GaussianDensityFunction</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1GaussianDensityFunction.html" title="GaussianDensityFunction class represents Gaussian Density Function.">GaussianDensityFunction</a> class represents Gaussian Density <a class="el" href="namespaceitk_1_1Function.html">Function</a>. <a href="classitk_1_1Statistics_1_1GaussianDensityFunction.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GaussianDistribution.html">GaussianDistribution</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1GaussianDistribution.html" title="GaussianDistribution class defines the interface for a univariate Gaussian distribution...">GaussianDistribution</a> class defines the interface for a univariate Gaussian distribution (pdfs, cdfs, etc.). <a href="classitk_1_1Statistics_1_1GaussianDistribution.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GaussianGoodnessOfFitComponent.html">GaussianGoodnessOfFitComponent</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">is a GoodnessOfFitComponent for Gaussian distribution. <a href="classitk_1_1Statistics_1_1GaussianGoodnessOfFitComponent.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GaussianMixtureModelComponent.html">GaussianMixtureModelComponent</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">is a component (derived from <a class="el" href="classitk_1_1Statistics_1_1MixtureModelComponentBase.html" title="base class for distribution modules that supports analytical way to update the distribution...">MixtureModelComponentBase</a>) for Gaussian class. This class is used in <a class="el" href="classitk_1_1Statistics_1_1ExpectationMaximizationMixtureModelEstimator.html" title="This class generates the parameter estimates for a mixture model using expectation...">ExpectationMaximizationMixtureModelEstimator</a>. <a href="classitk_1_1Statistics_1_1GaussianMixtureModelComponent.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GaussianRadialBasisFunction.html">GaussianRadialBasisFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GaussianTransferFunction.html">GaussianTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">struct </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1GetAdaptorMeasurementVectorLength.html">GetAdaptorMeasurementVectorLength</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">struct </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1GetHistogramDimension.html">GetHistogramDimension</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GoodnessOfFitComponentBase.html">GoodnessOfFitComponentBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">provides component (module) type specific functionalities for <a class="el" href="classitk_1_1Statistics_1_1GoodnessOfFitMixtureModelCostFunction.html" title="calculates the goodness-of-fit statstics for multivarate mixture model">GoodnessOfFitMixtureModelCostFunction</a>. <a href="classitk_1_1Statistics_1_1GoodnessOfFitComponentBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GoodnessOfFitFunctionBase.html">GoodnessOfFitFunctionBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">base class for classes calculates different types of goodness-of-fit statistics <a href="classitk_1_1Statistics_1_1GoodnessOfFitFunctionBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GoodnessOfFitMixtureModelCostFunction.html">GoodnessOfFitMixtureModelCostFunction</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">calculates the goodness-of-fit statstics for multivarate mixture model <a href="classitk_1_1Statistics_1_1GoodnessOfFitMixtureModelCostFunction.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.html">GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class computes texture feature coefficients from a grey level co-occurrence matrix. <a href="classitk_1_1Statistics_1_1GreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1HardLimitTransferFunction.html">HardLimitTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1Histogram.html">Histogram</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class stores measurement vectors in the context of n-dimensional histogram. <a href="classitk_1_1Statistics_1_1Histogram.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1HypersphereKernelMeanShiftModeSeeker.html">HypersphereKernelMeanShiftModeSeeker</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Evolves the mode using a hyperspherical kernel defined by a radius (which is set by SetRadius) method. <a href="classitk_1_1Statistics_1_1HypersphereKernelMeanShiftModeSeeker.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1IdentityTransferFunction.html">IdentityTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1ImageJointDomainTraits.html">ImageJointDomainTraits</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class provides the type defintion for the measurement vector in the joint domain (range domain -- pixel values + spatial domain -- pixel's physical coordinates). <a href="structitk_1_1Statistics_1_1ImageJointDomainTraits.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ImageToCooccurrenceListAdaptor.html">ImageToCooccurrenceListAdaptor</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Converts pixel data into a list of pairs in order to compute a cooccurrence <a class="el" href="classitk_1_1Statistics_1_1Histogram.html" title="This class stores measurement vectors in the context of n-dimensional histogram.">Histogram</a>. <a href="classitk_1_1Statistics_1_1ImageToCooccurrenceListAdaptor.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ImageToHistogramGenerator.html">ImageToHistogramGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class generates an histogram from an image. <a href="classitk_1_1Statistics_1_1ImageToHistogramGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ImageToListAdaptor.html">ImageToListAdaptor</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class provides <a class="el" href="classitk_1_1Statistics_1_1ListSampleBase.html" title="This class is the base class for Samples that store measurements in a list.">ListSampleBase</a> interfaces to ITK <a class="el" href="classitk_1_1Image.html" title="Templated n-dimensional image class.">Image</a>. <a href="classitk_1_1Statistics_1_1ImageToListAdaptor.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ImageToListGenerator.html">ImageToListGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">The class takes an image as input and generates a list sample as output. <a href="classitk_1_1Statistics_1_1ImageToListGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1InputFunctionBase.html">InputFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1IterativeSupervisedTrainingFunction.html">IterativeSupervisedTrainingFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1JointDomainImageToListAdaptor.html">JointDomainImageToListAdaptor</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This adaptor returns measurement vectors composed of an image pixel's range domain value (pixel value) and spatial domain value (pixel's physical coordiantes). <a href="classitk_1_1Statistics_1_1JointDomainImageToListAdaptor.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1KdTree.html">KdTree</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class provides methods for k-nearest neighbor search and related data structures for a k-d tree. <a href="classitk_1_1Statistics_1_1KdTree.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html">KdTreeBasedKmeansEstimator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">fast k-means algorithm implementation using k-d tree structure <a href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1KdTreeGenerator.html">KdTreeGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class generates a <a class="el" href="classitk_1_1Statistics_1_1KdTree.html" title="This class provides methods for k-nearest neighbor search and related data structures...">KdTree</a> object without centroid information. <a href="classitk_1_1Statistics_1_1KdTreeGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1KdTreeNode.html">KdTreeNode</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class defines the interface of its derived classes. <a href="structitk_1_1Statistics_1_1KdTreeNode.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1KdTreeNonterminalNode.html">KdTreeNonterminalNode</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This is a subclass of the <a class="el" href="structitk_1_1Statistics_1_1KdTreeNode.html" title="This class defines the interface of its derived classes.">KdTreeNode</a>. <a href="structitk_1_1Statistics_1_1KdTreeNonterminalNode.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1KdTreeTerminalNode.html">KdTreeTerminalNode</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">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. <a href="structitk_1_1Statistics_1_1KdTreeTerminalNode.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="structitk_1_1Statistics_1_1KdTreeWeightedCentroidNonterminalNode.html">KdTreeWeightedCentroidNonterminalNode</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This is a subclass of the <a class="el" href="structitk_1_1Statistics_1_1KdTreeNode.html" title="This class defines the interface of its derived classes.">KdTreeNode</a>. <a href="structitk_1_1Statistics_1_1KdTreeWeightedCentroidNonterminalNode.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1LayerBase.html">LayerBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1LearningFunctionBase.html">LearningFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ListSample.html">ListSample</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class is the native implementation of the <a class="el" href="classitk_1_1Statistics_1_1ListSampleBase.html" title="This class is the base class for Samples that store measurements in a list.">ListSampleBase</a>. <a href="classitk_1_1Statistics_1_1ListSample.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ListSampleBase.html">ListSampleBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class is the base class for Samples that store measurements in a list. <a href="classitk_1_1Statistics_1_1ListSampleBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ListSampleToHistogramFilter.html">ListSampleToHistogramFilter</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Imports data from <a class="el" href="classitk_1_1Statistics_1_1ListSample.html" title="This class is the native implementation of the ListSampleBase.">ListSample</a> object to <a class="el" href="classitk_1_1Statistics_1_1Histogram.html" title="This class stores measurement vectors in the context of n-dimensional histogram.">Histogram</a> object. <a href="classitk_1_1Statistics_1_1ListSampleToHistogramFilter.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ListSampleToHistogramGenerator.html">ListSampleToHistogramGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Generates a <a class="el" href="classitk_1_1Statistics_1_1Histogram.html" title="This class stores measurement vectors in the context of n-dimensional histogram.">Histogram</a> using the data from the <a class="el" href="classitk_1_1Statistics_1_1ListSample.html" title="This class is the native implementation of the ListSampleBase.">ListSample</a> object. <a href="classitk_1_1Statistics_1_1ListSampleToHistogramGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1LogLikelihoodGoodnessOfFitFunction.html">LogLikelihoodGoodnessOfFitFunction</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">calculates loglikelihood ratio statistics <a href="classitk_1_1Statistics_1_1LogLikelihoodGoodnessOfFitFunction.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1LogSigmoidTransferFunction.html">LogSigmoidTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MahalanobisDistanceMembershipFunction.html">MahalanobisDistanceMembershipFunction</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1MahalanobisDistanceMembershipFunction.html" title="MahalanobisDistanceMembershipFunction class represents MahalanobisDistance Density...">MahalanobisDistanceMembershipFunction</a> class represents MahalanobisDistance Density <a class="el" href="namespaceitk_1_1Function.html">Function</a>. <a href="classitk_1_1Statistics_1_1MahalanobisDistanceMembershipFunction.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator.html">MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class computes a grey-level co-occurence matrix (histogram) from a given image and mask. GLCM's are used for image texture description. <a href="classitk_1_1Statistics_1_1MaskedScalarImageToGreyLevelCooccurrenceMatrixGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MeanCalculator.html">MeanCalculator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">calculates sample mean <a href="classitk_1_1Statistics_1_1MeanCalculator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MeanShiftModeCacheMethod.html">MeanShiftModeCacheMethod</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class stores mappings between a query point and its resulting mode point. <a href="classitk_1_1Statistics_1_1MeanShiftModeCacheMethod.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MeanShiftModeSeekerBase.html">MeanShiftModeSeekerBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Evolves the mode. This is the base class for any mean shift mode seeking algorithm classes. <a href="classitk_1_1Statistics_1_1MeanShiftModeSeekerBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MeanSquaredErrorFunction.html">MeanSquaredErrorFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MembershipFunctionBase.html">MembershipFunctionBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1MembershipFunctionBase.html" title="MembershipFunctionBase class declares common interfaces for membership functions...">MembershipFunctionBase</a> class declares common interfaces for membership functions. <a href="classitk_1_1Statistics_1_1MembershipFunctionBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MembershipSample.html">MembershipSample</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Container for storing the instance-identifiers of other sample with their associated class labels. <a href="classitk_1_1Statistics_1_1MembershipSample.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MembershipSampleGenerator.html">MembershipSampleGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1MembershipSampleGenerator.html" title="MembershipSampleGenerator generates a MembershipSample object using a class mask...">MembershipSampleGenerator</a> generates a <a class="el" href="classitk_1_1Statistics_1_1MembershipSample.html" title="Container for storing the instance-identifiers of other sample with their associated...">MembershipSample</a> object using a class mask sample. <a href="classitk_1_1Statistics_1_1MembershipSampleGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MersenneTwisterRandomVariateGenerator.html">MersenneTwisterRandomVariateGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">MersenneTwisterRandom random variate generator. <a href="classitk_1_1Statistics_1_1MersenneTwisterRandomVariateGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MixtureModelComponentBase.html">MixtureModelComponentBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">base class for distribution modules that supports analytical way to update the distribution parameters <a href="classitk_1_1Statistics_1_1MixtureModelComponentBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MultilayerNeuralNetworkBase.html">MultilayerNeuralNetworkBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1MultiquadricRadialBasisFunction.html">MultiquadricRadialBasisFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1NeighborhoodSampler.html">NeighborhoodSampler</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">generates a <a class="el" href="classitk_1_1Statistics_1_1Subsample.html" title="This class stores a subset of instance identifiers from another sample object. You...">Subsample</a> that is sampled from the input sample using a spherical kernel. <a href="classitk_1_1Statistics_1_1NeighborhoodSampler.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1NeuralNetworkObject.html">NeuralNetworkObject</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1NNetDistanceMetricBase.html">NNetDistanceMetricBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1NormalVariateGenerator.html">NormalVariateGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Normal random variate generator. <a href="classitk_1_1Statistics_1_1NormalVariateGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1OneHiddenLayerBackPropagationNeuralNetwork.html">OneHiddenLayerBackPropagationNeuralNetwork</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1PointSetToListAdaptor.html">PointSetToListAdaptor</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class provides <a class="el" href="classitk_1_1Statistics_1_1ListSampleBase.html" title="This class is the base class for Samples that store measurements in a list.">ListSampleBase</a> interfaces to ITK <a class="el" href="classitk_1_1PointSet.html" title="A superclass of the N-dimensional mesh structure; supports point (geometric coordinate...">PointSet</a>. <a href="classitk_1_1Statistics_1_1PointSetToListAdaptor.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ProbabilityDistribution.html">ProbabilityDistribution</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1ProbabilityDistribution.html" title="ProbabilityDistribution class defines common interface for statistical distributions...">ProbabilityDistribution</a> class defines common interface for statistical distributions (pdfs, cdfs, etc.). <a href="classitk_1_1Statistics_1_1ProbabilityDistribution.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ProductInputFunction.html">ProductInputFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1QuickPropLearningRule.html">QuickPropLearningRule</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1RadialBasisFunctionBase.html">RadialBasisFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1RandomVariateGeneratorBase.html">RandomVariateGeneratorBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">this class defines common interfaces for random variate generators <a href="classitk_1_1Statistics_1_1RandomVariateGeneratorBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1RBFBackPropagationLearningFunction.html">RBFBackPropagationLearningFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1RBFLayer.html">RBFLayer</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1RBFNetwork.html">RBFNetwork</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1Sample.html">Sample</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classA.html">A</a> collection of measurements for statistical analysis. <a href="classitk_1_1Statistics_1_1Sample.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleAlgorithmBase.html">SampleAlgorithmBase</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class is a base class for algorithms that operate on <a class="el" href="classitk_1_1Statistics_1_1Sample.html" title="A collection of measurements for statistical analysis.">Sample</a> data. The class is templated over the SampleType, which it takes as input using the <a class="el" href="classitk_1_1Statistics_1_1SampleAlgorithmBase.html#e08306148af94feabe0ca8b4275d31d7">SetInputSample()</a> method. Derived classes that operate or calculate statistics on this input sample data and can access it using the <a class="el" href="classitk_1_1Statistics_1_1SampleAlgorithmBase.html#7fd28013f705d59e97d463b7b5aaa272">GetInputSample()</a> method. <a href="classitk_1_1Statistics_1_1SampleAlgorithmBase.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleClassifier.html">SampleClassifier</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Integration point for MembershipCalculator, DecisionRule, and target sample data. <a href="classitk_1_1Statistics_1_1SampleClassifier.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleClassifierWithMask.html">SampleClassifierWithMask</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Integration point for MembershipCalculator, DecisionRule, and target sample data. This class is functionally identical to the <a class="el" href="classitk_1_1Statistics_1_1SampleClassifier.html" title="Integration point for MembershipCalculator, DecisionRule, and target sample data...">SampleClassifier</a>, except that users can perform only part of the input sample that belongs to the subset of classes. <a href="classitk_1_1Statistics_1_1SampleClassifierWithMask.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleMeanShiftBlurringFilter.html">SampleMeanShiftBlurringFilter</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This filter blurs the input sample data using mean shift algorithm. <a href="classitk_1_1Statistics_1_1SampleMeanShiftBlurringFilter.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleMeanShiftClusteringFilter.html">SampleMeanShiftClusteringFilter</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This filter create a cluster map from an input sample. <a href="classitk_1_1Statistics_1_1SampleMeanShiftClusteringFilter.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleSelectiveMeanShiftBlurringFilter.html">SampleSelectiveMeanShiftBlurringFilter</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This filter blurs the input sample data using mean shift algorithm selectively. <a href="classitk_1_1Statistics_1_1SampleSelectiveMeanShiftBlurringFilter.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SampleToHistogramProjectionFilter.html">SampleToHistogramProjectionFilter</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">projects measurement vectors on to an axis to generate an 1D histogram. <a href="classitk_1_1Statistics_1_1SampleToHistogramProjectionFilter.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ScalarImageTextureCalculator.html">ScalarImageTextureCalculator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class computes texture descriptions from an image. <a href="classitk_1_1Statistics_1_1ScalarImageTextureCalculator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ScalarImageToGreyLevelCooccurrenceMatrixGenerator.html">ScalarImageToGreyLevelCooccurrenceMatrixGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class computes a grey-level co-occurence matrix (histogram) from a given image. GLCM's are used for image texture description. <a href="classitk_1_1Statistics_1_1ScalarImageToGreyLevelCooccurrenceMatrixGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ScalarImageToHistogramGenerator.html">ScalarImageToHistogramGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">TODO. <a href="classitk_1_1Statistics_1_1ScalarImageToHistogramGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1ScalarImageToListAdaptor.html">ScalarImageToListAdaptor</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class provides <a class="el" href="classitk_1_1Statistics_1_1ListSampleBase.html" title="This class is the base class for Samples that store measurements in a list.">ListSampleBase</a> interfaces to ITK <a class="el" href="classitk_1_1Image.html" title="Templated n-dimensional image class.">Image</a>. <a href="classitk_1_1Statistics_1_1ScalarImageToListAdaptor.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SelectiveSubsampleGenerator.html">SelectiveSubsampleGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1SelectiveSubsampleGenerator.html" title="SelectiveSubsampleGenerator generates a Subsample object that includes measurement...">SelectiveSubsampleGenerator</a> generates a <a class="el" href="classitk_1_1Statistics_1_1Subsample.html" title="This class stores a subset of instance identifiers from another sample object. You...">Subsample</a> object that includes measurement vectors that belong to the classes that are specified by the SetSelectedClassLabels method. <a href="classitk_1_1Statistics_1_1SelectiveSubsampleGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SigmoidTransferFunction.html">SigmoidTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SignedHardLimitTransferFunction.html">SignedHardLimitTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SparseFrequencyContainer.html">SparseFrequencyContainer</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">his class is a container for an histogram. <a href="classitk_1_1Statistics_1_1SparseFrequencyContainer.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SquaredDifferenceErrorFunction.html">SquaredDifferenceErrorFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1Subsample.html">Subsample</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">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 <a class="el" href="namespaceitk_1_1Statistics.html">Statistics</a> algorithms (that derive from StatisticsAlgorithmBase accept <a class="el" href="classitk_1_1Statistics_1_1Subsample.html" title="This class stores a subset of instance identifiers from another sample object. You...">Subsample</a> objects as inputs). <a href="classitk_1_1Statistics_1_1Subsample.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SumInputFunction.html">SumInputFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1SymmetricSigmoidTransferFunction.html">SymmetricSigmoidTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1TanHTransferFunction.html">TanHTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1TanSigmoidTransferFunction.html">TanSigmoidTransferFunction</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1TDistribution.html">TDistribution</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight"><a class="el" href="classitk_1_1Statistics_1_1TDistribution.html" title="TDistribution class defines the interface for a univariate Student-t distribution...">TDistribution</a> class defines the interface for a univariate Student-t distribution (pdfs, cdfs, etc.). <a href="classitk_1_1Statistics_1_1TDistribution.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1TrainingFunctionBase.html">TrainingFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1TransferFunctionBase.html">TransferFunctionBase</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1TwoHiddenLayerBackPropagationNeuralNetwork.html">TwoHiddenLayerBackPropagationNeuralNetwork</a></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1VariableDimensionHistogram.html">VariableDimensionHistogram</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class is similar to the <a class="el" href="classitk_1_1Statistics_1_1Histogram.html" title="This class stores measurement vectors in the context of n-dimensional histogram.">Histogram</a> 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 <a class="el" href="classitk_1_1Statistics_1_1Histogram.html" title="This class stores measurement vectors in the context of n-dimensional histogram.">Histogram</a> class instead. <a href="classitk_1_1Statistics_1_1VariableDimensionHistogram.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1WeightedCentroidKdTreeGenerator.html">WeightedCentroidKdTreeGenerator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">This class generates a <a class="el" href="classitk_1_1Statistics_1_1KdTree.html" title="This class provides methods for k-nearest neighbor search and related data structures...">KdTree</a> object with centroid information. <a href="classitk_1_1Statistics_1_1WeightedCentroidKdTreeGenerator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1WeightedCovarianceCalculator.html">WeightedCovarianceCalculator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">Calculates the covariance matrix of the target sample data where each measurement vector has an associated weight value. <a href="classitk_1_1Statistics_1_1WeightedCovarianceCalculator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1WeightedMeanCalculator.html">WeightedMeanCalculator</a></td></tr>
<tr><td class="mdescLeft"> </td><td class="mdescRight">calculates sample mean where each measurement vector has associated weight value <a href="classitk_1_1Statistics_1_1WeightedMeanCalculator.html#_details">More...</a><br></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">class </td><td class="memItemRight" valign="bottom"><a class="el" href="classitk_1_1Statistics_1_1WeightSetBase.html">WeightSetBase</a></td></tr>
<tr><td colspan="2"><br><h2>Enumerations</h2></td></tr>
<tr><td class="memItemLeft" nowrap align="right" valign="top">enum </td><td class="memItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b4">TextureFeatureName</a> { <br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b471211215dca071b28c737ba688393c4d">Energy</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b446f9b906126054d688b1cf479b4484e7">Entropy</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b45a78966112b3b5fbd76352681cede49f">Correlation</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b462b1cb2544396b2113deda1aa629fb3a">InverseDifferenceMoment</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b45920462c43bc231d33cff00830182de1">Inertia</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b42079f16a7bdf546944f5644bd220fbb0">ClusterShade</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b4b4c73123c87e05ee3e19ca187507cf2f">ClusterProminence</a>,
<br>
<a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b4e23819de2f6b82c9e7cb751af3aec04d">HaralickCorrelation</a>
<br>
}</td></tr>
<tr><td colspan="2"><br><h2>Functions</h2></td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#8f2f75348d201e13cd58c23b6ebddf7e">DownHeap</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int node)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#aa9cc3f62a838d02727507e7e4382f6f">FindSampleBound</a> (const TSample *sample, typename TSample::ConstIterator begin, typename TSample::ConstIterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#e481090f204cc9566d5e81def1227894">FindSampleBoundAndMean</a> (const TSubsample *sample, int beginIndex, int endIndex, typename TSubsample::MeasurementVectorType &min, typename TSubsample::MeasurementVectorType &max, typename TSubsample::MeasurementVectorType &mean)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSize > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">TSize </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#0ccbfc7827b6aea2c93261bfcfa32573">FloorLog</a> (TSize size)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#13685626aa0f5bdaea6fb8af9275695b">HeapSort</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#c75ffc368bc90494361bd9ffc079537a">InsertSort</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#c74850c310bd2e28b9ff2b96077b649f">IntrospectiveSort</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int sizeThreshold)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">void </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#7c4be418ca44301ca3bad644f833d5a1">IntrospectiveSortLoop</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int depthLimit, int sizeThreshold)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TValue > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">TValue </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#58218b209be0fb7ee3bb85cb55e33a58">MedianOfThree</a> (const TValue a, const TValue b, const TValue c)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">TSubsample::MeasurementType </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#fe7350816bf4bdad7aab29c3f463943a">NthElement</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int nth)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">int </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#9b440d36c9780f7e9265c628bfa2cb73">Partition</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, const typename TSubsample::MeasurementType partitionValue)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">TSubsample::MeasurementType </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#aba5a1bd4d68c35503023739dfd2dbea">QuickSelect</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth)</td></tr>
<tr><td class="memTemplParams" nowrap colspan="2">template<class TSubsample > </td></tr>
<tr><td class="memTemplItemLeft" nowrap align="right" valign="top">TSubsample::MeasurementType </td><td class="memTemplItemRight" valign="bottom"><a class="el" href="namespaceitk_1_1Statistics.html#e547372cee00ccd5f616943e6644c3f0">QuickSelect</a> (TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth, typename TSubsample::MeasurementType medianGuess)</td></tr>
</table>
<hr><h2>Enumeration Type Documentation</h2>
<a class="anchor" name="85ef4c19b7197649913814c0ee8165b4"></a><!-- doxytag: member="itk::Statistics::TextureFeatureName" ref="85ef4c19b7197649913814c0ee8165b4" args="" -->
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<td class="memname">enum <a class="el" href="namespaceitk_1_1Statistics.html#85ef4c19b7197649913814c0ee8165b4">itk::Statistics::TextureFeatureName</a> </td>
</tr>
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</div>
<div class="memdoc">
<p>
Texture feature types <dl compact><dt><b>Enumerator: </b></dt><dd>
<table border="0" cellspacing="2" cellpadding="0">
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b471211215dca071b28c737ba688393c4d"></a><!-- doxytag: member="Energy" ref="85ef4c19b7197649913814c0ee8165b471211215dca071b28c737ba688393c4d" args="" -->Energy</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b446f9b906126054d688b1cf479b4484e7"></a><!-- doxytag: member="Entropy" ref="85ef4c19b7197649913814c0ee8165b446f9b906126054d688b1cf479b4484e7" args="" -->Entropy</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b45a78966112b3b5fbd76352681cede49f"></a><!-- doxytag: member="Correlation" ref="85ef4c19b7197649913814c0ee8165b45a78966112b3b5fbd76352681cede49f" args="" -->Correlation</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b462b1cb2544396b2113deda1aa629fb3a"></a><!-- doxytag: member="InverseDifferenceMoment" ref="85ef4c19b7197649913814c0ee8165b462b1cb2544396b2113deda1aa629fb3a" args="" -->InverseDifferenceMoment</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b45920462c43bc231d33cff00830182de1"></a><!-- doxytag: member="Inertia" ref="85ef4c19b7197649913814c0ee8165b45920462c43bc231d33cff00830182de1" args="" -->Inertia</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b42079f16a7bdf546944f5644bd220fbb0"></a><!-- doxytag: member="ClusterShade" ref="85ef4c19b7197649913814c0ee8165b42079f16a7bdf546944f5644bd220fbb0" args="" -->ClusterShade</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b4b4c73123c87e05ee3e19ca187507cf2f"></a><!-- doxytag: member="ClusterProminence" ref="85ef4c19b7197649913814c0ee8165b4b4c73123c87e05ee3e19ca187507cf2f" args="" -->ClusterProminence</em> </td><td>
</td></tr>
<tr><td valign="top"><em><a class="anchor" name="85ef4c19b7197649913814c0ee8165b4e23819de2f6b82c9e7cb751af3aec04d"></a><!-- doxytag: member="HaralickCorrelation" ref="85ef4c19b7197649913814c0ee8165b4e23819de2f6b82c9e7cb751af3aec04d" args="" -->HaralickCorrelation</em> </td><td>
</td></tr>
</table>
</dl>
<p>Definition at line <a class="el" href="itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator_8h_source.html#l00098">98</a> of file <a class="el" href="itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator_8h_source.html">itkGreyLevelCooccurrenceMatrixTextureCoefficientsCalculator.h</a>.</p>
</div>
</div><p>
<hr><h2>Function Documentation</h2>
<a class="anchor" name="8f2f75348d201e13cd58c23b6ebddf7e"></a><!-- doxytag: member="itk::Statistics::DownHeap" ref="8f2f75348d201e13cd58c23b6ebddf7e" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int node)" -->
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template<class TSubsample > </div>
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<td class="memname">void itk::Statistics::DownHeap </td>
<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>endIndex</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>node</em></td><td> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td><td><code> [inline]</code></td>
</tr>
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</div>
<div class="memdoc">
<p>
</div>
</div><p>
<a class="anchor" name="aa9cc3f62a838d02727507e7e4382f6f"></a><!-- doxytag: member="itk::Statistics::FindSampleBound" ref="aa9cc3f62a838d02727507e7e4382f6f" args="(const TSample *sample, typename TSample::ConstIterator begin, typename TSample::ConstIterator end, typename TSample::MeasurementVectorType &min, typename TSample::MeasurementVectorType &max)" -->
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<td class="memname">void itk::Statistics::FindSampleBound </td>
<td>(</td>
<td class="paramtype">const TSample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSample::ConstIterator </td>
<td class="paramname"> <em>begin</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSample::ConstIterator </td>
<td class="paramname"> <em>end</em>, </td>
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<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSample::MeasurementVectorType & </td>
<td class="paramname"> <em>min</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSample::MeasurementVectorType & </td>
<td class="paramname"> <em>max</em></td><td> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td><td><code> [inline]</code></td>
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</div>
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<a class="anchor" name="e481090f204cc9566d5e81def1227894"></a><!-- doxytag: member="itk::Statistics::FindSampleBoundAndMean" ref="e481090f204cc9566d5e81def1227894" args="(const TSubsample *sample, int beginIndex, int endIndex, typename TSubsample::MeasurementVectorType &min, typename TSubsample::MeasurementVectorType &max, typename TSubsample::MeasurementVectorType &mean)" -->
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<td class="memname">void itk::Statistics::FindSampleBoundAndMean </td>
<td>(</td>
<td class="paramtype">const TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>endIndex</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSubsample::MeasurementVectorType & </td>
<td class="paramname"> <em>min</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSubsample::MeasurementVectorType & </td>
<td class="paramname"> <em>max</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">typename TSubsample::MeasurementVectorType & </td>
<td class="paramname"> <em>mean</em></td><td> </td>
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<td></td>
<td>)</td>
<td></td><td></td><td><code> [inline]</code></td>
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<a class="anchor" name="0ccbfc7827b6aea2c93261bfcfa32573"></a><!-- doxytag: member="itk::Statistics::FloorLog" ref="0ccbfc7827b6aea2c93261bfcfa32573" args="(TSize size)" -->
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<td class="memname">TSize itk::Statistics::FloorLog </td>
<td>(</td>
<td class="paramtype">TSize </td>
<td class="paramname"> <em>size</em> </td>
<td> ) </td>
<td><code> [inline]</code></td>
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<a class="anchor" name="13685626aa0f5bdaea6fb8af9275695b"></a><!-- doxytag: member="itk::Statistics::HeapSort" ref="13685626aa0f5bdaea6fb8af9275695b" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)" -->
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<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
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<tr>
<td class="paramkey"></td>
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<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>endIndex</em></td><td> </td>
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<td>)</td>
<td></td><td></td><td><code> [inline]</code></td>
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<a class="anchor" name="c75ffc368bc90494361bd9ffc079537a"></a><!-- doxytag: member="itk::Statistics::InsertSort" ref="c75ffc368bc90494361bd9ffc079537a" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex)" -->
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<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
</tr>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>endIndex</em></td><td> </td>
</tr>
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<td></td>
<td>)</td>
<td></td><td></td><td><code> [inline]</code></td>
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<a class="anchor" name="c74850c310bd2e28b9ff2b96077b649f"></a><!-- doxytag: member="itk::Statistics::IntrospectiveSort" ref="c74850c310bd2e28b9ff2b96077b649f" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int sizeThreshold)" -->
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<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>endIndex</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>sizeThreshold</em></td><td> </td>
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<a class="anchor" name="7c4be418ca44301ca3bad644f833d5a1"></a><!-- doxytag: member="itk::Statistics::IntrospectiveSortLoop" ref="7c4be418ca44301ca3bad644f833d5a1" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int depthLimit, int sizeThreshold)" -->
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<td class="memname">void itk::Statistics::IntrospectiveSortLoop </td>
<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
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<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramname"> <em>depthLimit</em>, </td>
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<td class="paramname"> <em>sizeThreshold</em></td><td> </td>
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<a class="anchor" name="58218b209be0fb7ee3bb85cb55e33a58"></a><!-- doxytag: member="itk::Statistics::MedianOfThree" ref="58218b209be0fb7ee3bb85cb55e33a58" args="(const TValue a, const TValue b, const TValue c)" -->
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<a class="anchor" name="fe7350816bf4bdad7aab29c3f463943a"></a><!-- doxytag: member="itk::Statistics::NthElement" ref="fe7350816bf4bdad7aab29c3f463943a" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int nth)" -->
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<td class="memname">TSubsample::MeasurementType itk::Statistics::NthElement </td>
<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramname"> <em>endIndex</em>, </td>
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<td class="paramname"> <em>nth</em></td><td> </td>
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<p>
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.
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<a class="anchor" name="9b440d36c9780f7e9265c628bfa2cb73"></a><!-- doxytag: member="itk::Statistics::Partition" ref="9b440d36c9780f7e9265c628bfa2cb73" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, const typename TSubsample::MeasurementType partitionValue)" -->
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<td class="memname">int itk::Statistics::Partition </td>
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<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
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<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramname"> <em>endIndex</em>, </td>
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<td class="paramtype">const typename TSubsample::MeasurementType </td>
<td class="paramname"> <em>partitionValue</em></td><td> </td>
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<td>)</td>
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<p>
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.
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<a class="anchor" name="aba5a1bd4d68c35503023739dfd2dbea"></a><!-- doxytag: member="itk::Statistics::QuickSelect" ref="aba5a1bd4d68c35503023739dfd2dbea" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth)" -->
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<td class="memname">TSubsample::MeasurementType itk::Statistics::QuickSelect </td>
<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
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<td class="paramkey"></td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramkey"></td>
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<td class="paramname"> <em>endIndex</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>kth</em></td><td> </td>
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<td>)</td>
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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. <a href="http://en.wikipedia.org/wiki/Selection_algorithm.">http://en.wikipedia.org/wiki/Selection_algorithm.</a>
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<a class="anchor" name="e547372cee00ccd5f616943e6644c3f0"></a><!-- doxytag: member="itk::Statistics::QuickSelect" ref="e547372cee00ccd5f616943e6644c3f0" args="(TSubsample *sample, unsigned int activeDimension, int beginIndex, int endIndex, int kth, typename TSubsample::MeasurementType medianGuess)" -->
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<td class="memname">TSubsample::MeasurementType itk::Statistics::QuickSelect </td>
<td>(</td>
<td class="paramtype">TSubsample * </td>
<td class="paramname"> <em>sample</em>, </td>
</tr>
<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">unsigned int </td>
<td class="paramname"> <em>activeDimension</em>, </td>
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<tr>
<td class="paramkey"></td>
<td></td>
<td class="paramtype">int </td>
<td class="paramname"> <em>beginIndex</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>endIndex</em>, </td>
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<td class="paramtype">int </td>
<td class="paramname"> <em>kth</em>, </td>
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<td class="paramkey"></td>
<td></td>
<td class="paramtype">typename TSubsample::MeasurementType </td>
<td class="paramname"> <em>medianGuess</em></td><td> </td>
</tr>
<tr>
<td></td>
<td>)</td>
<td></td><td></td><td><code> [inline]</code></td>
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<p>
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. <a href="http://en.wikipedia.org/wiki/Selection_algorithm">http://en.wikipedia.org/wiki/Selection_algorithm</a>
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