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<h1>itkKdTreeBasedKmeansEstimator.h</h1><a href="itkKdTreeBasedKmeansEstimator_8h.html">Go to the documentation of this file.</a><div class="fragment"><pre class="fragment"><a name="l00001"></a>00001 <span class="comment">/*=========================================================================</span>
<a name="l00002"></a>00002 <span class="comment"></span>
<a name="l00003"></a>00003 <span class="comment"> Program: Insight Segmentation & Registration Toolkit</span>
<a name="l00004"></a>00004 <span class="comment"> Module: $RCSfile: itkKdTreeBasedKmeansEstimator.h,v $</span>
<a name="l00005"></a>00005 <span class="comment"> Language: C++</span>
<a name="l00006"></a>00006 <span class="comment"> Date: $Date: 2005-07-26 15:54:59 $</span>
<a name="l00007"></a>00007 <span class="comment"> Version: $Revision: 1.18 $</span>
<a name="l00008"></a>00008 <span class="comment"></span>
<a name="l00009"></a>00009 <span class="comment"> Copyright (c) Insight Software Consortium. All rights reserved.</span>
<a name="l00010"></a>00010 <span class="comment"> See ITKCopyright.txt or http://www.itk.org/HTML/Copyright.htm for details.</span>
<a name="l00011"></a>00011 <span class="comment"></span>
<a name="l00012"></a>00012 <span class="comment"> This software is distributed WITHOUT ANY WARRANTY; without even </span>
<a name="l00013"></a>00013 <span class="comment"> the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR </span>
<a name="l00014"></a>00014 <span class="comment"> PURPOSE. See the above copyright notices for more information.</span>
<a name="l00015"></a>00015 <span class="comment"></span>
<a name="l00016"></a>00016 <span class="comment">=========================================================================*/</span>
<a name="l00017"></a>00017 <span class="preprocessor">#ifndef __itkKdTreeBasedKmeansEstimator_h</span>
<a name="l00018"></a>00018 <span class="preprocessor"></span><span class="preprocessor">#define __itkKdTreeBasedKmeansEstimator_h</span>
<a name="l00019"></a>00019 <span class="preprocessor"></span>
<a name="l00020"></a>00020 <span class="preprocessor">#include <vector></span>
<a name="l00021"></a>00021 <span class="preprocessor">#include "<a class="code" href="itk__hash__map_8h.html">itk_hash_map.h</a>"</span>
<a name="l00022"></a>00022
<a name="l00023"></a>00023 <span class="preprocessor">#include "<a class="code" href="itkObject_8h.html">itkObject.h</a>"</span>
<a name="l00024"></a>00024 <span class="preprocessor">#include "<a class="code" href="itkMeasurementVectorTraits_8h.html">itkMeasurementVectorTraits.h</a>"</span>
<a name="l00025"></a>00025
<a name="l00026"></a>00026 <span class="keyword">namespace </span>itk {
<a name="l00027"></a>00027 <span class="keyword">namespace </span>Statistics {
<a name="l00028"></a>00028
<a name="l00066"></a>00066 <span class="keyword">template</span>< <span class="keyword">class</span> TKdTree >
<a name="l00067"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html">00067</a> <span class="keyword">class </span><a class="code" href="itkWin32Header_8h.html#7d4384322557205d75f0719c012dded6">ITK_EXPORT</a> <a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html" title="fast k-means algorithm implementation using k-d tree structure">KdTreeBasedKmeansEstimator</a>:
<a name="l00068"></a>00068 <span class="keyword">public</span> <a class="code" href="classitk_1_1Object.html" title="Base class for most itk classes.">Object</a>
<a name="l00069"></a>00069 {
<a name="l00070"></a>00070 <span class="keyword">public</span>:
<a name="l00072"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#7430c6dcb6cfe96656730072e7413d87">00072</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html" title="fast k-means algorithm implementation using k-d tree structure">KdTreeBasedKmeansEstimator</a> <a class="code" href="classitk_1_1LightObject.html" title="Light weight base class for most itk classes.">Self</a> ;
<a name="l00073"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#26ee5f92112a5b2ae95500cfa47c3421">00073</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1Object.html" title="Base class for most itk classes.">Object</a> <a class="code" href="classitk_1_1LightObject.html" title="Light weight base class for most itk classes.">Superclass</a>;
<a name="l00074"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#ee0e22b89030d23c2c0cc460e838f04f">00074</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1SmartPointer.html">SmartPointer<Self></a> <a class="code" href="classitk_1_1SmartPointer.html">Pointer</a>;
<a name="l00075"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#b8f11df41a72a207b30b935b58b14f4b">00075</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1SmartPointer.html">SmartPointer<const Self></a> <a class="code" href="classitk_1_1SmartPointer.html">ConstPointer</a>;
<a name="l00076"></a>00076
<a name="l00078"></a>00078 <a class="code" href="itkMacro_8h.html#0fdc15685afa271857c57e7cd482f30c">itkNewMacro</a>(<a class="code" href="classitk_1_1LightObject.html" title="Light weight base class for most itk classes.">Self</a>);
<a name="l00079"></a>00079
<a name="l00081"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#50adad417b5f62782025e2749c4e2146">00081</a> <a class="code" href="itkMacro_8h.html#574c1f86661d409dd32c742307a0e96a">itkTypeMacro</a>(<a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html" title="fast k-means algorithm implementation using k-d tree structure">KdTreeBasedKmeansEstimator</a>, Obeject);
<a name="l00082"></a>00082
<a name="l00084"></a>00084 <span class="keyword">typedef</span> <span class="keyword">typename</span> TKdTree::KdTreeNodeType KdTreeNodeType ;
<a name="l00085"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#f9667af819521977d457436d41b4e34e">00085</a> <span class="keyword">typedef</span> <span class="keyword">typename</span> TKdTree::MeasurementType MeasurementType ;
<a name="l00086"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#0263b576da173f0911391e629fbb363b">00086</a> <span class="keyword">typedef</span> <span class="keyword">typename</span> TKdTree::MeasurementVectorType MeasurementVectorType ;
<a name="l00087"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#1a2cbaf674c84c40c906732beeda4d8d">00087</a> <span class="keyword">typedef</span> <span class="keyword">typename</span> TKdTree::InstanceIdentifier InstanceIdentifier ;
<a name="l00088"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#a0da04c8cbaa5d78fe367e2b37640e82">00088</a> <span class="keyword">typedef</span> <span class="keyword">typename</span> TKdTree::SampleType SampleType ;
<a name="l00089"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#c8327419b317bff7d0a0c5b13aa4f773">00089</a> <span class="keyword">typedef</span> <span class="keyword">typename</span> KdTreeNodeType::CentroidType CentroidType ;
<a name="l00090"></a>00090
<a name="l00091"></a>00091
<a name="l00093"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#d22f34e2e1bfcbd4f7dce3a4741e1793">00093</a> <span class="keyword">typedef</span> <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> MeasurementVectorSizeType;
<a name="l00094"></a>00094
<a name="l00097"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#2c46ab68b99b81b9a28f8d7f33ebdb6b">00097</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1Array.html">Array< double ></a> <a class="code" href="classitk_1_1Array.html">ParameterType</a> ;
<a name="l00098"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#66e2773addf243fc96cd4127ce1eec46">00098</a> <span class="keyword">typedef</span> std::vector< ParameterType > InternalParametersType;
<a name="l00099"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#57b7e6d0e222d569f4df85c14c314017">00099</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1Array.html">Array< double ></a> <a class="code" href="classitk_1_1Array.html">ParametersType</a>;
<a name="l00100"></a>00100
<a name="l00102"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#3b7129ce37b94a04b9ce34cb0a4c89cb">00102</a> <span class="keywordtype">void</span> SetParameters(<a class="code" href="classitk_1_1Array.html">ParametersType</a>& params)
<a name="l00103"></a>00103 { m_Parameters = params ; }
<a name="l00104"></a>00104
<a name="l00106"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#964c21decc48b381316102974087bad2">00106</a> <a class="code" href="classitk_1_1Array.html">ParametersType</a>& GetParameters()
<a name="l00107"></a>00107 { <span class="keywordflow">return</span> m_Parameters ; }
<a name="l00108"></a>00108
<a name="l00110"></a>00110 <a class="code" href="itkMacro_8h.html#7ebdd33cc5e7d74720ced9099c034faa">itkSetMacro</a>( MaximumIteration, <span class="keywordtype">int</span> );
<a name="l00111"></a>00111 <a class="code" href="itkMacro_8h.html#7d989c13a1a213168db99ae0f84185d4">itkGetConstReferenceMacro</a>( MaximumIteration, <span class="keywordtype">int</span> );
<a name="l00113"></a>00113
<a name="l00116"></a>00116 <a class="code" href="itkMacro_8h.html#7ebdd33cc5e7d74720ced9099c034faa">itkSetMacro</a>( CentroidPositionChangesThreshold, <span class="keywordtype">double</span> );
<a name="l00117"></a>00117 <a class="code" href="itkMacro_8h.html#7d989c13a1a213168db99ae0f84185d4">itkGetConstReferenceMacro</a>( CentroidPositionChangesThreshold, <span class="keywordtype">double</span> );
<a name="l00119"></a>00119
<a name="l00121"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#dcf17d1286ad96fd6474097f34389382">00121</a> <span class="keywordtype">void</span> SetKdTree(TKdTree* tree)
<a name="l00122"></a>00122 {
<a name="l00123"></a>00123 m_KdTree = tree ;
<a name="l00124"></a>00124 m_MeasurementVectorSize = tree->GetMeasurementVectorSize();
<a name="l00125"></a>00125 m_DistanceMetric->SetMeasurementVectorSize( m_MeasurementVectorSize );
<a name="l00126"></a>00126 <a class="code" href="classitk_1_1MeasurementVectorTraits.html#d7a46e993e7f3d690bc20f98971d25d5">MeasurementVectorTraits::SetLength</a>( m_TempVertex, m_MeasurementVectorSize );
<a name="l00127"></a>00127 }
<a name="l00129"></a>00129
<a name="l00130"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#9f1e9bb0bc77f5372a520a4b77748d1c">00130</a> TKdTree* GetKdTree()
<a name="l00131"></a>00131 { <span class="keywordflow">return</span> m_KdTree.GetPointer() ; }
<a name="l00132"></a>00132
<a name="l00134"></a>00134 <a class="code" href="itkMacro_8h.html#7d989c13a1a213168db99ae0f84185d4">itkGetConstReferenceMacro</a>( MeasurementVectorSize, MeasurementVectorSizeType );
<a name="l00135"></a>00135
<a name="l00136"></a>00136 <a class="code" href="itkMacro_8h.html#7d989c13a1a213168db99ae0f84185d4">itkGetConstReferenceMacro</a>( CurrentIteration, <span class="keywordtype">int</span>) ;
<a name="l00137"></a>00137 <a class="code" href="itkMacro_8h.html#7d989c13a1a213168db99ae0f84185d4">itkGetConstReferenceMacro</a>( CentroidPositionChanges, <span class="keywordtype">double</span>) ;
<a name="l00138"></a>00138
<a name="l00143"></a>00143 <span class="keywordtype">void</span> StartOptimization() ;
<a name="l00144"></a>00144
<a name="l00145"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#fac6f43a259932281dcb5f2e2772ac5c">00145</a> <span class="keyword">typedef</span> <a class="code" href="classitk_1_1hash__map.html">itk::hash_map< InstanceIdentifier, unsigned int ></a> <a class="code" href="classitk_1_1hash__map.html">ClusterLabelsType</a> ;
<a name="l00146"></a>00146
<a name="l00147"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#63c4a492c7e032a9b71252719f205a4c">00147</a> <span class="keywordtype">void</span> SetUseClusterLabels(<span class="keywordtype">bool</span> flag)
<a name="l00148"></a>00148 { m_UseClusterLabels = flag ; }
<a name="l00149"></a>00149
<a name="l00150"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#65d0fbc69886d0ca48ba33d3cfb22013">00150</a> <a class="code" href="classitk_1_1hash__map.html">ClusterLabelsType</a>* GetClusterLabels()
<a name="l00151"></a>00151 { <span class="keywordflow">return</span> &m_ClusterLabels ; }
<a name="l00152"></a>00152
<a name="l00153"></a>00153 <span class="keyword">protected</span>:
<a name="l00154"></a>00154 <a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html" title="fast k-means algorithm implementation using k-d tree structure">KdTreeBasedKmeansEstimator</a>() ;
<a name="l00155"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#17586e87059b21ab17613aef6a94a142">00155</a> <span class="keyword">virtual</span> <a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#17586e87059b21ab17613aef6a94a142">~KdTreeBasedKmeansEstimator</a>() {}
<a name="l00156"></a>00156
<a name="l00157"></a>00157 <span class="keywordtype">void</span> <a class="code" href="namespaceHardConnectedComponentImageFilter.html#920ae03048a5ad715d803ca2e92b52de">PrintSelf</a>(std::ostream& os, <a class="code" href="classitk_1_1Indent.html" title="Control indentation during Print() invocation.">Indent</a> indent) <span class="keyword">const</span>;
<a name="l00158"></a>00158
<a name="l00159"></a>00159 <span class="keywordtype">void</span> FillClusterLabels(KdTreeNodeType* node, <span class="keywordtype">int</span> closestIndex) ;
<a name="l00160"></a>00160
<a name="l00162"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html">00162</a> <span class="keyword">class </span><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html">CandidateVector</a>
<a name="l00163"></a>00163 {
<a name="l00164"></a>00164 <span class="keyword">public</span>:
<a name="l00165"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#bdffb034f2c3a49ab27023c75be3f13f">00165</a> <a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#bdffb034f2c3a49ab27023c75be3f13f">CandidateVector</a>() {}
<a name="l00166"></a>00166
<a name="l00167"></a><a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html">00167</a> <span class="keyword">struct </span><a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html">Candidate</a>
<a name="l00168"></a>00168 {
<a name="l00169"></a><a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#dc632dc8e83d7de3a8ce7ccfd16c14d3">00169</a> CentroidType Centroid ;
<a name="l00170"></a><a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#31b66eaadfb8cc1cc064a4cc9ba1a22a">00170</a> CentroidType WeightedCentroid ;
<a name="l00171"></a><a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#dd36e2e4b34c2301f814e2855ad62195">00171</a> <span class="keywordtype">int</span> <a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a> ;
<a name="l00172"></a>00172 } ; <span class="comment">// end of struct</span>
<a name="l00173"></a>00173
<a name="l00174"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#4b3ad6b095a00999e14244070f3acda1">00174</a> <span class="keyword">virtual</span> <a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#4b3ad6b095a00999e14244070f3acda1">~CandidateVector</a>() {}
<a name="l00175"></a>00175
<a name="l00177"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#c28b1b0be608c2040587e302befac4a3">00177</a> <span class="keywordtype">int</span> <a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a>()<span class="keyword"> const</span>
<a name="l00178"></a>00178 <span class="keyword"> </span>{ <span class="keywordflow">return</span> <span class="keyword">static_cast<</span><span class="keywordtype">int</span><span class="keyword">></span>( m_Candidates.size() ); }
<a name="l00179"></a>00179
<a name="l00182"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#01a6bfedd1b2bbd1786855b4f0ea2466">00182</a> <span class="keywordtype">void</span> SetCentroids(InternalParametersType& centroids)
<a name="l00183"></a>00183 {
<a name="l00184"></a>00184 this->m_MeasurementVectorSize = <a class="code" href="classitk_1_1MeasurementVectorTraits.html#4868d37fef7fcc55678062887cf44afd">MeasurementVectorTraits::GetLength</a>( centroids[0] );
<a name="l00185"></a>00185 m_Candidates.resize(centroids.size()) ;
<a name="l00186"></a>00186 <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < centroids.size() ; i++)
<a name="l00187"></a>00187 {
<a name="l00188"></a>00188 <a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html">Candidate</a> candidate ;
<a name="l00189"></a>00189 candidate.<a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#dc632dc8e83d7de3a8ce7ccfd16c14d3">Centroid</a> = centroids[i] ;
<a name="l00190"></a>00190 <a class="code" href="classitk_1_1MeasurementVectorTraits.html#d7a46e993e7f3d690bc20f98971d25d5">MeasurementVectorTraits::SetLength</a>( candidate.<a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#31b66eaadfb8cc1cc064a4cc9ba1a22a">WeightedCentroid</a>, m_MeasurementVectorSize );
<a name="l00191"></a>00191 candidate.<a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#31b66eaadfb8cc1cc064a4cc9ba1a22a">WeightedCentroid</a>.Fill(0.0) ;
<a name="l00192"></a>00192 candidate.<a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#dd36e2e4b34c2301f814e2855ad62195">Size</a> = 0 ;
<a name="l00193"></a>00193 m_Candidates[i] = candidate ;
<a name="l00194"></a>00194 }
<a name="l00195"></a>00195 }
<a name="l00197"></a>00197
<a name="l00199"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#aa7bcb710c42c127b3b8ff92c1313c69">00199</a> <span class="keywordtype">void</span> GetCentroids(InternalParametersType& centroids)
<a name="l00200"></a>00200 {
<a name="l00201"></a>00201 <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i ;
<a name="l00202"></a>00202 centroids.resize(this-><a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a>()) ;
<a name="l00203"></a>00203 <span class="keywordflow">for</span> (i = 0 ; i < (<span class="keywordtype">unsigned</span> int)this-><a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a>() ; i++)
<a name="l00204"></a>00204 {
<a name="l00205"></a>00205 centroids[i] = m_Candidates[i].<a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html#dc632dc8e83d7de3a8ce7ccfd16c14d3">Centroid</a> ;
<a name="l00206"></a>00206 }
<a name="l00207"></a>00207 }
<a name="l00209"></a>00209
<a name="l00212"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#e07d911bfaaa26bdfcb8f5ae73ae9463">00212</a> <span class="keywordtype">void</span> UpdateCentroids()
<a name="l00213"></a>00213 {
<a name="l00214"></a>00214 <span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i, j ;
<a name="l00215"></a>00215 <span class="keywordflow">for</span> (i = 0 ; i < (<span class="keywordtype">unsigned</span> int)this-><a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a>() ; i++)
<a name="l00216"></a>00216 {
<a name="l00217"></a>00217 <span class="keywordflow">if</span> (m_Candidates[i].<a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a> > 0)
<a name="l00218"></a>00218 {
<a name="l00219"></a>00219 <span class="keywordflow">for</span> (j = 0 ; j < m_MeasurementVectorSize; j++)
<a name="l00220"></a>00220 {
<a name="l00221"></a>00221 m_Candidates[i].Centroid[j] =
<a name="l00222"></a>00222 m_Candidates[i].WeightedCentroid[j] /
<a name="l00223"></a>00223 double(m_Candidates[i].<a class="code" href="classitk_1_1Size.html" title="Represent the size (bounds) of a n-dimensional image.">Size</a>) ;
<a name="l00224"></a>00224 }
<a name="l00225"></a>00225 }
<a name="l00226"></a>00226 }
<a name="l00227"></a>00227 }
<a name="l00229"></a>00229
<a name="l00231"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector.html#d0c1baee405271ad18dfeba97cac2139">00231</a> <a class="code" href="structitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator_1_1CandidateVector_1_1Candidate.html">Candidate</a>& operator[](<span class="keywordtype">int</span> index)
<a name="l00232"></a>00232 { <span class="keywordflow">return</span> m_Candidates[index] ; }
<a name="l00233"></a>00233
<a name="l00234"></a>00234
<a name="l00235"></a>00235 <span class="keyword">private</span>:
<a name="l00237"></a>00237 std::vector< Candidate > m_Candidates ;
<a name="l00238"></a>00238
<a name="l00240"></a>00240 MeasurementVectorSizeType m_MeasurementVectorSize;
<a name="l00241"></a>00241 } ; <span class="comment">// end of class</span>
<a name="l00242"></a>00242
<a name="l00248"></a>00248 <span class="keywordtype">double</span> GetSumOfSquaredPositionChanges(InternalParametersType &previous,
<a name="l00249"></a>00249 InternalParametersType &current) ;
<a name="l00250"></a>00250
<a name="l00253"></a>00253 <span class="keywordtype">int</span> GetClosestCandidate(ParameterType &measurements,
<a name="l00254"></a>00254 std::vector< int > &validIndexes) ;
<a name="l00255"></a>00255
<a name="l00257"></a>00257 <span class="keywordtype">bool</span> IsFarther(ParameterType &pointA,
<a name="l00258"></a>00258 ParameterType &pointB,
<a name="l00259"></a>00259 MeasurementVectorType &lowerBound,
<a name="l00260"></a>00260 MeasurementVectorType &upperBound) ;
<a name="l00261"></a>00261
<a name="l00264"></a>00264 <span class="keywordtype">void</span> Filter(KdTreeNodeType* node,
<a name="l00265"></a>00265 std::vector< int > validIndexes,
<a name="l00266"></a>00266 MeasurementVectorType &lowerBound,
<a name="l00267"></a>00267 MeasurementVectorType &upperBound) ;
<a name="l00268"></a>00268
<a name="l00270"></a>00270 <span class="keywordtype">void</span> CopyParameters(InternalParametersType &source, InternalParametersType &target) ;
<a name="l00271"></a>00271
<a name="l00273"></a>00273 <span class="keywordtype">void</span> CopyParameters(ParametersType &source, InternalParametersType &target) ;
<a name="l00274"></a>00274
<a name="l00276"></a>00276 <span class="keywordtype">void</span> CopyParameters(InternalParametersType &source, ParametersType &target) ;
<a name="l00277"></a>00277
<a name="l00279"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#f45acea203f7136a6bb7a23039d41ad3">00279</a> <span class="keywordtype">void</span> GetPoint(<a class="code" href="classitk_1_1Array.html">ParameterType</a> &point,
<a name="l00280"></a>00280 MeasurementVectorType measurements)
<a name="l00281"></a>00281 {
<a name="l00282"></a>00282 <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < m_MeasurementVectorSize ; i++)
<a name="l00283"></a>00283 {
<a name="l00284"></a>00284 point[i] = measurements[i] ;
<a name="l00285"></a>00285 }
<a name="l00286"></a>00286 }
<a name="l00288"></a>00288
<a name="l00289"></a><a class="code" href="classitk_1_1Statistics_1_1KdTreeBasedKmeansEstimator.html#787b1fef9a1daec693b360b1eae54a8c">00289</a> <span class="keywordtype">void</span> PrintPoint(<a class="code" href="classitk_1_1Array.html">ParameterType</a> &point)
<a name="l00290"></a>00290 {
<a name="l00291"></a>00291 std::cout << <span class="stringliteral">"[ "</span> ;
<a name="l00292"></a>00292 <span class="keywordflow">for</span> (<span class="keywordtype">unsigned</span> <span class="keywordtype">int</span> i = 0 ; i < m_MeasurementVectorSize ; i++)
<a name="l00293"></a>00293 {
<a name="l00294"></a>00294 std::cout << point[i] << <span class="stringliteral">" "</span> ;
<a name="l00295"></a>00295 }
<a name="l00296"></a>00296 std::cout << <span class="stringliteral">"]"</span> ;
<a name="l00297"></a>00297 }
<a name="l00298"></a>00298
<a name="l00299"></a>00299 <span class="keyword">private</span>:
<a name="l00301"></a>00301 <span class="keywordtype">int</span> m_CurrentIteration ;
<a name="l00302"></a>00302
<a name="l00304"></a>00304 <span class="keywordtype">int</span> m_MaximumIteration ;
<a name="l00305"></a>00305
<a name="l00307"></a>00307 <span class="keywordtype">double</span> m_CentroidPositionChanges ;
<a name="l00308"></a>00308
<a name="l00311"></a>00311 <span class="keywordtype">double</span> m_CentroidPositionChangesThreshold ;
<a name="l00312"></a>00312
<a name="l00314"></a>00314 <span class="keyword">typename</span> <a class="code" href="itkFEMMacro_8h.html#539cce1a3282ba59952dedcbf9cdb23f">TKdTree::Pointer</a> m_KdTree ;
<a name="l00315"></a>00315
<a name="l00317"></a>00317 <span class="keyword">typename</span> <a class="code" href="classitk_1_1Statistics_1_1EuclideanDistance.html" title="Euclidean distance function.">EuclideanDistance< ParameterType >::Pointer</a> m_DistanceMetric ;
<a name="l00318"></a>00318
<a name="l00320"></a>00320 ParametersType m_Parameters ;
<a name="l00321"></a>00321
<a name="l00322"></a>00322 CandidateVector m_CandidateVector ;
<a name="l00323"></a>00323
<a name="l00324"></a>00324 ParameterType m_TempVertex ;
<a name="l00325"></a>00325
<a name="l00326"></a>00326 <span class="keywordtype">bool</span> m_UseClusterLabels ;
<a name="l00327"></a>00327 <span class="keywordtype">bool</span> m_GenerateClusterLabels ;
<a name="l00328"></a>00328 ClusterLabelsType m_ClusterLabels ;
<a name="l00329"></a>00329 MeasurementVectorSizeType m_MeasurementVectorSize;
<a name="l00330"></a>00330 } ; <span class="comment">// end of class</span>
<a name="l00331"></a>00331
<a name="l00332"></a>00332 } <span class="comment">// end of namespace Statistics</span>
<a name="l00333"></a>00333 } <span class="comment">// end of namespace itk</span>
<a name="l00334"></a>00334
<a name="l00335"></a>00335 <span class="preprocessor">#ifndef ITK_MANUAL_INSTANTIATION</span>
<a name="l00336"></a>00336 <span class="preprocessor"></span><span class="preprocessor">#include "itkKdTreeBasedKmeansEstimator.txx"</span>
<a name="l00337"></a>00337 <span class="preprocessor">#endif</span>
<a name="l00338"></a>00338 <span class="preprocessor"></span>
<a name="l00339"></a>00339
<a name="l00340"></a>00340 <span class="preprocessor">#endif</span>
<a name="l00341"></a>00341 <span class="preprocessor"></span>
</pre></div></div>
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