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From l..@apache.org
Subject svn commit: r959802 [6/10] - in /websites/production/commons/content/proper/commons-math/xref/org/apache/commons/math3: fraction/ geometry/euclidean/threed/ geometry/euclidean/twod/ ml/neuralnet/ ml/neuralnet/sofm/ ode/ ode/events/ special/ stat/infere...
Date Mon, 27 Jul 2015 19:40:45 GMT
Modified: websites/production/commons/content/proper/commons-math/xref/org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html
==============================================================================
--- websites/production/commons/content/proper/commons-math/xref/org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html (original)
+++ websites/production/commons/content/proper/commons-math/xref/org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html Mon Jul 27 19:40:45 2015
@@ -28,196 +28,210 @@
 <a class="jxr_linenumber" name="L20" href="#L20">20</a>  <strong class="jxr_keyword">import</strong> java.util.Collection;
 <a class="jxr_linenumber" name="L21" href="#L21">21</a>  <strong class="jxr_keyword">import</strong> java.util.HashSet;
 <a class="jxr_linenumber" name="L22" href="#L22">22</a>  <strong class="jxr_keyword">import</strong> java.util.concurrent.atomic.AtomicLong;
-<a class="jxr_linenumber" name="L23" href="#L23">23</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.Network;
-<a class="jxr_linenumber" name="L24" href="#L24">24</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.MapUtils;
-<a class="jxr_linenumber" name="L25" href="#L25">25</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.Neuron;
-<a class="jxr_linenumber" name="L26" href="#L26">26</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.UpdateAction;
-<a class="jxr_linenumber" name="L27" href="#L27">27</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.distance.DistanceMeasure;
-<a class="jxr_linenumber" name="L28" href="#L28">28</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.linear.ArrayRealVector;
-<a class="jxr_linenumber" name="L29" href="#L29">29</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.analysis.function.Gaussian;
-<a class="jxr_linenumber" name="L30" href="#L30">30</a>  
-<a class="jxr_linenumber" name="L31" href="#L31">31</a>  <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L32" href="#L32">32</a>  <em class="jxr_javadoccomment"> * Update formula for &lt;a href="<a href="http://en.wikipedia.org/wiki/Kohonen" target="alexandria_uri">http://en.wikipedia.org/wiki/Kohonen</a>"&gt;</em>
-<a class="jxr_linenumber" name="L33" href="#L33">33</a>  <em class="jxr_javadoccomment"> * Kohonen's Self-Organizing Map&lt;/a&gt;.</em>
-<a class="jxr_linenumber" name="L34" href="#L34">34</a>  <em class="jxr_javadoccomment"> * &lt;br/&gt;</em>
-<a class="jxr_linenumber" name="L35" href="#L35">35</a>  <em class="jxr_javadoccomment"> * The {@link #update(Network,double[]) update} method modifies the</em>
-<a class="jxr_linenumber" name="L36" href="#L36">36</a>  <em class="jxr_javadoccomment"> * features {@code w} of the "winning" neuron and its neighbours</em>
-<a class="jxr_linenumber" name="L37" href="#L37">37</a>  <em class="jxr_javadoccomment"> * according to the following rule:</em>
-<a class="jxr_linenumber" name="L38" href="#L38">38</a>  <em class="jxr_javadoccomment"> * &lt;code&gt;</em>
-<a class="jxr_linenumber" name="L39" href="#L39">39</a>  <em class="jxr_javadoccomment"> *  w&lt;sub&gt;new&lt;/sub&gt; = w&lt;sub&gt;old&lt;/sub&gt; + &amp;alpha; e&lt;sup&gt;(-d / &amp;sigma;)&lt;/sup&gt; * (sample - w&lt;sub&gt;old&lt;/sub&gt;)</em>
-<a class="jxr_linenumber" name="L40" href="#L40">40</a>  <em class="jxr_javadoccomment"> * &lt;/code&gt;</em>
-<a class="jxr_linenumber" name="L41" href="#L41">41</a>  <em class="jxr_javadoccomment"> * where</em>
-<a class="jxr_linenumber" name="L42" href="#L42">42</a>  <em class="jxr_javadoccomment"> * &lt;ul&gt;</em>
-<a class="jxr_linenumber" name="L43" href="#L43">43</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;&amp;alpha; is the current &lt;em&gt;learning rate&lt;/em&gt;, &lt;/li&gt;</em>
-<a class="jxr_linenumber" name="L44" href="#L44">44</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;&amp;sigma; is the current &lt;em&gt;neighbourhood size&lt;/em&gt;, and&lt;/li&gt;</em>
-<a class="jxr_linenumber" name="L45" href="#L45">45</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;{@code d} is the number of links to traverse in order to reach</em>
-<a class="jxr_linenumber" name="L46" href="#L46">46</a>  <em class="jxr_javadoccomment"> *   the neuron from the winning neuron.&lt;/li&gt;</em>
-<a class="jxr_linenumber" name="L47" href="#L47">47</a>  <em class="jxr_javadoccomment"> * &lt;/ul&gt;</em>
-<a class="jxr_linenumber" name="L48" href="#L48">48</a>  <em class="jxr_javadoccomment"> * &lt;br/&gt;</em>
-<a class="jxr_linenumber" name="L49" href="#L49">49</a>  <em class="jxr_javadoccomment"> * This class is thread-safe as long as the arguments passed to the</em>
-<a class="jxr_linenumber" name="L50" href="#L50">50</a>  <em class="jxr_javadoccomment"> * {@link #KohonenUpdateAction(DistanceMeasure,LearningFactorFunction,</em>
-<a class="jxr_linenumber" name="L51" href="#L51">51</a>  <em class="jxr_javadoccomment"> * NeighbourhoodSizeFunction) constructor} are instances of thread-safe</em>
-<a class="jxr_linenumber" name="L52" href="#L52">52</a>  <em class="jxr_javadoccomment"> * classes.</em>
-<a class="jxr_linenumber" name="L53" href="#L53">53</a>  <em class="jxr_javadoccomment"> * &lt;br/&gt;</em>
-<a class="jxr_linenumber" name="L54" href="#L54">54</a>  <em class="jxr_javadoccomment"> * Each call to the {@link #update(Network,double[]) update} method</em>
-<a class="jxr_linenumber" name="L55" href="#L55">55</a>  <em class="jxr_javadoccomment"> * will increment the internal counter used to compute the current</em>
-<a class="jxr_linenumber" name="L56" href="#L56">56</a>  <em class="jxr_javadoccomment"> * values for</em>
-<a class="jxr_linenumber" name="L57" href="#L57">57</a>  <em class="jxr_javadoccomment"> * &lt;ul&gt;</em>
-<a class="jxr_linenumber" name="L58" href="#L58">58</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;the &lt;em&gt;learning rate&lt;/em&gt;, and&lt;/li&gt;</em>
-<a class="jxr_linenumber" name="L59" href="#L59">59</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;the &lt;em&gt;neighbourhood size&lt;/em&gt;.&lt;/li&gt;</em>
-<a class="jxr_linenumber" name="L60" href="#L60">60</a>  <em class="jxr_javadoccomment"> * &lt;/ul&gt;</em>
-<a class="jxr_linenumber" name="L61" href="#L61">61</a>  <em class="jxr_javadoccomment"> * Consequently, the function instances that compute those values (passed</em>
-<a class="jxr_linenumber" name="L62" href="#L62">62</a>  <em class="jxr_javadoccomment"> * to the constructor of this class) must take into account whether this</em>
-<a class="jxr_linenumber" name="L63" href="#L63">63</a>  <em class="jxr_javadoccomment"> * class's instance will be shared by multiple threads, as this will impact</em>
-<a class="jxr_linenumber" name="L64" href="#L64">64</a>  <em class="jxr_javadoccomment"> * the training process.</em>
-<a class="jxr_linenumber" name="L65" href="#L65">65</a>  <em class="jxr_javadoccomment"> *</em>
-<a class="jxr_linenumber" name="L66" href="#L66">66</a>  <em class="jxr_javadoccomment"> * @since 3.3</em>
-<a class="jxr_linenumber" name="L67" href="#L67">67</a>  <em class="jxr_javadoccomment"> */</em>
-<a class="jxr_linenumber" name="L68" href="#L68">68</a>  <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html">KohonenUpdateAction</a> <strong class="jxr_keyword">implements</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/UpdateAction.html">UpdateAction</a> {
-<a class="jxr_linenumber" name="L69" href="#L69">69</a>      <em class="jxr_javadoccomment">/** Distance function. */</em>
-<a class="jxr_linenumber" name="L70" href="#L70">70</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/distance/DistanceMeasure.html">DistanceMeasure</a> distance;
-<a class="jxr_linenumber" name="L71" href="#L71">71</a>      <em class="jxr_javadoccomment">/** Learning factor update function. */</em>
-<a class="jxr_linenumber" name="L72" href="#L72">72</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/LearningFactorFunction.html">LearningFactorFunction</a> learningFactor;
-<a class="jxr_linenumber" name="L73" href="#L73">73</a>      <em class="jxr_javadoccomment">/** Neighbourhood size update function. */</em>
-<a class="jxr_linenumber" name="L74" href="#L74">74</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/NeighbourhoodSizeFunction.html">NeighbourhoodSizeFunction</a> neighbourhoodSize;
-<a class="jxr_linenumber" name="L75" href="#L75">75</a>      <em class="jxr_javadoccomment">/** Number of calls to {@link #update(Network,double[])}. */</em>
-<a class="jxr_linenumber" name="L76" href="#L76">76</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> AtomicLong numberOfCalls = <strong class="jxr_keyword">new</strong> AtomicLong(-1);
-<a class="jxr_linenumber" name="L77" href="#L77">77</a>  
-<a class="jxr_linenumber" name="L78" href="#L78">78</a>      <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L79" href="#L79">79</a>  <em class="jxr_javadoccomment">     * @param distance Distance function.</em>
-<a class="jxr_linenumber" name="L80" href="#L80">80</a>  <em class="jxr_javadoccomment">     * @param learningFactor Learning factor update function.</em>
-<a class="jxr_linenumber" name="L81" href="#L81">81</a>  <em class="jxr_javadoccomment">     * @param neighbourhoodSize Neighbourhood size update function.</em>
-<a class="jxr_linenumber" name="L82" href="#L82">82</a>  <em class="jxr_javadoccomment">     */</em>
-<a class="jxr_linenumber" name="L83" href="#L83">83</a>      <strong class="jxr_keyword">public</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html">KohonenUpdateAction</a>(<a href="../../../../../../../org/apache/commons/math3/ml/distance/DistanceMeasure.html">DistanceMeasure</a> distance,
-<a class="jxr_linenumber" name="L84" href="#L84">84</a>                                 <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/LearningFactorFunction.html">LearningFactorFunction</a> learningFactor,
-<a class="jxr_linenumber" name="L85" href="#L85">85</a>                                 <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/NeighbourhoodSizeFunction.html">NeighbourhoodSizeFunction</a> neighbourhoodSize) {
-<a class="jxr_linenumber" name="L86" href="#L86">86</a>          <strong class="jxr_keyword">this</strong>.distance = distance;
-<a class="jxr_linenumber" name="L87" href="#L87">87</a>          <strong class="jxr_keyword">this</strong>.learningFactor = learningFactor;
-<a class="jxr_linenumber" name="L88" href="#L88">88</a>          <strong class="jxr_keyword">this</strong>.neighbourhoodSize = neighbourhoodSize;
-<a class="jxr_linenumber" name="L89" href="#L89">89</a>      }
-<a class="jxr_linenumber" name="L90" href="#L90">90</a>  
-<a class="jxr_linenumber" name="L91" href="#L91">91</a>      <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L92" href="#L92">92</a>  <em class="jxr_javadoccomment">     * {@inheritDoc}</em>
-<a class="jxr_linenumber" name="L93" href="#L93">93</a>  <em class="jxr_javadoccomment">     */</em>
-<a class="jxr_linenumber" name="L94" href="#L94">94</a>      <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> update(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Network.html">Network</a> net,
-<a class="jxr_linenumber" name="L95" href="#L95">95</a>                         <strong class="jxr_keyword">double</strong>[] features) {
-<a class="jxr_linenumber" name="L96" href="#L96">96</a>          <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">long</strong> numCalls = numberOfCalls.incrementAndGet();
-<a class="jxr_linenumber" name="L97" href="#L97">97</a>          <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> currentLearning = learningFactor.value(numCalls);
-<a class="jxr_linenumber" name="L98" href="#L98">98</a>          <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> best = findAndUpdateBestNeuron(net,
-<a class="jxr_linenumber" name="L99" href="#L99">99</a>                                                      features,
-<a class="jxr_linenumber" name="L100" href="#L100">100</a>                                                     currentLearning);
-<a class="jxr_linenumber" name="L101" href="#L101">101</a> 
-<a class="jxr_linenumber" name="L102" href="#L102">102</a>         <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> currentNeighbourhood = neighbourhoodSize.value(numCalls);
-<a class="jxr_linenumber" name="L103" href="#L103">103</a>         <em class="jxr_comment">// The farther away the neighbour is from the winning neuron, the</em>
-<a class="jxr_linenumber" name="L104" href="#L104">104</a>         <em class="jxr_comment">// smaller the learning rate will become.</em>
-<a class="jxr_linenumber" name="L105" href="#L105">105</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/analysis/function/Gaussian.html">Gaussian</a> neighbourhoodDecay
-<a class="jxr_linenumber" name="L106" href="#L106">106</a>             = <strong class="jxr_keyword">new</strong> <a href="../../../../../../../org/apache/commons/math3/analysis/function/Gaussian.html">Gaussian</a>(currentLearning,
-<a class="jxr_linenumber" name="L107" href="#L107">107</a>                            0,
-<a class="jxr_linenumber" name="L108" href="#L108">108</a>                            1d / currentNeighbourhood);
-<a class="jxr_linenumber" name="L109" href="#L109">109</a> 
-<a class="jxr_linenumber" name="L110" href="#L110">110</a>         <strong class="jxr_keyword">if</strong> (currentNeighbourhood &gt; 0) {
-<a class="jxr_linenumber" name="L111" href="#L111">111</a>             <em class="jxr_comment">// Initial set of neurons only contains the winning neuron.</em>
-<a class="jxr_linenumber" name="L112" href="#L112">112</a>             Collection&lt;Neuron&gt; neighbours = <strong class="jxr_keyword">new</strong> HashSet&lt;Neuron&gt;();
-<a class="jxr_linenumber" name="L113" href="#L113">113</a>             neighbours.add(best);
-<a class="jxr_linenumber" name="L114" href="#L114">114</a>             <em class="jxr_comment">// Winning neuron must be excluded from the neighbours.</em>
-<a class="jxr_linenumber" name="L115" href="#L115">115</a>             <strong class="jxr_keyword">final</strong> HashSet&lt;Neuron&gt; exclude = <strong class="jxr_keyword">new</strong> HashSet&lt;Neuron&gt;();
-<a class="jxr_linenumber" name="L116" href="#L116">116</a>             exclude.add(best);
-<a class="jxr_linenumber" name="L117" href="#L117">117</a> 
-<a class="jxr_linenumber" name="L118" href="#L118">118</a>             <strong class="jxr_keyword">int</strong> radius = 1;
-<a class="jxr_linenumber" name="L119" href="#L119">119</a>             <strong class="jxr_keyword">do</strong> {
-<a class="jxr_linenumber" name="L120" href="#L120">120</a>                 <em class="jxr_comment">// Retrieve immediate neighbours of the current set of neurons.</em>
-<a class="jxr_linenumber" name="L121" href="#L121">121</a>                 neighbours = net.getNeighbours(neighbours, exclude);
-<a class="jxr_linenumber" name="L122" href="#L122">122</a> 
-<a class="jxr_linenumber" name="L123" href="#L123">123</a>                 <em class="jxr_comment">// Update all the neighbours.</em>
-<a class="jxr_linenumber" name="L124" href="#L124">124</a>                 <strong class="jxr_keyword">for</strong> (Neuron n : neighbours) {
-<a class="jxr_linenumber" name="L125" href="#L125">125</a>                     updateNeighbouringNeuron(n, features, neighbourhoodDecay.value(radius));
-<a class="jxr_linenumber" name="L126" href="#L126">126</a>                 }
-<a class="jxr_linenumber" name="L127" href="#L127">127</a> 
-<a class="jxr_linenumber" name="L128" href="#L128">128</a>                 <em class="jxr_comment">// Add the neighbours to the exclude list so that they will</em>
-<a class="jxr_linenumber" name="L129" href="#L129">129</a>                 <em class="jxr_comment">// not be update more than once per training step.</em>
-<a class="jxr_linenumber" name="L130" href="#L130">130</a>                 exclude.addAll(neighbours);
-<a class="jxr_linenumber" name="L131" href="#L131">131</a>                 ++radius;
-<a class="jxr_linenumber" name="L132" href="#L132">132</a>             } <strong class="jxr_keyword">while</strong> (radius &lt;= currentNeighbourhood);
-<a class="jxr_linenumber" name="L133" href="#L133">133</a>         }
-<a class="jxr_linenumber" name="L134" href="#L134">134</a>     }
-<a class="jxr_linenumber" name="L135" href="#L135">135</a> 
-<a class="jxr_linenumber" name="L136" href="#L136">136</a>     <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L137" href="#L137">137</a> <em class="jxr_javadoccomment">     * Retrieves the number of calls to the {@link #update(Network,double[]) update}</em>
-<a class="jxr_linenumber" name="L138" href="#L138">138</a> <em class="jxr_javadoccomment">     * method.</em>
-<a class="jxr_linenumber" name="L139" href="#L139">139</a> <em class="jxr_javadoccomment">     *</em>
-<a class="jxr_linenumber" name="L140" href="#L140">140</a> <em class="jxr_javadoccomment">     * @return the current number of calls.</em>
-<a class="jxr_linenumber" name="L141" href="#L141">141</a> <em class="jxr_javadoccomment">     */</em>
-<a class="jxr_linenumber" name="L142" href="#L142">142</a>     <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">long</strong> getNumberOfCalls() {
-<a class="jxr_linenumber" name="L143" href="#L143">143</a>         <strong class="jxr_keyword">return</strong> numberOfCalls.get();
-<a class="jxr_linenumber" name="L144" href="#L144">144</a>     }
-<a class="jxr_linenumber" name="L145" href="#L145">145</a> 
-<a class="jxr_linenumber" name="L146" href="#L146">146</a>     <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L147" href="#L147">147</a> <em class="jxr_javadoccomment">     * Atomically updates the given neuron.</em>
-<a class="jxr_linenumber" name="L148" href="#L148">148</a> <em class="jxr_javadoccomment">     *</em>
-<a class="jxr_linenumber" name="L149" href="#L149">149</a> <em class="jxr_javadoccomment">     * @param n Neuron to be updated.</em>
-<a class="jxr_linenumber" name="L150" href="#L150">150</a> <em class="jxr_javadoccomment">     * @param features Training data.</em>
-<a class="jxr_linenumber" name="L151" href="#L151">151</a> <em class="jxr_javadoccomment">     * @param learningRate Learning factor.</em>
-<a class="jxr_linenumber" name="L152" href="#L152">152</a> <em class="jxr_javadoccomment">     */</em>
-<a class="jxr_linenumber" name="L153" href="#L153">153</a>     <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">void</strong> updateNeighbouringNeuron(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> n,
-<a class="jxr_linenumber" name="L154" href="#L154">154</a>                                           <strong class="jxr_keyword">double</strong>[] features,
-<a class="jxr_linenumber" name="L155" href="#L155">155</a>                                           <strong class="jxr_keyword">double</strong> learningRate) {
-<a class="jxr_linenumber" name="L156" href="#L156">156</a>         <strong class="jxr_keyword">while</strong> (<strong class="jxr_keyword">true</strong>) {
-<a class="jxr_linenumber" name="L157" href="#L157">157</a>             <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] expect = n.getFeatures();
-<a class="jxr_linenumber" name="L158" href="#L158">158</a>             <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] update = computeFeatures(expect,
-<a class="jxr_linenumber" name="L159" href="#L159">159</a>                                                     features,
-<a class="jxr_linenumber" name="L160" href="#L160">160</a>                                                     learningRate);
-<a class="jxr_linenumber" name="L161" href="#L161">161</a>             <strong class="jxr_keyword">if</strong> (n.compareAndSetFeatures(expect, update)) {
-<a class="jxr_linenumber" name="L162" href="#L162">162</a>                 <strong class="jxr_keyword">break</strong>;
-<a class="jxr_linenumber" name="L163" href="#L163">163</a>             }
-<a class="jxr_linenumber" name="L164" href="#L164">164</a>         }
-<a class="jxr_linenumber" name="L165" href="#L165">165</a>     }
-<a class="jxr_linenumber" name="L166" href="#L166">166</a> 
-<a class="jxr_linenumber" name="L167" href="#L167">167</a>     <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L168" href="#L168">168</a> <em class="jxr_javadoccomment">     * Searches for the neuron whose features are closest to the given</em>
-<a class="jxr_linenumber" name="L169" href="#L169">169</a> <em class="jxr_javadoccomment">     * sample, and atomically updates its features.</em>
+<a class="jxr_linenumber" name="L23" href="#L23">23</a>  
+<a class="jxr_linenumber" name="L24" href="#L24">24</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.analysis.function.Gaussian;
+<a class="jxr_linenumber" name="L25" href="#L25">25</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.linear.ArrayRealVector;
+<a class="jxr_linenumber" name="L26" href="#L26">26</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.distance.DistanceMeasure;
+<a class="jxr_linenumber" name="L27" href="#L27">27</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.MapUtils;
+<a class="jxr_linenumber" name="L28" href="#L28">28</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.Network;
+<a class="jxr_linenumber" name="L29" href="#L29">29</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.Neuron;
+<a class="jxr_linenumber" name="L30" href="#L30">30</a>  <strong class="jxr_keyword">import</strong> org.apache.commons.math3.ml.neuralnet.UpdateAction;
+<a class="jxr_linenumber" name="L31" href="#L31">31</a>  
+<a class="jxr_linenumber" name="L32" href="#L32">32</a>  <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L33" href="#L33">33</a>  <em class="jxr_javadoccomment"> * Update formula for &lt;a href="<a href="http://en.wikipedia.org/wiki/Kohonen" target="alexandria_uri">http://en.wikipedia.org/wiki/Kohonen</a>"&gt;</em>
+<a class="jxr_linenumber" name="L34" href="#L34">34</a>  <em class="jxr_javadoccomment"> * Kohonen's Self-Organizing Map&lt;/a&gt;.</em>
+<a class="jxr_linenumber" name="L35" href="#L35">35</a>  <em class="jxr_javadoccomment"> * &lt;br/&gt;</em>
+<a class="jxr_linenumber" name="L36" href="#L36">36</a>  <em class="jxr_javadoccomment"> * The {@link #update(Network,double[]) update} method modifies the</em>
+<a class="jxr_linenumber" name="L37" href="#L37">37</a>  <em class="jxr_javadoccomment"> * features {@code w} of the "winning" neuron and its neighbours</em>
+<a class="jxr_linenumber" name="L38" href="#L38">38</a>  <em class="jxr_javadoccomment"> * according to the following rule:</em>
+<a class="jxr_linenumber" name="L39" href="#L39">39</a>  <em class="jxr_javadoccomment"> * &lt;code&gt;</em>
+<a class="jxr_linenumber" name="L40" href="#L40">40</a>  <em class="jxr_javadoccomment"> *  w&lt;sub&gt;new&lt;/sub&gt; = w&lt;sub&gt;old&lt;/sub&gt; + &amp;alpha; e&lt;sup&gt;(-d / &amp;sigma;)&lt;/sup&gt; * (sample - w&lt;sub&gt;old&lt;/sub&gt;)</em>
+<a class="jxr_linenumber" name="L41" href="#L41">41</a>  <em class="jxr_javadoccomment"> * &lt;/code&gt;</em>
+<a class="jxr_linenumber" name="L42" href="#L42">42</a>  <em class="jxr_javadoccomment"> * where</em>
+<a class="jxr_linenumber" name="L43" href="#L43">43</a>  <em class="jxr_javadoccomment"> * &lt;ul&gt;</em>
+<a class="jxr_linenumber" name="L44" href="#L44">44</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;&amp;alpha; is the current &lt;em&gt;learning rate&lt;/em&gt;, &lt;/li&gt;</em>
+<a class="jxr_linenumber" name="L45" href="#L45">45</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;&amp;sigma; is the current &lt;em&gt;neighbourhood size&lt;/em&gt;, and&lt;/li&gt;</em>
+<a class="jxr_linenumber" name="L46" href="#L46">46</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;{@code d} is the number of links to traverse in order to reach</em>
+<a class="jxr_linenumber" name="L47" href="#L47">47</a>  <em class="jxr_javadoccomment"> *   the neuron from the winning neuron.&lt;/li&gt;</em>
+<a class="jxr_linenumber" name="L48" href="#L48">48</a>  <em class="jxr_javadoccomment"> * &lt;/ul&gt;</em>
+<a class="jxr_linenumber" name="L49" href="#L49">49</a>  <em class="jxr_javadoccomment"> * &lt;br/&gt;</em>
+<a class="jxr_linenumber" name="L50" href="#L50">50</a>  <em class="jxr_javadoccomment"> * This class is thread-safe as long as the arguments passed to the</em>
+<a class="jxr_linenumber" name="L51" href="#L51">51</a>  <em class="jxr_javadoccomment"> * {@link #KohonenUpdateAction(DistanceMeasure,LearningFactorFunction,</em>
+<a class="jxr_linenumber" name="L52" href="#L52">52</a>  <em class="jxr_javadoccomment"> * NeighbourhoodSizeFunction) constructor} are instances of thread-safe</em>
+<a class="jxr_linenumber" name="L53" href="#L53">53</a>  <em class="jxr_javadoccomment"> * classes.</em>
+<a class="jxr_linenumber" name="L54" href="#L54">54</a>  <em class="jxr_javadoccomment"> * &lt;br/&gt;</em>
+<a class="jxr_linenumber" name="L55" href="#L55">55</a>  <em class="jxr_javadoccomment"> * Each call to the {@link #update(Network,double[]) update} method</em>
+<a class="jxr_linenumber" name="L56" href="#L56">56</a>  <em class="jxr_javadoccomment"> * will increment the internal counter used to compute the current</em>
+<a class="jxr_linenumber" name="L57" href="#L57">57</a>  <em class="jxr_javadoccomment"> * values for</em>
+<a class="jxr_linenumber" name="L58" href="#L58">58</a>  <em class="jxr_javadoccomment"> * &lt;ul&gt;</em>
+<a class="jxr_linenumber" name="L59" href="#L59">59</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;the &lt;em&gt;learning rate&lt;/em&gt;, and&lt;/li&gt;</em>
+<a class="jxr_linenumber" name="L60" href="#L60">60</a>  <em class="jxr_javadoccomment"> *  &lt;li&gt;the &lt;em&gt;neighbourhood size&lt;/em&gt;.&lt;/li&gt;</em>
+<a class="jxr_linenumber" name="L61" href="#L61">61</a>  <em class="jxr_javadoccomment"> * &lt;/ul&gt;</em>
+<a class="jxr_linenumber" name="L62" href="#L62">62</a>  <em class="jxr_javadoccomment"> * Consequently, the function instances that compute those values (passed</em>
+<a class="jxr_linenumber" name="L63" href="#L63">63</a>  <em class="jxr_javadoccomment"> * to the constructor of this class) must take into account whether this</em>
+<a class="jxr_linenumber" name="L64" href="#L64">64</a>  <em class="jxr_javadoccomment"> * class's instance will be shared by multiple threads, as this will impact</em>
+<a class="jxr_linenumber" name="L65" href="#L65">65</a>  <em class="jxr_javadoccomment"> * the training process.</em>
+<a class="jxr_linenumber" name="L66" href="#L66">66</a>  <em class="jxr_javadoccomment"> *</em>
+<a class="jxr_linenumber" name="L67" href="#L67">67</a>  <em class="jxr_javadoccomment"> * @since 3.3</em>
+<a class="jxr_linenumber" name="L68" href="#L68">68</a>  <em class="jxr_javadoccomment"> */</em>
+<a class="jxr_linenumber" name="L69" href="#L69">69</a>  <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">class</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html">KohonenUpdateAction</a> <strong class="jxr_keyword">implements</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/UpdateAction.html">UpdateAction</a> {
+<a class="jxr_linenumber" name="L70" href="#L70">70</a>      <em class="jxr_javadoccomment">/** Distance function. */</em>
+<a class="jxr_linenumber" name="L71" href="#L71">71</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/distance/DistanceMeasure.html">DistanceMeasure</a> distance;
+<a class="jxr_linenumber" name="L72" href="#L72">72</a>      <em class="jxr_javadoccomment">/** Learning factor update function. */</em>
+<a class="jxr_linenumber" name="L73" href="#L73">73</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/LearningFactorFunction.html">LearningFactorFunction</a> learningFactor;
+<a class="jxr_linenumber" name="L74" href="#L74">74</a>      <em class="jxr_javadoccomment">/** Neighbourhood size update function. */</em>
+<a class="jxr_linenumber" name="L75" href="#L75">75</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/NeighbourhoodSizeFunction.html">NeighbourhoodSizeFunction</a> neighbourhoodSize;
+<a class="jxr_linenumber" name="L76" href="#L76">76</a>      <em class="jxr_javadoccomment">/** Number of calls to {@link #update(Network,double[])}. */</em>
+<a class="jxr_linenumber" name="L77" href="#L77">77</a>      <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">final</strong> AtomicLong numberOfCalls = <strong class="jxr_keyword">new</strong> AtomicLong(0);
+<a class="jxr_linenumber" name="L78" href="#L78">78</a>  
+<a class="jxr_linenumber" name="L79" href="#L79">79</a>      <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L80" href="#L80">80</a>  <em class="jxr_javadoccomment">     * @param distance Distance function.</em>
+<a class="jxr_linenumber" name="L81" href="#L81">81</a>  <em class="jxr_javadoccomment">     * @param learningFactor Learning factor update function.</em>
+<a class="jxr_linenumber" name="L82" href="#L82">82</a>  <em class="jxr_javadoccomment">     * @param neighbourhoodSize Neighbourhood size update function.</em>
+<a class="jxr_linenumber" name="L83" href="#L83">83</a>  <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L84" href="#L84">84</a>      <strong class="jxr_keyword">public</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html">KohonenUpdateAction</a>(<a href="../../../../../../../org/apache/commons/math3/ml/distance/DistanceMeasure.html">DistanceMeasure</a> distance,
+<a class="jxr_linenumber" name="L85" href="#L85">85</a>                                 <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/LearningFactorFunction.html">LearningFactorFunction</a> learningFactor,
+<a class="jxr_linenumber" name="L86" href="#L86">86</a>                                 <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/sofm/NeighbourhoodSizeFunction.html">NeighbourhoodSizeFunction</a> neighbourhoodSize) {
+<a class="jxr_linenumber" name="L87" href="#L87">87</a>          <strong class="jxr_keyword">this</strong>.distance = distance;
+<a class="jxr_linenumber" name="L88" href="#L88">88</a>          <strong class="jxr_keyword">this</strong>.learningFactor = learningFactor;
+<a class="jxr_linenumber" name="L89" href="#L89">89</a>          <strong class="jxr_keyword">this</strong>.neighbourhoodSize = neighbourhoodSize;
+<a class="jxr_linenumber" name="L90" href="#L90">90</a>      }
+<a class="jxr_linenumber" name="L91" href="#L91">91</a>  
+<a class="jxr_linenumber" name="L92" href="#L92">92</a>      <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L93" href="#L93">93</a>  <em class="jxr_javadoccomment">     * {@inheritDoc}</em>
+<a class="jxr_linenumber" name="L94" href="#L94">94</a>  <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L95" href="#L95">95</a>      @Override
+<a class="jxr_linenumber" name="L96" href="#L96">96</a>      <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">void</strong> update(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Network.html">Network</a> net,
+<a class="jxr_linenumber" name="L97" href="#L97">97</a>                         <strong class="jxr_keyword">double</strong>[] features) {
+<a class="jxr_linenumber" name="L98" href="#L98">98</a>          <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">long</strong> numCalls = numberOfCalls.incrementAndGet() - 1;
+<a class="jxr_linenumber" name="L99" href="#L99">99</a>          <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong> currentLearning = learningFactor.value(numCalls);
+<a class="jxr_linenumber" name="L100" href="#L100">100</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> best = findAndUpdateBestNeuron(net,
+<a class="jxr_linenumber" name="L101" href="#L101">101</a>                                                     features,
+<a class="jxr_linenumber" name="L102" href="#L102">102</a>                                                     currentLearning);
+<a class="jxr_linenumber" name="L103" href="#L103">103</a> 
+<a class="jxr_linenumber" name="L104" href="#L104">104</a>         <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">int</strong> currentNeighbourhood = neighbourhoodSize.value(numCalls);
+<a class="jxr_linenumber" name="L105" href="#L105">105</a>         <em class="jxr_comment">// The farther away the neighbour is from the winning neuron, the</em>
+<a class="jxr_linenumber" name="L106" href="#L106">106</a>         <em class="jxr_comment">// smaller the learning rate will become.</em>
+<a class="jxr_linenumber" name="L107" href="#L107">107</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/analysis/function/Gaussian.html">Gaussian</a> neighbourhoodDecay
+<a class="jxr_linenumber" name="L108" href="#L108">108</a>             = <strong class="jxr_keyword">new</strong> <a href="../../../../../../../org/apache/commons/math3/analysis/function/Gaussian.html">Gaussian</a>(currentLearning,
+<a class="jxr_linenumber" name="L109" href="#L109">109</a>                            0,
+<a class="jxr_linenumber" name="L110" href="#L110">110</a>                            1d / currentNeighbourhood);
+<a class="jxr_linenumber" name="L111" href="#L111">111</a> 
+<a class="jxr_linenumber" name="L112" href="#L112">112</a>         <strong class="jxr_keyword">if</strong> (currentNeighbourhood &gt; 0) {
+<a class="jxr_linenumber" name="L113" href="#L113">113</a>             <em class="jxr_comment">// Initial set of neurons only contains the winning neuron.</em>
+<a class="jxr_linenumber" name="L114" href="#L114">114</a>             Collection&lt;Neuron&gt; neighbours = <strong class="jxr_keyword">new</strong> HashSet&lt;Neuron&gt;();
+<a class="jxr_linenumber" name="L115" href="#L115">115</a>             neighbours.add(best);
+<a class="jxr_linenumber" name="L116" href="#L116">116</a>             <em class="jxr_comment">// Winning neuron must be excluded from the neighbours.</em>
+<a class="jxr_linenumber" name="L117" href="#L117">117</a>             <strong class="jxr_keyword">final</strong> HashSet&lt;Neuron&gt; exclude = <strong class="jxr_keyword">new</strong> HashSet&lt;Neuron&gt;();
+<a class="jxr_linenumber" name="L118" href="#L118">118</a>             exclude.add(best);
+<a class="jxr_linenumber" name="L119" href="#L119">119</a> 
+<a class="jxr_linenumber" name="L120" href="#L120">120</a>             <strong class="jxr_keyword">int</strong> radius = 1;
+<a class="jxr_linenumber" name="L121" href="#L121">121</a>             <strong class="jxr_keyword">do</strong> {
+<a class="jxr_linenumber" name="L122" href="#L122">122</a>                 <em class="jxr_comment">// Retrieve immediate neighbours of the current set of neurons.</em>
+<a class="jxr_linenumber" name="L123" href="#L123">123</a>                 neighbours = net.getNeighbours(neighbours, exclude);
+<a class="jxr_linenumber" name="L124" href="#L124">124</a> 
+<a class="jxr_linenumber" name="L125" href="#L125">125</a>                 <em class="jxr_comment">// Update all the neighbours.</em>
+<a class="jxr_linenumber" name="L126" href="#L126">126</a>                 <strong class="jxr_keyword">for</strong> (Neuron n : neighbours) {
+<a class="jxr_linenumber" name="L127" href="#L127">127</a>                     updateNeighbouringNeuron(n, features, neighbourhoodDecay.value(radius));
+<a class="jxr_linenumber" name="L128" href="#L128">128</a>                 }
+<a class="jxr_linenumber" name="L129" href="#L129">129</a> 
+<a class="jxr_linenumber" name="L130" href="#L130">130</a>                 <em class="jxr_comment">// Add the neighbours to the exclude list so that they will</em>
+<a class="jxr_linenumber" name="L131" href="#L131">131</a>                 <em class="jxr_comment">// not be update more than once per training step.</em>
+<a class="jxr_linenumber" name="L132" href="#L132">132</a>                 exclude.addAll(neighbours);
+<a class="jxr_linenumber" name="L133" href="#L133">133</a>                 ++radius;
+<a class="jxr_linenumber" name="L134" href="#L134">134</a>             } <strong class="jxr_keyword">while</strong> (radius &lt;= currentNeighbourhood);
+<a class="jxr_linenumber" name="L135" href="#L135">135</a>         }
+<a class="jxr_linenumber" name="L136" href="#L136">136</a>     }
+<a class="jxr_linenumber" name="L137" href="#L137">137</a> 
+<a class="jxr_linenumber" name="L138" href="#L138">138</a>     <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L139" href="#L139">139</a> <em class="jxr_javadoccomment">     * Retrieves the number of calls to the {@link #update(Network,double[]) update}</em>
+<a class="jxr_linenumber" name="L140" href="#L140">140</a> <em class="jxr_javadoccomment">     * method.</em>
+<a class="jxr_linenumber" name="L141" href="#L141">141</a> <em class="jxr_javadoccomment">     *</em>
+<a class="jxr_linenumber" name="L142" href="#L142">142</a> <em class="jxr_javadoccomment">     * @return the current number of calls.</em>
+<a class="jxr_linenumber" name="L143" href="#L143">143</a> <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L144" href="#L144">144</a>     <strong class="jxr_keyword">public</strong> <strong class="jxr_keyword">long</strong> getNumberOfCalls() {
+<a class="jxr_linenumber" name="L145" href="#L145">145</a>         <strong class="jxr_keyword">return</strong> numberOfCalls.get();
+<a class="jxr_linenumber" name="L146" href="#L146">146</a>     }
+<a class="jxr_linenumber" name="L147" href="#L147">147</a> 
+<a class="jxr_linenumber" name="L148" href="#L148">148</a>     <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L149" href="#L149">149</a> <em class="jxr_javadoccomment">     * Tries to update a neuron.</em>
+<a class="jxr_linenumber" name="L150" href="#L150">150</a> <em class="jxr_javadoccomment">     *</em>
+<a class="jxr_linenumber" name="L151" href="#L151">151</a> <em class="jxr_javadoccomment">     * @param n Neuron to be updated.</em>
+<a class="jxr_linenumber" name="L152" href="#L152">152</a> <em class="jxr_javadoccomment">     * @param features Training data.</em>
+<a class="jxr_linenumber" name="L153" href="#L153">153</a> <em class="jxr_javadoccomment">     * @param learningRate Learning factor.</em>
+<a class="jxr_linenumber" name="L154" href="#L154">154</a> <em class="jxr_javadoccomment">     * @return {@code true} if the update succeeded, {@code true} if a</em>
+<a class="jxr_linenumber" name="L155" href="#L155">155</a> <em class="jxr_javadoccomment">     * concurrent update has been detected.</em>
+<a class="jxr_linenumber" name="L156" href="#L156">156</a> <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L157" href="#L157">157</a>     <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">boolean</strong> attemptNeuronUpdate(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> n,
+<a class="jxr_linenumber" name="L158" href="#L158">158</a>                                         <strong class="jxr_keyword">double</strong>[] features,
+<a class="jxr_linenumber" name="L159" href="#L159">159</a>                                         <strong class="jxr_keyword">double</strong> learningRate) {
+<a class="jxr_linenumber" name="L160" href="#L160">160</a>         <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] expect = n.getFeatures();
+<a class="jxr_linenumber" name="L161" href="#L161">161</a>         <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] update = computeFeatures(expect,
+<a class="jxr_linenumber" name="L162" href="#L162">162</a>                                                 features,
+<a class="jxr_linenumber" name="L163" href="#L163">163</a>                                                 learningRate);
+<a class="jxr_linenumber" name="L164" href="#L164">164</a> 
+<a class="jxr_linenumber" name="L165" href="#L165">165</a>         <strong class="jxr_keyword">return</strong> n.compareAndSetFeatures(expect, update);
+<a class="jxr_linenumber" name="L166" href="#L166">166</a>     }
+<a class="jxr_linenumber" name="L167" href="#L167">167</a> 
+<a class="jxr_linenumber" name="L168" href="#L168">168</a>     <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L169" href="#L169">169</a> <em class="jxr_javadoccomment">     * Atomically updates the given neuron.</em>
 <a class="jxr_linenumber" name="L170" href="#L170">170</a> <em class="jxr_javadoccomment">     *</em>
-<a class="jxr_linenumber" name="L171" href="#L171">171</a> <em class="jxr_javadoccomment">     * @param net Network.</em>
-<a class="jxr_linenumber" name="L172" href="#L172">172</a> <em class="jxr_javadoccomment">     * @param features Sample data.</em>
-<a class="jxr_linenumber" name="L173" href="#L173">173</a> <em class="jxr_javadoccomment">     * @param learningRate Current learning factor.</em>
-<a class="jxr_linenumber" name="L174" href="#L174">174</a> <em class="jxr_javadoccomment">     * @return the winning neuron.</em>
-<a class="jxr_linenumber" name="L175" href="#L175">175</a> <em class="jxr_javadoccomment">     */</em>
-<a class="jxr_linenumber" name="L176" href="#L176">176</a>     <strong class="jxr_keyword">private</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> findAndUpdateBestNeuron(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Network.html">Network</a> net,
-<a class="jxr_linenumber" name="L177" href="#L177">177</a>                                            <strong class="jxr_keyword">double</strong>[] features,
-<a class="jxr_linenumber" name="L178" href="#L178">178</a>                                            <strong class="jxr_keyword">double</strong> learningRate) {
-<a class="jxr_linenumber" name="L179" href="#L179">179</a>         <strong class="jxr_keyword">while</strong> (<strong class="jxr_keyword">true</strong>) {
-<a class="jxr_linenumber" name="L180" href="#L180">180</a>             <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> best = MapUtils.findBest(features, net, distance);
-<a class="jxr_linenumber" name="L181" href="#L181">181</a> 
-<a class="jxr_linenumber" name="L182" href="#L182">182</a>             <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] expect = best.getFeatures();
-<a class="jxr_linenumber" name="L183" href="#L183">183</a>             <strong class="jxr_keyword">final</strong> <strong class="jxr_keyword">double</strong>[] update = computeFeatures(expect,
-<a class="jxr_linenumber" name="L184" href="#L184">184</a>                                                     features,
-<a class="jxr_linenumber" name="L185" href="#L185">185</a>                                                     learningRate);
-<a class="jxr_linenumber" name="L186" href="#L186">186</a>             <strong class="jxr_keyword">if</strong> (best.compareAndSetFeatures(expect, update)) {
-<a class="jxr_linenumber" name="L187" href="#L187">187</a>                 <strong class="jxr_keyword">return</strong> best;
-<a class="jxr_linenumber" name="L188" href="#L188">188</a>             }
-<a class="jxr_linenumber" name="L189" href="#L189">189</a> 
-<a class="jxr_linenumber" name="L190" href="#L190">190</a>             <em class="jxr_comment">// If another thread modified the state of the winning neuron,</em>
-<a class="jxr_linenumber" name="L191" href="#L191">191</a>             <em class="jxr_comment">// it may not be the best match anymore for the given training</em>
-<a class="jxr_linenumber" name="L192" href="#L192">192</a>             <em class="jxr_comment">// sample: Hence, the winner search is performed again.</em>
-<a class="jxr_linenumber" name="L193" href="#L193">193</a>         }
-<a class="jxr_linenumber" name="L194" href="#L194">194</a>     }
-<a class="jxr_linenumber" name="L195" href="#L195">195</a> 
-<a class="jxr_linenumber" name="L196" href="#L196">196</a>     <em class="jxr_javadoccomment">/**</em>
-<a class="jxr_linenumber" name="L197" href="#L197">197</a> <em class="jxr_javadoccomment">     * Computes the new value of the features set.</em>
-<a class="jxr_linenumber" name="L198" href="#L198">198</a> <em class="jxr_javadoccomment">     *</em>
-<a class="jxr_linenumber" name="L199" href="#L199">199</a> <em class="jxr_javadoccomment">     * @param current Current values of the features.</em>
-<a class="jxr_linenumber" name="L200" href="#L200">200</a> <em class="jxr_javadoccomment">     * @param sample Training data.</em>
-<a class="jxr_linenumber" name="L201" href="#L201">201</a> <em class="jxr_javadoccomment">     * @param learningRate Learning factor.</em>
-<a class="jxr_linenumber" name="L202" href="#L202">202</a> <em class="jxr_javadoccomment">     * @return the new values for the features.</em>
-<a class="jxr_linenumber" name="L203" href="#L203">203</a> <em class="jxr_javadoccomment">     */</em>
-<a class="jxr_linenumber" name="L204" href="#L204">204</a>     <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">double</strong>[] computeFeatures(<strong class="jxr_keyword">double</strong>[] current,
-<a class="jxr_linenumber" name="L205" href="#L205">205</a>                                      <strong class="jxr_keyword">double</strong>[] sample,
-<a class="jxr_linenumber" name="L206" href="#L206">206</a>                                      <strong class="jxr_keyword">double</strong> learningRate) {
-<a class="jxr_linenumber" name="L207" href="#L207">207</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a> c = <strong class="jxr_keyword">new</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a>(current, false);
-<a class="jxr_linenumber" name="L208" href="#L208">208</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a> s = <strong class="jxr_keyword">new</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a>(sample, false);
-<a class="jxr_linenumber" name="L209" href="#L209">209</a>         <em class="jxr_comment">// c + learningRate * (s - c)</em>
-<a class="jxr_linenumber" name="L210" href="#L210">210</a>         <strong class="jxr_keyword">return</strong> s.subtract(c).mapMultiplyToSelf(learningRate).add(c).toArray();
-<a class="jxr_linenumber" name="L211" href="#L211">211</a>     }
-<a class="jxr_linenumber" name="L212" href="#L212">212</a> }
+<a class="jxr_linenumber" name="L171" href="#L171">171</a> <em class="jxr_javadoccomment">     * @param n Neuron to be updated.</em>
+<a class="jxr_linenumber" name="L172" href="#L172">172</a> <em class="jxr_javadoccomment">     * @param features Training data.</em>
+<a class="jxr_linenumber" name="L173" href="#L173">173</a> <em class="jxr_javadoccomment">     * @param learningRate Learning factor.</em>
+<a class="jxr_linenumber" name="L174" href="#L174">174</a> <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L175" href="#L175">175</a>     <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">void</strong> updateNeighbouringNeuron(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> n,
+<a class="jxr_linenumber" name="L176" href="#L176">176</a>                                           <strong class="jxr_keyword">double</strong>[] features,
+<a class="jxr_linenumber" name="L177" href="#L177">177</a>                                           <strong class="jxr_keyword">double</strong> learningRate) {
+<a class="jxr_linenumber" name="L178" href="#L178">178</a>         <strong class="jxr_keyword">while</strong> (<strong class="jxr_keyword">true</strong>) {
+<a class="jxr_linenumber" name="L179" href="#L179">179</a>             <strong class="jxr_keyword">if</strong> (attemptNeuronUpdate(n, features, learningRate)) {
+<a class="jxr_linenumber" name="L180" href="#L180">180</a>                 <strong class="jxr_keyword">break</strong>;
+<a class="jxr_linenumber" name="L181" href="#L181">181</a>             }
+<a class="jxr_linenumber" name="L182" href="#L182">182</a>         }
+<a class="jxr_linenumber" name="L183" href="#L183">183</a>     }
+<a class="jxr_linenumber" name="L184" href="#L184">184</a> 
+<a class="jxr_linenumber" name="L185" href="#L185">185</a>     <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L186" href="#L186">186</a> <em class="jxr_javadoccomment">     * Searches for the neuron whose features are closest to the given</em>
+<a class="jxr_linenumber" name="L187" href="#L187">187</a> <em class="jxr_javadoccomment">     * sample, and atomically updates its features.</em>
+<a class="jxr_linenumber" name="L188" href="#L188">188</a> <em class="jxr_javadoccomment">     *</em>
+<a class="jxr_linenumber" name="L189" href="#L189">189</a> <em class="jxr_javadoccomment">     * @param net Network.</em>
+<a class="jxr_linenumber" name="L190" href="#L190">190</a> <em class="jxr_javadoccomment">     * @param features Sample data.</em>
+<a class="jxr_linenumber" name="L191" href="#L191">191</a> <em class="jxr_javadoccomment">     * @param learningRate Current learning factor.</em>
+<a class="jxr_linenumber" name="L192" href="#L192">192</a> <em class="jxr_javadoccomment">     * @return the winning neuron.</em>
+<a class="jxr_linenumber" name="L193" href="#L193">193</a> <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L194" href="#L194">194</a>     <strong class="jxr_keyword">private</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> findAndUpdateBestNeuron(<a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Network.html">Network</a> net,
+<a class="jxr_linenumber" name="L195" href="#L195">195</a>                                            <strong class="jxr_keyword">double</strong>[] features,
+<a class="jxr_linenumber" name="L196" href="#L196">196</a>                                            <strong class="jxr_keyword">double</strong> learningRate) {
+<a class="jxr_linenumber" name="L197" href="#L197">197</a>         <strong class="jxr_keyword">while</strong> (<strong class="jxr_keyword">true</strong>) {
+<a class="jxr_linenumber" name="L198" href="#L198">198</a>             <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/ml/neuralnet/Neuron.html">Neuron</a> best = MapUtils.findBest(features, net, distance);
+<a class="jxr_linenumber" name="L199" href="#L199">199</a> 
+<a class="jxr_linenumber" name="L200" href="#L200">200</a>             <strong class="jxr_keyword">if</strong> (attemptNeuronUpdate(best, features, learningRate)) {
+<a class="jxr_linenumber" name="L201" href="#L201">201</a>                 <strong class="jxr_keyword">return</strong> best;
+<a class="jxr_linenumber" name="L202" href="#L202">202</a>             }
+<a class="jxr_linenumber" name="L203" href="#L203">203</a> 
+<a class="jxr_linenumber" name="L204" href="#L204">204</a>             <em class="jxr_comment">// If another thread modified the state of the winning neuron,</em>
+<a class="jxr_linenumber" name="L205" href="#L205">205</a>             <em class="jxr_comment">// it may not be the best match anymore for the given training</em>
+<a class="jxr_linenumber" name="L206" href="#L206">206</a>             <em class="jxr_comment">// sample: Hence, the winner search is performed again.</em>
+<a class="jxr_linenumber" name="L207" href="#L207">207</a>         }
+<a class="jxr_linenumber" name="L208" href="#L208">208</a>     }
+<a class="jxr_linenumber" name="L209" href="#L209">209</a> 
+<a class="jxr_linenumber" name="L210" href="#L210">210</a>     <em class="jxr_javadoccomment">/**</em>
+<a class="jxr_linenumber" name="L211" href="#L211">211</a> <em class="jxr_javadoccomment">     * Computes the new value of the features set.</em>
+<a class="jxr_linenumber" name="L212" href="#L212">212</a> <em class="jxr_javadoccomment">     *</em>
+<a class="jxr_linenumber" name="L213" href="#L213">213</a> <em class="jxr_javadoccomment">     * @param current Current values of the features.</em>
+<a class="jxr_linenumber" name="L214" href="#L214">214</a> <em class="jxr_javadoccomment">     * @param sample Training data.</em>
+<a class="jxr_linenumber" name="L215" href="#L215">215</a> <em class="jxr_javadoccomment">     * @param learningRate Learning factor.</em>
+<a class="jxr_linenumber" name="L216" href="#L216">216</a> <em class="jxr_javadoccomment">     * @return the new values for the features.</em>
+<a class="jxr_linenumber" name="L217" href="#L217">217</a> <em class="jxr_javadoccomment">     */</em>
+<a class="jxr_linenumber" name="L218" href="#L218">218</a>     <strong class="jxr_keyword">private</strong> <strong class="jxr_keyword">double</strong>[] computeFeatures(<strong class="jxr_keyword">double</strong>[] current,
+<a class="jxr_linenumber" name="L219" href="#L219">219</a>                                      <strong class="jxr_keyword">double</strong>[] sample,
+<a class="jxr_linenumber" name="L220" href="#L220">220</a>                                      <strong class="jxr_keyword">double</strong> learningRate) {
+<a class="jxr_linenumber" name="L221" href="#L221">221</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a> c = <strong class="jxr_keyword">new</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a>(current, false);
+<a class="jxr_linenumber" name="L222" href="#L222">222</a>         <strong class="jxr_keyword">final</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a> s = <strong class="jxr_keyword">new</strong> <a href="../../../../../../../org/apache/commons/math3/linear/ArrayRealVector.html">ArrayRealVector</a>(sample, false);
+<a class="jxr_linenumber" name="L223" href="#L223">223</a>         <em class="jxr_comment">// c + learningRate * (s - c)</em>
+<a class="jxr_linenumber" name="L224" href="#L224">224</a>         <strong class="jxr_keyword">return</strong> s.subtract(c).mapMultiplyToSelf(learningRate).add(c).toArray();
+<a class="jxr_linenumber" name="L225" href="#L225">225</a>     }
+<a class="jxr_linenumber" name="L226" href="#L226">226</a> }
 </pre>
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