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From l..@apache.org
Subject svn commit: r959801 [7/10] - in /websites/production/commons/content/proper/commons-math/apidocs: ./ org/apache/commons/math3/exception/class-use/ org/apache/commons/math3/fraction/ org/apache/commons/math3/geometry/euclidean/oned/class-use/ org/apache...
Date Mon, 27 Jul 2015 19:40:06 GMT
Modified: websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html
==============================================================================
--- websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html (original)
+++ websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ml/neuralnet/sofm/KohonenUpdateAction.html Mon Jul 27 19:40:06 2015
@@ -28,196 +28,210 @@
 <span class="sourceLineNo">020</span>import java.util.Collection;<a name="line.20"></a>
 <span class="sourceLineNo">021</span>import java.util.HashSet;<a name="line.21"></a>
 <span class="sourceLineNo">022</span>import java.util.concurrent.atomic.AtomicLong;<a name="line.22"></a>
-<span class="sourceLineNo">023</span>import org.apache.commons.math3.ml.neuralnet.Network;<a name="line.23"></a>
-<span class="sourceLineNo">024</span>import org.apache.commons.math3.ml.neuralnet.MapUtils;<a name="line.24"></a>
-<span class="sourceLineNo">025</span>import org.apache.commons.math3.ml.neuralnet.Neuron;<a name="line.25"></a>
-<span class="sourceLineNo">026</span>import org.apache.commons.math3.ml.neuralnet.UpdateAction;<a name="line.26"></a>
-<span class="sourceLineNo">027</span>import org.apache.commons.math3.ml.distance.DistanceMeasure;<a name="line.27"></a>
-<span class="sourceLineNo">028</span>import org.apache.commons.math3.linear.ArrayRealVector;<a name="line.28"></a>
-<span class="sourceLineNo">029</span>import org.apache.commons.math3.analysis.function.Gaussian;<a name="line.29"></a>
-<span class="sourceLineNo">030</span><a name="line.30"></a>
-<span class="sourceLineNo">031</span>/**<a name="line.31"></a>
-<span class="sourceLineNo">032</span> * Update formula for &lt;a href="http://en.wikipedia.org/wiki/Kohonen"&gt;<a name="line.32"></a>
-<span class="sourceLineNo">033</span> * Kohonen's Self-Organizing Map&lt;/a&gt;.<a name="line.33"></a>
-<span class="sourceLineNo">034</span> * &lt;br/&gt;<a name="line.34"></a>
-<span class="sourceLineNo">035</span> * The {@link #update(Network,double[]) update} method modifies the<a name="line.35"></a>
-<span class="sourceLineNo">036</span> * features {@code w} of the "winning" neuron and its neighbours<a name="line.36"></a>
-<span class="sourceLineNo">037</span> * according to the following rule:<a name="line.37"></a>
-<span class="sourceLineNo">038</span> * &lt;code&gt;<a name="line.38"></a>
-<span class="sourceLineNo">039</span> *  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;)<a name="line.39"></a>
-<span class="sourceLineNo">040</span> * &lt;/code&gt;<a name="line.40"></a>
-<span class="sourceLineNo">041</span> * where<a name="line.41"></a>
-<span class="sourceLineNo">042</span> * &lt;ul&gt;<a name="line.42"></a>
-<span class="sourceLineNo">043</span> *  &lt;li&gt;&amp;alpha; is the current &lt;em&gt;learning rate&lt;/em&gt;, &lt;/li&gt;<a name="line.43"></a>
-<span class="sourceLineNo">044</span> *  &lt;li&gt;&amp;sigma; is the current &lt;em&gt;neighbourhood size&lt;/em&gt;, and&lt;/li&gt;<a name="line.44"></a>
-<span class="sourceLineNo">045</span> *  &lt;li&gt;{@code d} is the number of links to traverse in order to reach<a name="line.45"></a>
-<span class="sourceLineNo">046</span> *   the neuron from the winning neuron.&lt;/li&gt;<a name="line.46"></a>
-<span class="sourceLineNo">047</span> * &lt;/ul&gt;<a name="line.47"></a>
-<span class="sourceLineNo">048</span> * &lt;br/&gt;<a name="line.48"></a>
-<span class="sourceLineNo">049</span> * This class is thread-safe as long as the arguments passed to the<a name="line.49"></a>
-<span class="sourceLineNo">050</span> * {@link #KohonenUpdateAction(DistanceMeasure,LearningFactorFunction,<a name="line.50"></a>
-<span class="sourceLineNo">051</span> * NeighbourhoodSizeFunction) constructor} are instances of thread-safe<a name="line.51"></a>
-<span class="sourceLineNo">052</span> * classes.<a name="line.52"></a>
-<span class="sourceLineNo">053</span> * &lt;br/&gt;<a name="line.53"></a>
-<span class="sourceLineNo">054</span> * Each call to the {@link #update(Network,double[]) update} method<a name="line.54"></a>
-<span class="sourceLineNo">055</span> * will increment the internal counter used to compute the current<a name="line.55"></a>
-<span class="sourceLineNo">056</span> * values for<a name="line.56"></a>
-<span class="sourceLineNo">057</span> * &lt;ul&gt;<a name="line.57"></a>
-<span class="sourceLineNo">058</span> *  &lt;li&gt;the &lt;em&gt;learning rate&lt;/em&gt;, and&lt;/li&gt;<a name="line.58"></a>
-<span class="sourceLineNo">059</span> *  &lt;li&gt;the &lt;em&gt;neighbourhood size&lt;/em&gt;.&lt;/li&gt;<a name="line.59"></a>
-<span class="sourceLineNo">060</span> * &lt;/ul&gt;<a name="line.60"></a>
-<span class="sourceLineNo">061</span> * Consequently, the function instances that compute those values (passed<a name="line.61"></a>
-<span class="sourceLineNo">062</span> * to the constructor of this class) must take into account whether this<a name="line.62"></a>
-<span class="sourceLineNo">063</span> * class's instance will be shared by multiple threads, as this will impact<a name="line.63"></a>
-<span class="sourceLineNo">064</span> * the training process.<a name="line.64"></a>
-<span class="sourceLineNo">065</span> *<a name="line.65"></a>
-<span class="sourceLineNo">066</span> * @since 3.3<a name="line.66"></a>
-<span class="sourceLineNo">067</span> */<a name="line.67"></a>
-<span class="sourceLineNo">068</span>public class KohonenUpdateAction implements UpdateAction {<a name="line.68"></a>
-<span class="sourceLineNo">069</span>    /** Distance function. */<a name="line.69"></a>
-<span class="sourceLineNo">070</span>    private final DistanceMeasure distance;<a name="line.70"></a>
-<span class="sourceLineNo">071</span>    /** Learning factor update function. */<a name="line.71"></a>
-<span class="sourceLineNo">072</span>    private final LearningFactorFunction learningFactor;<a name="line.72"></a>
-<span class="sourceLineNo">073</span>    /** Neighbourhood size update function. */<a name="line.73"></a>
-<span class="sourceLineNo">074</span>    private final NeighbourhoodSizeFunction neighbourhoodSize;<a name="line.74"></a>
-<span class="sourceLineNo">075</span>    /** Number of calls to {@link #update(Network,double[])}. */<a name="line.75"></a>
-<span class="sourceLineNo">076</span>    private final AtomicLong numberOfCalls = new AtomicLong(-1);<a name="line.76"></a>
-<span class="sourceLineNo">077</span><a name="line.77"></a>
-<span class="sourceLineNo">078</span>    /**<a name="line.78"></a>
-<span class="sourceLineNo">079</span>     * @param distance Distance function.<a name="line.79"></a>
-<span class="sourceLineNo">080</span>     * @param learningFactor Learning factor update function.<a name="line.80"></a>
-<span class="sourceLineNo">081</span>     * @param neighbourhoodSize Neighbourhood size update function.<a name="line.81"></a>
-<span class="sourceLineNo">082</span>     */<a name="line.82"></a>
-<span class="sourceLineNo">083</span>    public KohonenUpdateAction(DistanceMeasure distance,<a name="line.83"></a>
-<span class="sourceLineNo">084</span>                               LearningFactorFunction learningFactor,<a name="line.84"></a>
-<span class="sourceLineNo">085</span>                               NeighbourhoodSizeFunction neighbourhoodSize) {<a name="line.85"></a>
-<span class="sourceLineNo">086</span>        this.distance = distance;<a name="line.86"></a>
-<span class="sourceLineNo">087</span>        this.learningFactor = learningFactor;<a name="line.87"></a>
-<span class="sourceLineNo">088</span>        this.neighbourhoodSize = neighbourhoodSize;<a name="line.88"></a>
-<span class="sourceLineNo">089</span>    }<a name="line.89"></a>
-<span class="sourceLineNo">090</span><a name="line.90"></a>
-<span class="sourceLineNo">091</span>    /**<a name="line.91"></a>
-<span class="sourceLineNo">092</span>     * {@inheritDoc}<a name="line.92"></a>
-<span class="sourceLineNo">093</span>     */<a name="line.93"></a>
-<span class="sourceLineNo">094</span>    public void update(Network net,<a name="line.94"></a>
-<span class="sourceLineNo">095</span>                       double[] features) {<a name="line.95"></a>
-<span class="sourceLineNo">096</span>        final long numCalls = numberOfCalls.incrementAndGet();<a name="line.96"></a>
-<span class="sourceLineNo">097</span>        final double currentLearning = learningFactor.value(numCalls);<a name="line.97"></a>
-<span class="sourceLineNo">098</span>        final Neuron best = findAndUpdateBestNeuron(net,<a name="line.98"></a>
-<span class="sourceLineNo">099</span>                                                    features,<a name="line.99"></a>
-<span class="sourceLineNo">100</span>                                                    currentLearning);<a name="line.100"></a>
-<span class="sourceLineNo">101</span><a name="line.101"></a>
-<span class="sourceLineNo">102</span>        final int currentNeighbourhood = neighbourhoodSize.value(numCalls);<a name="line.102"></a>
-<span class="sourceLineNo">103</span>        // The farther away the neighbour is from the winning neuron, the<a name="line.103"></a>
-<span class="sourceLineNo">104</span>        // smaller the learning rate will become.<a name="line.104"></a>
-<span class="sourceLineNo">105</span>        final Gaussian neighbourhoodDecay<a name="line.105"></a>
-<span class="sourceLineNo">106</span>            = new Gaussian(currentLearning,<a name="line.106"></a>
-<span class="sourceLineNo">107</span>                           0,<a name="line.107"></a>
-<span class="sourceLineNo">108</span>                           1d / currentNeighbourhood);<a name="line.108"></a>
-<span class="sourceLineNo">109</span><a name="line.109"></a>
-<span class="sourceLineNo">110</span>        if (currentNeighbourhood &gt; 0) {<a name="line.110"></a>
-<span class="sourceLineNo">111</span>            // Initial set of neurons only contains the winning neuron.<a name="line.111"></a>
-<span class="sourceLineNo">112</span>            Collection&lt;Neuron&gt; neighbours = new HashSet&lt;Neuron&gt;();<a name="line.112"></a>
-<span class="sourceLineNo">113</span>            neighbours.add(best);<a name="line.113"></a>
-<span class="sourceLineNo">114</span>            // Winning neuron must be excluded from the neighbours.<a name="line.114"></a>
-<span class="sourceLineNo">115</span>            final HashSet&lt;Neuron&gt; exclude = new HashSet&lt;Neuron&gt;();<a name="line.115"></a>
-<span class="sourceLineNo">116</span>            exclude.add(best);<a name="line.116"></a>
-<span class="sourceLineNo">117</span><a name="line.117"></a>
-<span class="sourceLineNo">118</span>            int radius = 1;<a name="line.118"></a>
-<span class="sourceLineNo">119</span>            do {<a name="line.119"></a>
-<span class="sourceLineNo">120</span>                // Retrieve immediate neighbours of the current set of neurons.<a name="line.120"></a>
-<span class="sourceLineNo">121</span>                neighbours = net.getNeighbours(neighbours, exclude);<a name="line.121"></a>
-<span class="sourceLineNo">122</span><a name="line.122"></a>
-<span class="sourceLineNo">123</span>                // Update all the neighbours.<a name="line.123"></a>
-<span class="sourceLineNo">124</span>                for (Neuron n : neighbours) {<a name="line.124"></a>
-<span class="sourceLineNo">125</span>                    updateNeighbouringNeuron(n, features, neighbourhoodDecay.value(radius));<a name="line.125"></a>
-<span class="sourceLineNo">126</span>                }<a name="line.126"></a>
-<span class="sourceLineNo">127</span><a name="line.127"></a>
-<span class="sourceLineNo">128</span>                // Add the neighbours to the exclude list so that they will<a name="line.128"></a>
-<span class="sourceLineNo">129</span>                // not be update more than once per training step.<a name="line.129"></a>
-<span class="sourceLineNo">130</span>                exclude.addAll(neighbours);<a name="line.130"></a>
-<span class="sourceLineNo">131</span>                ++radius;<a name="line.131"></a>
-<span class="sourceLineNo">132</span>            } while (radius &lt;= currentNeighbourhood);<a name="line.132"></a>
-<span class="sourceLineNo">133</span>        }<a name="line.133"></a>
-<span class="sourceLineNo">134</span>    }<a name="line.134"></a>
-<span class="sourceLineNo">135</span><a name="line.135"></a>
-<span class="sourceLineNo">136</span>    /**<a name="line.136"></a>
-<span class="sourceLineNo">137</span>     * Retrieves the number of calls to the {@link #update(Network,double[]) update}<a name="line.137"></a>
-<span class="sourceLineNo">138</span>     * method.<a name="line.138"></a>
-<span class="sourceLineNo">139</span>     *<a name="line.139"></a>
-<span class="sourceLineNo">140</span>     * @return the current number of calls.<a name="line.140"></a>
-<span class="sourceLineNo">141</span>     */<a name="line.141"></a>
-<span class="sourceLineNo">142</span>    public long getNumberOfCalls() {<a name="line.142"></a>
-<span class="sourceLineNo">143</span>        return numberOfCalls.get();<a name="line.143"></a>
-<span class="sourceLineNo">144</span>    }<a name="line.144"></a>
-<span class="sourceLineNo">145</span><a name="line.145"></a>
-<span class="sourceLineNo">146</span>    /**<a name="line.146"></a>
-<span class="sourceLineNo">147</span>     * Atomically updates the given neuron.<a name="line.147"></a>
-<span class="sourceLineNo">148</span>     *<a name="line.148"></a>
-<span class="sourceLineNo">149</span>     * @param n Neuron to be updated.<a name="line.149"></a>
-<span class="sourceLineNo">150</span>     * @param features Training data.<a name="line.150"></a>
-<span class="sourceLineNo">151</span>     * @param learningRate Learning factor.<a name="line.151"></a>
-<span class="sourceLineNo">152</span>     */<a name="line.152"></a>
-<span class="sourceLineNo">153</span>    private void updateNeighbouringNeuron(Neuron n,<a name="line.153"></a>
-<span class="sourceLineNo">154</span>                                          double[] features,<a name="line.154"></a>
-<span class="sourceLineNo">155</span>                                          double learningRate) {<a name="line.155"></a>
-<span class="sourceLineNo">156</span>        while (true) {<a name="line.156"></a>
-<span class="sourceLineNo">157</span>            final double[] expect = n.getFeatures();<a name="line.157"></a>
-<span class="sourceLineNo">158</span>            final double[] update = computeFeatures(expect,<a name="line.158"></a>
-<span class="sourceLineNo">159</span>                                                    features,<a name="line.159"></a>
-<span class="sourceLineNo">160</span>                                                    learningRate);<a name="line.160"></a>
-<span class="sourceLineNo">161</span>            if (n.compareAndSetFeatures(expect, update)) {<a name="line.161"></a>
-<span class="sourceLineNo">162</span>                break;<a name="line.162"></a>
-<span class="sourceLineNo">163</span>            }<a name="line.163"></a>
-<span class="sourceLineNo">164</span>        }<a name="line.164"></a>
-<span class="sourceLineNo">165</span>    }<a name="line.165"></a>
-<span class="sourceLineNo">166</span><a name="line.166"></a>
-<span class="sourceLineNo">167</span>    /**<a name="line.167"></a>
-<span class="sourceLineNo">168</span>     * Searches for the neuron whose features are closest to the given<a name="line.168"></a>
-<span class="sourceLineNo">169</span>     * sample, and atomically updates its features.<a name="line.169"></a>
+<span class="sourceLineNo">023</span><a name="line.23"></a>
+<span class="sourceLineNo">024</span>import org.apache.commons.math3.analysis.function.Gaussian;<a name="line.24"></a>
+<span class="sourceLineNo">025</span>import org.apache.commons.math3.linear.ArrayRealVector;<a name="line.25"></a>
+<span class="sourceLineNo">026</span>import org.apache.commons.math3.ml.distance.DistanceMeasure;<a name="line.26"></a>
+<span class="sourceLineNo">027</span>import org.apache.commons.math3.ml.neuralnet.MapUtils;<a name="line.27"></a>
+<span class="sourceLineNo">028</span>import org.apache.commons.math3.ml.neuralnet.Network;<a name="line.28"></a>
+<span class="sourceLineNo">029</span>import org.apache.commons.math3.ml.neuralnet.Neuron;<a name="line.29"></a>
+<span class="sourceLineNo">030</span>import org.apache.commons.math3.ml.neuralnet.UpdateAction;<a name="line.30"></a>
+<span class="sourceLineNo">031</span><a name="line.31"></a>
+<span class="sourceLineNo">032</span>/**<a name="line.32"></a>
+<span class="sourceLineNo">033</span> * Update formula for &lt;a href="http://en.wikipedia.org/wiki/Kohonen"&gt;<a name="line.33"></a>
+<span class="sourceLineNo">034</span> * Kohonen's Self-Organizing Map&lt;/a&gt;.<a name="line.34"></a>
+<span class="sourceLineNo">035</span> * &lt;br/&gt;<a name="line.35"></a>
+<span class="sourceLineNo">036</span> * The {@link #update(Network,double[]) update} method modifies the<a name="line.36"></a>
+<span class="sourceLineNo">037</span> * features {@code w} of the "winning" neuron and its neighbours<a name="line.37"></a>
+<span class="sourceLineNo">038</span> * according to the following rule:<a name="line.38"></a>
+<span class="sourceLineNo">039</span> * &lt;code&gt;<a name="line.39"></a>
+<span class="sourceLineNo">040</span> *  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;)<a name="line.40"></a>
+<span class="sourceLineNo">041</span> * &lt;/code&gt;<a name="line.41"></a>
+<span class="sourceLineNo">042</span> * where<a name="line.42"></a>
+<span class="sourceLineNo">043</span> * &lt;ul&gt;<a name="line.43"></a>
+<span class="sourceLineNo">044</span> *  &lt;li&gt;&amp;alpha; is the current &lt;em&gt;learning rate&lt;/em&gt;, &lt;/li&gt;<a name="line.44"></a>
+<span class="sourceLineNo">045</span> *  &lt;li&gt;&amp;sigma; is the current &lt;em&gt;neighbourhood size&lt;/em&gt;, and&lt;/li&gt;<a name="line.45"></a>
+<span class="sourceLineNo">046</span> *  &lt;li&gt;{@code d} is the number of links to traverse in order to reach<a name="line.46"></a>
+<span class="sourceLineNo">047</span> *   the neuron from the winning neuron.&lt;/li&gt;<a name="line.47"></a>
+<span class="sourceLineNo">048</span> * &lt;/ul&gt;<a name="line.48"></a>
+<span class="sourceLineNo">049</span> * &lt;br/&gt;<a name="line.49"></a>
+<span class="sourceLineNo">050</span> * This class is thread-safe as long as the arguments passed to the<a name="line.50"></a>
+<span class="sourceLineNo">051</span> * {@link #KohonenUpdateAction(DistanceMeasure,LearningFactorFunction,<a name="line.51"></a>
+<span class="sourceLineNo">052</span> * NeighbourhoodSizeFunction) constructor} are instances of thread-safe<a name="line.52"></a>
+<span class="sourceLineNo">053</span> * classes.<a name="line.53"></a>
+<span class="sourceLineNo">054</span> * &lt;br/&gt;<a name="line.54"></a>
+<span class="sourceLineNo">055</span> * Each call to the {@link #update(Network,double[]) update} method<a name="line.55"></a>
+<span class="sourceLineNo">056</span> * will increment the internal counter used to compute the current<a name="line.56"></a>
+<span class="sourceLineNo">057</span> * values for<a name="line.57"></a>
+<span class="sourceLineNo">058</span> * &lt;ul&gt;<a name="line.58"></a>
+<span class="sourceLineNo">059</span> *  &lt;li&gt;the &lt;em&gt;learning rate&lt;/em&gt;, and&lt;/li&gt;<a name="line.59"></a>
+<span class="sourceLineNo">060</span> *  &lt;li&gt;the &lt;em&gt;neighbourhood size&lt;/em&gt;.&lt;/li&gt;<a name="line.60"></a>
+<span class="sourceLineNo">061</span> * &lt;/ul&gt;<a name="line.61"></a>
+<span class="sourceLineNo">062</span> * Consequently, the function instances that compute those values (passed<a name="line.62"></a>
+<span class="sourceLineNo">063</span> * to the constructor of this class) must take into account whether this<a name="line.63"></a>
+<span class="sourceLineNo">064</span> * class's instance will be shared by multiple threads, as this will impact<a name="line.64"></a>
+<span class="sourceLineNo">065</span> * the training process.<a name="line.65"></a>
+<span class="sourceLineNo">066</span> *<a name="line.66"></a>
+<span class="sourceLineNo">067</span> * @since 3.3<a name="line.67"></a>
+<span class="sourceLineNo">068</span> */<a name="line.68"></a>
+<span class="sourceLineNo">069</span>public class KohonenUpdateAction implements UpdateAction {<a name="line.69"></a>
+<span class="sourceLineNo">070</span>    /** Distance function. */<a name="line.70"></a>
+<span class="sourceLineNo">071</span>    private final DistanceMeasure distance;<a name="line.71"></a>
+<span class="sourceLineNo">072</span>    /** Learning factor update function. */<a name="line.72"></a>
+<span class="sourceLineNo">073</span>    private final LearningFactorFunction learningFactor;<a name="line.73"></a>
+<span class="sourceLineNo">074</span>    /** Neighbourhood size update function. */<a name="line.74"></a>
+<span class="sourceLineNo">075</span>    private final NeighbourhoodSizeFunction neighbourhoodSize;<a name="line.75"></a>
+<span class="sourceLineNo">076</span>    /** Number of calls to {@link #update(Network,double[])}. */<a name="line.76"></a>
+<span class="sourceLineNo">077</span>    private final AtomicLong numberOfCalls = new AtomicLong(0);<a name="line.77"></a>
+<span class="sourceLineNo">078</span><a name="line.78"></a>
+<span class="sourceLineNo">079</span>    /**<a name="line.79"></a>
+<span class="sourceLineNo">080</span>     * @param distance Distance function.<a name="line.80"></a>
+<span class="sourceLineNo">081</span>     * @param learningFactor Learning factor update function.<a name="line.81"></a>
+<span class="sourceLineNo">082</span>     * @param neighbourhoodSize Neighbourhood size update function.<a name="line.82"></a>
+<span class="sourceLineNo">083</span>     */<a name="line.83"></a>
+<span class="sourceLineNo">084</span>    public KohonenUpdateAction(DistanceMeasure distance,<a name="line.84"></a>
+<span class="sourceLineNo">085</span>                               LearningFactorFunction learningFactor,<a name="line.85"></a>
+<span class="sourceLineNo">086</span>                               NeighbourhoodSizeFunction neighbourhoodSize) {<a name="line.86"></a>
+<span class="sourceLineNo">087</span>        this.distance = distance;<a name="line.87"></a>
+<span class="sourceLineNo">088</span>        this.learningFactor = learningFactor;<a name="line.88"></a>
+<span class="sourceLineNo">089</span>        this.neighbourhoodSize = neighbourhoodSize;<a name="line.89"></a>
+<span class="sourceLineNo">090</span>    }<a name="line.90"></a>
+<span class="sourceLineNo">091</span><a name="line.91"></a>
+<span class="sourceLineNo">092</span>    /**<a name="line.92"></a>
+<span class="sourceLineNo">093</span>     * {@inheritDoc}<a name="line.93"></a>
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-<span class="sourceLineNo">211</span>    }<a name="line.211"></a>
-<span class="sourceLineNo">212</span>}<a name="line.212"></a>
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+<span class="sourceLineNo">177</span>                                          double learningRate) {<a name="line.177"></a>
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+<span class="sourceLineNo">195</span>                                           double[] features,<a name="line.195"></a>
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+<span class="sourceLineNo">197</span>        while (true) {<a name="line.197"></a>
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+<span class="sourceLineNo">206</span>            // sample: Hence, the winner search is performed again.<a name="line.206"></a>
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+<span class="sourceLineNo">208</span>    }<a name="line.208"></a>
+<span class="sourceLineNo">209</span><a name="line.209"></a>
+<span class="sourceLineNo">210</span>    /**<a name="line.210"></a>
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+<span class="sourceLineNo">214</span>     * @param sample Training data.<a name="line.214"></a>
+<span class="sourceLineNo">215</span>     * @param learningRate Learning factor.<a name="line.215"></a>
+<span class="sourceLineNo">216</span>     * @return the new values for the features.<a name="line.216"></a>
+<span class="sourceLineNo">217</span>     */<a name="line.217"></a>
+<span class="sourceLineNo">218</span>    private double[] computeFeatures(double[] current,<a name="line.218"></a>
+<span class="sourceLineNo">219</span>                                     double[] sample,<a name="line.219"></a>
+<span class="sourceLineNo">220</span>                                     double learningRate) {<a name="line.220"></a>
+<span class="sourceLineNo">221</span>        final ArrayRealVector c = new ArrayRealVector(current, false);<a name="line.221"></a>
+<span class="sourceLineNo">222</span>        final ArrayRealVector s = new ArrayRealVector(sample, false);<a name="line.222"></a>
+<span class="sourceLineNo">223</span>        // c + learningRate * (s - c)<a name="line.223"></a>
+<span class="sourceLineNo">224</span>        return s.subtract(c).mapMultiplyToSelf(learningRate).add(c).toArray();<a name="line.224"></a>
+<span class="sourceLineNo">225</span>    }<a name="line.225"></a>
+<span class="sourceLineNo">226</span>}<a name="line.226"></a>
 
 
 

Modified: websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ode/AbstractIntegrator.html
==============================================================================
--- websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ode/AbstractIntegrator.html (original)
+++ websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ode/AbstractIntegrator.html Mon Jul 27 19:40:06 2015
@@ -286,183 +286,186 @@
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 <span class="sourceLineNo">279</span>     * @exception MaxCountExceededException if the number of functions evaluations is exceeded<a name="line.279"></a>
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+<span class="sourceLineNo">404</span>                interpolator.setSoftPreviousTime(eventT);<a name="line.404"></a>
+<span class="sourceLineNo">405</span>                interpolator.setSoftCurrentTime(currentT);<a name="line.405"></a>
+<span class="sourceLineNo">406</span><a name="line.406"></a>
+<span class="sourceLineNo">407</span>                // check if the same event occurs again in the remaining part of the step<a name="line.407"></a>
+<span class="sourceLineNo">408</span>                if (currentEvent.evaluateStep(interpolator)) {<a name="line.408"></a>
+<span class="sourceLineNo">409</span>                    // the event occurs during the current step<a name="line.409"></a>
+<span class="sourceLineNo">410</span>                    occurringEvents.add(currentEvent);<a name="line.410"></a>
+<span class="sourceLineNo">411</span>                }<a name="line.411"></a>
+<span class="sourceLineNo">412</span><a name="line.412"></a>
+<span class="sourceLineNo">413</span>            }<a name="line.413"></a>
+<span class="sourceLineNo">414</span><a name="line.414"></a>
+<span class="sourceLineNo">415</span>            // last part of the step, after the last event<a name="line.415"></a>
+<span class="sourceLineNo">416</span>            interpolator.setInterpolatedTime(currentT);<a name="line.416"></a>
+<span class="sourceLineNo">417</span>            final double[] currentY = new double[y.length];<a name="line.417"></a>
+<span class="sourceLineNo">418</span>            expandable.getPrimaryMapper().insertEquationData(interpolator.getInterpolatedState(),<a name="line.418"></a>
+<span class="sourceLineNo">419</span>                                                             currentY);<a name="line.419"></a>
+<span class="sourceLineNo">420</span>            int index = 0;<a name="line.420"></a>
+<span class="sourceLineNo">421</span>            for (EquationsMapper secondary : expandable.getSecondaryMappers()) {<a name="line.421"></a>
+<span class="sourceLineNo">422</span>                secondary.insertEquationData(interpolator.getInterpolatedSecondaryState(index++),<a name="line.422"></a>
+<span class="sourceLineNo">423</span>                                             currentY);<a name="line.423"></a>
+<span class="sourceLineNo">424</span>            }<a name="line.424"></a>
+<span class="sourceLineNo">425</span>            for (final EventState state : eventsStates) {<a name="line.425"></a>
+<span class="sourceLineNo">426</span>                state.stepAccepted(currentT, currentY);<a name="line.426"></a>
+<span class="sourceLineNo">427</span>                isLastStep = isLastStep || state.stop();<a name="line.427"></a>
+<span class="sourceLineNo">428</span>            }<a name="line.428"></a>
+<span class="sourceLineNo">429</span>            isLastStep = isLastStep || Precision.equals(currentT, tEnd, 1);<a name="line.429"></a>
+<span class="sourceLineNo">430</span><a name="line.430"></a>
+<span class="sourceLineNo">431</span>            // handle the remaining part of the step, after all events if any<a name="line.431"></a>
+<span class="sourceLineNo">432</span>            for (StepHandler handler : stepHandlers) {<a name="line.432"></a>
+<span class="sourceLineNo">433</span>                handler.handleStep(interpolator, isLastStep);<a name="line.433"></a>
+<span class="sourceLineNo">434</span>            }<a name="line.434"></a>
+<span class="sourceLineNo">435</span><a name="line.435"></a>
+<span class="sourceLineNo">436</span>            return currentT;<a name="line.436"></a>
+<span class="sourceLineNo">437</span><a name="line.437"></a>
+<span class="sourceLineNo">438</span>    }<a name="line.438"></a>
+<span class="sourceLineNo">439</span><a name="line.439"></a>
+<span class="sourceLineNo">440</span>    /** Check the integration span.<a name="line.440"></a>
+<span class="sourceLineNo">441</span>     * @param equations set of differential equations<a name="line.441"></a>
+<span class="sourceLineNo">442</span>     * @param t target time for the integration<a name="line.442"></a>
+<span class="sourceLineNo">443</span>     * @exception NumberIsTooSmallException if integration span is too small<a name="line.443"></a>
+<span class="sourceLineNo">444</span>     * @exception DimensionMismatchException if adaptive step size integrators<a name="line.444"></a>
+<span class="sourceLineNo">445</span>     * tolerance arrays dimensions are not compatible with equations settings<a name="line.445"></a>
+<span class="sourceLineNo">446</span>     */<a name="line.446"></a>
+<span class="sourceLineNo">447</span>    protected void sanityChecks(final ExpandableStatefulODE equations, final double t)<a name="line.447"></a>
+<span class="sourceLineNo">448</span>        throws NumberIsTooSmallException, DimensionMismatchException {<a name="line.448"></a>
+<span class="sourceLineNo">449</span><a name="line.449"></a>
+<span class="sourceLineNo">450</span>        final double threshold = 1000 * FastMath.ulp(FastMath.max(FastMath.abs(equations.getTime()),<a name="line.450"></a>
+<span class="sourceLineNo">451</span>                                                                  FastMath.abs(t)));<a name="line.451"></a>
+<span class="sourceLineNo">452</span>        final double dt = FastMath.abs(equations.getTime() - t);<a name="line.452"></a>
+<span class="sourceLineNo">453</span>        if (dt &lt;= threshold) {<a name="line.453"></a>
+<span class="sourceLineNo">454</span>            throw new NumberIsTooSmallException(LocalizedFormats.TOO_SMALL_INTEGRATION_INTERVAL,<a name="line.454"></a>
+<span class="sourceLineNo">455</span>                                                dt, threshold, false);<a name="line.455"></a>
+<span class="sourceLineNo">456</span>        }<a name="line.456"></a>
+<span class="sourceLineNo">457</span><a name="line.457"></a>
+<span class="sourceLineNo">458</span>    }<a name="line.458"></a>
+<span class="sourceLineNo">459</span><a name="line.459"></a>
+<span class="sourceLineNo">460</span>}<a name="line.460"></a>
 
 
 

Modified: websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ode/UnknownParameterException.html
==============================================================================
--- websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ode/UnknownParameterException.html (original)
+++ websites/production/commons/content/proper/commons-math/apidocs/src-html/org/apache/commons/math3/ode/UnknownParameterException.html Mon Jul 27 19:40:06 2015
@@ -46,7 +46,7 @@
 <span class="sourceLineNo">038</span>     * @param name parameter name.<a name="line.38"></a>
 <span class="sourceLineNo">039</span>     */<a name="line.39"></a>
 <span class="sourceLineNo">040</span>    public UnknownParameterException(final String name) {<a name="line.40"></a>
-<span class="sourceLineNo">041</span>        super(LocalizedFormats.UNKNOWN_PARAMETER);<a name="line.41"></a>
+<span class="sourceLineNo">041</span>        super(LocalizedFormats.UNKNOWN_PARAMETER, name);<a name="line.41"></a>
 <span class="sourceLineNo">042</span>        this.name = name;<a name="line.42"></a>
 <span class="sourceLineNo">043</span>    }<a name="line.43"></a>
 <span class="sourceLineNo">044</span><a name="line.44"></a>



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