Self Organizing Map

Salut,

Je travaille sur les cartes de Kohonen (Self-Organizing Map) avec Eclipse.
Quelqu'un pourrait-il m'aider sur ce sujet?
D'avance merci.

Salut,

http://club.developpez.com/aidenouveaux/

Nous ne sommes pas là pour faire ton travail.
Qu'est-ce qui te pose problème ?

I'm training the self organizig map, I need to set the number of the input neuron and how to affect to this neuron a weight ? in eclipse with weka
some one can help me please
this my code

Code:

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/*  * This program is free software; you can redistribute it and/or modify  * it under the terms of the GNU General Public License as published by  * the Free Software Foundation; either version 2 of the License, or  * (at your option) any later version.  *  * This program is distributed in the hope that it will be useful,  * but WITHOUT ANY WARRANTY; without even the implied warranty of  * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the  * GNU General Public License for more details.  *  * You should have received a copy of the GNU General Public License  * along with this program; if not, write to the Free Software  * Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.  */   /*  * SelfOrganizingMap.java  * Copyright (C) 2000-2010 University of Waikato, Hamilton, New Zealand  *  */ package weka.clusterers;   import java.util.Enumeration; import java.util.Vector; import weka.core.Attribute; import weka.core.Capabilities; import weka.core.Capabilities.Capability; import weka.core.DenseInstance; import weka.core.Instance; import weka.core.Instances; import weka.core.Option; import weka.core.OptionHandler; import weka.core.RevisionUtils; import weka.core.Utils; import weka.core.EuclideanDistance;   /** <!-- globalinfo-start -->  * A Clusterer that implements Kohonen's Self-Organizing Map algorithm for  * unsupervised clustering. <br/>  * See T. Kohonen, Self-Organization and Associative Memory, 3rd Edition, Springer, 1989  * <p/> <!-- globalinfo-end -->  * <!-- options-start -->  * Valid options are: <p/>  *  * <pre> -L &lt; initial learning rate&gt;  *  The initial learning rate for the training algorithm.  *  (Value should be greater than 0.01 and less or equal to 1, Default = 1).</pre>  *  * <pre> -O  &lt;number of epochs in ordering phase&gt;  *  Number of epochs in ordering phase.  *  (Value should be greater than or equal to 2000, Default = 2000).</pre>  *  * <pre> -C  &lt;number of epochs in convergence phase&gt;  *  Number of epochs to train through.  *  (Value should be greater than or equal to 1000, Default = 1000).</pre>  *  * <pre> -H &lt;height of lattice&gt;  *  The height of lattice.  *  (Value should be &gt; 0, Default = 1).</pre>  *  * <pre> -W &lt;width of lattice&gt;  *  The width of lattice.  *  (Value should be &gt; 0, Default = 1).</pre>  *  * <pre> -I  *  Normalizing the attributes will NOT be done.  *  (Set this to not normalize the attributes).</pre>  *  * <pre> -S  *  Statistics will NOT be calculated after training.  *  (Set this to not calculate statistics).</pre>  * <!-- options-end -->  *  * @author John Salatas (jsalatas at gmail.com)  * @version $Revision: 4 $  */ public class SelfOrganizingMap extends AbstractClusterer implements OptionHandler {   /** for serialization */ static final long serialVersionUID = -3028490959617832916L; /** the distance function used. */ private EuclideanDistance m_euclideanDistance = new EuclideanDistance(); /** The width of lattice */ private int m_width; /** The height of lattice */ private int m_height; /** The number of epochs in convergence phase  */ private int m_convergenceEpochs; /** The number of epochs in ordering phase */ private int m_orderingEpochs; /** The initial learning rate for the network */ private double m_learningRate; /** The training instances.  */ private Instances m_instances; /** The weights for each unit in the lattice. */ private Instances m_clusters; // It is used in EuclideanDistance.closestPoint private int[] m_clusterList; /** This flag states that the user wants the input values normalized. */ private boolean m_normalizeAttributes; /** The maximum value for all the attributes. */ private double[] m_attributeMax; /** The minimum value for all the attributes. */ private double[] m_attributeMin; /**  This flag states that the user wants to calculate statistics after training. */ private boolean m_calcStats; /** holds the training instances to clusters assignments */ private Instances[] m_clusterInstances; /** holds the cluster statistics */ private double[][][] m_clusterStats; public static final int INPUT_COUNT=4;   public static final int SAMPLE_COUNT=100; /**      * @return The width of lattice.      */ public int getWidth() { return m_width; }   /**      * Sets the width of lattice.      * @param width The width of lattice.      */ public void setWidth(int width) { if (width > 0) { this.m_width = width; } }   /**      * @return The height of lattice.      */ public int getHeight() { return m_height; }   /**      * Sets the height of lattice.      * Must be greater than 0.      * @param height The height.      */ public void setHeight(int height) { if (height > 0) { this.m_height = height; } }   /**      * @return The number of epochs in convergence phase.      */ public int getConvergenceEpochs() { return m_convergenceEpochs; }   /**      * Set the number of epochs in convergence phase.      * Must be greater than or equal to 1000.      * @param n The number of epochs.      */ public void setConvergenceEpochs(int n) { if (n >= 1000) { m_convergenceEpochs = n; } }   /**      * @return The number of epochs in ordering phase.      */ public int getOrderingEpochs() { return m_orderingEpochs; }   /**      * Set the number of epochs in ordering phase.      * Must be greater than or equal to 2000.      * @param n The number of epochs.      */ public void setOrderingEpochs(int n) { if (n >= 2000) { m_orderingEpochs = n; } }   /**      * @return The initial learning rate for the nodes.      */ public double getLearningRate() { return m_learningRate; }   /**      * The initial learning rate can be set using this command.      * Must be greater than 0 and no more than 1.      * @param l The initial learning rate.      */ public void setLearningRate(double l) { if (l > 0.01 && l <= 1) { m_learningRate = l; } }   /**      * @return The flag for normalizing attributes.      */ public boolean getNormalizeAttributes() { return m_normalizeAttributes; }   /**      * @param a True if the attributes should be normalized (even nominal      * attributes will get normalized here) (range goes between -1 - 1).      */ public void setNormalizeAttributes(boolean a) { m_normalizeAttributes = a; }   /**      * @return The flag for calculating statistics after training.      */ public boolean getCalcStats() { return m_calcStats; }   /**      *      * @param c True if statistics should be calculated.      */ public void setCalcStats(boolean c) { this.m_calcStats = c; }   /**      * @return a string to describe the height option.      */ public String heightTipText() { return "The height of lattice."; }   /**      * @return a string to describe the caclulate statistics option.      */ public String calcStatsTipText() { return "This should calculate statistics for each cluster after training."; }   /**      * @return a string to describe the width option.      */ public String widthTipText() { return "The width of lattice."; }   /**      * @return a string to describe the learning rate option.      */ public String learningRateTipText() { return "The initial amount the weights are updated."; }   /**      * @return a string to describe the number of epochs in convergence option.      */ public String convergenceEpochsTipText() { return "The number of epochs in convergence phase."; }   /**      * @return a string to describe the number of epochs in ordering phase option.      */ public String orderingEpochsTipText() { return "The number of epochs in ordering phase."; }   /**      * @return a string to describe the normalize attributes option.      */ public String normalizeAttributesTipText() { return "This will normalize the attributes."; }   /**      * This will return a string describing the clusterer.      * @return The string.      */ public String globalInfo() { return "A Clusterer that implements Kohonen's Self Organizing Map\n" + "algorithm for unsupervised clustering."; }   /**      * Returns the revision string.      *      * @return          the revision      */ public String getRevision() { return RevisionUtils.extract("$Revision: 4 $"); }   /**      * The constructor.      */ public SelfOrganizingMap() { m_clusters = null; m_width = 2; m_height = 2; m_convergenceEpochs = 1000; m_orderingEpochs = 2000; m_learningRate = 1.0; m_normalizeAttributes = true; m_calcStats = true; }   /**      * Returns default capabilities of the classifier.      *      * @return      the capabilities of this classifier      */ public Capabilities getCapabilities() { Capabilities result = super.getCapabilities(); result.disableAll(); result.enable(Capability.NO_CLASS);   // attributes result.enable(Capability.NUMERIC_ATTRIBUTES); result.enable(Capability.NOMINAL_ATTRIBUTES); result.enable(Capability.MISSING_VALUES);   return result; }   /**      * Parses a given list of options. <p/>      *     <!-- options-start -->      * Valid options are: <p/>      *      * <pre> -L &lt;initial learning rate&gt;      *  The initial learning rate for the training algorithm.      *  (Value should be greater than 0.01 and less or equal to 1, Default = 1).</pre>      *      * <pre> -O  &lt;number of epochs in ordering phase&gt;      *  Number of epochs in ordering phase.      *  (Value should be greater than or equal to 2000, Default = 2000).</pre>      *      * <pre> -C &lt;number of epochs in convergence phase&gt;      *  Number of epochs in convergence phase.      *  (Value should be greater than or equal to 1000, Default = 1000).</pre>      *      * <pre> -I      *  Normalizing the attributes will NOT be done.      *  (Set this to not normalize the attributes).</pre>      *      * <pre> -H &lt;height of lattice&gt;      *  The height of lattice.      *  (Value should be &gt; 0, Default = 2).</pre>      *      * <pre> -W &lt;width of lattice&gt;      *  The width of lattice.      *  (Value should be &gt; 0, Default = 2).</pre>      *      * <pre> -S      *  Statistics will NOT be calculated after training.      *  (Set this to not calculate statistics).</pre>      *     <!-- options-end -->      *      * @param options the list of options as an array of strings      * @throws Exception if an option is not supported      */ public void setOptions(String[] options) throws Exception { //the defaults can be found here!!!! String learningString = Utils.getOption('L', options); if (learningString.length() != 0) { setLearningRate((new Double(learningString)).doubleValue()); } else { setLearningRate(1); } String orderingEpochsString = Utils.getOption('O', options); if (orderingEpochsString.length() != 0) { setOrderingEpochs(Integer.parseInt(orderingEpochsString)); } else { setOrderingEpochs(2000); } String convergenceEpochsString = Utils.getOption('C', options); if (convergenceEpochsString.length() != 0) { setConvergenceEpochs(Integer.parseInt(convergenceEpochsString)); } else { setConvergenceEpochs(1000); } String heightString = Utils.getOption('H', options); if (heightString.length() != 0) { setHeight(Integer.parseInt(heightString)); } else { setHeight(2); } String widthString = Utils.getOption('W', options); if (widthString.length() != 0) { setWidth(Integer.parseInt(widthString)); } else { setWidth(2); } if (Utils.getFlag('I', options)) { setNormalizeAttributes(false); } else { setNormalizeAttributes(true); } if (Utils.getFlag('S', options)) { setCalcStats(false); } else { setCalcStats(true); } Utils.checkForRemainingOptions(options); }   /**      * Gets the current settings of NeuralNet.      *      * @return an array of strings suitable for passing to setOptions()      */ public String[] getOptions() {   String[] options = new String[12]; int current = 0; options[current++] = "-L"; options[current++] = "" + getLearningRate(); options[current++] = "-O"; options[current++] = "" + getOrderingEpochs(); options[current++] = "-C"; options[current++] = "" + getConvergenceEpochs(); options[current++] = "-H"; options[current++] = "" + getHeight(); options[current++] = "-W"; options[current++] = "" + getWidth(); if (!getNormalizeAttributes()) { options[current++] = "-I"; } if (!getCalcStats()) { options[current++] = "-S"; } while (current < options.length) { options[current++] = ""; } return options; }   /**      * Classifies a given instance.      *      * @param i the instance to be assigned to a cluster      * @return the number of the assigned cluster as an interger      * if the class is enumerated, otherwise the predicted value      * @throws Exception if instance could not be classified      * successfully      */ public int clusterInstance(Instance i) throws Exception { if ((m_clusters == null) || (m_instances == null)) { return 0; }   Instance instance = new DenseInstance(i);   if (m_normalizeAttributes) { instance = normalizeInstance(instance); }   int a = m_euclideanDistance.closestPoint(instance, m_clusters, m_clusterList);   return a; }   /**      * Returns an enumeration describing the available options.      *      * @return an enumeration of all the available options.      */ public Enumeration listOptions() { Vector result = new Vector();   result.addElement(new Option( "\tLearning Rate for the training algorithm.\n" + "\t(default 1)", "L", 1, "-L <num>"));   result.addElement(new Option( "\tNumber of epochs in ordering phase.\n" + "\t(default 2000)", "O", 1, "-O <num>"));   result.addElement(new Option( "\tNumber of epochs in convergence phase.\n" + "\t(default 1000)", "C", 1, "-C <num>"));   result.addElement(new Option( "\tThe height of lattice.\n" + "\t(default 1)", "H", 1, "-H <num>"));   result.addElement(new Option( "\tThe width of lattice.\n" + "\t(default 1)", "W", 1, "-W <num>"));   result.addElement(new Option( "\tNormalizing the attributes will NOT be done.\n" + "\t(Set this to not normalize the attributes).", "I", 0, "-I"));   return result.elements(); }   private String pad(String source, String padChar, int length, boolean leftPad) { StringBuffer temp = new StringBuffer();   if (leftPad) { for (int i = 0; i < length; i++) { temp.append(padChar); } temp.append(source); } else { temp.append(source); for (int i = 0; i < length; i++) { temp.append(padChar); } } return temp.toString(); }   /**      * return a string describing this clusterer.      *      * @return a description of the clusterer as a string      */ public String toString() { StringBuilder sb = new StringBuilder(); sb.append("\nSelf Organized Map\n==================\n"); if ((m_clusters == null) || (m_instances == null)) { sb.append("No clusterer built yet!\n"); sb.append("==================\n\n"); return sb.toString(); }   sb.append("\nNumber of clusters: " + (m_width * m_height) + "\n");   int maxWidth = 0; int maxAttWidth = 0;   if (m_calcStats) { // set up max widths // attributes for (int i = 0; i < m_clusters.numAttributes(); i++) { Attribute a = m_clusters.attribute(i); if (a.name().length() > maxAttWidth) { maxAttWidth = m_clusters.attribute(i).name().length(); } } for (int i = 0; i < m_clusters.numInstances(); i++) { for (int j = 0; j < m_clusters.numAttributes(); j++) { // check mean and std. dev. against maxWidth double mean = Math.log(Math.abs(m_clusterStats[j][i][2])) / Math.log(10.0); double stdD = Math.log(Math.abs(m_clusterStats[j][i][3])) / Math.log(10.0); double width = (mean > stdD) ? mean : stdD; if (width < 0) { width = 1; } // decimal + # decimal places + 1 width += 6.0; if ((int) width > maxWidth) { maxWidth = (int) width; } } }   if (maxAttWidth < "Attribute".length()) { maxAttWidth = "Attribute".length(); }   maxAttWidth += 2;   sb.append("\n\n"); sb.append(pad("Cluster", " ", (maxAttWidth + maxWidth + 1) - "Cluster".length(), true));   sb.append("\n"); sb.append(pad("Attribute", " ", maxAttWidth - "Attribute".length(), false));   // cluster #'s for (int i = 0; i < m_clusters.numInstances(); i++) { String classL = "" + i; sb.append(pad(classL, " ", maxWidth + 1 - classL.length(), true)); } sb.append("\n");   sb.append(pad("", " ", maxAttWidth, true)); for (int i = 0; i < m_clusters.numInstances(); i++) { String numInst = Utils.doubleToString(m_clusterInstances[i].numInstances(), maxWidth, 2).trim(); numInst = "(" + numInst + ")"; sb.append(pad(numInst, " ", maxWidth + 1 - numInst.length(), true)); }   sb.append("\n"); sb.append(pad("", "=", maxAttWidth + (maxWidth * m_clusters.numInstances()) + m_clusters.numInstances() + 1, true)); sb.append("\n");   for (int i = 0; i < m_clusters.numAttributes(); i++) { String attName = m_clusters.attribute(i).name(); sb.append(attName + "\n");   String valueL = " value"; sb.append(pad(valueL, " ", maxAttWidth + 1 - valueL.length(), false)); for (int j = 0; j < m_clusters.numInstances(); j++) { // values String value = Utils.doubleToString(denormalizeInstance(m_clusters.get(j)).value(i), maxWidth, 4).trim(); sb.append(pad(value, " ", maxWidth + 1 - value.length(), true)); } sb.append("\n"); String minL = " min"; sb.append(pad(minL, " ", maxAttWidth + 1 - minL.length(), false)); for (int j = 0; j < m_clusters.numInstances(); j++) { // means String min = Utils.doubleToString(m_clusterStats[i][j][0], maxWidth, 4).trim(); sb.append(pad(min, " ", maxWidth + 1 - min.length(), true)); } sb.append("\n"); String maxL = " max"; sb.append(pad(maxL, " ", maxAttWidth + 1 - maxL.length(), false)); for (int j = 0; j < m_clusters.numInstances(); j++) { // means String max = Utils.doubleToString(m_clusterStats[i][j][1], maxWidth, 4).trim(); sb.append(pad(max, " ", maxWidth + 1 - max.length(), true)); } sb.append("\n"); String meanL = " mean"; sb.append(pad(meanL, " ", maxAttWidth + 1 - meanL.length(), false)); for (int j = 0; j < m_clusters.numInstances(); j++) { // means String mean = Utils.doubleToString(m_clusterStats[i][j][2], maxWidth, 4).trim(); sb.append(pad(mean, " ", maxWidth + 1 - mean.length(), true)); } sb.append("\n"); // now do std deviations String stdDevL = " std. dev."; sb.append(pad(stdDevL, " ", maxAttWidth + 1 - stdDevL.length(), false)); for (int j = 0; j < m_clusters.numInstances(); j++) { String stdDev = Utils.doubleToString(m_clusterStats[i][j][3], maxWidth, 4).trim(); sb.append(pad(stdDev, " ", maxWidth + 1 - stdDev.length(), true)); } sb.append("\n\n"); } }   return sb.toString(); }   /**      * Generates a clusterer. Has to initialize all fields of the clusterer      * that are not being set via options.      *      * @param data set of instances serving as training data      * @throws Exception if the clusterer has not been      * generated successfully      */ public void buildClusterer(Instances data) throws Exception { // can clusterer handle the data? getCapabilities().testWithFail(data);   // copy the original instances m_instances = new Instances(data);   // normalize instances m_instances = normalize(m_instances);   // init clusters m_clusters = initClusters();   // init the pointList (used in EuclideanDistance.closestPoint) m_clusterList = new int[m_clusters.numInstances()]; for (int i = 0; i < m_clusterList.length; i++) { m_clusterList[i] = i; }   // the actual learning rate double learningRate; // the actual effective neighborhood double effectiveWidth; // init euclidean distance m_euclideanDistance.setDontNormalize(true); m_euclideanDistance.setInstances(m_clusters); // the winner neuron int winningNeuron;   for (int epoch = 1; epoch <= m_convergenceEpochs + m_orderingEpochs; epoch++) { learningRate = calcLearningRate(epoch); effectiveWidth = calcEffectiveWidth(epoch); for (int instance = 0; instance < m_instances.numInstances(); instance++) { winningNeuron = m_euclideanDistance.closestPoint(m_instances.get(instance), m_clusters, m_clusterList);   // update the weights for (int neuron = 0; neuron < m_clusters.numInstances(); neuron++) { updateWeights(neuron, winningNeuron, instance, learningRate, effectiveWidth); } } }   if (m_calcStats) { calcStatistics(); } }   /**      * This function calculates the clusterer's statistics      */ private void calcStatistics() { // init cluster statistics m_clusterStats = new double[m_instances.numAttributes()][m_width * m_height][4];   // init clusters assignements m_clusterInstances = new Instances[m_width * m_height];   // keep cluster's attributes for (int i = 0; i < m_clusters.numInstances(); i++) { m_clusterInstances[i] = new Instances(m_instances); m_clusterInstances[i].clear();   try { Instances clusters = getClusters(); for (int j = 0; j < clusters.numAttributes(); j++) { m_clusterStats[j][i][0] = clusters.get(i).value(j); } } catch (Exception ex) { } }   // get instances in each class for (int instance = 0; instance < m_instances.numInstances(); instance++) { Instance inst = m_instances.get(instance); int cluster = -1; try { cluster = clusterInstance(denormalizeInstance(inst));   } catch (Exception ex) { } if (cluster != -1) { m_clusterInstances[cluster].add(denormalizeInstance(inst)); } }   //calc min, max, mean, stdev for each cluster for (int cluster = 0; cluster < m_clusters.numInstances(); cluster++) { for (int attr = 0; attr < m_clusters.numAttributes(); attr++) { int unknownValues = 0; double min = Double.POSITIVE_INFINITY; double max = Double.NEGATIVE_INFINITY; double mean = 0; double stdev = 0; for (int instance = 0; instance < m_clusterInstances[cluster].numInstances(); instance++) { Instance inst = m_clusterInstances[cluster].get(instance); if (!Double.isNaN(inst.value(attr))) { mean += inst.value(attr); if (inst.value(attr) < min) { min = inst.value(attr); } if (inst.value(attr) > max) { max = inst.value(attr); } } else { unknownValues++; } } mean /= (m_clusterInstances[cluster].numInstances() - unknownValues); for (int instance = 0; instance < m_clusterInstances[cluster].numInstances(); instance++) { Instance inst = m_clusterInstances[cluster].get(instance); if (!Double.isNaN(inst.value(attr))) { stdev += (inst.value(attr) - mean) * (inst.value(attr) - mean); } } stdev /= (m_clusterInstances[cluster].numInstances() - 1 - unknownValues); stdev = Math.sqrt(stdev); if (min == Double.POSITIVE_INFINITY) { min = 0; } if (max == Double.NEGATIVE_INFINITY) { max = 0; } if((m_clusterInstances[cluster].numInstances() - unknownValues)==0) { min = Double.NaN; max = Double.NaN; stdev = Double.NaN; }   m_clusterStats[attr][cluster][0] = min; m_clusterStats[attr][cluster][1] = max; m_clusterStats[attr][cluster][2] = mean; m_clusterStats[attr][cluster][3] = stdev; } } }   /**      * This function updates the weights of a cluster      * @param updateCluster the cluster to update      * @param winningCluster the winning cluster for the current training instance      * @param instance the index of current training instance      * @param learningRate the current learning rate      * @param effWidth the current effective width      */ private void updateWeights(int updateCluster, int winningCluster, int instance, double learningRate, double effWidth) { double diff; int winnerWidth = winningCluster % m_width; int winnerHeight = winningCluster / m_width; int width = updateCluster % m_width; int height = updateCluster / m_width;   double distance = Math.pow((winnerWidth - width), 2) + Math.pow((winnerHeight - height), 2); double h = Math.exp(-distance / (2 * Math.pow(effWidth, 2))); for (int j = 0; j < m_clusters.numAttributes(); j++) { diff = learningRate * h * (m_instances.get(instance).value(j) - m_clusters.get(updateCluster).value(j)); if (!Double.isNaN(diff)) { m_clusters.get(updateCluster).setValue(j, m_clusters.get(updateCluster).value(j) + diff); } } }   /**      * This function calculates the effective width of the topological      * netowork for the current iteration      * @param iteration the current iteration.      * @return the effective width .      */ private double calcEffectiveWidth(int iteration) { double effectiveWidth;   if (iteration <= m_orderingEpochs) { // Ordering phase double latticeRadius = Math.sqrt(m_width * m_width + m_height * m_height) / 2; effectiveWidth = latticeRadius * Math.exp(-iteration * Math.log(latticeRadius) / m_orderingEpochs); } else { // Convergence phase effectiveWidth = 0.0001; }   return effectiveWidth; }   /**      * This function calculates the learning rate for the current iteration      * @param iteration the current iteration.      * @return the learning rate.      */ private double calcLearningRate(int iteration) { double learningRate;   if (iteration <= m_orderingEpochs) { // Ordering phase learningRate = m_learningRate * Math.exp(-(double) iteration * Math.log(100. * m_learningRate) / m_orderingEpochs); } else { // Convergence phase learningRate = 0.01; }   return learningRate; }   /**      * This function performs the denormalization of the attributes of an instance.      *      * @param inst the instance.      * @return The modified instance. This needs to be done as it deep copies      * the instance which will need to be passed back out.      */ protected Instance denormalizeInstance(Instance inst) { inst = new DenseInstance(inst); if (m_normalizeAttributes) { for (int noa = 0; noa < inst.numAttributes(); noa++) { inst.setValue(noa, (inst.value(noa) * (m_attributeMax[noa] - m_attributeMin[noa]) + (m_attributeMax[noa] + m_attributeMin[noa])) / 2); } } return inst; }   /**      * This function performs the normalization of the attributes of an instance.      *      * @param inst the instance.      * @return The modified instance. This needs to be done as it deep copies      * the instance which will need to be passed back out.      */ protected Instance normalizeInstance(Instance inst) { inst = new DenseInstance(inst); double min; double max; for (int noa = 0; noa < m_instances.numAttributes(); noa++) { if (inst.value(noa) > m_attributeMax[noa]) { max = inst.value(noa); } else { max = m_attributeMax[noa]; } if (inst.value(noa) < m_attributeMin[noa]) { min = inst.value(noa); } else { min = m_attributeMin[noa]; } if ((max - min) != 0) { inst.setValue(noa, -1 + 2 * (inst.value(noa) - min) / (max - min)); } else { inst.setValue(noa, inst.value(noa)); } } return inst; }   /**      * This function performs the normalization of the attributes if applicable.      * (note that regardless of the options it will fill an array with the range      * and base, set to normalize all attributes and the class to be between -1      * and 1)      * @param inst the instances.      * @return The modified instances. This needs to be done. If the attributes      * are normalized then deep copies will be made of all the instances which      * will need to be passed back out.      */ private Instances normalize(Instances inst) throws Exception { if (inst != null) { inst = new Instances(inst); // x bounds double min = Double.POSITIVE_INFINITY; double max = Double.NEGATIVE_INFINITY; double value; m_attributeMax = new double[inst.numAttributes()]; m_attributeMin = new double[inst.numAttributes()];   for (int noa = 0; noa < inst.numAttributes(); noa++) { min = Double.POSITIVE_INFINITY; max = Double.NEGATIVE_INFINITY; for (int i = 0; i < inst.numInstances(); i++) { if (!inst.instance(i).isMissing(noa)) { value = inst.instance(i).value(noa); if (value < min) { min = value; } if (value > max) { max = value; } } }   m_attributeMax[noa] = max; m_attributeMin[noa] = min; } }   if (m_normalizeAttributes) { for (int i = 0; i < inst.numInstances(); i++) { inst.set(i, normalizeInstance(inst.instance(i))); } }   return inst; }   /**      * This function initializes the clusters' weights.      *      * @return  The initialized clusters      */ protected Instances initClusters() { Instances weights = new Instances(m_instances, m_width * m_height); for (int i = 0; i < m_width * m_height; i++) { double[] instValues = new double[m_instances.numAttributes()]; for (int j = 0; j < m_instances.numAttributes(); j++) { if (m_normalizeAttributes) { instValues[j] = 0; } else { instValues[j] = (m_attributeMax[j] + m_attributeMin[j]) / 2; } } Instance inst = new DenseInstance(1, instValues); weights.add(i, inst); }   return weights; }   /**      * Returns the number of clusters.      *      * @return the number of clusters generated for a training dataset.      * @exception Exception if number of clusters could not be returned      * successfully      */ public int numberOfClusters() throws Exception { return m_height * m_width; }   /**      * This function returns the clusters if the clusterer is build      * or an exception if the clusterer is not build.      *      * The clusters are returned dernomalized even if normalizeAttributes      * option is set.      *      * @return The clusters      * @throws Exception      */ public Instances getClusters() throws Exception { if (m_clusters == null) { throw new Exception("No clusterer built yet!"); } Instances inst = new Instances(m_clusters); if (m_normalizeAttributes) { for (int i = 0; i < inst.numInstances(); i++) { inst.set(i, denormalizeInstance(inst.instance(i))); } }   return inst; }   /**      * This function returns the training statistics in a 3-dimension array      * as follows:<br/>      * First dimension: the attribute index<br/>      * Second dimension: the cluster index<br/>      * Third dimension: the static index (Valid values are 0: min, 1: max, 2: mean, 3: st. dev.)      *      * @return the statistics array      * @throws Exception      */ public double[][][] getStatistics() throws Exception { if (m_calcStats) { if (m_clusterStats == null) { throw new Exception("No clusterer built yet!"); } } else { throw new Exception("Statistics are not calculated"); } return m_clusterStats; }   /**      * This function returns the cluster assignment for each of the      * training instances. The array's index indicates the corresponding      * cluster.      *      * @return the cluster assignments array      * @throws Exception      */ public Instances[] getClusterInstances() throws Exception { if (m_calcStats) { if (m_clusterInstances == null) { throw new Exception("No clusterer built yet!"); } } else { throw new Exception("Statistics are not calculated"); } return m_clusterInstances; } }

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