/* *    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. *//* *    RBFNetwork.java *    Copyright (C) 2004 University of Waikato, Hamilton, New Zealand * */package weka.classifiers.functions;import weka.classifiers.Classifier;import weka.clusterers.MakeDensityBasedClusterer;import weka.clusterers.SimpleKMeans;import weka.core.Capabilities;import weka.core.Instance;import weka.core.Instances;import weka.core.Option;import weka.core.OptionHandler;import weka.core.SelectedTag;import weka.core.Utils;import weka.filters.Filter;import weka.filters.unsupervised.attribute.ClusterMembership;import weka.filters.unsupervised.attribute.Standardize;import java.util.Enumeration;import java.util.Vector;/** <!-- globalinfo-start --> * Class that implements a normalized Gaussian radial basisbasis function network.<br/> * It uses the k-means clustering algorithm to provide the basis functions and learns either a logistic regression (discrete class problems) or linear regression (numeric class problems) on top of that. Symmetric multivariate Gaussians are fit to the data from each cluster. If the class is nominal it uses the given number of clusters per class.It standardizes all numeric attributes to zero mean and unit variance. * <p/> <!-- globalinfo-end --> * <!-- options-start --> * Valid options are: <p/> *  * <pre> -B &lt;number&gt; *  Set the number of clusters (basis functions) to generate. (default = 2).</pre> *  * <pre> -S &lt;seed&gt; *  Set the random seed to be used by K-means. (default = 1).</pre> *  * <pre> -R &lt;ridge&gt; *  Set the ridge value for the logistic or linear regression.</pre> *  * <pre> -M &lt;number&gt; *  Set the maximum number of iterations for the logistic regression. (default -1, until convergence).</pre> *  * <pre> -W &lt;number&gt; *  Set the minimum standard deviation for the clusters. (default 0.1).</pre> *  <!-- options-end --> * * @author Mark Hall * @author Eibe Frank * @version $Revision: 1.9 $ */public class RBFNetwork extends Classifier implements OptionHandler {  /** for serialization */  static final long serialVersionUID = -3669814959712675720L;    /** The logistic regression for classification problems */  private Logistic m_logistic;  /** The linear regression for numeric problems */  private LinearRegression m_linear;  /** The filter for producing the meta data */  private ClusterMembership m_basisFilter;  /** Filter used for normalizing the data */  private Standardize m_standardize;  /** The number of clusters (basis functions to generate) */  private int m_numClusters = 2;  /** The ridge parameter for the logistic regression. */  protected double m_ridge = 1e-8;  /** The maximum number of iterations for logistic regression. */  private int m_maxIts = -1;  /** The seed to pass on to K-means */  private int m_clusteringSeed = 1;  /** The minimum standard deviation */  private double m_minStdDev = 0.1;  /** a ZeroR model in case no model can be built from the data */  private Classifier m_ZeroR;      /**   * Returns a string describing this classifier   * @return a description of the classifier suitable for   * displaying in the explorer/experimenter gui   */  public String globalInfo() {    return "Class that implements a normalized Gaussian radial basis"       + "basis function network.\n"      + "It uses the k-means clustering algorithm to provide the basis "      + "functions and learns either a logistic regression (discrete "      + "class problems) or linear regression (numeric class problems) "      + "on top of that. Symmetric multivariate Gaussians are fit to "      + "the data from each cluster. If the class is "      + "nominal it uses the given number of clusters per class."      + "It standardizes all numeric "      + "attributes to zero mean and unit variance." ;  }  /**   * Returns default capabilities of the classifier, i.e.,  and "or" of   * Logistic and LinearRegression.   *   * @return      the capabilities of this classifier   * @see         Logistic   * @see         LinearRegression   */  public Capabilities getCapabilities() {    Capabilities result = new Logistic().getCapabilities();    result.or(new LinearRegression().getCapabilities());    Capabilities classes = result.getClassCapabilities();    result.and(new SimpleKMeans().getCapabilities());    result.or(classes);    return result;  }  /**   * Builds the classifier   *   * @param instances the training data   * @throws Exception if the classifier could not be built successfully   */  public void buildClassifier(Instances instances) throws Exception {    // can classifier handle the data?    getCapabilities().testWithFail(instances);    // remove instances with missing class    instances = new Instances(instances);    instances.deleteWithMissingClass();        // only class? -> build ZeroR model    if (instances.numAttributes() == 1) {      System.err.println(	  "Cannot build model (only class attribute present in data!), "	  + "using ZeroR model instead!");      m_ZeroR = new weka.classifiers.rules.ZeroR();      m_ZeroR.buildClassifier(instances);      return;    }    else {      m_ZeroR = null;    }        m_standardize = new Standardize();    m_standardize.setInputFormat(instances);    instances = Filter.useFilter(instances, m_standardize);    SimpleKMeans sk = new SimpleKMeans();    sk.setNumClusters(m_numClusters);    sk.setSeed(m_clusteringSeed);    MakeDensityBasedClusterer dc = new MakeDensityBasedClusterer();    dc.setClusterer(sk);    dc.setMinStdDev(m_minStdDev);    m_basisFilter = new ClusterMembership();    m_basisFilter.setDensityBasedClusterer(dc);    m_basisFilter.setInputFormat(instances);    Instances transformed = Filter.useFilter(instances, m_basisFilter);    if (instances.classAttribute().isNominal()) {      m_linear = null;      m_logistic = new Logistic();      m_logistic.setRidge(m_ridge);      m_logistic.setMaxIts(m_maxIts);      m_logistic.buildClassifier(transformed);    } else {      m_logistic = null;      m_linear = new LinearRegression();      m_linear.setAttributeSelectionMethod(new SelectedTag(LinearRegression.SELECTION_NONE,							   LinearRegression.TAGS_SELECTION));      m_linear.setRidge(m_ridge);      m_linear.buildClassifier(transformed);    }  }  /**   * Computes the distribution for a given instance   *   * @param instance the instance for which distribution is computed   * @return the distribution   * @throws Exception if the distribution can't be computed successfully   */  public double [] distributionForInstance(Instance instance)     throws Exception {    // default model?    if (m_ZeroR != null) {      return m_ZeroR.distributionForInstance(instance);    }        m_standardize.input(instance);    m_basisFilter.input(m_standardize.output());    Instance transformed = m_basisFilter.output();        return ((instance.classAttribute().isNominal()	     ? m_logistic.distributionForInstance(transformed)	     : m_linear.distributionForInstance(transformed)));  }    /**   * Returns a description of this classifier as a String   *   * @return a description of this classifier   */  public String toString() {    // only ZeroR model?    if (m_ZeroR != null) {      StringBuffer buf = new StringBuffer();      buf.append(this.getClass().getName().replaceAll(".*\\.", "") + "\n");      buf.append(this.getClass().getName().replaceAll(".*\\.", "").replaceAll(".", "=") + "\n\n");      buf.append("Warning: No model could be built, hence ZeroR model is used:\n\n");      buf.append(m_ZeroR.toString());      return buf.toString();    }        if (m_basisFilter == null) {      return "No classifier built yet!";    }    StringBuffer sb = new StringBuffer();    sb.append("Radial basis function network\n");    sb.append((m_linear == null) 	      ? "(Logistic regression "	      : "(Linear regression ");    sb.append("applied to K-means clusters as basis functions):\n\n");    sb.append((m_linear == null)	      ? m_logistic.toString()	      : m_linear.toString());    return sb.toString();  }  /**   * Returns the tip text for this property   * @return tip text for this property suitable for