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The Waikato Environment for Knowledge Analysis (WEKA), a machine learning workbench. This is the stable version. Apart from bugfixes, this version does not receive any other updates.
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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.
 */

/*
 * SingleClustererEnhancer.java
 * Copyright (C) 2006 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.clusterers;

import weka.core.Capabilities;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Utils;
import weka.core.Capabilities.Capability;

import java.util.Enumeration;
import java.util.Vector;

/**
 * Meta-clusterer for enhancing a base clusterer.
 *
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 1.4 $
 */
public abstract class SingleClustererEnhancer
  extends AbstractClusterer
  implements OptionHandler {

  /** for serialization */
  private static final long serialVersionUID = 4893928362926428671L;

  /** the clusterer */
  protected Clusterer m_Clusterer = new SimpleKMeans();

  /**
   * String describing default clusterer.
   * 
   * @return 		the default clusterer classname
   */
  protected String defaultClustererString() {
    return SimpleKMeans.class.getName();
  }

  /**
   * 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(
	"\tFull name of base clusterer.\n"
	+ "\t(default: " + defaultClustererString() +")",
	"W", 1, "-W"));

    if (m_Clusterer instanceof OptionHandler) {
      result.addElement(new Option(
	  "",
	  "", 0, "\nOptions specific to clusterer "
	  + m_Clusterer.getClass().getName() + ":"));
      Enumeration enu = ((OptionHandler) m_Clusterer).listOptions();
      while (enu.hasMoreElements()) {
	result.addElement(enu.nextElement());
      }
    }

    return result.elements();
  }

  /**
   * Parses a given list of options.
   *
   * @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 {
    String	tmpStr;
    
    tmpStr = Utils.getOption('W', options);
    if (tmpStr.length() > 0) { 
      // This is just to set the classifier in case the option 
      // parsing fails.
      setClusterer(AbstractClusterer.forName(tmpStr, null));
      setClusterer(AbstractClusterer.forName(tmpStr, Utils.partitionOptions(options)));
    } 
    else {
      // This is just to set the classifier in case the option 
      // parsing fails.
      setClusterer(AbstractClusterer.forName(defaultClustererString(), null));
      setClusterer(AbstractClusterer.forName(defaultClustererString(), Utils.partitionOptions(options)));
    }
  }

  /**
   * Gets the current settings of the clusterer.
   *
   * @return 		an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    Vector	result;
    String[]	options;
    int		i;
    
    result = new Vector();

    result.add("-W");
    result.add(getClusterer().getClass().getName());
    
    if (getClusterer() instanceof OptionHandler) {
      result.add("--");
      options = ((OptionHandler) getClusterer()).getOptions();
      for (i = 0; i < options.length; i++)
	result.add(options[i]);
    }
    
    return (String[]) result.toArray(new String[result.size()]);
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return 		tip text for this property suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String clustererTipText() {
    return "The base clusterer to be used.";
  }

  /**
   * Set the base clusterer.
   *
   * @param value 	the classifier to use.
   */
  public void setClusterer(Clusterer value) {
    m_Clusterer = value;
  }

  /**
   * Get the clusterer used as the base clusterer.
   *
   * @return 		the base clusterer
   */
  public Clusterer getClusterer() {
    return m_Clusterer;
  }
  
  /**
   * Gets the clusterer specification string, which contains the class name of
   * the clusterer and any options to the clusterer
   *
   * @return 		the clusterer string
   */
  protected String getClustererSpec() {
    String	result;
    Clusterer 	clusterer;
    
    clusterer = getClusterer();
    result    = clusterer.getClass().getName();
    
    if (clusterer instanceof OptionHandler)
      result += " " + Utils.joinOptions(((OptionHandler) clusterer).getOptions());
    
    return result;
  }

  /**
   * Returns default capabilities of the clusterer.
   *
   * @return		the capabilities of this clusterer
   */
  public Capabilities getCapabilities() {
    Capabilities	result;
    
    if (getClusterer() == null)
      result = super.getCapabilities();
    else
      result = getClusterer().getCapabilities();
    
    // set dependencies
    for (Capability cap: Capability.values())
      result.enableDependency(cap);
    
    return result;
  }

  /**
   * Returns the number of clusters.
   *
   * @return 		the number of clusters generated for a training dataset.
   * @throws Exception 	if number of clusters could not be returned
   * 			successfully
   */
  public int numberOfClusters() throws Exception {
    return m_Clusterer.numberOfClusters();
  }
}