content: /*
   *    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();
    }
  }
  