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.
   */
  
  /*
   * RandomSubset.java
   * Copyright (C) 2007 University of Waikato, Hamilton, New Zealand
   */
  
  package weka.filters.unsupervised.attribute;
  
  import weka.core.Capabilities;
  import weka.core.FastVector;
  import weka.core.Instance; 
  import weka.core.DenseInstance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.RevisionUtils;
  import weka.core.Utils;
  import weka.core.Capabilities.Capability;
  import weka.filters.SimpleStreamFilter;
  
  import java.util.Collections;
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Chooses a random subset of attributes, either an absolute number or a percentage. The class is always included in the output (as the last attribute).
   * <p/>
   <!-- globalinfo-end -->
   * 
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -D
   *  Turns on output of debugging information.</pre>
   * 
   * <pre> -N &lt;double&gt;
   *  The number of attributes to randomly select.
   *  If &lt; 1 then percentage, &gt;= 1 absolute number.
   *  (default: 0.5)</pre>
   * 
   * <pre> -S &lt;int&gt;
   *  The seed value.
   *  (default: 1)</pre>
   * 
   <!-- options-end -->
   *
   * @author  fracpete (fracpete at waikato dot ac dot nz)
   * @version $Revision: 5987 $
   */
  public class RandomSubset
    extends SimpleStreamFilter {
  
    /** for serialization. */
    private static final long serialVersionUID = 2911221724251628050L;
  
    /** The number of attributes to randomly choose (&gt;= 1 absolute number of
     * attributes, &lt; 1 percentage). */
    protected double m_NumAttributes = 0.5;
    
    /** The seed value. */
    protected int m_Seed = 1;
    
    /** The indices of the attributes that got selected. */
    protected int[] m_Indices = null;
    
    /**
     * Returns a string describing this filter.
     *
     * @return 		a description of the filter suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return 
          "Chooses a random subset of attributes, either an absolute number "
        + "or a percentage. The class is always included in the output ("
        + "as the last attribute).";
    }
  
    /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options.
     */
    public Enumeration listOptions() {
      Vector        result;
      Enumeration   enm;
  
      result = new Vector();
  
      enm = super.listOptions();
      while (enm.hasMoreElements())
        result.addElement(enm.nextElement());
  
      result.addElement(new Option(
  	"\tThe number of attributes to randomly select.\n"
  	+ "\tIf < 1 then percentage, >= 1 absolute number.\n"
  	+ "\t(default: 0.5)",
  	"N", 1, "-N <double>"));
      
      result.addElement(new Option(
  	"\tThe seed value.\n"
  	+ "\t(default: 1)",
  	"S", 1, "-S <int>"));
  
      return result.elements();
    }	  
  
    /**
     * Gets the current settings of the filter.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {
      int			i;
      Vector<String>	result;
      String[]		options;
  
      result  = new Vector<String>();
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
  
      result.add("-N"); 
      result.add("" + m_NumAttributes);
  
      result.add("-S"); 
      result.add("" + m_Seed);
  
      return result.toArray(new String[result.size()]);	  
    }	  
  
    /**
     * Parses a given list of options. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -D
     *  Turns on output of debugging information.</pre>
     * 
     * <pre> -N &lt;double&gt;
     *  The number of attributes to randomly select.
     *  If &lt; 1 then percentage, &gt;= 1 absolute number.
     *  (default: 0.5)</pre>
     * 
     * <pre> -S &lt;int&gt;
     *  The seed value.
     *  (default: 1)</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 {
      String	tmpStr;
  
      tmpStr = Utils.getOption("N", options);
      if (tmpStr.length() != 0)
        setNumAttributes(Double.parseDouble(tmpStr));
      else
        setNumAttributes(0.5);
      
      tmpStr = Utils.getOption("S", options);
      if (tmpStr.length() != 0)
        setSeed(Integer.parseInt(tmpStr));
      else
        setSeed(1);
      
      super.setOptions(options);
    }	  
  
    /**
     * Returns the tip text for this property.
     * 
     * @return 		tip text for this property suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String numAttributesTipText() {
      return "The number of attributes to choose: < 1 percentage, >= 1 absolute number.";
    }
  
    /**
     * Get the number of attributes (&lt; 1 percentage, &gt;= 1 absolute number).
     *
     * @return 		the number of attributes.
     */
    public double getNumAttributes() {
      return m_NumAttributes;
    }
  
    /**
     * Set the number of attributes. 
     *
     * @param value	the number of attributes to use.
     */
    public void setNumAttributes(double value) {
      m_NumAttributes = value;
    }
  
    /**
     * Returns the tip text for this property.
     * 
     * @return 		tip text for this property suitable for
     * 			displaying in the explorer/experimenter gui
     */
    public String seedTipText() {
      return "The seed value for the random number generator.";
    }
  
    /**
     * Get the seed value for the random number generator.
     *
     * @return 		the seed value.
     */
    public int getSeed() {
      return m_Seed;
    }
  
    /**
     * Set the seed value for the random number generator. 
     *
     * @param value	the seed value.
     */
    public void setSeed(int value) {
      m_Seed = value;
    }
  
    /** 
     * Returns the Capabilities of this filter.
     *
     * @return            the capabilities of this object
     * @see               Capabilities
     */
    public Capabilities getCapabilities() {
      Capabilities result = super.getCapabilities();
      result.disableAll();
  
      // attributes
      result.enableAllAttributes();
      result.enable(Capability.MISSING_VALUES);
      
      // class
      result.enableAllClasses();
      result.enable(Capability.MISSING_CLASS_VALUES);
      result.enable(Capability.NO_CLASS);
      
      return result;
    }
  
    /**
     * Determines the output format based on the input format and returns 
     * this. In case the output format cannot be returned immediately, i.e.,
     * hasImmediateOutputFormat() returns false, then this method will called
     * from batchFinished() after the call of preprocess(Instances), in which,
     * e.g., statistics for the actual processing step can be gathered.
     *
     * @param inputFormat     the input format to base the output format on
     * @return                the output format
     * @throws Exception      in case the determination goes wrong
     */
    protected Instances determineOutputFormat(Instances inputFormat) throws Exception {
      Instances		result;
      FastVector		atts;
      int			i;
      int			numAtts;
      Vector<Integer>	indices;
      Vector<Integer>	subset;
      Random		rand;
      int			index;
   
      // determine the number of attributes
      numAtts = inputFormat.numAttributes();
      if (inputFormat.classIndex() > -1)
        numAtts--;
      
      if (m_NumAttributes < 1) {
        numAtts = (int) Math.round((double) numAtts * m_NumAttributes);
      }
      else {
        if (m_NumAttributes < numAtts)
  	numAtts = (int) m_NumAttributes;
      }
      if (getDebug())
        System.out.println("# of atts: " + numAtts);
      
      // determine random indices
      indices = new Vector<Integer>();
      for (i = 0; i < inputFormat.numAttributes(); i++) {
        if (i == inputFormat.classIndex())
  	continue;
        indices.add(i);
      }
      
      subset = new Vector<Integer>();
      rand   = new Random(m_Seed);
      for (i = 0; i < numAtts; i++) {
        index = rand.nextInt(indices.size());
        subset.add(indices.get(index));
        indices.remove(index);
      }
      Collections.sort(subset);
      if (inputFormat.classIndex() > -1)
        subset.add(inputFormat.classIndex());
      if (getDebug())
        System.out.println("indices: " + subset);
      
      // generate output format
      atts      = new FastVector();
      m_Indices = new int[subset.size()];
      for (i = 0; i < subset.size(); i++) {
        atts.addElement(inputFormat.attribute(subset.get(i)));
        m_Indices[i] = subset.get(i);
      }
      result = new Instances(inputFormat.relationName(), atts, 0);
      if (inputFormat.classIndex() > -1)
        result.setClassIndex(result.numAttributes() - 1);
  
      return result;
    }
  
    /**
     * processes the given instance (may change the provided instance) and
     * returns the modified version.
     *
     * @param instance    the instance to process
     * @return            the modified data
     * @throws Exception  in case the processing goes wrong
     */
    protected Instance process(Instance instance) throws Exception {
      Instance	result;
      double[]	values;
      int		i;
      
      values = new double[m_Indices.length];
      for (i = 0; i < m_Indices.length; i++)
        values[i] = instance.value(m_Indices[i]);
  
      result = new DenseInstance(instance.weight(), values);
      result.setDataset(getOutputFormat());
      
      copyValues(result, false, instance.dataset(), getOutputFormat());
      result.setDataset(getOutputFormat());
      
      return result;
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5987 $");
    }
  
    /**
     * Runs the filter with the given parameters. Use -h to list options.
     * 
     * @param args	the commandline options
     */
    public static void main(String[] args) {
      runFilter(new RandomSubset(), args);
    }
  }
  