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.
   */
  
  /*
   * LED24.java
   * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.datagenerators.classifiers.classification;
  
  import weka.core.Attribute;
  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.TechnicalInformation;
  import weka.core.TechnicalInformationHandler;
  import weka.core.Utils;
  import weka.core.TechnicalInformation.Field;
  import weka.core.TechnicalInformation.Type;
  import weka.datagenerators.ClassificationGenerator;
  
  import java.util.Enumeration;
  import java.util.Random;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * This generator produces data for a display with 7 LEDs. The original output consists of 10 concepts and 7 boolean attributes. Here, in addition to the 7 necessary boolean attributes, 17 other, irrelevant boolean attributes with random values are added to make it harder. By default 10 percent of noise are added to the data.<br/>
   * <br/>
   * More information can be found here:<br/>
   * L. Breiman J.H. Friedman R.A. Olshen, C.J. Stone (1984). Classification and Regression Trees. Belmont, California. URL http://www.ics.uci.edu/~mlearn/databases/led-display-creator/.
   * <p/>
   <!-- globalinfo-end -->
   *
   * Link: <br/>
   * <a href="http://www.ics.uci.edu/~mlearn/databases/led-display-creator/">http://www.ics.uci.edu/~mlearn/databases/led-display-creator/</a> <p/>
   * 
   <!-- technical-bibtex-start -->
   * BibTeX:
   * <pre>
   * &#64;inbook{Olshen1984,
   *    address = {Belmont, California},
   *    author = {L. Breiman J.H. Friedman R.A. Olshen and C.J. Stone},
   *    pages = {43-49},
   *    publisher = {Wadsworth International Group},
   *    title = {Classification and Regression Trees},
   *    year = {1984},
   *    ISBN = {0412048418},
   *    URL = {http://www.ics.uci.edu/\~mlearn/databases/led-display-creator/}
   * }
   * </pre>
   * <p/>
   <!-- technical-bibtex-end -->
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -h
   *  Prints this help.</pre>
   * 
   * <pre> -o &lt;file&gt;
   *  The name of the output file, otherwise the generated data is
   *  printed to stdout.</pre>
   * 
   * <pre> -r &lt;name&gt;
   *  The name of the relation.</pre>
   * 
   * <pre> -d
   *  Whether to print debug informations.</pre>
   * 
   * <pre> -S
   *  The seed for random function (default 1)</pre>
   * 
   * <pre> -n &lt;num&gt;
   *  The number of examples to generate (default 100)</pre>
   * 
   * <pre> -N &lt;num&gt;
   *  The noise percentage. (default 10.0)</pre>
   * 
   <!-- options-end -->
   *
   * @author Richard Kirkby (rkirkby at cs dot waikato dot ac dot nz)
   * @author FracPete (fracpete at waikato dot ac dot nz)
   * @version $Revision: 5987 $
   */
  
  public class LED24
    extends ClassificationGenerator
    implements TechnicalInformationHandler {
    
    /** for serialization */
    static final long serialVersionUID = -7880209100415868737L;  
    
    /** the noise rate */
    protected double m_NoisePercent;
    
    /** the 7-bit LEDs */
    protected static final int m_originalInstances[][] = {
      { 1, 1, 1, 0, 1, 1, 1 }, { 0, 0, 1, 0, 0, 1, 0 },
      { 1, 0, 1, 1, 1, 0, 1 }, { 1, 0, 1, 1, 0, 1, 1 },
      { 0, 1, 1, 1, 0, 1, 0 }, { 1, 1, 0, 1, 0, 1, 1 },
      { 1, 1, 0, 1, 1, 1, 1 }, { 1, 0, 1, 0, 0, 1, 0 },
      { 1, 1, 1, 1, 1, 1, 1 }, { 1, 1, 1, 1, 0, 1, 1 } };
  
    /** used for generating the output, i.e., the additional noise attributes */
    protected int m_numIrrelevantAttributes = 17;
  
    /**
     * initializes the generator with default values
     */
    public LED24() {
      super();
  
      setNoisePercent(defaultNoisePercent());
    }
  
    /**
     * Returns a string describing this data generator.
     *
     * @return a description of the data generator suitable for
     * displaying in the explorer/experimenter gui
     */
    public String globalInfo() {
      return 
           "This generator produces data for a display with 7 LEDs. The original "
         + "output consists of 10 concepts and 7 boolean attributes. Here, in "
         + "addition to the 7 necessary boolean attributes, 17 other, irrelevant "
         + "boolean attributes with random values are added to make it harder. "
         + "By default 10 percent of noise are added to the data.\n"
         + "\n"
         + "More information can be found here:\n"
         + getTechnicalInformation().toString();
    }
  
    /**
     * Returns an instance of a TechnicalInformation object, containing 
     * detailed information about the technical background of this class,
     * e.g., paper reference or book this class is based on.
     * 
     * @return the technical information about this class
     */
    public TechnicalInformation getTechnicalInformation() {
      TechnicalInformation 	result;
      
      result = new TechnicalInformation(Type.INBOOK);
      result.setValue(Field.AUTHOR, "L. Breiman J.H. Friedman R.A. Olshen and C.J. Stone");
      result.setValue(Field.YEAR, "1984");
      result.setValue(Field.TITLE, "Classification and Regression Trees");
      result.setValue(Field.PUBLISHER, "Wadsworth International Group");
      result.setValue(Field.ADDRESS, "Belmont, California");
      result.setValue(Field.PAGES, "43-49");
      result.setValue(Field.ISBN, "0412048418");
      result.setValue(Field.URL, "http://www.ics.uci.edu/~mlearn/databases/led-display-creator/");
      
      return result;
    }
  
   /**
     * Returns an enumeration describing the available options.
     *
     * @return an enumeration of all the available options
     */
    public Enumeration listOptions() {
      Vector result = enumToVector(super.listOptions());
  
      result.add(new Option(
                "\tThe noise percentage. (default " 
                + defaultNoisePercent() + ")",
                "N", 1, "-N <num>"));
  
      return result.elements();
    }
  
    /**
     * Parses a list of options for this object. <p/>
     *
     <!-- options-start -->
     * Valid options are: <p/>
     * 
     * <pre> -h
     *  Prints this help.</pre>
     * 
     * <pre> -o &lt;file&gt;
     *  The name of the output file, otherwise the generated data is
     *  printed to stdout.</pre>
     * 
     * <pre> -r &lt;name&gt;
     *  The name of the relation.</pre>
     * 
     * <pre> -d
     *  Whether to print debug informations.</pre>
     * 
     * <pre> -S
     *  The seed for random function (default 1)</pre>
     * 
     * <pre> -n &lt;num&gt;
     *  The number of examples to generate (default 100)</pre>
     * 
     * <pre> -N &lt;num&gt;
     *  The noise percentage. (default 10.0)</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;
  
      super.setOptions(options);
  
      tmpStr = Utils.getOption('N', options);
      if (tmpStr.length() != 0)
        setNoisePercent(Double.parseDouble(tmpStr));
      else
        setNoisePercent(defaultNoisePercent());
    }
  
    /**
     * Gets the current settings of the datagenerator.
     *
     * @return an array of strings suitable for passing to setOptions
     */
    public String[] getOptions() {
      Vector        result;
      String[]      options;
      int           i;
      
      result  = new Vector();
      options = super.getOptions();
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
      
      result.add("-N");
      result.add("" + getNoisePercent());
      
      return (String[]) result.toArray(new String[result.size()]);
    }
  
    /**
     * returns the default noise percentage
     * 
     * @return the default noise percentage
     */
    protected double defaultNoisePercent() {
      return 10;
    }
    
    /**
     * Gets the noise percentage.
     *
     * @return the noise percentage.
     */
    public double getNoisePercent() { 
      return m_NoisePercent; 
    }
    
    /**
     * Sets the noise percentage.
     *
     * @param value the noise percentage.
     */
    public void setNoisePercent(double value) { 
      if ( (value >= 0.0) && (value <= 100.0) )
        m_NoisePercent = value;
      else
        throw new IllegalArgumentException(
            "Noise percent must be in [0,100] (provided: " + value + ")!");
    }  
    
    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String noisePercentTipText() {
      return "The noise percent: 0 <= perc <= 100.";
    }
  
    /**
     * Return if single mode is set for the given data generator
     * mode depends on option setting and or generator type.
     * 
     * @return single mode flag
     * @throws Exception if mode is not set yet
     */
    public boolean getSingleModeFlag() throws Exception {
      return true;
    }
  
    /**
     * Initializes the format for the dataset produced. 
     * Must be called before the generateExample or generateExamples
     * methods are used.
     * Re-initializes the random number generator with the given seed.
     *
     * @return the format for the dataset 
     * @throws Exception if the generating of the format failed
     * @see  #getSeed()
     */
    public Instances defineDataFormat() throws Exception {
      FastVector      atts;
      FastVector      attValues;
      int             i;
      int             n;
  
      m_Random = new Random(getSeed());
  
      // number of examples is the same as given per option
      setNumExamplesAct(getNumExamples());
  
      // set up attributes
      atts = new FastVector();
      
      for (n = 1; n <= 24; n++) {
        attValues = new FastVector();
        for (i = 0; i < 2; i++)
          attValues.addElement("" + i);
        atts.addElement(new Attribute("att" + n, attValues));
      }
      
      attValues = new FastVector();
      for (i = 0; i < 10; i++)
        attValues.addElement("" + i);
      atts.addElement(new Attribute("class", attValues));
      
      // dataset
      m_DatasetFormat = new Instances(getRelationNameToUse(), atts, 0);
      
      return m_DatasetFormat;
    }
  
    /**
     * Generates one example of the dataset. 
     *
     * @return the generated example
     * @throws Exception if the format of the dataset is not yet defined
     * @throws Exception if the generator only works with generateExamples
     * which means in non single mode
     */
    public Instance generateExample() throws Exception {
      Instance      result;
      double[]      atts;
      int           i;
      int           selected;
      Random        random;
  
      result = null;
      random = getRandom();
  
      if (m_DatasetFormat == null)
        throw new Exception("Dataset format not defined.");
  
      atts     = new double[m_DatasetFormat.numAttributes()];
      selected = random.nextInt(10);
      for (i = 0; i < 7; i++) {
        if ((1 + (random.nextInt(100))) <= getNoisePercent())
          atts[i] = m_originalInstances[selected][i] == 0 ? 1 : 0;
        else
          atts[i] = m_originalInstances[selected][i];
      }
  
      for (i = 0; i < m_numIrrelevantAttributes; i++)
        atts[i + 7] = random.nextInt(2);
  
      atts[atts.length - 1] = selected;
  
      // create instance
      result  = new DenseInstance(1.0, atts);
      result.setDataset(m_DatasetFormat);
  
      return result;
    }
  
    /**
     * Generates all examples of the dataset. Re-initializes the random number
     * generator with the given seed, before generating instances.
     *
     * @return the generated dataset
     * @throws Exception if the format of the dataset is not yet defined
     * @throws Exception if the generator only works with generateExample,
     * which means in single mode
     * @see   #getSeed()
     */
    public Instances generateExamples() throws Exception {
      Instances       result;
      int             i;
  
      result   = new Instances(m_DatasetFormat, 0);
      m_Random = new Random(getSeed());
  
      for (i = 0; i < getNumExamplesAct(); i++)
        result.add(generateExample());
      
      return result;
    }
  
    /**
     * Generates a comment string that documentates the data generator.
     * By default this string is added at the beginning of the produced output
     * as ARFF file type, next after the options.
     * 
     * @return string contains info about the generated rules
     */
    public String generateStart () {
      return "";
    }
  
    /**
     * Generates a comment string that documentats the data generator.
     * By default this string is added at the end of theproduces output
     * as ARFF file type.
     * 
     * @return string contains info about the generated rules
     * @throws Exception if the generating of the documentaion fails
     */
    public String generateFinished() throws Exception {
      return "";
    }
    
    /**
     * Returns the revision string.
     * 
     * @return		the revision
     */
    public String getRevision() {
      return RevisionUtils.extract("$Revision: 5987 $");
    }
  
    /**
     * Main method for executing this class.
     *
     * @param args should contain arguments for the data producer: 
     */
    public static void main(String[] args) {
      runDataGenerator(new LED24(), args);
    }
  }
  