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.
   */
  
  /*
   * BayesNet.java
   * Copyright (C) 2005 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.datagenerators.classifiers.classification;
  
  import weka.classifiers.bayes.net.BayesNetGenerator;
  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.datagenerators.ClassificationGenerator;
  
  import java.util.Enumeration;
  import java.util.Vector;
  
  /**
   <!-- globalinfo-start -->
   * Generates random instances based on a Bayes network.
   * <p/>
   <!-- globalinfo-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> -A &lt;num&gt;
   *  The number of arcs to use. (default 20)</pre>
   * 
   * <pre> -C &lt;num&gt;
   *  The cardinality of the attributes and the class. (default 2)</pre>
   * 
   <!-- options-end -->
   *
   * @author FracPete (fracpete at waikato dot ac dot nz)
   * @version $Revision: 5987 $
   * @see BayesNetGenerator
   */
  
  public class BayesNet
    extends ClassificationGenerator {
    
    /** for serialization */
    static final long serialVersionUID = -796118162379901512L;
    
    /** the bayesian net generator, that produces the actual data */
    protected BayesNetGenerator m_Generator;
  
    /**
     * initializes the generator
     */
    public BayesNet() {
      super();
  
      setNumAttributes(defaultNumAttributes());
      setNumArcs(defaultNumArcs());
      setCardinality(defaultCardinality());
    }
    
    /**
     * 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 
          "Generates random instances based on a Bayes network.";
    }
  
   /**
     * 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 number of arcs to use. (default " 
                + defaultNumArcs() + ")",
                "A", 1, "-A <num>"));
  
      result.add(new Option(
                "\tThe cardinality of the attributes and the class. (default " 
                + defaultCardinality() + ")",
                "C", 1, "-C <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> -A &lt;num&gt;
     *  The number of arcs to use. (default 20)</pre>
     * 
     * <pre> -C &lt;num&gt;
     *  The cardinality of the attributes and the class. (default 2)</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;
      Vector        list;
  
      super.setOptions(options);
  
      list = new Vector();
  
      list.add("-N");
      list.add("" + getNumAttributes());
  
      list.add("-M");
      list.add("" + getNumExamples());
      
      list.add("-S");
      list.add("" + getSeed());
      
      list.add("-A");
      tmpStr = Utils.getOption('A', options);
      if (tmpStr.length() != 0)
        list.add(tmpStr);
      else
        list.add("" + defaultNumArcs());
  
      list.add("-C");
      tmpStr = Utils.getOption('C', options);
      if (tmpStr.length() != 0)
        list.add(tmpStr);
      else
        list.add("" + defaultCardinality());
  
      setGeneratorOptions(list);
    }
  
    /**
     * 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 = removeBlacklist(super.getOptions());
      for (i = 0; i < options.length; i++)
        result.add(options[i]);
  
      // determine options from generator
      options = getGenerator().getOptions();
  
      try {
        result.add("-A");
        result.add(Utils.getOption('A', options));
      }
      catch (Exception e) {
        e.printStackTrace();
      }
  
      try {
        result.add("-C");
        result.add(Utils.getOption('C', options));
      }
      catch (Exception e) {
        e.printStackTrace();
      }
      
      return (String[]) result.toArray(new String[result.size()]);
    }
  
    /**
     * sets the given options of the BayesNetGenerator
     * 
     * @param generator the generator to set the options for
     * @param options the options to set
     */
    protected void setGeneratorOptions(
        BayesNetGenerator generator, Vector options) {
  
      try {
        generator.setOptions(
            (String[]) options.toArray(new String[options.size()]));
      }
      catch (Exception e) {
        e.printStackTrace();
      }
    }
  
    /**
     * returns the actual datagenerator
     * 
     * @return the actual datagenerator
     */
    protected BayesNetGenerator getGenerator() {
      if (m_Generator == null)
        m_Generator = new BayesNetGenerator();
  
      return m_Generator;
    }
  
    /**
     * sets the given options of the BayesNetGenerator
     * 
     * @param options the options to set
     */
    protected void setGeneratorOptions(Vector options) {
      setGeneratorOptions(getGenerator(), options);
    }
  
    /**
     * sets a specific option/value of the generator (option must be w/o
     * then '-')
     * @param generator       the generator to set the option for
     * @param option          the option to set
     * @param value           the new value for the option
     */
    protected void setGeneratorOption( BayesNetGenerator generator, 
                                       String option, String value ) {
  
      String[]      options;
      Vector        list;
      int           i;
  
      try {
        // get options and remove specific option
        options = generator.getOptions();
        Utils.getOption(option, options);
  
        // add option and set the new options
        list = new Vector();
        for (i = 0; i < options.length; i++) {
          if (options[i].length() != 0)
            list.add(options[i]);
        }
        list.add("-" + option);
        list.add(value);
        setGeneratorOptions(generator, list);
      }
      catch (Exception e) {
        e.printStackTrace();
      }
    }
  
    /**
     * sets a specific option/value of the generator (option must be w/o
     * then '-')
     * @param option          the option to set
     * @param value           the new value for the option
     */
    protected void setGeneratorOption(String option, String value) {
      setGeneratorOption(getGenerator(), option, value);
    }
  
    /**
     * returns the default number of attributes
     * 
     * @return the default number of attributes
     */
    protected int defaultNumAttributes() {
      return 10;
    }
  
    /**
     * Sets the number of attributes the dataset should have.
     * @param numAttributes the new number of attributes
     */
    public void setNumAttributes(int numAttributes) {
      setGeneratorOption("N", "" + numAttributes);
    }
  
    /**
     * Gets the number of attributes that should be produced.
     * @return the number of attributes that should be produced
     */
    public int getNumAttributes() { 
      int       result;
      
      result = -1;
      try {
        result = Integer.parseInt(
            Utils.getOption('N', getGenerator().getOptions()));
      }
      catch (Exception e) {
        e.printStackTrace();
        result = -1;
      }
  
      return result;
    }
    
    /**
     * 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 the generated data will contain (including class attribute), ie the number of nodes in the bayesian net.";
    }
  
    /**
     * returns the default cardinality
     * 
     * @return the default cardinality
     */
    protected int defaultCardinality() {
      return 2;
    }
  
    /**
     * Sets the cardinality of the attributes (incl class attribute)
     * @param value the cardinality
     */
    public void setCardinality(int value) { 
      setGeneratorOption("C", "" + value);
    }
  
    /**
     * Gets the cardinality of the attributes (incl class attribute)
     * @return the cardinality of the attributes
     */
    public int getCardinality() { 
      int       result;
      
      result = -1;
      try {
        result = Integer.parseInt(
            Utils.getOption('C', getGenerator().getOptions()));
      }
      catch (Exception e) {
        e.printStackTrace();
        result = -1;
      }
  
      return result;
    }
    
    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String cardinalityTipText() {
      return "The cardinality of the attributes, incl the class attribute.";
    }
  
    /**
     * returns the default number of arcs
     * 
     * @return the default number of arcs
     */
    protected int defaultNumArcs() {
      return 20;
    }
  
    /**
     * Sets the number of arcs for the bayesian net
     * @param value the number of arcs
     */
    public void setNumArcs(int value) {
      int       nodes;
      int       minArcs;
      int       maxArcs;
  
      nodes   = getNumAttributes();
      minArcs = nodes - 1;
      maxArcs = nodes * (nodes - 1) / 2;
      
      if (value > maxArcs)
        throw new IllegalArgumentException(
            "Number of arcs should be at most nodes * (nodes - 1) / 2 = " 
            + maxArcs + " instead of " + value + " (nodes = numAttributes)!");
      else if (value < minArcs)
        throw new IllegalArgumentException(
            "Number of arcs should be at least (nodes - 1) = " + minArcs 
            + " instead of " + value + " (nodes = numAttributes)!");
      else
        setGeneratorOption("A", "" + value);
    }
  
    /**
     * Gets the number of arcs for the bayesian net
     * @return the number of arcs
     */
    public int getNumArcs() { 
      int       result;
      
      result = -1;
      try {
        result = Integer.parseInt(
            Utils.getOption('A', getGenerator().getOptions()));
      }
      catch (Exception e) {
        e.printStackTrace();
        result = -1;
      }
  
      return result;
    }
    
    /**
     * Returns the tip text for this property
     * 
     * @return tip text for this property suitable for
     *         displaying in the explorer/experimenter gui
     */
    public String numArcsTipText() {
      return "The number of arcs in the bayesian net, at most: n * (n - 1) / 2 and at least: (n - 1); with n = numAttributes";
    }
  
    /**
     * Sets the number of examples, given by option.
     * @param numExamples the new number of examples
     */
    public void setNumExamples(int numExamples) { 
      super.setNumExamples(numExamples);
      setGeneratorOption("M", "" + numExamples);
    }
  
    /**
     * Gets the number of examples, given by option.
     * @return the number of examples, given by option 
     */
    public int getNumExamples() { 
      int       result;
      
      result = -1;
      try {
        result = Integer.parseInt(
            Utils.getOption('M', getGenerator().getOptions()));
      }
      catch (Exception e) {
        e.printStackTrace();
        result = -1;
      }
  
      return result;
    }
  
    /**
     * 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 false;
    }
  
    /**
     * 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 {
      BayesNetGenerator   bng;
  
      bng = new BayesNetGenerator();
      bng.setOptions(getGenerator().getOptions());
      setGeneratorOption(bng, "M", "1");
      bng.generateRandomNetwork();
      bng.generateInstances();
      bng.m_Instances.renameAttribute(0, "class");
      bng.m_Instances.setRelationName(getRelationNameToUse());
      
      return bng.m_Instances;
    }
  
    /**
     * 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 {
      throw new Exception("Cannot generate examples one-by-one!");
    }
  
    /**
     * 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 {
      getGenerator().setOptions(getGenerator().getOptions());
      getGenerator().generateRandomNetwork();
      getGenerator().generateInstances();
      getGenerator().m_Instances.renameAttribute(0, "class");
      getGenerator().m_Instances.setRelationName(getRelationNameToUse());
      
      return getGenerator().m_Instances;
    }
  
    /**
     * 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 BayesNet(), args);
    }
  }
  