rbfnetwork.java

Weka

   * displaying in the explorer/experimenter gui
   */
  public String maxItsTipText() {
    return "Maximum number of iterations for the logistic regression to perform. "
      +"Only applied to discrete class problems.";
  }

  /**
   * Get the value of MaxIts.
   *
   * @return Value of MaxIts.
   */
  public int getMaxIts() {
	
    return m_maxIts;
  }
    
  /**
   * Set the value of MaxIts.
   *
   * @param newMaxIts Value to assign to MaxIts.
   */
  public void setMaxIts(int newMaxIts) {
	
    m_maxIts = newMaxIts;
  }    

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String ridgeTipText() {
    return "Set the Ridge value for the logistic or linear regression.";
  }

  /**
   * Sets the ridge value for logistic or linear regression.
   *
   * @param ridge the ridge
   */
  public void setRidge(double ridge) {
    m_ridge = ridge;
  }
    
  /**
   * Gets the ridge value.
   *
   * @return the ridge
   */
  public double getRidge() {
    return m_ridge;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numClustersTipText() {
    return "The number of clusters for K-Means to generate.";
  }

  /**
   * Set the number of clusters for K-means to generate.
   *
   * @param numClusters the number of clusters to generate.
   */
  public void setNumClusters(int numClusters) {
    if (numClusters > 0) {
      m_numClusters = numClusters;
    }
  }

  /**
   * Return the number of clusters to generate.
   *
   * @return the number of clusters to generate.
   */
  public int getNumClusters() {
    return m_numClusters;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String clusteringSeedTipText() {
    return "The random seed to pass on to K-means.";
  }
  
  /**
   * Set the random seed to be passed on to K-means.
   *
   * @param seed a seed value.
   */
  public void setClusteringSeed(int seed) {
    m_clusteringSeed = seed;
  }

  /**
   * Get the random seed used by K-means.
   *
   * @return the seed value.
   */
  public int getClusteringSeed() {
    return m_clusteringSeed;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minStdDevTipText() {
    return "Sets the minimum standard deviation for the clusters.";
  }

  /**
   * Get the MinStdDev value.
   * @return the MinStdDev value.
   */
  public double getMinStdDev() {
    return m_minStdDev;
  }

  /**
   * Set the MinStdDev value.
   * @param newMinStdDev The new MinStdDev value.
   */
  public void setMinStdDev(double newMinStdDev) {
    m_minStdDev = newMinStdDev;
  }

  
  /**
   * Returns an enumeration describing the available options
   *
   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {
    Vector newVector = new Vector(4);

    newVector.addElement(new Option("\tSet the number of clusters (basis functions) "
				    +"to generate. (default = 2).",
				    "B", 1, "-B <number>"));
    newVector.addElement(new Option("\tSet the random seed to be used by K-means. "
				    +"(default = 1).",
				    "S", 1, "-S <seed>"));
    newVector.addElement(new Option("\tSet the ridge value for the logistic or "
				    +"linear regression.",
				    "R", 1, "-R <ridge>"));
    newVector.addElement(new Option("\tSet the maximum number of iterations "
				    +"for the logistic regression."
				    + " (default -1, until convergence).",
				    "M", 1, "-M <number>"));
    newVector.addElement(new Option("\tSet the minimum standard "
				    +"deviation for the clusters."
				    + " (default 0.1).",
				    "W", 1, "-W <number>"));
    return newVector.elements();
  }

  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are: <p/>
   * 
   * <pre> -B &lt;number&gt;
   *  Set the number of clusters (basis functions) to generate. (default = 2).</pre>
   * 
   * <pre> -S &lt;seed&gt;
   *  Set the random seed to be used by K-means. (default = 1).</pre>
   * 
   * <pre> -R &lt;ridge&gt;
   *  Set the ridge value for the logistic or linear regression.</pre>
   * 
   * <pre> -M &lt;number&gt;
   *  Set the maximum number of iterations for the logistic regression. (default -1, until convergence).</pre>
   * 
   * <pre> -W &lt;number&gt;
   *  Set the minimum standard deviation for the clusters. (default 0.1).</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 {
    setDebug(Utils.getFlag('D', options));

    String ridgeString = Utils.getOption('R', options);
    if (ridgeString.length() != 0) {
      m_ridge = Double.parseDouble(ridgeString);
    } else {
      m_ridge = 1.0e-8;
    }
	
    String maxItsString = Utils.getOption('M', options);
    if (maxItsString.length() != 0) {
      m_maxIts = Integer.parseInt(maxItsString);
    } else {
      m_maxIts = -1;
    }

    String numClustersString = Utils.getOption('B', options);
    if (numClustersString.length() != 0) {
      setNumClusters(Integer.parseInt(numClustersString));
    }

    String seedString = Utils.getOption('S', options);
    if (seedString.length() != 0) {
      setClusteringSeed(Integer.parseInt(seedString));
    }
    String stdString = Utils.getOption('W', options);
    if (stdString.length() != 0) {
      setMinStdDev(Double.parseDouble(stdString));
    }
    Utils.checkForRemainingOptions(options);
  }

  /**
   * Gets the current settings of the classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {
	
    String [] options = new String [10];
    int current = 0;
    
    options[current++] = "-B";
    options[current++] = "" + m_numClusters;
    options[current++] = "-S";
    options[current++] = "" + m_clusteringSeed;
    options[current++] = "-R";
    options[current++] = ""+m_ridge;	
    options[current++] = "-M";
    options[current++] = ""+m_maxIts;
    options[current++] = "-W";
    options[current++] = ""+m_minStdDev;

    while (current < options.length) 
      options[current++] = "";
    return options;
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the command line arguments to the
   * scheme (see Evaluation)
   */
  public static void main(String [] argv) {
    runClassifier(new RBFNetwork(), argv);
  }
}