* 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);  }}