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• SubspaceCluster.java

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weka.datagenerators.clusterers.SubspaceCluster Maven / Gradle / Ivy

/*
 *   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 3 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, see .
 */

/*
 *    SubspaceCluster.java
 *    Copyright (C) 2001-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.datagenerators.clusterers;

import java.util.ArrayList;
import java.util.Collections;
import java.util.Enumeration;
import java.util.Random;
import java.util.Vector;

import weka.core.Attribute;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.Tag;
import weka.core.Utils;
import weka.datagenerators.ClusterDefinition;
import weka.datagenerators.ClusterGenerator;

/**
 *  A data generator that produces data points in
 * hyperrectangular subspace clusters.
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -h
 *  Prints this help.
 * 
 * 
 * 
 * -o <file>
 *  The name of the output file, otherwise the generated data is
 *  printed to stdout.
 * 
 * 
 * 
 * -r <name>
 *  The name of the relation.
 * 
 * 
 * 
 * -d
 *  Whether to print debug informations.
 * 
 * 
 * 
 * -S
 *  The seed for random function (default 1)
 * 
 * 
 * 
 * -a <num>
 *  The number of attributes (default 1).
 * 
 * 
 * 
 * -c
 *  Class Flag, if set, the cluster is listed in extra attribute.
 * 
 * 
 * 
 * -b <range>
 *  The indices for boolean attributes.
 * 
 * 
 * 
 * -m <range>
 *  The indices for nominal attributes.
 * 
 * 
 * 
 * -P <num>
 *  The noise rate in percent (default 0.0).
 *  Can be between 0% and 30%. (Remark: The original 
 *  algorithm only allows noise up to 10%.)
 * 
 * 
 * 
 * -C <cluster-definition>
 *  A cluster definition of class 'SubspaceClusterDefinition'
 *  (definition needs to be quoted to be recognized as 
 *  a single argument).
 * 
 * 
 * 
 * Options specific to weka.datagenerators.clusterers.SubspaceClusterDefinition:
 * 
 * 
 * 
 * -A <range>
 *  Generates randomly distributed instances in the cluster.
 * 
 * 
 * 
 * -U <range>
 *  Generates uniformly distributed instances in the cluster.
 * 
 * 
 * 
 * -G <range>
 *  Generates gaussian distributed instances in the cluster.
 * 
 * 
 * 
 * -D <num>,<num>
 *  The attribute min/max (-A and -U) or mean/stddev (-G) for
 *  the cluster.
 * 
 * 
 * 
 * -N <num>..<num>
 *  The range of number of instances per cluster (default 1..50).
 * 
 * 
 * 
 * -I
 *  Uses integer instead of continuous values (default continuous).
 * 
 * 
 * 
 * 
 * @author Gabi Schmidberger ([email protected])
 * @author FracPete (fracpete at waikato dot ac dot nz)
 * @version $Revision: 12478 $
 */
public class SubspaceCluster extends ClusterGenerator {

  /** for serialization */
  static final long serialVersionUID = -3454999858505621128L;

  /** noise rate in percent (option P, between 0 and 30) */
  protected double m_NoiseRate;

  /** cluster list */
  protected ClusterDefinition[] m_Clusters;

  /** if nominal, store number of values */
  protected int[] m_numValues;

  /** cluster type: uniform/random */
  public static final int UNIFORM_RANDOM = 0;
  /** cluster type: total uniform */
  public static final int TOTAL_UNIFORM = 1;
  /** cluster type: gaussian */
  public static final int GAUSSIAN = 2;
  /** the tags for the cluster types */
  public static final Tag[] TAGS_CLUSTERTYPE = {
    new Tag(UNIFORM_RANDOM, "uniform/random"),
    new Tag(TOTAL_UNIFORM, "total uniform"), new Tag(GAUSSIAN, "gaussian") };

  /** cluster subtype: continuous */
  public static final int CONTINUOUS = 0;
  /** cluster subtype: integer */
  public static final int INTEGER = 1;
  /** the tags for the cluster types */
  public static final Tag[] TAGS_CLUSTERSUBTYPE = {
    new Tag(CONTINUOUS, "continuous"), new Tag(INTEGER, "integer") };

  /**
   * initializes the generator, sets the number of clusters to 0, since user has
   * to specify them explicitly
   */
  public SubspaceCluster() {
    super();

    setNoiseRate(defaultNoiseRate());
  }

  /**
   * 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 "A data generator that produces data points in "
      + "hyperrectangular subspace clusters.";
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options
   */
  @Override
  public Enumeration listOptions() {
    Vector result = enumToVector(super.listOptions());

    result.addElement(new Option("\tThe noise rate in percent (default "
      + defaultNoiseRate() + ").\n"
      + "\tCan be between 0% and 30%. (Remark: The original \n"
      + "\talgorithm only allows noise up to 10%.)", "P", 1, "-P "));

    result.addElement(new Option("\tA cluster definition of class '"
      + SubspaceClusterDefinition.class.getName().replaceAll(".*\\.", "")
      + "'\n" + "\t(definition needs to be quoted to be recognized as \n"
      + "\ta single argument).", "C", 1, "-C "));

    result.addElement(new Option("", "", 0, "\nOptions specific to "
      + SubspaceClusterDefinition.class.getName() + ":"));

    result.addAll(enumToVector(new SubspaceClusterDefinition(this)
      .listOptions()));

    return result.elements();
  }

  /**
   * Parses a list of options for this object.
   * 

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -h
   *  Prints this help.
   * 
   * 
   * 
   * -o <file>
   *  The name of the output file, otherwise the generated data is
   *  printed to stdout.
   * 
   * 
   * 
   * -r <name>
   *  The name of the relation.
   * 
   * 
   * 
   * -d
   *  Whether to print debug informations.
   * 
   * 
   * 
   * -S
   *  The seed for random function (default 1)
   * 
   * 
   * 
   * -a <num>
   *  The number of attributes (default 1).
   * 
   * 
   * 
   * -c
   *  Class Flag, if set, the cluster is listed in extra attribute.
   * 
   * 
   * 
   * -b <range>
   *  The indices for boolean attributes.
   * 
   * 
   * 
   * -m <range>
   *  The indices for nominal attributes.
   * 
   * 
   * 
   * -P <num>
   *  The noise rate in percent (default 0.0).
   *  Can be between 0% and 30%. (Remark: The original 
   *  algorithm only allows noise up to 10%.)
   * 
   * 
   * 
   * -C <cluster-definition>
   *  A cluster definition of class 'SubspaceClusterDefinition'
   *  (definition needs to be quoted to be recognized as 
   *  a single argument).
   * 
   * 
   * 
   * Options specific to weka.datagenerators.clusterers.SubspaceClusterDefinition:
   * 
   * 
   * 
   * -A <range>
   *  Generates randomly distributed instances in the cluster.
   * 
   * 
   * 
   * -U <range>
   *  Generates uniformly distributed instances in the cluster.
   * 
   * 
   * 
   * -G <range>
   *  Generates gaussian distributed instances in the cluster.
   * 
   * 
   * 
   * -D <num>,<num>
   *  The attribute min/max (-A and -U) or mean/stddev (-G) for
   *  the cluster.
   * 
   * 
   * 
   * -N <num>..<num>
   *  The range of number of instances per cluster (default 1..50).
   * 
   * 
   * 
   * -I
   *  Uses integer instead of continuous values (default continuous).
   * 
   * 
   * 
   * 
   * @param options the list of options as an array of strings
   * @throws Exception if an option is not supported
   */
  @Override
  public void setOptions(String[] options) throws Exception {
    String tmpStr;
    SubspaceClusterDefinition cl;
    Vector list;
    super.setOptions(options);

    m_numValues = new int[getNumAttributes()];
    // numValues might be changed by a cluster definition
    // (only relevant for nominal data)
    for (int i = 0; i < getNumAttributes(); i++) {
      m_numValues[i] = 1;
    }

    tmpStr = Utils.getOption('P', options);
    if (tmpStr.length() != 0) {
      setNoiseRate(Double.parseDouble(tmpStr));
    } else {
      setNoiseRate(defaultNoiseRate());
    }

    // cluster definitions
    list = new Vector();

    do {
      tmpStr = Utils.getOption('C', options);
      if (tmpStr.length() != 0) {
        cl = new SubspaceClusterDefinition(this);
        cl.setOptions(Utils.splitOptions(tmpStr));
        list.add(cl);
      }
    } while (tmpStr.length() != 0);

    m_Clusters = list.toArray(new ClusterDefinition[list.size()]);
    // in case no cluster definition was provided, make sure that there's at
    // least one definition present -> see getClusters()
    getClusters();
  }

  /**
   * Gets the current settings of the datagenerator.
   * 
   * @return an array of strings suitable for passing to setOptions
   */
  @Override
  public String[] getOptions() {

    Vector result = new Vector();

    Collections.addAll(result, super.getOptions());

    result.add("-P");
    result.add("" + getNoiseRate());

    for (int i = 0; i < getClusters().length; i++) {
      result.add("-C");
      result.add(Utils.joinOptions(getClusters()[i].getOptions()));
    }

    return result.toArray(new String[result.size()]);
  }

  /**
   * returns the current cluster definitions, if necessary initializes them
   * 
   * @return the current cluster definitions
   */
  protected ClusterDefinition[] getClusters() {
    if ((m_Clusters == null) || (m_Clusters.length == 0)) {
      if (m_Clusters != null) {
        System.out.println("NOTE: at least 1 cluster definition is necessary, "
          + "created default one.");
      }
      m_Clusters = new ClusterDefinition[] { new SubspaceClusterDefinition(this) };
    }

    return m_Clusters;
  }

  /**
   * returns the default number of attributes
   * 
   * @return the default number of attributes
   */
  @Override
  protected int defaultNumAttributes() {
    return 1;
  }

  /**
   * Sets the number of attributes the dataset should have.
   * 
   * @param numAttributes the new number of attributes
   */
  @Override
  public void setNumAttributes(int numAttributes) {
    super.setNumAttributes(numAttributes);
    m_numValues = new int[getNumAttributes()];
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  @Override
  public String numAttributesTipText() {
    return "The number of attributes the generated data will contain (Note: they must be covered by the cluster definitions!)";
  }

  /**
   * returns the default noise rate
   * 
   * @return the default noise rate
   */
  protected double defaultNoiseRate() {
    return 0.0;
  }

  /**
   * Gets the percentage of noise set.
   * 
   * @return the percentage of noise set
   */
  public double getNoiseRate() {
    return m_NoiseRate;
  }

  /**
   * Sets the percentage of noise set.
   * 
   * @param newNoiseRate new percentage of noise
   */
  public void setNoiseRate(double newNoiseRate) {
    m_NoiseRate = newNoiseRate;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String noiseRateTipText() {
    return "The noise rate to use.";
  }

  /**
   * returns the currently set clusters
   * 
   * @return the currently set clusters
   */
  public ClusterDefinition[] getClusterDefinitions() {
    return getClusters();
  }

  /**
   * sets the clusters to use
   * 
   * @param value the clusters do use
   * @throws Exception if clusters are not the correct class
   */
  public void setClusterDefinitions(ClusterDefinition[] value) throws Exception {

    String indexStr;

    indexStr = "";
    m_Clusters = value;
    for (int i = 0; i < getClusters().length; i++) {
      if (!(getClusters()[i] instanceof SubspaceClusterDefinition)) {
        if (indexStr.length() != 0) {
          indexStr += ",";
        }
        indexStr += "" + (i + 1);
      }
      getClusters()[i].setParent(this);
      getClusters()[i].setOptions(getClusters()[i].getOptions()); // for
                                                                  // initializing!
    }

    // any wrong classes encountered?
    if (indexStr.length() != 0) {
      throw new Exception("These cluster definitions are not '"
        + SubspaceClusterDefinition.class.getName() + "': " + indexStr);
    }
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String clusterDefinitionsTipText() {
    return "The clusters to use.";
  }

  /**
   * Checks, whether all attributes are covered by cluster definitions and
   * returns TRUE in that case.
   * 
   * @return whether all attributes are covered
   */
  protected boolean checkCoverage() {
    int i;
    int n;
    int[] count;
    Range r;
    String attrIndex;
    SubspaceClusterDefinition cl;

    // check whether all the attributes are covered
    count = new int[getNumAttributes()];
    for (i = 0; i < getNumAttributes(); i++) {
      if (m_nominalCols.isInRange(i)) {
        count[i]++;
      }
      if (m_booleanCols.isInRange(i)) {
        count[i]++;
      }
      for (n = 0; n < getClusters().length; n++) {
        cl = (SubspaceClusterDefinition) getClusters()[n];
        r = new Range(cl.getAttrIndexRange());
        r.setUpper(getNumAttributes());
        if (r.isInRange(i)) {
          count[i]++;
        }
      }
    }

    // list all indices that are not covered
    attrIndex = "";
    for (i = 0; i < count.length; i++) {
      if (count[i] == 0) {
        if (attrIndex.length() != 0) {
          attrIndex += ",";
        }
        attrIndex += (i + 1);
      }
    }

    if (attrIndex.length() != 0) {
      throw new IllegalArgumentException(
        "The following attributes are not covered by a cluster "
          + "definition: " + attrIndex + "\n");
    }

    return true;
  }

  /**
   * Gets the single mode flag.
   * 
   * @return true if methode generateExample can be used.
   */
  @Override
  public boolean getSingleModeFlag() {
    return false;
  }

  /**
   * Initializes the format for the dataset produced.
   * 
   * @return the output data format
   * @throws Exception data format could not be defined
   */

  @Override
  public Instances defineDataFormat() throws Exception {

    // initialize
    setOptions(getOptions());

    checkCoverage();

    Random random = new Random(getSeed());
    setRandom(random);
    Instances dataset;
    ArrayList attributes = new ArrayList(3);
    Attribute attribute;
    boolean classFlag = getClassFlag();

    ArrayList classValues = null;
    if (classFlag) {
      classValues = new ArrayList(getClusters().length);
    }
    ArrayList boolValues = new ArrayList(2);
    boolValues.add("false");
    boolValues.add("true");
    ArrayList nomValues = null;

    // define dataset
    for (int i = 0; i < getNumAttributes(); i++) {
      // define boolean attribute
      if (m_booleanCols.isInRange(i)) {
        attribute = new Attribute("B" + i, boolValues);
      } else if (m_nominalCols.isInRange(i)) {
        // define nominal attribute
        nomValues = new ArrayList(m_numValues[i]);
        for (int j = 0; j < m_numValues[i]; j++) {
          nomValues.add("value-" + j);
        }
        attribute = new Attribute("N" + i, nomValues);
      } else {
        // numerical attribute
        attribute = new Attribute("X" + i);
      }
      attributes.add(attribute);
    }

    if (classFlag) {
      for (int i = 0; i < getClusters().length; i++) {
        classValues.add("c" + i);
      }
      attribute = new Attribute("class", classValues);
      attributes.add(attribute);
    }

    dataset = new Instances(getRelationNameToUse(), attributes, 0);
    if (classFlag) {
      dataset.setClassIndex(m_NumAttributes);
    }

    // set dataset format of this class
    Instances format = new Instances(dataset, 0);
    setDatasetFormat(format);

    for (int i = 0; i < getClusters().length; i++) {
      SubspaceClusterDefinition cl = (SubspaceClusterDefinition) getClusters()[i];
      cl.setNumInstances(random);
      cl.setParent(this);
    }

    return dataset;
  }

  /**
   * Returns true if attribute is boolean
   * 
   * @param index of the attribute
   * @return true if the attribute is boolean
   */
  public boolean isBoolean(int index) {
    return m_booleanCols.isInRange(index);
  }

  /**
   * Returns true if attribute is nominal
   * 
   * @param index of the attribute
   * @return true if the attribute is nominal
   */
  public boolean isNominal(int index) {
    return m_nominalCols.isInRange(index);
  }

  /**
   * returns array that stores the number of values for a nominal attribute.
   * 
   * @return the array that stores the number of values for a nominal attribute
   */
  public int[] getNumValues() {
    return m_numValues;
  }

  /**
   * Generate an example of the dataset.
   * 
   * @return the instance generated
   * @throws Exception if format not defined or generating 

   *           examples one by one is not possible, because voting is chosen
   */

  @Override
  public Instance generateExample() throws Exception {
    throw new Exception("Examples cannot be generated one by one.");
  }

  /**
   * Generate all examples of the dataset.
   * 
   * @return the instance generated
   * @throws Exception if format not defined
   */

  @Override
  public Instances generateExamples() throws Exception {
    Instances format = getDatasetFormat();
    Instance example = null;

    if (format == null) {
      throw new Exception("Dataset format not defined.");
    }

    // generate examples for one cluster after another
    for (int cNum = 0; cNum < getClusters().length; cNum++) {
      SubspaceClusterDefinition cl = (SubspaceClusterDefinition) getClusters()[cNum];

      // get the number of instances to create
      int instNum = cl.getNumInstances();

      // class value is c + cluster number
      String cName = "c" + cNum;

      switch (cl.getClusterType().getSelectedTag().getID()) {
      case (UNIFORM_RANDOM):
        for (int i = 0; i < instNum; i++) {
          // generate example
          example = generateExample(format, getRandom(), cl, cName);
          if (example != null) {
            format.add(example);
          }
        }
        break;
      case (TOTAL_UNIFORM):
        // generate examples
        if (!cl.isInteger()) {
          generateUniformExamples(format, instNum, cl, cName);
        } else {
          generateUniformIntegerExamples(format, instNum, cl, cName);
        }
        break;
      case (GAUSSIAN):
        // generate examples
        generateGaussianExamples(format, instNum, getRandom(), cl, cName);
        break;
      }
    }

    return format;
  }

  /**
   * Generate an example of the dataset.
   * 
   * @param format the dataset format
   * @param randomG the random number generator to use
   * @param cl the cluster definition
   * @param cName the class value
   * @return the generated instance
   */
  private Instance generateExample(Instances format, Random randomG,
    SubspaceClusterDefinition cl, String cName) {

    boolean makeInteger = cl.isInteger();
    int num = -1;
    int numAtts = m_NumAttributes;
    if (getClassFlag()) {
      numAtts++;
    }

    double[] values = new double[numAtts];
    boolean[] attributes = cl.getAttributes();
    double[] minValue = cl.getMinValue();
    double[] maxValue = cl.getMaxValue();
    double value;

    int clusterI = -1;
    for (int i = 0; i < m_NumAttributes; i++) {
      if (attributes[i]) {
        clusterI++;
        num++;
        // boolean or nominal attribute
        if (isBoolean(i) || isNominal(i)) {

          if (minValue[clusterI] == maxValue[clusterI]) {
            value = minValue[clusterI];
          } else {
            int numValues = (int) (maxValue[clusterI] - minValue[clusterI] + 1.0);
            value = randomG.nextInt(numValues);
            value += minValue[clusterI];
          }
        } else {
          // numeric attribute
          value = randomG.nextDouble() * (maxValue[num] - minValue[num])
            + minValue[num];
          if (makeInteger) {
            value = Math.round(value);
          }
        }
        values[i] = value;
      } else {
        values[i] = Utils.missingValue();
      }
    }

    if (getClassFlag()) {
      values[format.classIndex()] = format.classAttribute().indexOfValue(cName);
    }

    DenseInstance example = new DenseInstance(1.0, values);
    example.setDataset(format);

    return example;
  }

  /**
   * Generate examples for a uniform cluster dataset.
   * 
   * @param format the dataset format
   * @param numInstances the number of instances to generator
   * @param cl the cluster definition
   * @param cName the class value
   */
  private void generateUniformExamples(Instances format, int numInstances,
    SubspaceClusterDefinition cl, String cName) {

    int numAtts = m_NumAttributes;
    if (getClassFlag()) {
      numAtts++;
    }
   boolean[] attributes = cl.getAttributes();
    double[] minValue = cl.getMinValue();
    double[] maxValue = cl.getMaxValue();
    double[] diff = new double[minValue.length];

    for (int i = 0; i < minValue.length; i++) {
      diff[i] = (maxValue[i] - minValue[i]);
    }

    for (int j = 0; j < numInstances; j++) {
      double[] values = new double[numAtts];
      int num = -1;
      for (int i = 0; i < m_NumAttributes; i++) {
        if (attributes[i]) {
          num++;
          double value = minValue[num]
            + (diff[num] * ((double) j / (double) (numInstances - 1)));
          values[i] = value;
        } else {
          values[i] = Utils.missingValue();
        }
      }
      if (getClassFlag()) {
        values[format.classIndex()] = format.classAttribute().indexOfValue(cName);
      }

      DenseInstance example = new DenseInstance(1.0, values);
      example.setDataset(format);
      format.add(example);
    }
  }

  /**
   * Generate examples for a uniform cluster dataset.
   * 
   * @param format the dataset format
   * @param numInstances the number of instances to generator
   * @param cl the cluster definition
   * @param cName the class value
   */
  private void generateUniformIntegerExamples(Instances format,
    int numInstances, SubspaceClusterDefinition cl, String cName) {

    int numAtts = m_NumAttributes;
    if (getClassFlag()) {
      numAtts++;
    }

    double[] values = new double[numAtts];
    boolean[] attributes = cl.getAttributes();
    double[] minValue = cl.getMinValue();
    double[] maxValue = cl.getMaxValue();
    int[] minInt = new int[minValue.length];
    int[] maxInt = new int[maxValue.length];
    int[] intValue = new int[maxValue.length];
    int[] numInt = new int[minValue.length];

    int num = 1;
    for (int i = 0; i < minValue.length; i++) {
      minInt[i] = (int) Math.ceil(minValue[i]);
      maxInt[i] = (int) Math.floor(maxValue[i]);
      numInt[i] = (maxInt[i] - minInt[i] + 1);
      num = num * numInt[i];
    }
    int numEach = numInstances / num;
    int rest = numInstances - numEach * num;

    // initialize with smallest values combination
    for (int i = 0; i < m_NumAttributes; i++) {
      if (attributes[i]) {
        values[i] = minInt[i];
        intValue[i] = minInt[i];
      } else {
        values[i] = Utils.missingValue();
      }
    }
    if (getClassFlag()) {
      values[format.classIndex()] = format.classAttribute().indexOfValue(cName);
    }

    DenseInstance example = new DenseInstance(1.0, values);
    example.setDataset(format);

    int added = 0;
    int attr = 0;
    // do while not added all
    do {
      // add all for one value combination
      for (int k = 0; k < numEach; k++) {
        format.add(example); // Instance will be copied here
        added++;
      }
      if (rest > 0) {
        format.add(example); // Instance will be copied here
        added++;
        rest--;
      }

      if (added >= numInstances) {
        break;
      }
      // switch to the next value combination
      boolean done = false;
      do {
        if (attributes[attr] && (intValue[attr] + 1 <= maxInt[attr])) {
          intValue[attr]++;
          done = true;
        } else {
          attr++;
        }
      } while (!done);

      example.setValue(attr, intValue[attr]);
    } while (added < numInstances);
  }

  /**
   * Generate examples for a uniform cluster dataset.
   * 
   * @param format the dataset format
   * @param numInstances the number of instances to generate
   * @param random the random number generator
   * @param cl the cluster definition
   * @param cName the class value
   */
  private void generateGaussianExamples(Instances format, int numInstances,
    Random random, SubspaceClusterDefinition cl, String cName) {

    boolean makeInteger = cl.isInteger();
    int numAtts = m_NumAttributes;
    if (getClassFlag()) {
      numAtts++;
    }

    boolean[] attributes = cl.getAttributes();
    double[] meanValue = cl.getMeanValue();
    double[] stddevValue = cl.getStddevValue();

    for (int j = 0; j < numInstances; j++) {
      double[] values = new double[numAtts];
      int num = -1;
      for (int i = 0; i < m_NumAttributes; i++) {
        if (attributes[i]) {
          num++;
          double value = meanValue[num]
            + (random.nextGaussian() * stddevValue[num]);
          if (makeInteger) {
            value = Math.round(value);
          }
          values[i] = value;
        } else {
          values[i] = Utils.missingValue();
        }
      }
      if (getClassFlag()) {
        values[format.classIndex()] = format.classAttribute().indexOfValue(cName);
      }

      DenseInstance example = new DenseInstance(1.0, values);
      example.setDataset(format);
      format.add(example);
    }
  }

  /**
   * Compiles documentation about the data generation after the generation
   * process
   * 
   * @return string with additional information about generated dataset
   * @throws Exception no input structure has been defined
   */
  @Override
  public String generateFinished() throws Exception {
    return "";
  }

  /**
   * Compiles documentation about the data generation before the generation
   * process
   * 
   * @return string with additional information
   */
  @Override
  public String generateStart() {
    StringBuffer docu = new StringBuffer();

    int sumInst = 0;
    for (int cNum = 0; cNum < getClusters().length; cNum++) {
      SubspaceClusterDefinition cl = (SubspaceClusterDefinition) getClusters()[cNum];
      docu.append("%\n");
      docu.append("% Cluster: c" + cNum + "   ");
      switch (cl.getClusterType().getSelectedTag().getID()) {
      case UNIFORM_RANDOM:
        docu.append("Uniform Random");
        break;
      case TOTAL_UNIFORM:
        docu.append("Total Random");
        break;
      case GAUSSIAN:
        docu.append("Gaussian");
        break;
      }
      if (cl.isInteger()) {
        docu.append(" / INTEGER");
      }

      docu.append("\n% ----------------------------------------------\n");
      docu.append("%" + cl.attributesToString());

      docu.append("\n% Number of Instances:            " + cl.getInstNums()
        + "\n");
      docu.append("% Generated Number of Instances:  " + cl.getNumInstances()
        + "\n");
      sumInst += cl.getNumInstances();
    }
    docu.append("%\n% ----------------------------------------------\n");
    docu.append("% Total Number of Instances: " + sumInst + "\n");
    docu.append("%                            in " + getClusters().length
      + " Cluster(s)\n%");

    return docu.toString();
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 12478 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param args should contain arguments for the data producer:
   */
  public static void main(String[] args) {
    runDataGenerator(new SubspaceCluster(), args);
  }
}