/*
 *    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.
 */

/*
 *    Apriori.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.associations;

import java.util.Enumeration;
import java.util.Hashtable;

import weka.core.AttributeStats;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.FastVector;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.SelectedTag;
import weka.core.Tag;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.filters.Filter;
import weka.filters.unsupervised.attribute.Remove;

/**
 * <!-- globalinfo-start --> Class implementing an Apriori-type algorithm.
 * Iteratively reduces the minimum support until it finds the required number of
 * rules with the given minimum confidence.<br/>
 * The algorithm has an option to mine class association rules. It is adapted as
 * explained in the second reference.<br/>
 * <br/>
 * For more information see:<br/>
 * <br/>
 * R. Agrawal, R. Srikant: Fast Algorithms for Mining Association Rules in Large
 * Databases. In: 20th International Conference on Very Large Data Bases,
 * 478-499, 1994.<br/>
 * <br/>
 * Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association
 * Rule Mining. In: Fourth International Conference on Knowledge Discovery and
 * Data Mining, 80-86, 1998.
 * <p/>
 * <!-- globalinfo-end -->
 * 
 * <!-- technical-bibtex-start --> BibTeX:
 * 
 * <pre>
 * &#64;inproceedings{Agrawal1994,
 *    author = {R. Agrawal and R. Srikant},
 *    booktitle = {20th International Conference on Very Large Data Bases},
 *    pages = {478-499},
 *    publisher = {Morgan Kaufmann, Los Altos, CA},
 *    title = {Fast Algorithms for Mining Association Rules in Large Databases},
 *    year = {1994}
 * }
 * 
 * &#64;inproceedings{Liu1998,
 *    author = {Bing Liu and Wynne Hsu and Yiming Ma},
 *    booktitle = {Fourth International Conference on Knowledge Discovery and Data Mining},
 *    pages = {80-86},
 *    publisher = {AAAI Press},
 *    title = {Integrating Classification and Association Rule Mining},
 *    year = {1998}
 * }
 * </pre>
 * <p/>
 * <!-- technical-bibtex-end -->
 * 
 * <!-- options-start --> Valid options are:
 * <p/>
 * 
 * <pre>
 * -N &lt;required number of rules output&gt;
 *  The required number of rules. (default = 10)
 * </pre>
 * 
 * <pre>
 * -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
 *  The metric type by which to rank rules. (default = confidence)
 * </pre>
 * 
 * <pre>
 * -C &lt;minimum metric score of a rule&gt;
 *  The minimum confidence of a rule. (default = 0.9)
 * </pre>
 * 
 * <pre>
 * -D &lt;delta for minimum support&gt;
 *  The delta by which the minimum support is decreased in
 *  each iteration. (default = 0.05)
 * </pre>
 * 
 * <pre>
 * -U &lt;upper bound for minimum support&gt;
 *  Upper bound for minimum support. (default = 1.0)
 * </pre>
 * 
 * <pre>
 * -M &lt;lower bound for minimum support&gt;
 *  The lower bound for the minimum support. (default = 0.1)
 * </pre>
 * 
 * <pre>
 * -S &lt;significance level&gt;
 *  If used, rules are tested for significance at
 *  the given level. Slower. (default = no significance testing)
 * </pre>
 * 
 * <pre>
 * -I
 *  If set the itemsets found are also output. (default = no)
 * </pre>
 * 
 * <pre>
 * -R
 *  Remove columns that contain all missing values (default = no)
 * </pre>
 * 
 * <pre>
 * -V
 *  Report progress iteratively. (default = no)
 * </pre>
 * 
 * <pre>
 * -A
 *  If set class association rules are mined. (default = no)
 * </pre>
 * 
 * <pre>
 * -c &lt;the class index&gt;
 *  The class index. (default = last)
 * </pre>
 * 
 * <!-- options-end -->
 * 
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @author Mark Hall (mhall@cs.waikato.ac.nz)
 * @author Stefan Mutter (mutter@cs.waikato.ac.nz)
 * @version $Revision: 9096 $
 */
public class Apriori extends AbstractAssociator implements OptionHandler,
    CARuleMiner, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = 3277498842319212687L;

  /** The minimum support. */
  protected double m_minSupport;

  /** The upper bound on the support */
  protected double m_upperBoundMinSupport;

  /** The lower bound for the minimum support. */
  protected double m_lowerBoundMinSupport;

  /** Metric type: Confidence */
  protected static final int CONFIDENCE = 0;
  /** Metric type: Lift */
  protected static final int LIFT = 1;
  /** Metric type: Leverage */
  protected static final int LEVERAGE = 2;
  /** Metric type: Conviction */
  protected static final int CONVICTION = 3;
  /** Metric types. */
  public static final Tag[] TAGS_SELECTION = {
      new Tag(CONFIDENCE, "Confidence"), new Tag(LIFT, "Lift"),
      new Tag(LEVERAGE, "Leverage"), new Tag(CONVICTION, "Conviction") };

  /** The selected metric type. */
  protected int m_metricType = CONFIDENCE;

  /** The minimum metric score. */
  protected double m_minMetric;

  /** The maximum number of rules that are output. */
  protected int m_numRules;

  /** Delta by which m_minSupport is decreased in each iteration. */
  protected double m_delta;

  /** Significance level for optional significance test. */
  protected double m_significanceLevel;

  /** Number of cycles used before required number of rules was one. */
  protected int m_cycles;

  /** The set of all sets of itemsets L. */
  protected FastVector m_Ls;

  /** The same information stored in hash tables. */
  protected FastVector m_hashtables;

  /** The list of all generated rules. */
  protected FastVector[] m_allTheRules;

  /**
   * The instances (transactions) to be used for generating the association
   * rules.
   */
  protected Instances m_instances;

  /** Output itemsets found? */
  protected boolean m_outputItemSets;

  /** Remove columns with all missing values */
  protected boolean m_removeMissingCols;

  /** Report progress iteratively */
  protected boolean m_verbose;

  /** Only the class attribute of all Instances. */
  protected Instances m_onlyClass;

  /** The class index. */
  protected int m_classIndex;

  /** Flag indicating whether class association rules are mined. */
  protected boolean m_car;

  /**
   * Returns a string describing this associator
   * 
   * @return a description of the evaluator suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "Class implementing an Apriori-type algorithm. Iteratively reduces "
        + "the minimum support until it finds the required number of rules with "
        + "the given minimum confidence.\n"
        + "The algorithm has an option to mine class association rules. It is "
        + "adapted as explained in the second reference.\n\n"
        + "For more information see:\n\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;
    TechnicalInformation additional;

    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR,
        Messages.getInstance().getString("APRIORI_AUTHOR"));
    result.setValue(Field.TITLE,
        "Fast Algorithms for Mining Association Rules in Large Databases");
    result.setValue(Field.BOOKTITLE,
        "20th International Conference on Very Large Data Bases");
    result.setValue(Field.YEAR, "1994");
    result.setValue(Field.PAGES, "478-499");
    result.setValue(Field.PUBLISHER, "Morgan Kaufmann, Los Altos, CA");

    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR, "Bing Liu and Wynne Hsu and Yiming Ma");
    additional.setValue(Field.TITLE,
        "Integrating Classification and Association Rule Mining");
    additional
        .setValue(Field.BOOKTITLE,
            "Fourth International Conference on Knowledge Discovery and Data Mining");
    additional.setValue(Field.YEAR, "1998");
    additional.setValue(Field.PAGES, "80-86");
    additional.setValue(Field.PUBLISHER, "AAAI Press");

    return result;
  }

  /**
   * Constructor that allows to sets default values for the minimum confidence
   * and the maximum number of rules the minimum confidence.
   */
  public Apriori() {

    resetOptions();
  }

  /**
   * Resets the options to the default values.
   */
  public void resetOptions() {

    m_removeMissingCols = false;
    m_verbose = false;
    m_delta = 0.05;
    m_minMetric = 0.90;
    m_numRules = 10;
    m_lowerBoundMinSupport = 0.1;
    m_upperBoundMinSupport = 1.0;
    m_significanceLevel = -1;
    m_outputItemSets = false;
    m_car = false;
    m_classIndex = -1;
  }

  /**
   * Removes columns that are all missing from the data
   * 
   * @param instances the instances
   * @return a new set of instances with all missing columns removed
   * @throws Exception if something goes wrong
   */
  protected Instances removeMissingColumns(Instances instances)
      throws Exception {

    int numInstances = instances.numInstances();
    StringBuffer deleteString = new StringBuffer();
    int removeCount = 0;
    boolean first = true;
    int maxCount = 0;

    for (int i = 0; i < instances.numAttributes(); i++) {
      AttributeStats as = instances.attributeStats(i);
      if (m_upperBoundMinSupport == 1.0 && maxCount != numInstances) {
        // see if we can decrease this by looking for the most frequent value
        int[] counts = as.nominalCounts;
        if (counts[Utils.maxIndex(counts)] > maxCount) {
          maxCount = counts[Utils.maxIndex(counts)];
        }
      }
      if (as.missingCount == numInstances) {
        if (first) {
          deleteString.append((i + 1));
          first = false;
        } else {
          deleteString.append("," + (i + 1));
        }
        removeCount++;
      }
    }
    if (m_verbose) {
      System.err.println("Removed : " + removeCount
          + " columns with all missing " + "values.");
    }
    if (m_upperBoundMinSupport == 1.0 && maxCount != numInstances) {
      m_upperBoundMinSupport = (double) maxCount / (double) numInstances;
      if (m_verbose) {
        System.err.println("Setting upper bound min support to : "
            + m_upperBoundMinSupport);
      }
    }

    if (deleteString.toString().length() > 0) {
      Remove af = new Remove();
      af.setAttributeIndices(deleteString.toString());
      af.setInvertSelection(false);
      af.setInputFormat(instances);
      Instances newInst = Filter.useFilter(instances, af);

      return newInst;
    }
    return instances;
  }

  /**
   * Returns default capabilities of the classifier.
   * 
   * @return the capabilities of this classifier
   */
  @Override
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class (can handle a nominal class if CAR rules are selected). This
    result.enable(Capability.NO_CLASS);
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    return result;
  }

  /**
   * Method that generates all large itemsets with a minimum support, and from
   * these all association rules with a minimum confidence.
   * 
   * @param instances the instances to be used for generating the associations
   * @throws Exception if rules can't be built successfully
   */
  public void buildAssociations(Instances instances) throws Exception {

    double[] confidences, supports;
    int[] indices;
    FastVector[] sortedRuleSet;
    int necSupport = 0;

    instances = new Instances(instances);

    if (m_removeMissingCols) {
      instances = removeMissingColumns(instances);
    }
    if (m_car && m_metricType != CONFIDENCE)
      throw new Exception("For CAR-Mining metric type has to be confidence!");

    // only set class index if CAR is requested
    if (m_car) {
      if (m_classIndex == -1) {
        instances.setClassIndex(instances.numAttributes() - 1);
      } else if (m_classIndex <= instances.numAttributes() && m_classIndex > 0) {
        instances.setClassIndex(m_classIndex - 1);
      } else {
        throw new Exception("Invalid class index.");
      }
    }

    // can associator handle the data?
    getCapabilities().testWithFail(instances);

    m_cycles = 0;

    // make sure that the lower bound is equal to at least one instance
    double lowerBoundMinSupportToUse = (m_lowerBoundMinSupport
        * instances.numInstances() < 1.0) ? 1.0 / instances.numInstances()
        : m_lowerBoundMinSupport;

    if (m_car) {
      // m_instances does not contain the class attribute
      m_instances = LabeledItemSet.divide(instances, false);

      // m_onlyClass contains only the class attribute
      m_onlyClass = LabeledItemSet.divide(instances, true);
    } else
      m_instances = instances;

    if (m_car && m_numRules == Integer.MAX_VALUE) {
      // Set desired minimum support
      m_minSupport = lowerBoundMinSupportToUse;
    } else {
      // Decrease minimum support until desired number of rules found.
      m_minSupport = m_upperBoundMinSupport - m_delta;
      m_minSupport = (m_minSupport < lowerBoundMinSupportToUse) ? lowerBoundMinSupportToUse
          : m_minSupport;
    }

    do {

      // Reserve space for variables
      m_Ls = new FastVector();
      m_hashtables = new FastVector();
      m_allTheRules = new FastVector[6];
      m_allTheRules[0] = new FastVector();
      m_allTheRules[1] = new FastVector();
      m_allTheRules[2] = new FastVector();
      if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
        m_allTheRules[3] = new FastVector();
        m_allTheRules[4] = new FastVector();
        m_allTheRules[5] = new FastVector();
      }
      sortedRuleSet = new FastVector[6];
      sortedRuleSet[0] = new FastVector();
      sortedRuleSet[1] = new FastVector();
      sortedRuleSet[2] = new FastVector();
      if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
        sortedRuleSet[3] = new FastVector();
        sortedRuleSet[4] = new FastVector();
        sortedRuleSet[5] = new FastVector();
      }
      if (!m_car) {
        // Find large itemsets and rules
        findLargeItemSets();
        if (m_significanceLevel != -1 || m_metricType != CONFIDENCE)
          findRulesBruteForce();
        else
          findRulesQuickly();
      } else {
        findLargeCarItemSets();
        findCarRulesQuickly();
      }

      // prune rules for upper bound min support
      if (m_upperBoundMinSupport < 1.0) {
        pruneRulesForUpperBoundSupport();
      }

      // Sort rules according to their support
      /*
       * supports = new double[m_allTheRules[2].size()]; for (int i = 0; i <
       * m_allTheRules[2].size(); i++) supports[i] =
       * (double)((AprioriItemSet)m_allTheRules[1].elementAt(i)).support();
       * indices = Utils.stableSort(supports); for (int i = 0; i <
       * m_allTheRules[2].size(); i++) {
       * sortedRuleSet[0].addElement(m_allTheRules[0].elementAt(indices[i]));
       * sortedRuleSet[1].addElement(m_allTheRules[1].elementAt(indices[i]));
       * sortedRuleSet[2].addElement(m_allTheRules[2].elementAt(indices[i])); if
       * (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
       * sortedRuleSet[3].addElement(m_allTheRules[3].elementAt(indices[i]));
       * sortedRuleSet[4].addElement(m_allTheRules[4].elementAt(indices[i]));
       * sortedRuleSet[5].addElement(m_allTheRules[5].elementAt(indices[i])); }
       * }
       */
      int j = m_allTheRules[2].size() - 1;
      supports = new double[m_allTheRules[2].size()];
      for (int i = 0; i < (j + 1); i++)
        supports[j - i] = ((double) ((ItemSet) m_allTheRules[1]
            .elementAt(j - i)).support()) * (-1);
      indices = Utils.stableSort(supports);
      for (int i = 0; i < (j + 1); i++) {
        sortedRuleSet[0].addElement(m_allTheRules[0].elementAt(indices[j - i]));
        sortedRuleSet[1].addElement(m_allTheRules[1].elementAt(indices[j - i]));
        sortedRuleSet[2].addElement(m_allTheRules[2].elementAt(indices[j - i]));
        if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
          sortedRuleSet[3].addElement(m_allTheRules[3]
              .elementAt(indices[j - i]));
          sortedRuleSet[4].addElement(m_allTheRules[4]
              .elementAt(indices[j - i]));
          sortedRuleSet[5].addElement(m_allTheRules[5]
              .elementAt(indices[j - i]));
        }
      }

      // Sort rules according to their confidence
      m_allTheRules[0].removeAllElements();
      m_allTheRules[1].removeAllElements();
      m_allTheRules[2].removeAllElements();
      if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
        m_allTheRules[3].removeAllElements();
        m_allTheRules[4].removeAllElements();
        m_allTheRules[5].removeAllElements();
      }
      confidences = new double[sortedRuleSet[2].size()];
      int sortType = 2 + m_metricType;

      for (int i = 0; i < sortedRuleSet[2].size(); i++)
        confidences[i] = ((Double) sortedRuleSet[sortType].elementAt(i))
            .doubleValue();
      indices = Utils.stableSort(confidences);
      for (int i = sortedRuleSet[0].size() - 1; (i >= (sortedRuleSet[0].size() - m_numRules))
          && (i >= 0); i--) {
        m_allTheRules[0].addElement(sortedRuleSet[0].elementAt(indices[i]));
        m_allTheRules[1].addElement(sortedRuleSet[1].elementAt(indices[i]));
        m_allTheRules[2].addElement(sortedRuleSet[2].elementAt(indices[i]));
        if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
          m_allTheRules[3].addElement(sortedRuleSet[3].elementAt(indices[i]));
          m_allTheRules[4].addElement(sortedRuleSet[4].elementAt(indices[i]));
          m_allTheRules[5].addElement(sortedRuleSet[5].elementAt(indices[i]));
        }
      }

      if (m_verbose) {
        if (m_Ls.size() > 1) {
          System.out.println(toString());
        }
      }

      if (m_minSupport == lowerBoundMinSupportToUse
          || m_minSupport - m_delta > lowerBoundMinSupportToUse)
        m_minSupport -= m_delta;
      else
        m_minSupport = lowerBoundMinSupportToUse;

      necSupport = Math.round((float) ((m_minSupport * m_instances
          .numInstances()) + 0.5));

      m_cycles++;
    } while ((m_allTheRules[0].size() < m_numRules)
        && (Utils.grOrEq(m_minSupport, lowerBoundMinSupportToUse))
        /* (necSupport >= lowerBoundNumInstancesSupport) */
        /* (Utils.grOrEq(m_minSupport, m_lowerBoundMinSupport)) */&& (necSupport >= 1));
    m_minSupport += m_delta;
  }

  private void pruneRulesForUpperBoundSupport() {
    int necMaxSupport = (int) (m_upperBoundMinSupport
        * m_instances.numInstances() + 0.5);

    FastVector[] prunedRules = new FastVector[6];
    for (int i = 0; i < 6; i++) {
      prunedRules[i] = new FastVector();
    }

    for (int i = 0; i < m_allTheRules[0].size(); i++) {
      if (((ItemSet) m_allTheRules[1].elementAt(i)).support() <= necMaxSupport) {
        prunedRules[0].addElement(m_allTheRules[0].elementAt(i));
        prunedRules[1].addElement(m_allTheRules[1].elementAt(i));
        prunedRules[2].addElement(m_allTheRules[2].elementAt(i));

        if (!m_car) {
          prunedRules[3].addElement(m_allTheRules[3].elementAt(i));
          prunedRules[4].addElement(m_allTheRules[4].elementAt(i));
          prunedRules[5].addElement(m_allTheRules[5].elementAt(i));
        }
      }
    }

    m_allTheRules[0] = prunedRules[0];
    m_allTheRules[1] = prunedRules[1];
    m_allTheRules[2] = prunedRules[2];
    m_allTheRules[3] = prunedRules[3];
    m_allTheRules[4] = prunedRules[4];
    m_allTheRules[5] = prunedRules[5];
  }

  /**
   * Method that mines all class association rules with minimum support and with
   * a minimum confidence.
   * 
   * @return an sorted array of FastVector (confidence depended) containing the
   *         rules and metric information
   * @param data the instances for which class association rules should be mined
   * @throws Exception if rules can't be built successfully
   */
  public FastVector[] mineCARs(Instances data) throws Exception {

    m_car = true;
    buildAssociations(data);
    return m_allTheRules;
  }

  /**
   * Gets the instances without the class atrribute.
   * 
   * @return the instances without the class attribute.
   */
  public Instances getInstancesNoClass() {

    return m_instances;
  }

  /**
   * Gets only the class attribute of the instances.
   * 
   * @return the class attribute of all instances.
   */
  public Instances getInstancesOnlyClass() {

    return m_onlyClass;
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   */
  public Enumeration listOptions() {

    String string1 = "\tThe required number of rules. (default = " + m_numRules
        + ")", string2 = "\tThe minimum confidence of a rule. (default = "
        + m_minMetric + ")", string3 = "\tThe delta by which the minimum support is decreased in\n", string4 = "\teach iteration. (default = "
        + m_delta + ")", string5 = "\tThe lower bound for the minimum support. (default = "
        + m_lowerBoundMinSupport + ")", string6 = "\tIf used, rules are tested for significance at\n", string7 = "\tthe given level. Slower. (default = no significance testing)", string8 = "\tIf set the itemsets found are also output. (default = no)", string9 = "\tIf set class association rules are mined. (default = no)", string10 = "\tThe class index. (default = last)", stringType = "\tThe metric type by which to rank rules. (default = "
        + "confidence)";

    FastVector newVector = new FastVector(11);

    newVector.addElement(new Option(string1, "N", 1,
        "-N <required number of rules output>"));
    newVector.addElement(new Option(stringType, "T", 1,
        "-T <0=confidence | 1=lift | " + "2=leverage | 3=Conviction>"));
    newVector.addElement(new Option(string2, "C", 1,
        "-C <minimum metric score of a rule>"));
    newVector.addElement(new Option(string3 + string4, "D", 1,
        "-D <delta for minimum support>"));
    newVector.addElement(new Option("\tUpper bound for minimum support. "
        + "(default = 1.0)", "U", 1, "-U <upper bound for minimum support>"));
    newVector.addElement(new Option(string5, "M", 1,
        "-M <lower bound for minimum support>"));
    newVector.addElement(new Option(string6 + string7, "S", 1,
        "-S <significance level>"));
    newVector.addElement(new Option(string8, "I", 0, "-I"));
    newVector.addElement(new Option("\tRemove columns that contain "
        + "all missing values (default = no)", "R", 0, "-R"));
    newVector.addElement(new Option("\tReport progress iteratively. (default "
        + "= no)", "V", 0, "-V"));
    newVector.addElement(new Option(string9, "A", 0, "-A"));
    newVector.addElement(new Option(string10, "c", 1, "-c <the class index>"));

    return newVector.elements();
  }

  /**
   * Parses a given list of options.
   * <p/>
   * 
   * <!-- options-start --> Valid options are:
   * <p/>
   * 
   * <pre>
   * -N &lt;required number of rules output&gt;
   *  The required number of rules. (default = 10)
   * </pre>
   * 
   * <pre>
   * -T &lt;0=confidence | 1=lift | 2=leverage | 3=Conviction&gt;
   *  The metric type by which to rank rules. (default = confidence)
   * </pre>
   * 
   * <pre>
   * -C &lt;minimum metric score of a rule&gt;
   *  The minimum confidence of a rule. (default = 0.9)
   * </pre>
   * 
   * <pre>
   * -D &lt;delta for minimum support&gt;
   *  The delta by which the minimum support is decreased in
   *  each iteration. (default = 0.05)
   * </pre>
   * 
   * <pre>
   * -U &lt;upper bound for minimum support&gt;
   *  Upper bound for minimum support. (default = 1.0)
   * </pre>
   * 
   * <pre>
   * -M &lt;lower bound for minimum support&gt;
   *  The lower bound for the minimum support. (default = 0.1)
   * </pre>
   * 
   * <pre>
   * -S &lt;significance level&gt;
   *  If used, rules are tested for significance at
   *  the given level. Slower. (default = no significance testing)
   * </pre>
   * 
   * <pre>
   * -I
   *  If set the itemsets found are also output. (default = no)
   * </pre>
   * 
   * <pre>
   * -R
   *  Remove columns that contain all missing values (default = no)
   * </pre>
   * 
   * <pre>
   * -V
   *  Report progress iteratively. (default = no)
   * </pre>
   * 
   * <pre>
   * -A
   *  If set class association rules are mined. (default = no)
   * </pre>
   * 
   * <pre>
   * -c &lt;the class index&gt;
   *  The class index. (default = last)
   * </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 {

    resetOptions();
    String numRulesString = Utils.getOption('N', options), minConfidenceString = Utils
        .getOption('C', options), deltaString = Utils.getOption('D', options), maxSupportString = Utils
        .getOption('U', options), minSupportString = Utils.getOption('M',
        options), significanceLevelString = Utils.getOption('S', options), classIndexString = Utils
        .getOption('c', options);
    String metricTypeString = Utils.getOption('T', options);
    if (metricTypeString.length() != 0) {
      setMetricType(new SelectedTag(Integer.parseInt(metricTypeString),
          TAGS_SELECTION));
    }

    if (numRulesString.length() != 0) {
      m_numRules = Integer.parseInt(numRulesString);
    }
    if (classIndexString.length() != 0) {
      if (classIndexString.equalsIgnoreCase("last")) {
        m_classIndex = -1;
      } else if (classIndexString.equalsIgnoreCase("first")) {
        m_classIndex = 0;
      } else {
        m_classIndex = Integer.parseInt(classIndexString);
      }
    }
    if (minConfidenceString.length() != 0) {
      m_minMetric = (new Double(minConfidenceString)).doubleValue();
    }
    if (deltaString.length() != 0) {
      m_delta = (new Double(deltaString)).doubleValue();
    }
    if (maxSupportString.length() != 0) {
      setUpperBoundMinSupport((new Double(maxSupportString)).doubleValue());
    }
    if (minSupportString.length() != 0) {
      m_lowerBoundMinSupport = (new Double(minSupportString)).doubleValue();
    }
    if (significanceLevelString.length() != 0) {
      m_significanceLevel = (new Double(significanceLevelString)).doubleValue();
    }
    m_outputItemSets = Utils.getFlag('I', options);
    m_car = Utils.getFlag('A', options);
    m_verbose = Utils.getFlag('V', options);
    setRemoveAllMissingCols(Utils.getFlag('R', options));
  }

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

    String[] options = new String[20];
    int current = 0;

    if (m_outputItemSets) {
      options[current++] = "-I";
    }

    if (getRemoveAllMissingCols()) {
      options[current++] = "-R";
    }

    options[current++] = "-N";
    options[current++] = "" + m_numRules;
    options[current++] = "-T";
    options[current++] = "" + m_metricType;
    options[current++] = "-C";
    options[current++] = "" + m_minMetric;
    options[current++] = "-D";
    options[current++] = "" + m_delta;
    options[current++] = "-U";
    options[current++] = "" + m_upperBoundMinSupport;
    options[current++] = "-M";
    options[current++] = "" + m_lowerBoundMinSupport;
    options[current++] = "-S";
    options[current++] = "" + m_significanceLevel;
    if (m_car)
      options[current++] = "-A";
    if (m_verbose)
      options[current++] = "-V";
    options[current++] = "-c";
    options[current++] = "" + m_classIndex;

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

  /**
   * Outputs the size of all the generated sets of itemsets and the rules.
   * 
   * @return a string representation of the model
   */
  @Override
  public String toString() {

    StringBuffer text = new StringBuffer();

    if (m_Ls.size() <= 1)
      return "\nNo large itemsets and rules found!\n";
    text.append("\nApriori\n=======\n\n");
    text.append("Minimum support: " + Utils.doubleToString(m_minSupport, 2)
        + " (" + ((int) (m_minSupport * m_instances.numInstances() + 0.5))
        + " instances)" + '\n');
    text.append("Minimum metric <");
    switch (m_metricType) {
    case CONFIDENCE:
      text.append("confidence>: ");
      break;
    case LIFT:
      text.append("lift>: ");
      break;
    case LEVERAGE:
      text.append("leverage>: ");
      break;
    case CONVICTION:
      text.append("conviction>: ");
      break;
    }
    text.append(Utils.doubleToString(m_minMetric, 2) + '\n');

    if (m_significanceLevel != -1)
      text.append("Significance level: "
          + Utils.doubleToString(m_significanceLevel, 2) + '\n');
    text.append("Number of cycles performed: " + m_cycles + '\n');
    text.append("\nGenerated sets of large itemsets:\n");
    if (!m_car) {
      for (int i = 0; i < m_Ls.size(); i++) {
        text.append("\nSize of set of large itemsets L(" + (i + 1) + "): "
            + ((FastVector) m_Ls.elementAt(i)).size() + '\n');
        if (m_outputItemSets) {
          text.append("\nLarge Itemsets L(" + (i + 1) + "):\n");
          for (int j = 0; j < ((FastVector) m_Ls.elementAt(i)).size(); j++)
            text.append(((AprioriItemSet) ((FastVector) m_Ls.elementAt(i))
                .elementAt(j)).toString(m_instances) + "\n");
        }
      }
      text.append("\nBest rules found:\n\n");
      for (int i = 0; i < m_allTheRules[0].size(); i++) {
        text.append(Utils.doubleToString((double) i + 1,
            (int) (Math.log(m_numRules) / Math.log(10) + 1), 0)
            + ". "
            + ((AprioriItemSet) m_allTheRules[0].elementAt(i))
                .toString(m_instances)
            + " ==> "
            + ((AprioriItemSet) m_allTheRules[1].elementAt(i))
                .toString(m_instances)
            + "    conf:("
            + Utils.doubleToString(
                ((Double) m_allTheRules[2].elementAt(i)).doubleValue(), 2)
            + ")");
        if (m_metricType != CONFIDENCE || m_significanceLevel != -1) {
          text.append((m_metricType == LIFT ? " <" : "")
              + " lift:("
              + Utils.doubleToString(
                  ((Double) m_allTheRules[3].elementAt(i)).doubleValue(), 2)
              + ")" + (m_metricType == LIFT ? ">" : ""));
          text.append((m_metricType == LEVERAGE ? " <" : "")
              + " lev:("
              + Utils.doubleToString(
                  ((Double) m_allTheRules[4].elementAt(i)).doubleValue(), 2)
              + ")");
          text.append(" ["
              + (int) (((Double) m_allTheRules[4].elementAt(i)).doubleValue() * m_instances
                  .numInstances()) + "]"
              + (m_metricType == LEVERAGE ? ">" : ""));
          text.append((m_metricType == CONVICTION ? " <" : "")
              + " conv:("
              + Utils.doubleToString(
                  ((Double) m_allTheRules[5].elementAt(i)).doubleValue(), 2)
              + ")" + (m_metricType == CONVICTION ? ">" : ""));
        }
        text.append('\n');
      }
    } else {
      for (int i = 0; i < m_Ls.size(); i++) {
        text.append("\nSize of set of large itemsets L(" + (i + 1) + "): "
            + ((FastVector) m_Ls.elementAt(i)).size() + '\n');
        if (m_outputItemSets) {
          text.append("\nLarge Itemsets L(" + (i + 1) + "):\n");
          for (int j = 0; j < ((FastVector) m_Ls.elementAt(i)).size(); j++) {
            text.append(((ItemSet) ((FastVector) m_Ls.elementAt(i))
                .elementAt(j)).toString(m_instances) + "\n");
            text.append(((LabeledItemSet) ((FastVector) m_Ls.elementAt(i))
                .elementAt(j)).m_classLabel + "  ");
            text.append(((LabeledItemSet) ((FastVector) m_Ls.elementAt(i))
                .elementAt(j)).support() + "\n");
          }
        }
      }
      text.append("\nBest rules found:\n\n");
      for (int i = 0; i < m_allTheRules[0].size(); i++) {
        text.append(Utils.doubleToString((double) i + 1,
            (int) (Math.log(m_numRules) / Math.log(10) + 1), 0)
            + ". "
            + ((ItemSet) m_allTheRules[0].elementAt(i)).toString(m_instances)
            + " ==> "
            + ((ItemSet) m_allTheRules[1].elementAt(i)).toString(m_onlyClass)
            + "    conf:("
            + Utils.doubleToString(
                ((Double) m_allTheRules[2].elementAt(i)).doubleValue(), 2)
            + ")");

        text.append('\n');
      }
    }
    return text.toString();
  }

  /**
   * Returns the metric string for the chosen metric type
   * 
   * @return a string describing the used metric for the interestingness of a
   *         class association rule
   */
  public String metricString() {

    switch (m_metricType) {
    case LIFT:
      return "lif";
    case LEVERAGE:
      return "leverage";
    case CONVICTION:
      return "conviction";
    default:
      return "conf";
    }
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String removeAllMissingColsTipText() {
    return "Remove columns with all missing values.";
  }

  /**
   * Remove columns containing all missing values.
   * 
   * @param r true if cols are to be removed.
   */
  public void setRemoveAllMissingCols(boolean r) {
    m_removeMissingCols = r;
  }

  /**
   * Returns whether columns containing all missing values are to be removed
   * 
   * @return true if columns are to be removed.
   */
  public boolean getRemoveAllMissingCols() {
    return m_removeMissingCols;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String upperBoundMinSupportTipText() {
    return "Upper bound for minimum support. Start iteratively decreasing "
        + "minimum support from this value.";
  }

  /**
   * Get the value of upperBoundMinSupport.
   * 
   * @return Value of upperBoundMinSupport.
   */
  public double getUpperBoundMinSupport() {

    return m_upperBoundMinSupport;
  }

  /**
   * Set the value of upperBoundMinSupport.
   * 
   * @param v Value to assign to upperBoundMinSupport.
   */
  public void setUpperBoundMinSupport(double v) {

    m_upperBoundMinSupport = v;
  }

  /**
   * Sets the class index
   * 
   * @param index the class index
   */
  public void setClassIndex(int index) {

    m_classIndex = index;
  }

  /**
   * Gets the class index
   * 
   * @return the index of the class attribute
   */
  public int getClassIndex() {

    return m_classIndex;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String classIndexTipText() {
    return "Index of the class attribute. If set to -1, the last attribute is taken as class attribute.";

  }

  /**
   * Sets class association rule mining
   * 
   * @param flag if class association rules are mined, false otherwise
   */
  public void setCar(boolean flag) {
    m_car = flag;
  }

  /**
   * Gets whether class association ruels are mined
   * 
   * @return true if class association rules are mined, false otherwise
   */
  public boolean getCar() {
    return m_car;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String carTipText() {
    return "If enabled class association rules are mined instead of (general) association rules.";
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String lowerBoundMinSupportTipText() {
    return "Lower bound for minimum support.";
  }

  /**
   * Get the value of lowerBoundMinSupport.
   * 
   * @return Value of lowerBoundMinSupport.
   */
  public double getLowerBoundMinSupport() {

    return m_lowerBoundMinSupport;
  }

  /**
   * Set the value of lowerBoundMinSupport.
   * 
   * @param v Value to assign to lowerBoundMinSupport.
   */
  public void setLowerBoundMinSupport(double v) {

    m_lowerBoundMinSupport = v;
  }

  /**
   * Get the metric type
   * 
   * @return the type of metric to use for ranking rules
   */
  public SelectedTag getMetricType() {
    return new SelectedTag(m_metricType, TAGS_SELECTION);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String metricTypeTipText() {
    return "Set the type of metric by which to rank rules. Confidence is "
        + "the proportion of the examples covered by the premise that are also "
        + "covered by the consequence(Class association rules can only be mined using confidence). Lift is confidence divided by the "
        + "proportion of all examples that are covered by the consequence. This "
        + "is a measure of the importance of the association that is independent "
        + "of support. Leverage is the proportion of additional examples covered "
        + "by both the premise and consequence above those expected if the "
        + "premise and consequence were independent of each other. The total "
        + "number of examples that this represents is presented in brackets "
        + "following the leverage. Conviction is "
        + "another measure of departure from independence. Conviction is given "
        + "by P(premise)P(!consequence) / P(premise, !consequence).";
  }

  /**
   * Set the metric type for ranking rules
   * 
   * @param d the type of metric
   */
  public void setMetricType(SelectedTag d) {

    if (d.getTags() == TAGS_SELECTION) {
      m_metricType = d.getSelectedTag().getID();
    }

    if (m_metricType == CONFIDENCE) {
      setMinMetric(0.9);
    }

    if (m_metricType == LIFT || m_metricType == CONVICTION) {
      setMinMetric(1.1);
    }

    if (m_metricType == LEVERAGE) {
      setMinMetric(0.1);
    }
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String minMetricTipText() {
    return "Minimum metric score. Consider only rules with scores higher than "
        + "this value.";
  }

  /**
   * Get the value of minConfidence.
   * 
   * @return Value of minConfidence.
   */
  public double getMinMetric() {

    return m_minMetric;
  }

  /**
   * Set the value of minConfidence.
   * 
   * @param v Value to assign to minConfidence.
   */
  public void setMinMetric(double v) {

    m_minMetric = v;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numRulesTipText() {
    return "Number of rules to find.";
  }

  /**
   * Get the value of numRules.
   * 
   * @return Value of numRules.
   */
  public int getNumRules() {

    return m_numRules;
  }

  /**
   * Set the value of numRules.
   * 
   * @param v Value to assign to numRules.
   */
  public void setNumRules(int v) {

    m_numRules = v;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String deltaTipText() {
    return "Iteratively decrease support by this factor. Reduces support "
        + "until min support is reached or required number of rules has been "
        + "generated.";
  }

  /**
   * Get the value of delta.
   * 
   * @return Value of delta.
   */
  public double getDelta() {

    return m_delta;
  }

  /**
   * Set the value of delta.
   * 
   * @param v Value to assign to delta.
   */
  public void setDelta(double v) {

    m_delta = v;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String significanceLevelTipText() {
    return "Significance level. Significance test (confidence metric only).";
  }

  /**
   * Get the value of significanceLevel.
   * 
   * @return Value of significanceLevel.
   */
  public double getSignificanceLevel() {

    return m_significanceLevel;
  }

  /**
   * Set the value of significanceLevel.
   * 
   * @param v Value to assign to significanceLevel.
   */
  public void setSignificanceLevel(double v) {

    m_significanceLevel = v;
  }

  /**
   * Sets whether itemsets are output as well
   * 
   * @param flag true if itemsets are to be output as well
   */
  public void setOutputItemSets(boolean flag) {
    m_outputItemSets = flag;
  }

  /**
   * Gets whether itemsets are output as well
   * 
   * @return true if itemsets are output as well
   */
  public boolean getOutputItemSets() {
    return m_outputItemSets;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String outputItemSetsTipText() {
    return "If enabled the itemsets are output as well.";
  }

  /**
   * Sets verbose mode
   * 
   * @param flag true if algorithm should be run in verbose mode
   */
  public void setVerbose(boolean flag) {
    m_verbose = flag;
  }

  /**
   * Gets whether algorithm is run in verbose mode
   * 
   * @return true if algorithm is run in verbose mode
   */
  public boolean getVerbose() {
    return m_verbose;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String verboseTipText() {
    return "If enabled the algorithm will be run in verbose mode.";
  }

  /**
   * Method that finds all large itemsets for the given set of instances.
   * 
   * @throws Exception if an attribute is numeric
   */
  private void findLargeItemSets() throws Exception {

    FastVector kMinusOneSets, kSets;
    Hashtable hashtable;
    int necSupport, necMaxSupport, i = 0;

    // Find large itemsets

    // minimum support
    necSupport = (int) (m_minSupport * m_instances.numInstances() + 0.5);
    necMaxSupport = (int) (m_upperBoundMinSupport * m_instances.numInstances() + 0.5);

    kSets = AprioriItemSet.singletons(m_instances);
    AprioriItemSet.upDateCounters(kSets, m_instances);
    kSets = AprioriItemSet.deleteItemSets(kSets, necSupport,
        m_instances.numInstances());
    if (kSets.size() == 0)
      return;
    do {
      m_Ls.addElement(kSets);
      kMinusOneSets = kSets;
      kSets = AprioriItemSet.mergeAllItemSets(kMinusOneSets, i,
          m_instances.numInstances());
      hashtable = AprioriItemSet.getHashtable(kMinusOneSets,
          kMinusOneSets.size());
      m_hashtables.addElement(hashtable);
      kSets = AprioriItemSet.pruneItemSets(kSets, hashtable);
      AprioriItemSet.upDateCounters(kSets, m_instances);
      kSets = AprioriItemSet.deleteItemSets(kSets, necSupport,
          m_instances.numInstances());
      i++;
    } while (kSets.size() > 0);
  }

  /**
   * Method that finds all association rules and performs significance test.
   * 
   * @throws Exception if an attribute is numeric
   */
  private void findRulesBruteForce() throws Exception {

    FastVector[] rules;

    // Build rules
    for (int j = 1; j < m_Ls.size(); j++) {
      FastVector currentItemSets = (FastVector) m_Ls.elementAt(j);
      Enumeration enumItemSets = currentItemSets.elements();
      while (enumItemSets.hasMoreElements()) {
        AprioriItemSet currentItemSet = (AprioriItemSet) enumItemSets
            .nextElement();
        // AprioriItemSet currentItemSet = new
        // AprioriItemSet((ItemSet)enumItemSets.nextElement());
        rules = currentItemSet.generateRulesBruteForce(m_minMetric,
            m_metricType, m_hashtables, j + 1, m_instances.numInstances(),
            m_significanceLevel);
        for (int k = 0; k < rules[0].size(); k++) {
          m_allTheRules[0].addElement(rules[0].elementAt(k));
          m_allTheRules[1].addElement(rules[1].elementAt(k));
          m_allTheRules[2].addElement(rules[2].elementAt(k));

          m_allTheRules[3].addElement(rules[3].elementAt(k));
          m_allTheRules[4].addElement(rules[4].elementAt(k));
          m_allTheRules[5].addElement(rules[5].elementAt(k));
        }
      }
    }
  }

  /**
   * Method that finds all association rules.
   * 
   * @throws Exception if an attribute is numeric
   */
  private void findRulesQuickly() throws Exception {

    FastVector[] rules;

    // Build rules
    for (int j = 1; j < m_Ls.size(); j++) {
      FastVector currentItemSets = (FastVector) m_Ls.elementAt(j);
      Enumeration enumItemSets = currentItemSets.elements();
      while (enumItemSets.hasMoreElements()) {
        AprioriItemSet currentItemSet = (AprioriItemSet) enumItemSets
            .nextElement();
        // AprioriItemSet currentItemSet = new
        // AprioriItemSet((ItemSet)enumItemSets.nextElement());
        rules = currentItemSet.generateRules(m_minMetric, m_hashtables, j + 1);
        for (int k = 0; k < rules[0].size(); k++) {
          m_allTheRules[0].addElement(rules[0].elementAt(k));
          m_allTheRules[1].addElement(rules[1].elementAt(k));
          m_allTheRules[2].addElement(rules[2].elementAt(k));
        }
      }
    }
  }

  /**
   * 
   * Method that finds all large itemsets for class association rules for the
   * given set of instances.
   * 
   * @throws Exception if an attribute is numeric
   */
  private void findLargeCarItemSets() throws Exception {

    FastVector kMinusOneSets, kSets;
    Hashtable hashtable;
    int necSupport, necMaxSupport, i = 0;

    // Find large itemsets

    // minimum support
    double nextMinSupport = m_minSupport * m_instances.numInstances();
    double nextMaxSupport = m_upperBoundMinSupport * m_instances.numInstances();
    if (Math.rint(nextMinSupport) == nextMinSupport) {
      necSupport = (int) nextMinSupport;
    } else {
      necSupport = Math.round((float) (nextMinSupport + 0.5));
    }
    if (Math.rint(nextMaxSupport) == nextMaxSupport) {
      necMaxSupport = (int) nextMaxSupport;
    } else {
      necMaxSupport = Math.round((float) (nextMaxSupport + 0.5));
    }

    // find item sets of length one
    kSets = LabeledItemSet.singletons(m_instances, m_onlyClass);
    LabeledItemSet.upDateCounters(kSets, m_instances, m_onlyClass);

    // check if a item set of lentgh one is frequent, if not delete it
    kSets = LabeledItemSet.deleteItemSets(kSets, necSupport,
        m_instances.numInstances());
    if (kSets.size() == 0)
      return;
    do {
      m_Ls.addElement(kSets);
      kMinusOneSets = kSets;
      kSets = LabeledItemSet.mergeAllItemSets(kMinusOneSets, i,
          m_instances.numInstances());
      hashtable = LabeledItemSet.getHashtable(kMinusOneSets,
          kMinusOneSets.size());
      kSets = LabeledItemSet.pruneItemSets(kSets, hashtable);
      LabeledItemSet.upDateCounters(kSets, m_instances, m_onlyClass);
      kSets = LabeledItemSet.deleteItemSets(kSets, necSupport,
          m_instances.numInstances());
      i++;
    } while (kSets.size() > 0);
  }

  /**
   * Method that finds all class association rules.
   * 
   * @throws Exception if an attribute is numeric
   */
  private void findCarRulesQuickly() throws Exception {

    FastVector[] rules;

    // Build rules
    for (int j = 0; j < m_Ls.size(); j++) {
      FastVector currentLabeledItemSets = (FastVector) m_Ls.elementAt(j);
      Enumeration enumLabeledItemSets = currentLabeledItemSets.elements();
      while (enumLabeledItemSets.hasMoreElements()) {
        LabeledItemSet currentLabeledItemSet = (LabeledItemSet) enumLabeledItemSets
            .nextElement();
        rules = currentLabeledItemSet.generateRules(m_minMetric, false);
        for (int k = 0; k < rules[0].size(); k++) {
          m_allTheRules[0].addElement(rules[0].elementAt(k));
          m_allTheRules[1].addElement(rules[1].elementAt(k));
          m_allTheRules[2].addElement(rules[2].elementAt(k));
        }
      }
    }
  }

  /**
   * returns all the rules
   * 
   * @return all the rules
   * @see #m_allTheRules
   */
  public FastVector[] getAllTheRules() {
    return m_allTheRules;
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 9096 $");
  }

  /**
   * Main method.
   * 
   * @param args the commandline options
   */
  public static void main(String[] args) {
    runAssociator(new Apriori(), args);
  }
}
