• Home
• nz.ac.waikato.cms.weka
• weka-dev
• 3.7.11
• source code
• Discretize.java

×

Project download

Many resources are needed to download a project. Please understand that we have to compensate our server costs. Thank you in advance.
Project price only 1 $

You can buy this project and download/modify it how often you want.

weka.filters.supervised.attribute.Discretize 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 .
 */

/*
 *    Discretize.java
 *    Copyright (C) 1999-2012 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.filters.supervised.attribute;

import java.util.ArrayList;
import java.util.Enumeration;
import java.util.HashSet;
import java.util.List;
import java.util.Set;
import java.util.Vector;

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.ContingencyTables;
import weka.core.DenseInstance;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.Range;
import weka.core.RevisionUtils;
import weka.core.SparseInstance;
import weka.core.SpecialFunctions;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.filters.Filter;
import weka.filters.SupervisedFilter;

/**
 
 * An instance filter that discretizes a range of numeric attributes in the dataset into nominal attributes. Discretization is by Fayyad & Irani's MDL method (the default).

 * 

 * For more information, see:

 * 

 * Usama M. Fayyad, Keki B. Irani: Multi-interval discretization of continuousvalued attributes for classification learning. In: Thirteenth International Joint Conference on Articial Intelligence, 1022-1027, 1993.

 * 

 * Igor Kononenko: On Biases in Estimating Multi-Valued Attributes. In: 14th International Joint Conference on Articial Intelligence, 1034-1040, 1995.
 * 

 
 * 
 
 * BibTeX:
 * 
 * @inproceedings{Fayyad1993,
 *    author = {Usama M. Fayyad and Keki B. Irani},
 *    booktitle = {Thirteenth International Joint Conference on Articial Intelligence},
 *    pages = {1022-1027},
 *    publisher = {Morgan Kaufmann Publishers},
 *    title = {Multi-interval discretization of continuousvalued attributes for classification learning},
 *    volume = {2},
 *    year = {1993}
 * }
 * 
 * @inproceedings{Kononenko1995,
 *    author = {Igor Kononenko},
 *    booktitle = {14th International Joint Conference on Articial Intelligence},
 *    pages = {1034-1040},
 *    title = {On Biases in Estimating Multi-Valued Attributes},
 *    year = {1995},
 *    PS = {http://ai.fri.uni-lj.si/papers/kononenko95-ijcai.ps.gz}
 * }
 * 
 * 

 
 * 
 
 * Valid options are: 

 * 
 * 
 -R <col1,col2-col4,...>
 *  Specifies list of columns to Discretize. First and last are valid indexes.
 *  (default none)
 * 
 * 
 -V
 *  Invert matching sense of column indexes.
 * 
 * 
 -D
 *  Output binary attributes for discretized attributes.
 * 
 * 
 -Y
 *  Use bin numbers rather than ranges for discretized attributes.
 * 
 * 
 -E
 *  Use better encoding of split point for MDL.
 * 
 * 
 -K
 *  Use Kononenko's MDL criterion.
 * 
 * 
 -precision <integer>
 *  Precision for bin boundary labels.
 *  (default = 6 decimal places).
 * 
 
 * 
 * @author Len Trigg ([email protected])
 * @author Eibe Frank ([email protected])
 * @version $Revision: 10231 $
 */
public class Discretize extends Filter implements SupervisedFilter,
  OptionHandler, WeightedInstancesHandler, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = -3141006402280129097L;

  /** Stores which columns to Discretize */
  protected Range m_DiscretizeCols = new Range();

  /** Store the current cutpoints */
  protected double[][] m_CutPoints = null;

  /** Output binary attributes for discretized attributes. */
  protected boolean m_MakeBinary = false;

  /** Use bin numbers rather than ranges for discretized attributes. */
  protected boolean m_UseBinNumbers = false;

  /** Use better encoding of split point for MDL. */
  protected boolean m_UseBetterEncoding = false;

  /** Use Kononenko's MDL criterion instead of Fayyad et al.'s */
  protected boolean m_UseKononenko = false;

  /** Precision for bin range labels */
  protected int m_BinRangePrecision = 6;

  /** Constructor - initialises the filter */
  public Discretize() {

    setAttributeIndices("first-last");
  }

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

    Vector newVector = new Vector(6);

    newVector.addElement(new Option(
      "\tSpecifies list of columns to Discretize. First"
        + " and last are valid indexes.\n" + "\t(default none)", "R", 1,
      "-R "));

    newVector.addElement(new Option(
      "\tInvert matching sense of column indexes.", "V", 0, "-V"));

    newVector.addElement(new Option(
      "\tOutput binary attributes for discretized attributes.", "D", 0, "-D"));

    newVector.addElement(new Option(
      "\tUse bin numbers rather than ranges for discretized attributes.", "Y",
      0, "-Y"));

    newVector.addElement(new Option(
      "\tUse better encoding of split point for MDL.", "E", 0, "-E"));

    newVector.addElement(new Option("\tUse Kononenko's MDL criterion.", "K", 0,
      "-K"));

    newVector
      .addElement(new Option("\tPrecision for bin boundary labels.\n\t"
        + "(default = 6 decimal places).", "precision", 1,
        "-precision "));

    return newVector.elements();
  }

  /**
   * Parses a given list of options.
   * 

   * 
   
   * Valid options are: 

   * 
   * 
 -R <col1,col2-col4,...>
   *  Specifies list of columns to Discretize. First and last are valid indexes.
   *  (default none)
   * 
   * 
 -V
   *  Invert matching sense of column indexes.
   * 
   * 
 -D
   *  Output binary attributes for discretized attributes.
   * 
   * 
 -Y
   *  Use bin numbers rather than ranges for discretized attributes.
   * 
   * 
 -E
   *  Use better encoding of split point for MDL.
   * 
   * 
 -K
   *  Use Kononenko's MDL criterion.
   * 
   * 
 -precision <integer>
   *  Precision for bin boundary labels.
   *  (default = 6 decimal places).
   * 
   
   * 
   * @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 {

    setMakeBinary(Utils.getFlag('D', options));
    setUseBinNumbers(Utils.getFlag('Y', options));
    setUseBetterEncoding(Utils.getFlag('E', options));
    setUseKononenko(Utils.getFlag('K', options));
    setInvertSelection(Utils.getFlag('V', options));

    String convertList = Utils.getOption('R', options);
    if (convertList.length() != 0) {
      setAttributeIndices(convertList);
    } else {
      setAttributeIndices("first-last");
    }

    String precisionS = Utils.getOption("precision", options);
    if (precisionS.length() > 0) {
      setBinRangePrecision(Integer.parseInt(precisionS));
    }

    if (getInputFormat() != null) {
      setInputFormat(getInputFormat());
    }

    Utils.checkForRemainingOptions(options);
  }

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

    List options = new ArrayList();

    if (getMakeBinary()) {
      options.add("-D");
    }
    if (getUseBinNumbers()) {
      options.add("-Y");
    }
    if (getUseBetterEncoding()) {
      options.add("-E");
    }
    if (getUseKononenko()) {
      options.add("-K");
    }
    if (getInvertSelection()) {
      options.add("-V");
    }
    if (!getAttributeIndices().equals("")) {
      options.add("-R");
      options.add(getAttributeIndices());
    }

    options.add("-precision");
    options.add("" + getBinRangePrecision());

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

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

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

    // class
    result.enable(Capability.NOMINAL_CLASS);

    return result;
  }

  /**
   * Sets the format of the input instances.
   * 
   * @param instanceInfo an Instances object containing the input instance
   *          structure (any instances contained in the object are ignored -
   *          only the structure is required).
   * @return true if the outputFormat may be collected immediately
   * @throws Exception if the input format can't be set successfully
   */
  @Override
  public boolean setInputFormat(Instances instanceInfo) throws Exception {

    super.setInputFormat(instanceInfo);

    m_DiscretizeCols.setUpper(instanceInfo.numAttributes() - 1);
    m_CutPoints = null;

    // If we implement loading cutfiles, then load
    // them here and set the output format
    return false;
  }

  /**
   * Input an instance for filtering. Ordinarily the instance is processed and
   * made available for output immediately. Some filters require all instances
   * be read before producing output.
   * 
   * @param instance the input instance
   * @return true if the filtered instance may now be collected with output().
   * @throws IllegalStateException if no input format has been defined.
   */
  @Override
  public boolean input(Instance instance) {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_NewBatch) {
      resetQueue();
      m_NewBatch = false;
    }

    if (m_CutPoints != null) {
      convertInstance(instance);
      return true;
    }

    bufferInput(instance);
    return false;
  }

  /**
   * Signifies that this batch of input to the filter is finished. If the filter
   * requires all instances prior to filtering, output() may now be called to
   * retrieve the filtered instances.
   * 
   * @return true if there are instances pending output
   * @throws IllegalStateException if no input structure has been defined
   */
  @Override
  public boolean batchFinished() {

    if (getInputFormat() == null) {
      throw new IllegalStateException("No input instance format defined");
    }
    if (m_CutPoints == null) {
      calculateCutPoints();

      setOutputFormat();

      // If we implement saving cutfiles, save the cuts here

      // Convert pending input instances
      for (int i = 0; i < getInputFormat().numInstances(); i++) {
        convertInstance(getInputFormat().instance(i));
      }
    }
    flushInput();

    m_NewBatch = true;
    return (numPendingOutput() != 0);
  }

  /**
   * Returns a string describing this filter
   * 
   * @return a description of the filter suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {

    return "An instance filter that discretizes a range of numeric"
      + " attributes in the dataset into nominal attributes."
      + " Discretization is by Fayyad & Irani's MDL method (the default).\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
   */
  @Override
  public TechnicalInformation getTechnicalInformation() {
    TechnicalInformation result;
    TechnicalInformation additional;

    result = new TechnicalInformation(Type.INPROCEEDINGS);
    result.setValue(Field.AUTHOR, "Usama M. Fayyad and Keki B. Irani");
    result
      .setValue(
        Field.TITLE,
        "Multi-interval discretization of continuousvalued attributes for classification learning");
    result.setValue(Field.BOOKTITLE,
      "Thirteenth International Joint Conference on Articial Intelligence");
    result.setValue(Field.YEAR, "1993");
    result.setValue(Field.VOLUME, "2");
    result.setValue(Field.PAGES, "1022-1027");
    result.setValue(Field.PUBLISHER, "Morgan Kaufmann Publishers");

    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR, "Igor Kononenko");
    additional.setValue(Field.TITLE,
      "On Biases in Estimating Multi-Valued Attributes");
    additional.setValue(Field.BOOKTITLE,
      "14th International Joint Conference on Articial Intelligence");
    additional.setValue(Field.YEAR, "1995");
    additional.setValue(Field.PAGES, "1034-1040");
    additional.setValue(Field.PS,
      "http://ai.fri.uni-lj.si/papers/kononenko95-ijcai.ps.gz");

    return result;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String binRangePrecisionTipText() {
    return "The number of decimal places for cut points to use when generating bin labels";
  }

  /**
   * Set the precision for bin boundaries. Only affects the boundary values used
   * in the labels for the converted attributes; internal cutpoints are at full
   * double precision.
   * 
   * @param p the precision for bin boundaries
   */
  public void setBinRangePrecision(int p) {
    m_BinRangePrecision = p;
  }

  /**
   * Get the precision for bin boundaries. Only affects the boundary values used
   * in the labels for the converted attributes; internal cutpoints are at full
   * double precision.
   * 
   * @return the precision for bin boundaries
   */
  public int getBinRangePrecision() {
    return m_BinRangePrecision;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String makeBinaryTipText() {

    return "Make resulting attributes binary.";
  }

  /**
   * Gets whether binary attributes should be made for discretized ones.
   * 
   * @return true if attributes will be binarized
   */
  public boolean getMakeBinary() {

    return m_MakeBinary;
  }

  /**
   * Sets whether binary attributes should be made for discretized ones.
   * 
   * @param makeBinary if binary attributes are to be made
   */
  public void setMakeBinary(boolean makeBinary) {

    m_MakeBinary = makeBinary;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String useBinNumbersTipText() {
    return "Use bin numbers (eg BXofY) rather than ranges for for discretized attributes";
  }

  /**
   * Gets whether bin numbers rather than ranges should be used for discretized
   * attributes.
   * 
   * @return true if bin numbers should be used
   */
  public boolean getUseBinNumbers() {

    return m_UseBinNumbers;
  }

  /**
   * Sets whether bin numbers rather than ranges should be used for discretized
   * attributes.
   * 
   * @param useBinNumbers if bin numbers should be used
   */
  public void setUseBinNumbers(boolean useBinNumbers) {

    m_UseBinNumbers = useBinNumbers;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String useKononenkoTipText() {

    return "Use Kononenko's MDL criterion. If set to false"
      + " uses the Fayyad & Irani criterion.";
  }

  /**
   * Gets whether Kononenko's MDL criterion is to be used.
   * 
   * @return true if Kononenko's criterion will be used.
   */
  public boolean getUseKononenko() {

    return m_UseKononenko;
  }

  /**
   * Sets whether Kononenko's MDL criterion is to be used.
   * 
   * @param useKon true if Kononenko's one is to be used
   */
  public void setUseKononenko(boolean useKon) {

    m_UseKononenko = useKon;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String useBetterEncodingTipText() {

    return "Uses a more efficient split point encoding.";
  }

  /**
   * Gets whether better encoding is to be used for MDL.
   * 
   * @return true if the better MDL encoding will be used
   */
  public boolean getUseBetterEncoding() {

    return m_UseBetterEncoding;
  }

  /**
   * Sets whether better encoding is to be used for MDL.
   * 
   * @param useBetterEncoding true if better encoding to be used.
   */
  public void setUseBetterEncoding(boolean useBetterEncoding) {

    m_UseBetterEncoding = useBetterEncoding;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String invertSelectionTipText() {

    return "Set attribute selection mode. If false, only selected"
      + " (numeric) attributes in the range will be discretized; if"
      + " true, only non-selected attributes will be discretized.";
  }

  /**
   * Gets whether the supplied columns are to be removed or kept
   * 
   * @return true if the supplied columns will be kept
   */
  public boolean getInvertSelection() {

    return m_DiscretizeCols.getInvert();
  }

  /**
   * Sets whether selected columns should be removed or kept. If true the
   * selected columns are kept and unselected columns are deleted. If false
   * selected columns are deleted and unselected columns are kept.
   * 
   * @param invert the new invert setting
   */
  public void setInvertSelection(boolean invert) {

    m_DiscretizeCols.setInvert(invert);
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String attributeIndicesTipText() {
    return "Specify range of attributes to act on."
      + " This is a comma separated list of attribute indices, with"
      + " \"first\" and \"last\" valid values. Specify an inclusive"
      + " range with \"-\". E.g: \"first-3,5,6-10,last\".";
  }

  /**
   * Gets the current range selection
   * 
   * @return a string containing a comma separated list of ranges
   */
  public String getAttributeIndices() {

    return m_DiscretizeCols.getRanges();
  }

  /**
   * Sets which attributes are to be Discretized (only numeric attributes among
   * the selection will be Discretized).
   * 
   * @param rangeList a string representing the list of attributes. Since the
   *          string will typically come from a user, attributes are indexed
   *          from 1. 

   *          eg: first-3,5,6-last
   * @throws IllegalArgumentException if an invalid range list is supplied
   */
  public void setAttributeIndices(String rangeList) {

    m_DiscretizeCols.setRanges(rangeList);
  }

  /**
   * Sets which attributes are to be Discretized (only numeric attributes among
   * the selection will be Discretized).
   * 
   * @param attributes an array containing indexes of attributes to Discretize.
   *          Since the array will typically come from a program, attributes are
   *          indexed from 0.
   * @throws IllegalArgumentException if an invalid set of ranges is supplied
   */
  public void setAttributeIndicesArray(int[] attributes) {

    setAttributeIndices(Range.indicesToRangeList(attributes));
  }

  /**
   * Gets the cut points for an attribute
   * 
   * @param attributeIndex the index (from 0) of the attribute to get the cut
   *          points of
   * @return an array containing the cutpoints (or null if the attribute
   *         requested isn't being Discretized
   */
  public double[] getCutPoints(int attributeIndex) {

    if (m_CutPoints == null) {
      return null;
    }
    return m_CutPoints[attributeIndex];
  }

  /**
   * Gets the bin ranges string for an attribute
   * 
   * @param attributeIndex the index (from 0) of the attribute to get the bin
   *          ranges string of
   * @return the bin ranges string (or null if the attribute requested has been
   *         discretized into only one interval.)
   */
  public String getBinRangesString(int attributeIndex) {

    if (m_CutPoints == null) {
      return null;
    }

    double[] cutPoints = m_CutPoints[attributeIndex];

    if (cutPoints == null) {
      return "All";
    }

    StringBuilder sb = new StringBuilder();
    boolean first = true;

    for (int j = 0, n = cutPoints.length; j <= n; ++j) {
      if (first) {
        first = false;
      } else {
        sb.append(',');
      }

      sb.append(binRangeString(cutPoints, j, m_BinRangePrecision));
    }

    return sb.toString();
  }

  /**
   * Get a bin range string for a specified bin of some attribute's cut points.
   * 
   * @param cutPoints The attribute's cut points; never null.
   * @param j The bin number (zero based); never out of range.
   * @param precision the precision for the range values
   * 
   * @return The bin range string.
   */
  private static String binRangeString(double[] cutPoints, int j, int precision) {
    assert cutPoints != null;

    int n = cutPoints.length;
    assert 0 <= j && j <= n;

    return j == 0 ? "" + "(" + "-inf" + "-"
      + Utils.doubleToString(cutPoints[0], precision) + "]" : j == n ? "" + "("
      + Utils.doubleToString(cutPoints[n - 1], precision) + "-" + "inf" + ")"
      : "" + "(" + Utils.doubleToString(cutPoints[j - 1], precision) + "-"
        + Utils.doubleToString(cutPoints[j], precision) + "]";
  }

  /** Generate the cutpoints for each attribute */
  protected void calculateCutPoints() {

    Instances copy = null;

    m_CutPoints = new double[getInputFormat().numAttributes()][];
    for (int i = getInputFormat().numAttributes() - 1; i >= 0; i--) {
      if ((m_DiscretizeCols.isInRange(i))
        && (getInputFormat().attribute(i).isNumeric())) {

        // Use copy to preserve order
        if (copy == null) {
          copy = new Instances(getInputFormat());
        }
        calculateCutPointsByMDL(i, copy);
      }
    }
  }

  /**
   * Set cutpoints for a single attribute using MDL.
   * 
   * @param index the index of the attribute to set cutpoints for
   * @param data the data to work with
   */
  protected void calculateCutPointsByMDL(int index, Instances data) {

    // Sort instances
    data.sort(data.attribute(index));

    // Find first instances that's missing
    int firstMissing = data.numInstances();
    for (int i = 0; i < data.numInstances(); i++) {
      if (data.instance(i).isMissing(index)) {
        firstMissing = i;
        break;
      }
    }
    m_CutPoints[index] = cutPointsForSubset(data, index, 0, firstMissing);
  }

  /**
   * Test using Kononenko's MDL criterion.
   * 
   * @param priorCounts
   * @param bestCounts
   * @param numInstances
   * @param numCutPoints
   * @return true if the split is acceptable
   */
  private boolean KononenkosMDL(double[] priorCounts, double[][] bestCounts,
    double numInstances, int numCutPoints) {

    double distPrior, instPrior, distAfter = 0, sum, instAfter = 0;
    double before, after;
    int numClassesTotal;

    // Number of classes occuring in the set
    numClassesTotal = 0;
    for (double priorCount : priorCounts) {
      if (priorCount > 0) {
        numClassesTotal++;
      }
    }

    // Encode distribution prior to split
    distPrior = SpecialFunctions.log2Binomial(numInstances + numClassesTotal
      - 1, numClassesTotal - 1);

    // Encode instances prior to split.
    instPrior = SpecialFunctions.log2Multinomial(numInstances, priorCounts);

    before = instPrior + distPrior;

    // Encode distributions and instances after split.
    for (double[] bestCount : bestCounts) {
      sum = Utils.sum(bestCount);
      distAfter += SpecialFunctions.log2Binomial(sum + numClassesTotal - 1,
        numClassesTotal - 1);
      instAfter += SpecialFunctions.log2Multinomial(sum, bestCount);
    }

    // Coding cost after split
    after = Utils.log2(numCutPoints) + distAfter + instAfter;

    // Check if split is to be accepted
    return (before > after);
  }

  /**
   * Test using Fayyad and Irani's MDL criterion.
   * 
   * @param priorCounts
   * @param bestCounts
   * @param numInstances
   * @param numCutPoints
   * @return true if the splits is acceptable
   */
  private boolean FayyadAndIranisMDL(double[] priorCounts,
    double[][] bestCounts, double numInstances, int numCutPoints) {

    double priorEntropy, entropy, gain;
    double entropyLeft, entropyRight, delta;
    int numClassesTotal, numClassesRight, numClassesLeft;

    // Compute entropy before split.
    priorEntropy = ContingencyTables.entropy(priorCounts);

    // Compute entropy after split.
    entropy = ContingencyTables.entropyConditionedOnRows(bestCounts);

    // Compute information gain.
    gain = priorEntropy - entropy;

    // Number of classes occuring in the set
    numClassesTotal = 0;
    for (double priorCount : priorCounts) {
      if (priorCount > 0) {
        numClassesTotal++;
      }
    }

    // Number of classes occuring in the left subset
    numClassesLeft = 0;
    for (int i = 0; i < bestCounts[0].length; i++) {
      if (bestCounts[0][i] > 0) {
        numClassesLeft++;
      }
    }

    // Number of classes occuring in the right subset
    numClassesRight = 0;
    for (int i = 0; i < bestCounts[1].length; i++) {
      if (bestCounts[1][i] > 0) {
        numClassesRight++;
      }
    }

    // Entropy of the left and the right subsets
    entropyLeft = ContingencyTables.entropy(bestCounts[0]);
    entropyRight = ContingencyTables.entropy(bestCounts[1]);

    // Compute terms for MDL formula
    delta = Utils.log2(Math.pow(3, numClassesTotal) - 2)
      - ((numClassesTotal * priorEntropy) - (numClassesRight * entropyRight) - (numClassesLeft * entropyLeft));

    // Check if split is to be accepted
    return (gain > (Utils.log2(numCutPoints) + delta) / numInstances);
  }

  /**
   * Selects cutpoints for sorted subset.
   * 
   * @param instances
   * @param attIndex
   * @param first
   * @param lastPlusOne
   * @return
   */
  private double[] cutPointsForSubset(Instances instances, int attIndex,
    int first, int lastPlusOne) {

    double[][] counts, bestCounts;
    double[] priorCounts, left, right, cutPoints;
    double currentCutPoint = -Double.MAX_VALUE, bestCutPoint = -1, currentEntropy, bestEntropy, priorEntropy, gain;
    int bestIndex = -1, numCutPoints = 0;
    double numInstances = 0;

    // Compute number of instances in set
    if ((lastPlusOne - first) < 2) {
      return null;
    }

    // Compute class counts.
    counts = new double[2][instances.numClasses()];
    for (int i = first; i < lastPlusOne; i++) {
      numInstances += instances.instance(i).weight();
      counts[1][(int) instances.instance(i).classValue()] += instances
        .instance(i).weight();
    }

    // Save prior counts
    priorCounts = new double[instances.numClasses()];
    System.arraycopy(counts[1], 0, priorCounts, 0, instances.numClasses());

    // Entropy of the full set
    priorEntropy = ContingencyTables.entropy(priorCounts);
    bestEntropy = priorEntropy;

    // Find best entropy.
    bestCounts = new double[2][instances.numClasses()];
    for (int i = first; i < (lastPlusOne - 1); i++) {
      counts[0][(int) instances.instance(i).classValue()] += instances
        .instance(i).weight();
      counts[1][(int) instances.instance(i).classValue()] -= instances
        .instance(i).weight();
      if (instances.instance(i).value(attIndex) < instances.instance(i + 1)
        .value(attIndex)) {
        currentCutPoint = (instances.instance(i).value(attIndex) + instances
          .instance(i + 1).value(attIndex)) / 2.0;
        currentEntropy = ContingencyTables.entropyConditionedOnRows(counts);
        if (currentEntropy < bestEntropy) {
          bestCutPoint = currentCutPoint;
          bestEntropy = currentEntropy;
          bestIndex = i;
          System.arraycopy(counts[0], 0, bestCounts[0], 0,
            instances.numClasses());
          System.arraycopy(counts[1], 0, bestCounts[1], 0,
            instances.numClasses());
        }
        numCutPoints++;
      }
    }

    // Use worse encoding?
    if (!m_UseBetterEncoding) {
      numCutPoints = (lastPlusOne - first) - 1;
    }

    // Checks if gain is zero
    gain = priorEntropy - bestEntropy;
    if (gain <= 0) {
      return null;
    }

    // Check if split is to be accepted
    if ((m_UseKononenko && KononenkosMDL(priorCounts, bestCounts, numInstances,
      numCutPoints))
      || (!m_UseKononenko && FayyadAndIranisMDL(priorCounts, bestCounts,
        numInstances, numCutPoints))) {

      // Select split points for the left and right subsets
      left = cutPointsForSubset(instances, attIndex, first, bestIndex + 1);
      right = cutPointsForSubset(instances, attIndex, bestIndex + 1,
        lastPlusOne);

      // Merge cutpoints and return them
      if ((left == null) && (right) == null) {
        cutPoints = new double[1];
        cutPoints[0] = bestCutPoint;
      } else if (right == null) {
        cutPoints = new double[left.length + 1];
        System.arraycopy(left, 0, cutPoints, 0, left.length);
        cutPoints[left.length] = bestCutPoint;
      } else if (left == null) {
        cutPoints = new double[1 + right.length];
        cutPoints[0] = bestCutPoint;
        System.arraycopy(right, 0, cutPoints, 1, right.length);
      } else {
        cutPoints = new double[left.length + right.length + 1];
        System.arraycopy(left, 0, cutPoints, 0, left.length);
        cutPoints[left.length] = bestCutPoint;
        System.arraycopy(right, 0, cutPoints, left.length + 1, right.length);
      }

      return cutPoints;
    } else {
      return null;
    }
  }

  /**
   * Set the output format. Takes the currently defined cutpoints and
   * m_InputFormat and calls setOutputFormat(Instances) appropriately.
   */
  protected void setOutputFormat() {

    if (m_CutPoints == null) {
      setOutputFormat(null);
      return;
    }
    ArrayList attributes = new ArrayList(getInputFormat()
      .numAttributes());
    int classIndex = getInputFormat().classIndex();
    for (int i = 0, m = getInputFormat().numAttributes(); i < m; ++i) {
      if ((m_DiscretizeCols.isInRange(i))
        && (getInputFormat().attribute(i).isNumeric())) {

        Set cutPointsCheck = new HashSet();
        double[] cutPoints = m_CutPoints[i];
        if (!m_MakeBinary) {
          ArrayList attribValues;
          if (cutPoints == null) {
            attribValues = new ArrayList(1);
            attribValues.add("'All'");
          } else {
            attribValues = new ArrayList(cutPoints.length + 1);
            if (m_UseBinNumbers) {
              for (int j = 0, n = cutPoints.length; j <= n; ++j) {
                attribValues.add("'B" + (j + 1) + "of" + (n + 1) + "'");
              }
            } else {
              for (int j = 0, n = cutPoints.length; j <= n; ++j) {
                String newBinRangeString = binRangeString(cutPoints, j,
                  m_BinRangePrecision);
                if (cutPointsCheck.contains(newBinRangeString)) {
                  throw new IllegalArgumentException(
                    "A duplicate bin range was detected. "
                      + "Try increasing the bin range precision.");
                }
                attribValues.add("'" + newBinRangeString + "'");
              }
            }
          }
          Attribute newAtt = new Attribute(
            getInputFormat().attribute(i).name(), attribValues);
          newAtt.setWeight(getInputFormat().attribute(i).weight());
          attributes.add(newAtt);
        } else {
          if (cutPoints == null) {
            ArrayList attribValues = new ArrayList(1);
            attribValues.add("'All'");
            Attribute newAtt = new Attribute(getInputFormat().attribute(i)
              .name(), attribValues);
            newAtt.setWeight(getInputFormat().attribute(i).weight());
            attributes.add(newAtt);
          } else {
            if (i < getInputFormat().classIndex()) {
              classIndex += cutPoints.length - 1;
            }
            for (int j = 0, n = cutPoints.length; j < n; ++j) {
              ArrayList attribValues = new ArrayList(2);
              if (m_UseBinNumbers) {
                attribValues.add("'B1of2'");
                attribValues.add("'B2of2'");
              } else {
                double[] binaryCutPoint = { cutPoints[j] };
                String newBinRangeString1 = binRangeString(binaryCutPoint, 0,
                  m_BinRangePrecision);
                String newBinRangeString2 = binRangeString(binaryCutPoint, 1,
                  m_BinRangePrecision);
                if (newBinRangeString1.equals(newBinRangeString2)) {
                  throw new IllegalArgumentException(
                    "A duplicate bin range was detected. "
                      + "Try increasing the bin range precision.");
                }
                attribValues.add("'" + newBinRangeString1 + "'");
                attribValues.add("'" + newBinRangeString2 + "'");
              }
              Attribute newAtt = new Attribute(getInputFormat().attribute(i)
                .name() + "_" + (j + 1), attribValues);
              newAtt.setWeight(getInputFormat().attribute(i).weight());
              attributes.add(newAtt);
            }
          }
        }
      } else {
        attributes.add((Attribute) getInputFormat().attribute(i).copy());
      }
    }
    Instances outputFormat = new Instances(getInputFormat().relationName(),
      attributes, 0);
    outputFormat.setClassIndex(classIndex);
    setOutputFormat(outputFormat);
  }

  /**
   * Convert a single instance over. The converted instance is added to the end
   * of the output queue.
   * 
   * @param instance the instance to convert
   */
  protected void convertInstance(Instance instance) {

    int index = 0;
    double[] vals = new double[outputFormatPeek().numAttributes()];
    // Copy and convert the values
    for (int i = 0; i < getInputFormat().numAttributes(); i++) {
      if (m_DiscretizeCols.isInRange(i)
        && getInputFormat().attribute(i).isNumeric()) {
        int j;
        double currentVal = instance.value(i);
        if (m_CutPoints[i] == null) {
          if (instance.isMissing(i)) {
            vals[index] = Utils.missingValue();
          } else {
            vals[index] = 0;
          }
          index++;
        } else {
          if (!m_MakeBinary) {
            if (instance.isMissing(i)) {
              vals[index] = Utils.missingValue();
            } else {
              for (j = 0; j < m_CutPoints[i].length; j++) {
                if (currentVal <= m_CutPoints[i][j]) {
                  break;
                }
              }
              vals[index] = j;
            }
            index++;
          } else {
            for (j = 0; j < m_CutPoints[i].length; j++) {
              if (instance.isMissing(i)) {
                vals[index] = Utils.missingValue();
              } else if (currentVal <= m_CutPoints[i][j]) {
                vals[index] = 0;
              } else {
                vals[index] = 1;
              }
              index++;
            }
          }
        }
      } else {
        vals[index] = instance.value(i);
        index++;
      }
    }

    Instance inst = null;
    if (instance instanceof SparseInstance) {
      inst = new SparseInstance(instance.weight(), vals);
    } else {
      inst = new DenseInstance(instance.weight(), vals);
    }
    inst.setDataset(getOutputFormat());
    copyValues(inst, false, instance.dataset(), getOutputFormat());
    inst.setDataset(getOutputFormat());
    push(inst);
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 10231 $");
  }

  /**
   * Main method for testing this class.
   * 
   * @param argv should contain arguments to the filter: use -h for help
   */
  public static void main(String[] argv) {
    runFilter(new Discretize(), argv);
  }
}