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weka.attributeSelection.ReliefFAttributeEval 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 .
 */

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

package weka.attributeSelection;

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

import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Capabilities.Capability;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.Option;
import weka.core.OptionHandler;
import weka.core.RevisionUtils;
import weka.core.TechnicalInformation;
import weka.core.TechnicalInformation.Field;
import weka.core.TechnicalInformation.Type;
import weka.core.TechnicalInformationHandler;
import weka.core.Utils;

/**
 *  ReliefFAttributeEval :

 * 

 * Evaluates the worth of an attribute by repeatedly sampling an instance and
 * considering the value of the given attribute for the nearest instance of the
 * same and different class. Can operate on both discrete and continuous class
 * data.

 * 

 * For more information see:

 * 

 * Kenji Kira, Larry A. Rendell: A Practical Approach to Feature Selection. In:
 * Ninth International Workshop on Machine Learning, 249-256, 1992.

 * 

 * Igor Kononenko: Estimating Attributes: Analysis and Extensions of RELIEF. In:
 * European Conference on Machine Learning, 171-182, 1994.

 * 

 * Marko Robnik-Sikonja, Igor Kononenko: An adaptation of Relief for attribute
 * estimation in regression. In: Fourteenth International Conference on Machine
 * Learning, 296-304, 1997.
 * 

 * 
 * 
 *  BibTeX:
 * 
 * 
 * @inproceedings{Kira1992,
 *    author = {Kenji Kira and Larry A. Rendell},
 *    booktitle = {Ninth International Workshop on Machine Learning},
 *    editor = {Derek H. Sleeman and Peter Edwards},
 *    pages = {249-256},
 *    publisher = {Morgan Kaufmann},
 *    title = {A Practical Approach to Feature Selection},
 *    year = {1992}
 * }
 * 
 * @inproceedings{Kononenko1994,
 *    author = {Igor Kononenko},
 *    booktitle = {European Conference on Machine Learning},
 *    editor = {Francesco Bergadano and Luc De Raedt},
 *    pages = {171-182},
 *    publisher = {Springer},
 *    title = {Estimating Attributes: Analysis and Extensions of RELIEF},
 *    year = {1994}
 * }
 * 
 * @inproceedings{Robnik-Sikonja1997,
 *    author = {Marko Robnik-Sikonja and Igor Kononenko},
 *    booktitle = {Fourteenth International Conference on Machine Learning},
 *    editor = {Douglas H. Fisher},
 *    pages = {296-304},
 *    publisher = {Morgan Kaufmann},
 *    title = {An adaptation of Relief for attribute estimation in regression},
 *    year = {1997}
 * }
 * 
 * 

 * 
 * 
 *  Valid options are:
 * 

 * 
 * 
 * -M <num instances>
 *  Specify the number of instances to
 *  sample when estimating attributes.
 *  If not specified, then all instances
 *  will be used.
 * 
 * 
 * 
 * -D <seed>
 *  Seed for randomly sampling instances.
 *  (Default = 1)
 * 
 * 
 * 
 * -K <number of neighbours>
 *  Number of nearest neighbours (k) used
 *  to estimate attribute relevances
 *  (Default = 10).
 * 
 * 
 * 
 * -W
 *  Weight nearest neighbours by distance
 * 
 * 
 * 
 * -A <num>
 *  Specify sigma value (used in an exp
 *  function to control how quickly
 *  weights for more distant instances
 *  decrease. Use in conjunction with -W.
 *  Sensible value=1/5 to 1/10 of the
 *  number of nearest neighbours.
 *  (Default = 2)
 * 
 * 
 * 
 * 
 * @author Mark Hall ([email protected])
 * @version $Revision: 13374 $
 */
public class ReliefFAttributeEval extends ASEvaluation implements
  AttributeEvaluator, OptionHandler, TechnicalInformationHandler {

  /** for serialization */
  static final long serialVersionUID = -8422186665795839379L;

  /** The training instances */
  private Instances m_trainInstances;

  /** The class index */
  private int m_classIndex;

  /** The number of attributes */
  private int m_numAttribs;

  /** The number of instances */
  private int m_numInstances;

  /** Numeric class */
  private boolean m_numericClass;

  /** The number of classes if class is nominal */
  private int m_numClasses;

  /**
   * Used to hold the probability of a different class val given nearest
   * instances (numeric class)
   */
  private double m_ndc;

  /**
   * Used to hold the prob of different value of an attribute given nearest
   * instances (numeric class case)
   */
  private double[] m_nda;

  /**
   * Used to hold the prob of a different class val and different att val given
   * nearest instances (numeric class case)
   */
  private double[] m_ndcda;

  /** Holds the weights that relief assigns to attributes */
  private double[] m_weights;

  /** Prior class probabilities (discrete class case) */
  private double[] m_classProbs;

  /**
   * The number of instances to sample when estimating attributes default == -1,
   * use all instances
   */
  private int m_sampleM;

  /** The number of nearest hits/misses */
  private int m_Knn;

  /** k nearest scores + instance indexes for n classes */
  private double[][][] m_karray;

  /** Upper bound for numeric attributes */
  private double[] m_maxArray;

  /** Lower bound for numeric attributes */
  private double[] m_minArray;

  /** Keep track of the farthest instance for each class */
  private double[] m_worst;

  /** Index in the m_karray of the farthest instance for each class */
  private int[] m_index;

  /** Number of nearest neighbours stored of each class */
  private int[] m_stored;

  /** Random number seed used for sampling instances */
  private int m_seed;

  /**
   * used to (optionally) weight nearest neighbours by their distance from the
   * instance in question. Each entry holds exp(-((rank(r_i, i_j)/sigma)^2))
   * where rank(r_i,i_j) is the rank of instance i_j in a sequence of instances
   * ordered by the distance from r_i. sigma is a user defined parameter,
   * default=20
   **/
  private double[] m_weightsByRank;
  private int m_sigma;

  /** Weight by distance rather than equal weights */
  private boolean m_weightByDistance;

  /**
   * Constructor
   */
  public ReliefFAttributeEval() {
    resetOptions();
  }

  /**
   * Returns a string describing this attribute evaluator
   * 
   * @return a description of the evaluator suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String globalInfo() {
    return "ReliefFAttributeEval :\n\nEvaluates the worth of an attribute by "
      + "repeatedly sampling an instance and considering the value of the "
      + "given attribute for the nearest instance of the same and different "
      + "class. Can operate on both discrete and continuous class data.\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, "Kenji Kira and Larry A. Rendell");
    result.setValue(Field.TITLE, "A Practical Approach to Feature Selection");
    result.setValue(Field.BOOKTITLE,
      "Ninth International Workshop on Machine Learning");
    result.setValue(Field.EDITOR, "Derek H. Sleeman and Peter Edwards");
    result.setValue(Field.YEAR, "1992");
    result.setValue(Field.PAGES, "249-256");
    result.setValue(Field.PUBLISHER, "Morgan Kaufmann");

    additional = result.add(Type.INPROCEEDINGS);
    additional.setValue(Field.AUTHOR, "Igor Kononenko");
    additional.setValue(Field.TITLE,
      "Estimating Attributes: Analysis and Extensions of RELIEF");
    additional.setValue(Field.BOOKTITLE,
      "European Conference on Machine Learning");
    additional.setValue(Field.EDITOR, "Francesco Bergadano and Luc De Raedt");
    additional.setValue(Field.YEAR, "1994");
    additional.setValue(Field.PAGES, "171-182");
    additional.setValue(Field.PUBLISHER, "Springer");

    additional = result.add(Type.INPROCEEDINGS);
    additional
      .setValue(Field.AUTHOR, "Marko Robnik-Sikonja and Igor Kononenko");
    additional.setValue(Field.TITLE,
      "An adaptation of Relief for attribute estimation in regression");
    additional.setValue(Field.BOOKTITLE,
      "Fourteenth International Conference on Machine Learning");
    additional.setValue(Field.EDITOR, "Douglas H. Fisher");
    additional.setValue(Field.YEAR, "1997");
    additional.setValue(Field.PAGES, "296-304");
    additional.setValue(Field.PUBLISHER, "Morgan Kaufmann");

    return result;
  }

  /**
   * Returns an enumeration describing the available options.
   * 
   * @return an enumeration of all the available options.
   **/
  @Override
  public Enumeration listOptions() {
    Vector newVector = new Vector(4);
    newVector.addElement(new Option("\tSpecify the number of instances to\n"
      + "\tsample when estimating attributes.\n"
      + "\tIf not specified, then all instances\n" + "\twill be used.", "M", 1,
      "-M "));
    newVector.addElement(new Option("\tSeed for randomly sampling instances.\n"
      + "\t(Default = 1)", "D", 1, "-D "));
    newVector.addElement(new Option("\tNumber of nearest neighbours (k) used\n"
      + "\tto estimate attribute relevances\n" + "\t(Default = 10).", "K", 1,
      "-K "));
    newVector.addElement(new Option("\tWeight nearest neighbours by distance",
      "W", 0, "-W"));
    newVector.addElement(new Option("\tSpecify sigma value (used in an exp\n"
      + "\tfunction to control how quickly\n"
      + "\tweights for more distant instances\n"
      + "\tdecrease. Use in conjunction with -W.\n"
      + "\tSensible value=1/5 to 1/10 of the\n"
      + "\tnumber of nearest neighbours.\n" + "\t(Default = 2)", "A", 1,
      "-A "));
    return newVector.elements();
  }

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

   * 
   *  Valid options are:
   * 

   * 
   * 
   * -M <num instances>
   *  Specify the number of instances to
   *  sample when estimating attributes.
   *  If not specified, then all instances
   *  will be used.
   * 
   * 
   * 
   * -D <seed>
   *  Seed for randomly sampling instances.
   *  (Default = 1)
   * 
   * 
   * 
   * -K <number of neighbours>
   *  Number of nearest neighbours (k) used
   *  to estimate attribute relevances
   *  (Default = 10).
   * 
   * 
   * 
   * -W
   *  Weight nearest neighbours by distance
   * 
   * 
   * 
   * -A <num>
   *  Specify sigma value (used in an exp
   *  function to control how quickly
   *  weights for more distant instances
   *  decrease. Use in conjunction with -W.
   *  Sensible value=1/5 to 1/10 of the
   *  number of nearest neighbours.
   *  (Default = 2)
   * 
   * 
   * 
   * 
   * @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 optionString;
    resetOptions();
    setWeightByDistance(Utils.getFlag('W', options));
    optionString = Utils.getOption('M', options);

    if (optionString.length() != 0) {
      setSampleSize(Integer.parseInt(optionString));
    }

    optionString = Utils.getOption('D', options);

    if (optionString.length() != 0) {
      setSeed(Integer.parseInt(optionString));
    }

    optionString = Utils.getOption('K', options);

    if (optionString.length() != 0) {
      setNumNeighbours(Integer.parseInt(optionString));
    }

    optionString = Utils.getOption('A', options);

    if (optionString.length() != 0) {
      setWeightByDistance(true); // turn on weighting by distance
      setSigma(Integer.parseInt(optionString));
    }
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String sigmaTipText() {
    return "Set influence of nearest neighbours. Used in an exp function to "
      + "control how quickly weights decrease for more distant instances. "
      + "Use in conjunction with weightByDistance. Sensible values = 1/5 to "
      + "1/10 the number of nearest neighbours.";
  }

  /**
   * Sets the sigma value.
   * 
   * @param s the value of sigma (> 0)
   * @throws Exception if s is not positive
   */
  public void setSigma(int s) throws Exception {
    if (s <= 0) {
      throw new Exception("value of sigma must be > 0!");
    }

    m_sigma = s;
  }

  /**
   * Get the value of sigma.
   * 
   * @return the sigma value.
   */
  public int getSigma() {
    return m_sigma;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String numNeighboursTipText() {
    return "Number of nearest neighbours for attribute estimation.";
  }

  /**
   * Set the number of nearest neighbours
   * 
   * @param n the number of nearest neighbours.
   */
  public void setNumNeighbours(int n) {
    m_Knn = n;
  }

  /**
   * Get the number of nearest neighbours
   * 
   * @return the number of nearest neighbours
   */
  public int getNumNeighbours() {
    return m_Knn;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String seedTipText() {
    return "Random seed for sampling instances.";
  }

  /**
   * Set the random number seed for randomly sampling instances.
   * 
   * @param s the random number seed.
   */
  public void setSeed(int s) {
    m_seed = s;
  }

  /**
   * Get the seed used for randomly sampling instances.
   * 
   * @return the random number seed.
   */
  public int getSeed() {
    return m_seed;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String sampleSizeTipText() {
    return "Number of instances to sample. Default (-1) indicates that all "
      + "instances will be used for attribute estimation.";
  }

  /**
   * Set the number of instances to sample for attribute estimation
   * 
   * @param s the number of instances to sample.
   */
  public void setSampleSize(int s) {
    m_sampleM = s;
  }

  /**
   * Get the number of instances used for estimating attributes
   * 
   * @return the number of instances.
   */
  public int getSampleSize() {
    return m_sampleM;
  }

  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for displaying in the
   *         explorer/experimenter gui
   */
  public String weightByDistanceTipText() {
    return "Weight nearest neighbours by their distance.";
  }

  /**
   * Set the nearest neighbour weighting method
   * 
   * @param b true nearest neighbours are to be weighted by distance.
   */
  public void setWeightByDistance(boolean b) {
    m_weightByDistance = b;
  }

  /**
   * Get whether nearest neighbours are being weighted by distance
   * 
   * @return m_weightByDiffernce
   */
  public boolean getWeightByDistance() {
    return m_weightByDistance;
  }

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

    Vector options = new Vector();

    if (getWeightByDistance()) {
      options.add("-W");
    }

    options.add("-M");
    options.add("" + getSampleSize());
    options.add("-D");
    options.add("" + getSeed());
    options.add("-K");
    options.add("" + getNumNeighbours());

    if (getWeightByDistance()) {
      options.add("-A");
      options.add("" + getSigma());
    }

    return options.toArray(new String[0]);
  }

  /**
   * Return a description of the ReliefF attribute evaluator.
   * 
   * @return a description of the evaluator as a String.
   */
  @Override
  public String toString() {
    StringBuffer text = new StringBuffer();

    if (m_trainInstances == null) {
      text.append("ReliefF feature evaluator has not been built yet\n");
    } else {
      text.append("\tReliefF Ranking Filter");
      text.append("\n\tInstances sampled: ");

      if (m_sampleM == -1) {
        text.append("all\n");
      } else {
        text.append(m_sampleM + "\n");
      }

      text.append("\tNumber of nearest neighbours (k): " + m_Knn + "\n");

      if (m_weightByDistance) {
        text.append("\tExponentially decreasing (with distance) "
          + "influence for\n" + "\tnearest neighbours. Sigma: " + m_sigma
          + "\n");
      } else {
        text.append("\tEqual influence nearest neighbours\n");
      }
    }

    return text.toString();
  }

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

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

    // class
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.NUMERIC_CLASS);
    result.enable(Capability.DATE_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    return result;
  }

  /**
   * Initializes a ReliefF attribute evaluator.
   * 
   * @param data set of instances serving as training data
   * @throws Exception if the evaluator has not been generated successfully
   */
  @Override
  public void buildEvaluator(Instances data) throws Exception {

    int z, totalInstances;
    Random r = new Random(m_seed);

    // can evaluator handle data?
    getCapabilities().testWithFail(data);

    m_trainInstances = data;
    m_classIndex = m_trainInstances.classIndex();
    m_numAttribs = m_trainInstances.numAttributes();
    m_numInstances = m_trainInstances.numInstances();

    if (m_trainInstances.attribute(m_classIndex).isNumeric()) {
      m_numericClass = true;
    } else {
      m_numericClass = false;
    }

    if (!m_numericClass) {
      m_numClasses = m_trainInstances.attribute(m_classIndex).numValues();
    } else {
      m_ndc = 0;
      m_numClasses = 1;
      m_nda = new double[m_numAttribs];
      m_ndcda = new double[m_numAttribs];
    }

    if (m_weightByDistance) // set up the rank based weights
    {
      m_weightsByRank = new double[m_Knn];

      for (int i = 0; i < m_Knn; i++) {
        m_weightsByRank[i] = Math
          .exp(-((i / (double) m_sigma) * (i / (double) m_sigma)));
      }
    }

    // the final attribute weights
    m_weights = new double[m_numAttribs];
    // num classes (1 for numeric class) knn neighbours,
    // and 0 = distance, 1 = instance index
    m_karray = new double[m_numClasses][m_Knn][2];

    if (!m_numericClass) {
      m_classProbs = new double[m_numClasses];

      for (int i = 0; i < m_numInstances; i++) {
        if (!m_trainInstances.instance(i).classIsMissing()) {
          m_classProbs[(int) m_trainInstances.instance(i).value(m_classIndex)]++;
        }
      }

      for (int i = 0; i < m_numClasses; i++) {
        m_classProbs[i] /= m_numInstances;
      }
    }

    m_worst = new double[m_numClasses];
    m_index = new int[m_numClasses];
    m_stored = new int[m_numClasses];
    m_minArray = new double[m_numAttribs];
    m_maxArray = new double[m_numAttribs];

    for (int i = 0; i < m_numAttribs; i++) {
      m_minArray[i] = m_maxArray[i] = Double.NaN;
    }

    for (int i = 0; i < m_numInstances; i++) {
      updateMinMax(m_trainInstances.instance(i));
    }

    if ((m_sampleM > m_numInstances) || (m_sampleM < 0)) {
      totalInstances = m_numInstances;
    } else {
      totalInstances = m_sampleM;
    }

    // process each instance, updating attribute weights
    for (int i = 0; i < totalInstances; i++) {
      if (totalInstances == m_numInstances) {
        z = i;
      } else {
        z = r.nextInt() % m_numInstances;
      }

      if (z < 0) {
        z *= -1;
      }

      if (!(m_trainInstances.instance(z).isMissing(m_classIndex))) {
        // first clear the knn and worst index stuff for the classes
        for (int j = 0; j < m_numClasses; j++) {
          m_index[j] = m_stored[j] = 0;

          for (int k = 0; k < m_Knn; k++) {
            m_karray[j][k][0] = m_karray[j][k][1] = 0;
          }
        }

        findKHitMiss(z);

        if (m_numericClass) {
          updateWeightsNumericClass(z);
        } else {
          updateWeightsDiscreteClass(z);
        }
      }
    }

    // now scale weights by 1/m_numInstances (nominal class) or
    // calculate weights numeric class
    // System.out.println("num inst:"+m_numInstances+" r_ndc:"+r_ndc);
    for (int i = 0; i < m_numAttribs; i++) {
      if (i != m_classIndex) {
        if (m_numericClass) {
          m_weights[i] = m_ndcda[i] / m_ndc
            - ((m_nda[i] - m_ndcda[i]) / (totalInstances - m_ndc));
        } else {
          m_weights[i] *= (1.0 / totalInstances);
        }

        // System.out.println(r_weights[i]);
      }
    }
  }

  /**
   * Evaluates an individual attribute using ReliefF's instance based approach.
   * The actual work is done by buildEvaluator which evaluates all features.
   * 
   * @param attribute the index of the attribute to be evaluated
   * @throws Exception if the attribute could not be evaluated
   */
  @Override
  public double evaluateAttribute(int attribute) throws Exception {
    return m_weights[attribute];
  }

  /**
   * Reset options to their default values
   */
  protected void resetOptions() {
    m_trainInstances = null;
    m_sampleM = -1;
    m_Knn = 10;
    m_sigma = 2;
    m_weightByDistance = false;
    m_seed = 1;
  }

  /**
   * Normalizes a given value of a numeric attribute.
   * 
   * @param x the value to be normalized
   * @param i the attribute's index
   * @return the normalized value
   */
  private double norm(double x, int i) {
    if (Double.isNaN(m_minArray[i]) || Utils.eq(m_maxArray[i], m_minArray[i])) {
      return 0;
    } else {
      return (x - m_minArray[i]) / (m_maxArray[i] - m_minArray[i]);
    }
  }

  /**
   * Updates the minimum and maximum values for all the attributes based on a
   * new instance.
   * 
   * @param instance the new instance
   */
  private void updateMinMax(Instance instance) {
    // for (int j = 0; j < m_numAttribs; j++) {
    try {
      for (int j = 0; j < instance.numValues(); j++) {
        if ((instance.attributeSparse(j).isNumeric())
          && (!instance.isMissingSparse(j))) {
          if (Double.isNaN(m_minArray[instance.index(j)])) {
            m_minArray[instance.index(j)] = instance.valueSparse(j);
            m_maxArray[instance.index(j)] = instance.valueSparse(j);
          } else {
            if (instance.valueSparse(j) < m_minArray[instance.index(j)]) {
              m_minArray[instance.index(j)] = instance.valueSparse(j);
            } else {
              if (instance.valueSparse(j) > m_maxArray[instance.index(j)]) {
                m_maxArray[instance.index(j)] = instance.valueSparse(j);
              }
            }
          }
        }
      }
    } catch (Exception ex) {
      System.err.println(ex);
      ex.printStackTrace();
    }
  }

  /**
   * Computes the difference between two given attribute values.
   */
  private double difference(int index, double val1, double val2) {

    switch (m_trainInstances.attribute(index).type()) {
    case Attribute.NOMINAL:

      // If attribute is nominal
      if (Utils.isMissingValue(val1) || Utils.isMissingValue(val2)) {
        return (1.0 - (1.0 / (m_trainInstances.attribute(index).numValues())));
      } else if ((int) val1 != (int) val2) {
        return 1;
      } else {
        return 0;
      }
    case Attribute.NUMERIC:

      // If attribute is numeric
      if (Utils.isMissingValue(val1) || Utils.isMissingValue(val2)) {
        if (Utils.isMissingValue(val1) && Utils.isMissingValue(val2)) {
          return 1;
        } else {
          double diff;
          if (Utils.isMissingValue(val2)) {
            diff = norm(val1, index);
          } else {
            diff = norm(val2, index);
          }
          if (diff < 0.5) {
            diff = 1.0 - diff;
          }
          return diff;
        }
      } else {
        return Math.abs(norm(val1, index) - norm(val2, index));
      }
    default:
      return 0;
    }
  }

  /**
   * Calculates the distance between two instances
   * 
   * @param first the first instance
   * @param second the second instance
   * @return the distance between the two given instances, between 0 and 1
   */
  private double distance(Instance first, Instance second) {

    double distance = 0;
    int firstI, secondI;

    for (int p1 = 0, p2 = 0; p1 < first.numValues() || p2 < second.numValues();) {
      if (p1 >= first.numValues()) {
        firstI = m_trainInstances.numAttributes();
      } else {
        firstI = first.index(p1);
      }
      if (p2 >= second.numValues()) {
        secondI = m_trainInstances.numAttributes();
      } else {
        secondI = second.index(p2);
      }
      if (firstI == m_trainInstances.classIndex()) {
        p1++;
        continue;
      }
      if (secondI == m_trainInstances.classIndex()) {
        p2++;
        continue;
      }
      double diff;
      if (firstI == secondI) {
        diff = difference(firstI, first.valueSparse(p1), second.valueSparse(p2));
        p1++;
        p2++;
      } else if (firstI > secondI) {
        diff = difference(secondI, 0, second.valueSparse(p2));
        p2++;
      } else {
        diff = difference(firstI, first.valueSparse(p1), 0);
        p1++;
      }
      // distance += diff * diff;
      distance += diff;
    }

    // return Math.sqrt(distance / m_NumAttributesUsed);
    return distance;
  }

  /**
   * update attribute weights given an instance when the class is numeric
   * 
   * @param instNum the index of the instance to use when updating weights
   */
  private void updateWeightsNumericClass(int instNum) {
    int i, j;
    double temp, temp2;
    int[] tempSorted = null;
    double[] tempDist = null;
    double distNorm = 1.0;
    int firstI, secondI;

    Instance inst = m_trainInstances.instance(instNum);

    // sort nearest neighbours and set up normalization variable
    if (m_weightByDistance) {
      tempDist = new double[m_stored[0]];

      for (j = 0, distNorm = 0; j < m_stored[0]; j++) {
        // copy the distances
        tempDist[j] = m_karray[0][j][0];
        // sum normalizer
        distNorm += m_weightsByRank[j];
      }

      tempSorted = Utils.sort(tempDist);
    }

    for (i = 0; i < m_stored[0]; i++) {
      // P diff prediction (class) given nearest instances
      if (m_weightByDistance) {
        temp = difference(
          m_classIndex,
          inst.value(m_classIndex),
          m_trainInstances.instance((int) m_karray[0][tempSorted[i]][1]).value(
            m_classIndex));
        temp *= (m_weightsByRank[i] / distNorm);
      } else {
        temp = difference(m_classIndex, inst.value(m_classIndex),
          m_trainInstances.instance((int) m_karray[0][i][1])
            .value(m_classIndex));
        temp *= (1.0 / m_stored[0]); // equal influence
      }

      m_ndc += temp;

      Instance cmp;
      cmp = (m_weightByDistance) ? m_trainInstances
        .instance((int) m_karray[0][tempSorted[i]][1]) : m_trainInstances
        .instance((int) m_karray[0][i][1]);

      double temp_diffP_diffA_givNearest = difference(m_classIndex,
        inst.value(m_classIndex), cmp.value(m_classIndex));
      // now the attributes
      for (int p1 = 0, p2 = 0; p1 < inst.numValues() || p2 < cmp.numValues();) {
        if (p1 >= inst.numValues()) {
          firstI = m_trainInstances.numAttributes();
        } else {
          firstI = inst.index(p1);
        }
        if (p2 >= cmp.numValues()) {
          secondI = m_trainInstances.numAttributes();
        } else {
          secondI = cmp.index(p2);
        }
        if (firstI == m_trainInstances.classIndex()) {
          p1++;
          continue;
        }
        if (secondI == m_trainInstances.classIndex()) {
          p2++;
          continue;
        }
        temp = 0.0;
        temp2 = 0.0;

        if (firstI == secondI) {
          j = firstI;
          temp = difference(j, inst.valueSparse(p1), cmp.valueSparse(p2));
          p1++;
          p2++;
        } else if (firstI > secondI) {
          j = secondI;
          temp = difference(j, 0, cmp.valueSparse(p2));
          p2++;
        } else {
          j = firstI;
          temp = difference(j, inst.valueSparse(p1), 0);
          p1++;
        }

        temp2 = temp_diffP_diffA_givNearest * temp;
        // P of different prediction and different att value given
        // nearest instances
        if (m_weightByDistance) {
          temp2 *= (m_weightsByRank[i] / distNorm);
        } else {
          temp2 *= (1.0 / m_stored[0]); // equal influence
        }

        m_ndcda[j] += temp2;

        // P of different attribute val given nearest instances
        if (m_weightByDistance) {
          temp *= (m_weightsByRank[i] / distNorm);
        } else {
          temp *= (1.0 / m_stored[0]); // equal influence
        }

        m_nda[j] += temp;
      }
    }
  }

  /**
   * update attribute weights given an instance when the class is discrete
   * 
   * @param instNum the index of the instance to use when updating weights
   */
  private void updateWeightsDiscreteClass(int instNum) {
    int i, j, k;
    int cl;
    double temp_diff, w_norm = 1.0;
    double[] tempDistClass;
    int[] tempSortedClass = null;
    double distNormClass = 1.0;
    double[] tempDistAtt;
    int[][] tempSortedAtt = null;
    double[] distNormAtt = null;
    int firstI, secondI;

    // store the indexes (sparse instances) of non-zero elements
    Instance inst = m_trainInstances.instance(instNum);

    // get the class of this instance
    cl = (int) m_trainInstances.instance(instNum).value(m_classIndex);

    // sort nearest neighbours and set up normalization variables
    if (m_weightByDistance) {
      // do class (hits) first
      // sort the distances
      tempDistClass = new double[m_stored[cl]];

      for (j = 0, distNormClass = 0; j < m_stored[cl]; j++) {
        // copy the distances
        tempDistClass[j] = m_karray[cl][j][0];
        // sum normalizer
        distNormClass += m_weightsByRank[j];
      }

      tempSortedClass = Utils.sort(tempDistClass);
      // do misses (other classes)
      tempSortedAtt = new int[m_numClasses][1];
      distNormAtt = new double[m_numClasses];

      for (k = 0; k < m_numClasses; k++) {
        if (k != cl) // already done cl
        {
          // sort the distances
          tempDistAtt = new double[m_stored[k]];

          for (j = 0, distNormAtt[k] = 0; j < m_stored[k]; j++) {
            // copy the distances
            tempDistAtt[j] = m_karray[k][j][0];
            // sum normalizer
            distNormAtt[k] += m_weightsByRank[j];
          }

          tempSortedAtt[k] = Utils.sort(tempDistAtt);
        }
      }
    }

    if (m_numClasses > 2) {
      // the amount of probability space left after removing the
      // probability of this instance's class value
      w_norm = (1.0 - m_classProbs[cl]);
    }

    // do the k nearest hits of the same class
    for (j = 0, temp_diff = 0.0; j < m_stored[cl]; j++) {
      Instance cmp;
      cmp = (m_weightByDistance) ? m_trainInstances
        .instance((int) m_karray[cl][tempSortedClass[j]][1]) : m_trainInstances
        .instance((int) m_karray[cl][j][1]);

      for (int p1 = 0, p2 = 0; p1 < inst.numValues() || p2 < cmp.numValues();) {
        if (p1 >= inst.numValues()) {
          firstI = m_trainInstances.numAttributes();
        } else {
          firstI = inst.index(p1);
        }
        if (p2 >= cmp.numValues()) {
          secondI = m_trainInstances.numAttributes();
        } else {
          secondI = cmp.index(p2);
        }
        if (firstI == m_trainInstances.classIndex()) {
          p1++;
          continue;
        }
        if (secondI == m_trainInstances.classIndex()) {
          p2++;
          continue;
        }
        if (firstI == secondI) {
          i = firstI;
          temp_diff = difference(i, inst.valueSparse(p1), cmp.valueSparse(p2));
          p1++;
          p2++;
        } else if (firstI > secondI) {
          i = secondI;
          temp_diff = difference(i, 0, cmp.valueSparse(p2));
          p2++;
        } else {
          i = firstI;
          temp_diff = difference(i, inst.valueSparse(p1), 0);
          p1++;
        }

        if (m_weightByDistance) {
          temp_diff *= (m_weightsByRank[j] / distNormClass);
        } else {
          if (m_stored[cl] > 0) {
            temp_diff /= m_stored[cl];
          }
        }
        m_weights[i] -= temp_diff;

      }
    }

    // now do k nearest misses from each of the other classes
    temp_diff = 0.0;

    for (k = 0; k < m_numClasses; k++) {
      if (k != cl) // already done cl
      {
        for (j = 0; j < m_stored[k]; j++) {
          Instance cmp;
          cmp = (m_weightByDistance) ? m_trainInstances
            .instance((int) m_karray[k][tempSortedAtt[k][j]][1])
            : m_trainInstances.instance((int) m_karray[k][j][1]);

          for (int p1 = 0, p2 = 0; p1 < inst.numValues()
            || p2 < cmp.numValues();) {
            if (p1 >= inst.numValues()) {
              firstI = m_trainInstances.numAttributes();
            } else {
              firstI = inst.index(p1);
            }
            if (p2 >= cmp.numValues()) {
              secondI = m_trainInstances.numAttributes();
            } else {
              secondI = cmp.index(p2);
            }
            if (firstI == m_trainInstances.classIndex()) {
              p1++;
              continue;
            }
            if (secondI == m_trainInstances.classIndex()) {
              p2++;
              continue;
            }
            if (firstI == secondI) {
              i = firstI;
              temp_diff = difference(i, inst.valueSparse(p1),
                cmp.valueSparse(p2));
              p1++;
              p2++;
            } else if (firstI > secondI) {
              i = secondI;
              temp_diff = difference(i, 0, cmp.valueSparse(p2));
              p2++;
            } else {
              i = firstI;
              temp_diff = difference(i, inst.valueSparse(p1), 0);
              p1++;
            }

            if (m_weightByDistance) {
              temp_diff *= (m_weightsByRank[j] / distNormAtt[k]);
            } else {
              if (m_stored[k] > 0) {
                temp_diff /= m_stored[k];
              }
            }
            if (m_numClasses > 2) {
              m_weights[i] += ((m_classProbs[k] / w_norm) * temp_diff);
            } else {
              m_weights[i] += temp_diff;
            }
          }
        }
      }
    }
  }

  /**
   * Find the K nearest instances to supplied instance if the class is numeric,
   * or the K nearest Hits (same class) and Misses (K from each of the other
   * classes) if the class is discrete.
   * 
   * @param instNum the index of the instance to find nearest neighbours of
   */
  private void findKHitMiss(int instNum) {
    int i, j;
    int cl;
    double ww;
    double temp_diff = 0.0;
    Instance thisInst = m_trainInstances.instance(instNum);

    for (i = 0; i < m_numInstances; i++) {
      if (i != instNum) {
        Instance cmpInst = m_trainInstances.instance(i);
        temp_diff = distance(cmpInst, thisInst);

        // class of this training instance or 0 if numeric
        if (m_numericClass) {
          cl = 0;
        } else {
          if (m_trainInstances.instance(i).classIsMissing()) {
            // skip instances with missing class values in the nominal class case
            continue;
          }
          cl = (int) m_trainInstances.instance(i).value(m_classIndex);
        }

        // add this diff to the list for the class of this instance
        if (m_stored[cl] < m_Knn) {
          m_karray[cl][m_stored[cl]][0] = temp_diff;
          m_karray[cl][m_stored[cl]][1] = i;
          m_stored[cl]++;

          // note the worst diff for this class
          for (j = 0, ww = -1.0; j < m_stored[cl]; j++) {
            if (m_karray[cl][j][0] > ww) {
              ww = m_karray[cl][j][0];
              m_index[cl] = j;
            }
          }

          m_worst[cl] = ww;
        } else
        /*
         * if we already have stored knn for this class then check to see if
         * this instance is better than the worst
         */
        {
          if (temp_diff < m_karray[cl][m_index[cl]][0]) {
            m_karray[cl][m_index[cl]][0] = temp_diff;
            m_karray[cl][m_index[cl]][1] = i;

            for (j = 0, ww = -1.0; j < m_stored[cl]; j++) {
              if (m_karray[cl][j][0] > ww) {
                ww = m_karray[cl][j][0];
                m_index[cl] = j;
              }
            }

            m_worst[cl] = ww;
          }
        }
      }
    }
  }

  /**
   * Returns the revision string.
   * 
   * @return the revision
   */
  @Override
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 13374 $");
  }
  
  @Override
  public int[] postProcess(int[] attributeSet) {

    // save memory
    m_trainInstances = new Instances(m_trainInstances, 0);

    return attributeSet;
  }

  // ============
  // Test method.
  // ============
  /**
   * Main method for testing this class.
   * 
   * @param args the options
   */
  public static void main(String[] args) {
    runEvaluator(new ReliefFAttributeEval(), args);
  }
}