flr.java

代码是一个分类器的实现,其中使用了部分weka的源代码。可以将项目导入eclipse运行

   *  the metric space for the iris dataset:
   *  [ 4.3  7.9 ]  [ 2.0  4.4 ]  [ 1.0  6.9 ]  [ 0.1  2.5 ]  in Class:  -1
   *  This lattice just contains the min and max value in each 
   *  dimension.
   *  In other kind of problems this may not be just a min-max 
   *  operation, but it could contain limits defined by the problem 
   *  itself.
   *  Thus, this option should be set by the user.
   *  If ommited, the metric space used contains the mins and maxs 
   *  of the training split.</pre>
   * 
   * <pre> -Y
   *  Show Rules</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 {
    // Option -Y
    m_showRules = Utils.getFlag('Y', options);
    // Option -R
    String rhoaString = Utils.getOption('R', options);
    if (rhoaString.length() != 0) {
      m_Rhoa = Double.parseDouble(rhoaString);
      if (m_Rhoa < 0 || m_Rhoa > 1) {
        throw new Exception(
            "Vigilance parameter (rhoa) should be a real number in range [0,1]");
      }
    }
    else
      m_Rhoa = 0.5;

      // Option -B
    String boundsString = Utils.getOption('B', options);
    if (boundsString.length() != 0) {
      m_BoundsFile = new File(boundsString);
    } //fi
    Utils.checkForRemainingOptions(options);
  } //setOptions

  /**
   * Gets the current settings of the Classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    String[] options = new String[5];
    int current = 0;
    options[current++] = "-R";
    options[current++] = "" + getRhoa();
    if (m_showRules) {
      options[current++] = "-Y";
    }
    if (m_BoundsFile.toString() != "") {
      options[current++] = "-B";
      options[current++] = "" + getBoundsFile();
    }
    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  } // getOptions

  /**
   * Get rhoa
   * @return the value of this parameter
   */
  public double getRhoa() {
    return m_Rhoa;
  }

  /**
   * Get boundaries File
   * @return the value of this parameter
   */
  public String getBoundsFile() {
    return m_BoundsFile.toString();
  }

  /**
   * Get ShowRules parameter
   * @return the value of this parameter
   */
  public boolean getShowRules() {
    return m_showRules;
  }

  /**
   * Set rhoa
   * @param newRhoa sets the rhoa value
   * @throws Exception if rhoa is not in range [0,1]
   */
  public void setRhoa(double newRhoa) throws Exception {
    if (newRhoa < 0 || newRhoa > 1) {
      throw new Exception(
          "Vigilance parameter (rhoa) should be a real number in range [0,1]!!!");
    }
    m_Rhoa = newRhoa;
  }

  /**
   * Set Boundaries File
   * @param newBoundsFile a new file containing the boundaries
   */
  public void setBoundsFile(String newBoundsFile) {
    m_BoundsFile = new File(newBoundsFile);
  }

  /**
   * Set ShowRules flag
   * @param flag the new value of this parameter
   */
  public void setShowRules(boolean flag) {
    m_showRules = flag;
  }

  /**
   * Sets the metric space from the training set using the min-max stats, in case -B option is not used.
   * @param data is the training set
   */
  public void setBounds(Instances data) {
    // Initialize minmax stats
    bounds = new FuzzyLattice(data.numAttributes() - 1);
    int k = 0;
    for (int i = 0; i < data.numAttributes(); i++) {
      if (i != data.classIndex()) {
        AttributeStats stats = data.attributeStats(i);
        bounds.setMin(k, stats.numericStats.min);
        bounds.setMax(k, stats.numericStats.max);
        k = k + 1;
      } //if
    } //for
  } //setBounds

  /**
   * Checks the metric space
   */
  public void checkBounds() {
    for (int i = 0; i < bounds.length(); i++) {
      if (bounds.getMin(i) == bounds.getMax(i))
        bounds.setMax(i, bounds.getMax(i) + EPSILON);
    }
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String rhoaTipText() {
    return " The vigilance parameter value" + " (default = 0.75)";
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String boundsFileTipText() {
    return " Point the filename containing the metric space";
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String showRulesTipText() {
    return " If true, displays the ruleset.";
  }

  /**
   * Returns the value of the named measure
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @throws IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) {
      return measureNumRules();
    }
    else {
      throw new IllegalArgumentException(additionalMeasureName +
                                         " not supported (FLR)");
    }
  }

  /**
   * Returns an enumeration of the additional measure names
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector(1);
    newVector.addElement("measureNumRules");
    return newVector.elements();
  }

  /**
   * Additional measure Number of Rules
   * @return the number of rules induced
   */
  public double measureNumRules() {
    if (learnedCode == null)
      return 0.0;
    else
      return (double) learnedCode.size();
  }

  /**
   * Returns a description of the classifier suitable for
   * displaying in the explorer/experimenter gui
   * @return the description
   */
  public String globalInfo() {
    return 
        "Fuzzy Lattice Reasoning Classifier (FLR) v5.0\n\n"
      + "The Fuzzy Lattice Reasoning Classifier uses the notion of Fuzzy "
      + "Lattices for creating a Reasoning Environment.\n"
      + "The current version can be used for classification using numeric predictors.\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, "I. N. Athanasiadis and V. G. Kaburlasos and P. A. Mitkas and V. Petridis");
    result.setValue(Field.TITLE, "Applying Machine Learning Techniques on Air Quality Data for Real-Time Decision Support");
    result.setValue(Field.BOOKTITLE, "1st Intl. NAISO Symposium on Information Technologies in Environmental Engineering (ITEE-2003)");
    result.setValue(Field.YEAR, "2003");
    result.setValue(Field.ADDRESS, "Gdansk, Poland");
    result.setValue(Field.PUBLISHER, "ICSC-NAISO Academic Press");
    result.setValue(Field.NOTE, "Abstract in ICSC-NAISO Academic Press, Canada (ISBN:3906454339), pg.51");
    
    additional = result.add(Type.UNPUBLISHED);
    additional.setValue(Field.AUTHOR, "V. G. Kaburlasos and I. N. Athanasiadis and P. A. Mitkas and V. Petridis");
    additional.setValue(Field.TITLE, "Fuzzy Lattice Reasoning (FLR) Classifier and its Application on Improved Estimation of Ambient Ozone Concentration");
    additional.setValue(Field.YEAR, "2003");
    
    return result;
  }

  /**
   * Main method for testing this class.
   *
   * @param args should contain command line arguments for evaluation
   * (see Evaluation).
   */

  public static void main(String[] args) {
    runClassifier(new FLR(), args);
  }

  /**
   * <p>Fuzzy Lattice implementation in WEKA </p>
   *
   * @author Ioannis N. Athanasiadis
   * email: [e-mail]
   * alias: [e-mail]
   * @version 5.0
   */
  private class FuzzyLattice
      implements Serializable {

    /** for serialization */
    static final long serialVersionUID = -3568003680327062404L;
    
    private double min[];
    private double max[];
    private int categ;
    private String className;

    //Constructors

    /**
     * Constructs a Fuzzy Lattice from a instance
     * @param dR the instance
     * @param bounds the boundaries file
     */
    public FuzzyLattice(Instance dR, FuzzyLattice bounds) {
      min = new double[dR.numAttributes() - 1];
      max = new double[dR.numAttributes() - 1];
      int k = 0;
      for (int i = 0; i < dR.numAttributes(); i++) {
        if (i != dR.classIndex()) {
          if (!dR.isMissing(i)) {
            min[k] = (dR.value(i) > bounds.getMin(k)) ? dR.value(i) :
                bounds.getMin(k);
            max[k] = (dR.value(i) < bounds.getMax(k)) ? dR.value(i) :
                bounds.getMax(k);
            k = k + 1;
          } //if(!dR.isMissing(i))
          else {
            min[k] = bounds.getMax(k);
            max[k] = bounds.getMin(k);
            k = k + 1;
          } //else
        } //if(i!=dR.classIndex())
      } //for (int i=0; i<dR.numAttributes();i++)
      categ = (int) dR.value(dR.classIndex());
      className = dR.stringValue(dR.classIndex());
    } //FuzzyLattice

    /**
     * Constructs an empty Fuzzy Lattice of a specific dimension pointing
     * in Class "Metric Space" (-1)
     * @param length the dimention of the Lattice
     */
    public FuzzyLattice(int length) {
      min = new double[length];
      max = new double[length];

      for (int i = 0; i < length; i++) {
        min[i] = 0;
        max[i] = 0;
      }
      categ = -1;
      className = "Metric Space";
    }

    /**
     * Converts a String to a Fuzzy Lattice pointing in Class "Metric Space" (-1)
     * Note that the input String should be compatible with the toString() method.
     * @param rule the input String.
     */
    public FuzzyLattice(String rule) {
      int size = 0;
      for (int i = 0; i < rule.length(); i++) {
        String s = rule.substring(i, i + 1);
        if (s.equalsIgnoreCase("[")) {
          size++;
        }
      }
      min = new double[size];
      max = new double[size];

      int i = 0;
      int k = 0;
      String temp = "";
      int s = 0;
      do {
        String character = rule.substring(s, s + 1);
        temp = temp + character;
        if (character.equalsIgnoreCase(" ")) {
          if (!temp.equalsIgnoreCase(" ")) {
            k = k + 1;
            if (k % 4 == 2) {
              min[i] = Double.parseDouble(temp);
            } //if
            else if (k % 4 == 3) {
              max[i] = Double.parseDouble(temp);
              i = i + 1;
            } //else
          } // if (!temp.equalsIgnoreCase(" ") ){
          temp = "";
        } //if (character.equalsIgnoreCase(seperator)){
        s = s + 1;
      }
      while (i < size);
      categ = -1;
      className = "Metric Space";
    }

    // Functions

    /**
     * Calculates the valuation function of the FuzzyLattice
     * @param bounds corresponding boundaries
     * @return the value of the valuation function
     */
    public double valuation(FuzzyLattice bounds) {
      double resp = 0.0;
      for (int i = 0; i < min.length; i++) {
        resp += 1 -
            (min[i] - bounds.getMin(i)) / (bounds.getMax(i) - bounds.getMin(i));
        resp += (max[i] - bounds.getMin(i)) /
            (bounds.getMax(i) - bounds.getMin(i));
      }
      return resp;
    }

    /**
     * Calcualtes the length of the FuzzyLattice
     * @return the length
     */
    public int length() {
      return min.length;
    }

    /**
     * Implements the Join Function
     * @param lattice the second fuzzy lattice
     * @return the joint lattice
     */
    public FuzzyLattice join(FuzzyLattice lattice) { // Lattice Join
      FuzzyLattice b = new FuzzyLattice(lattice.length());
      int i;
      for (i = 0; i < lattice.min.length; i++) {
        b.min[i] = (lattice.min[i] < min[i]) ? lattice.min[i] :
            min[i];
        b.max[i] = (lattice.max[i] > max[i]) ? lattice.max[i] :
            max[i];
      }
      b.categ = categ;
      b.className = className;
      return b;
    }

    // Get-Set Functions

    public int getCateg() {
      return categ;
    }

    public void setCateg(int i) {
      categ = i;
    }

    public String getClassName() {
      return className;
    }

    public void setClassName(String s) {
      className = s;
    }

    public double getMin(int i) {
      return min[i];
    }

    public double getMax(int i) {
      return max[i];
    }

    public void setMin(int i, double val) {
      min[i] = val;
    }

    public void setMax(int i, double val) {
      max[i] = val;
    }

    /**
     * Returns a description of the Fuzzy Lattice
     * @return the Fuzzy Lattice and the corresponding Class
     */
    public String toString() {
      String rule = "";
      for (int i = 0; i < min.length; i++) {
        rule = rule + "[ " + min[i] + "  " + max[i] + " ]  ";
      }
      rule = rule + "in Class:  " + className + " \n";
      return rule;
    }
  }
}