aodesr.java

来自「Weka」· Java 代码 · 共 916 行 · 第 1/2 页

 
    // calculate probabilities for each possible class value
    for(int classVal = 0; classVal < m_NumClasses; classVal++) {
 
       probs[classVal] = 0;
       double x = 0;
       parentCount = 0;
 
       countsForClass = m_CondiCounts[classVal];

       // each attribute has a turn of being the parent
       for(int parent = 0; parent < m_NumAttributes; parent++) {
          if(attIndex[parent] == -1)
             continue;  // skip class attribute or missing value

          // determine correct index for the parent in m_CondiCounts matrix
          pIndex = attIndex[parent];

          // check that the att value has a frequency of m_Limit or greater
	  if(m_Frequencies[pIndex] < m_Limit) 
             continue;
          
          // delete the generalization attributes.
          if(SpecialGeneralArray[parent] != -1)
             continue;

          countsForClassParent = countsForClass[pIndex];

          // block the parent from being its own child
          attIndex[parent] = -1;

          parentCount++;

          double classparentfreq = countsForClassParent[pIndex];

          // find the number of missing values for parent's attribute
          double missing4ParentAtt = 
            m_Frequencies[m_StartAttIndex[parent] + m_NumAttValues[parent]];

          // calculate the prior probability -- P(parent & classVal)
           if (m_Laplace){
             x = LaplaceEstimate(classparentfreq, m_SumInstances - missing4ParentAtt, 
                                    m_NumClasses * m_NumAttValues[parent]);
          } else {
          
             x = MEstimate(classparentfreq, m_SumInstances - missing4ParentAtt, 
                                    m_NumClasses * m_NumAttValues[parent]);
          }


    
          // take into account the value of each attribute
          for(int att = 0; att < m_NumAttributes; att++) {
             if(attIndex[att] == -1) // skip class attribute or missing value
                continue;
             // delete the generalization attributes.
             if(SpecialGeneralArray[att] != -1)
                continue;
            
 
             double missingForParentandChildAtt = 
                      countsForClassParent[m_StartAttIndex[att] + m_NumAttValues[att]];

             if (m_Laplace){
                x *= LaplaceEstimate(countsForClassParent[attIndex[att]], 
                    classparentfreq - missingForParentandChildAtt, m_NumAttValues[att]);
             } else {
                x *= MEstimate(countsForClassParent[attIndex[att]], 
                    classparentfreq - missingForParentandChildAtt, m_NumAttValues[att]);
             }
          }

          // add this probability to the overall probability
          probs[classVal] += x;
 
          // unblock the parent
          attIndex[parent] = pIndex;
       }
 
       // check that at least one att was a parent
       if(parentCount < 1) {

          // do plain naive bayes conditional prob
	  probs[classVal] = NBconditionalProb(instance, classVal);
          //probs[classVal] = Double.NaN;

       } else {
 
          // divide by number of parent atts to get the mean
          probs[classVal] /= (double)(parentCount);
       }
    }
    Utils.normalize(probs);
    return probs;
  }


  /**
   * Calculates the probability of the specified class for the given test
   * instance, using naive Bayes.
   *
   * @param instance the instance to be classified
   * @param classVal the class for which to calculate the probability
   * @return predicted class probability
   * @throws Exception if there is a problem generating the prediction
   */
  public double NBconditionalProb(Instance instance, int classVal)
                                                     throws Exception {
    double prob;
    int attIndex;
    double [][] pointer;

    // calculate the prior probability
    if(m_Laplace) {
       prob = LaplaceEstimate(m_ClassCounts[classVal],m_SumInstances,m_NumClasses); 
    } else {
       prob = MEstimate(m_ClassCounts[classVal], m_SumInstances, m_NumClasses);
    }
    pointer = m_CondiCounts[classVal];
    
    // consider effect of each att value
    for(int att = 0; att < m_NumAttributes; att++) {
       if(att == m_ClassIndex || instance.isMissing(att))
          continue;
       
       // determine correct index for att in m_CondiCounts
       attIndex = m_StartAttIndex[att] + (int)instance.value(att);
       if (m_Laplace){
         prob *= LaplaceEstimate((double)pointer[attIndex][attIndex], 
                   (double)m_SumForCounts[classVal][att], m_NumAttValues[att]);
       } else {
           prob *= MEstimate((double)pointer[attIndex][attIndex], 
                   (double)m_SumForCounts[classVal][att], m_NumAttValues[att]);
       }
    }
    return prob;
  }


  /**
   * Returns the probability estimate, using m-estimate
   *
   * @param frequency frequency of value of interest
   * @param total count of all values
   * @param numValues number of different values
   * @return the probability estimate
   */
  public double MEstimate(double frequency, double total,
                          double numValues) {
    
    return (frequency + m_MWeight / numValues) / (total + m_MWeight);
  }
   
  /**
   * Returns the probability estimate, using laplace correction
   *
   * @param frequency frequency of value of interest
   * @param total count of all values
   * @param numValues number of different values
   * @return the probability estimate
   */
  public double LaplaceEstimate(double frequency, double total,
                                double numValues) {
    
    return (frequency + 1.0) / (total + numValues);
  }
    
   
  /**
   * Returns an enumeration describing the available options
   *
   * @return an enumeration of all the available options
   */
  public Enumeration listOptions() {

    Vector newVector = new Vector(5);
        
    newVector.addElement(
       new Option("\tOutput debugging information\n",
                  "D", 0,"-D"));
    newVector.addElement(
       new Option("\tImpose a critcal value for specialization-generalization relationship\n"
                  + "\t(default is 50)", "C", 1,"-C"));
    newVector.addElement(
       new Option("\tImpose a frequency limit for superParents\n"
                  + "\t(default is 1)", "F", 2,"-F"));
    newVector.addElement(
       new Option("\tUsing Laplace estimation\n"
                  + "\t(default is m-esimation (m=1))",
                  "L", 3,"-L"));
    newVector.addElement(
       new Option("\tWeight value for m-estimation\n"
                  + "\t(default is 1.0)", "M", 4,"-M"));

    return newVector.elements();
  }


  /**
   * Parses a given list of options. <p/>
   *
   <!-- options-start -->
   * Valid options are:<p/>
   *
   * <pre> -D
   *  Output debugging information
   * </pre>
   * 
   * <pre> -F &lt;int&gt;
   *  Impose a frequency limit for superParents
   *  (default is 1)</pre>
   *
   * <pre> -L
   *  Use Laplace estimation
   *  (default is m-estimation)</pre>
   *
   * <pre> -M &lt;double&gt;
   *  Specify the m value of m-estimation
   *  (default is 1)</pre>
   *
   * <pre>-C &lt;int&gt;
   *  Specify critical value for specialization-generalization.
   *  (default is 50).
   *  Larger values than the default of 50 substantially reduce
   *  the risk of incorrectly inferring that one value subsumes
   *  another, but also reduces the number of true subsumptions
   *  that are detected.</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 {

    m_Debug = Utils.getFlag('D', options);

    String Critical = Utils.getOption('C', options);
    if(Critical.length() != 0) 
       m_Critical = Integer.parseInt(Critical);
    else
       m_Critical = 50;
    
    String Freq = Utils.getOption('F', options);
    if(Freq.length() != 0) 
       m_Limit = Integer.parseInt(Freq);
    else
       m_Limit = 1;
    
    m_Laplace = Utils.getFlag('L', options);
    String MWeight = Utils.getOption('M', options); 
    if(MWeight.length() != 0) {
       if(m_Laplace)
          throw new Exception("weight for m-estimate is pointless if using laplace estimation!");
       m_MWeight = Double.parseDouble(MWeight);
    } else
       m_MWeight = 1.0;
    
    Utils.checkForRemainingOptions(options);
  }
    
  /**
   * Gets the current settings of the classifier.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String [] getOptions() {
        
    Vector result  = new Vector();

    if (m_Debug)
       result.add("-D");

    result.add("-F");
    result.add("" + m_Limit);

    if (m_Laplace) {
       result.add("-L");
    } else {
       result.add("-M");
       result.add("" + m_MWeight);
    }
        
    result.add("-C");
    result.add("" + m_Critical);

    return (String[]) result.toArray(new String[result.size()]);
  }
 
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String mestWeightTipText() {
    return "Set the weight for m-estimate.";
  }

  /**
   * Sets the weight for m-estimate
   *
   * @param w the weight
   */
  public void setMestWeight(double w) {
    if (getUseLaplace()) {
       System.out.println(
          "Weight is only used in conjunction with m-estimate - ignored!");
    } else {
      if(w > 0)
         m_MWeight = w;
      else
         System.out.println("M-Estimate Weight must be greater than 0!");
    }
  }

  /**
   * Gets the weight used in m-estimate
   *
   * @return the weight for m-estimation
   */
  public double getMestWeight() {
    return m_MWeight;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useLaplaceTipText() {
    return "Use Laplace correction instead of m-estimation.";
  }

  /**
   * Gets if laplace correction is being used.
   *
   * @return Value of m_Laplace.
   */
  public boolean getUseLaplace() {
    return m_Laplace;
  }

  /**
   * Sets if laplace correction is to be used.
   *
   * @param value Value to assign to m_Laplace.
   */
  public void setUseLaplace(boolean value) {
    m_Laplace = value;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String frequencyLimitTipText() {
    return "Attributes with a frequency in the train set below "
           + "this value aren't used as parents.";
  }

  /**
   * Sets the frequency limit
   *
   * @param f the frequency limit
   */
  public void setFrequencyLimit(int f) {
    m_Limit = f;
  }

  /**
   * Gets the frequency limit.
   *
   * @return the frequency limit
   */
  public int getFrequencyLimit() {
    return m_Limit;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String criticalValueTipText() {
    return "Specify critical value for specialization-generalization "
           + "relationship (default 50).";
  }

  /**
   * Sets the critical value
   *
   * @param c the critical value
   */
  public void setCriticalValue(int c) {
    m_Critical = c;
  }

  /**
   * Gets the critical value.
   *
   * @return the critical value
   */
  public int getCriticalValue() {
    return m_Critical;
  }

  /**
   * Returns a description of the classifier.
   *
   * @return a description of the classifier as a string.
   */
  public String toString() {
 
    StringBuffer text = new StringBuffer();
        
    text.append("The AODEsr Classifier");
    if (m_Instances == null) {
       text.append(": No model built yet.");
    } else {
       try {
          for (int i = 0; i < m_NumClasses; i++) {
             // print to string, the prior probabilities of class values
             text.append("\nClass " + m_Instances.classAttribute().value(i) +
                       ": Prior probability = " + Utils.
                          doubleToString(((m_ClassCounts[i] + 1)
                       /(m_SumInstances + m_NumClasses)), 4, 2)+"\n\n");
          }
                
          text.append("Dataset: " + m_Instances.relationName() + "\n"
                      + "Instances: " + m_NumInstances + "\n"
                      + "Attributes: " + m_NumAttributes + "\n"
		      + "Frequency limit for superParents: " + m_Limit + "\n"
                      + "Critical value for the specializtion-generalization "
                      + "relationship: " + m_Critical + "\n");
          if(m_Laplace) {
            text.append("Using LapLace estimation.");
          } else {
              text.append("Using m-estimation, m = " + m_MWeight); 
          }
       } catch (Exception ex) {
          text.append(ex.getMessage());
       }
    }
    return text.toString();
  }
    
    
  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String [] argv) {
    runClassifier(new AODEsr(), argv);
  }
}