vote.java

来自「Weka」· Java 代码 · 共 661 行 · 第 1/2 页

    switch (m_CombinationRule) {
      case AVERAGE_RULE:
      case PRODUCT_RULE:
      case MAJORITY_VOTING_RULE:
      case MIN_RULE:
      case MAX_RULE:
	dist = distributionForInstance(instance);
	if (instance.classAttribute().isNominal()) {
	  index = Utils.maxIndex(dist);
	  if (dist[index] == 0)
	    result = Instance.missingValue();
	  else
	    result = index;
	}
	else if (instance.classAttribute().isNumeric()){
	  result = dist[0];
	}
	else {
	  result = Instance.missingValue();
	}
	break;
      case MEDIAN_RULE:
	result = classifyInstanceMedian(instance);
	break;
      default:
	throw new IllegalStateException("Unknown combination rule '" + m_CombinationRule + "'!");
    }
    
    return result;
  }

  /**
   * Classifies the given test instance, returning the median from all
   * classifiers.
   *
   * @param instance the instance to be classified
   * @return the predicted most likely class for the instance or 
   * Instance.missingValue() if no prediction is made
   * @throws Exception if an error occurred during the prediction
   */
  protected double classifyInstanceMedian(Instance instance) throws Exception {
    double[] results = new double[m_Classifiers.length];
    double result;

    for (int i = 0; i < results.length; i++)
      results[i] = m_Classifiers[i].classifyInstance(instance);
    
    if (results.length == 0)
      result = 0;
    else if (results.length == 1)
      result = results[0];
    else
      result = Utils.kthSmallestValue(results, results.length / 2);
    
    return result;
  }

  /**
   * Classifies a given instance using the selected combination rule.
   *
   * @param instance the instance to be classified
   * @return the distribution
   * @throws Exception if instance could not be classified
   * successfully
   */
  public double[] distributionForInstance(Instance instance) throws Exception {
    double[] result = new double[instance.numClasses()];
    
    switch (m_CombinationRule) {
      case AVERAGE_RULE:
	result = distributionForInstanceAverage(instance);
	break;
      case PRODUCT_RULE:
	result = distributionForInstanceProduct(instance);
	break;
      case MAJORITY_VOTING_RULE:
	result = distributionForInstanceMajorityVoting(instance);
	break;
      case MIN_RULE:
	result = distributionForInstanceMin(instance);
	break;
      case MAX_RULE:
	result = distributionForInstanceMax(instance);
	break;
      case MEDIAN_RULE:
	result[0] = classifyInstance(instance);
	break;
      default:
	throw new IllegalStateException("Unknown combination rule '" + m_CombinationRule + "'!");
    }
    
    if (!instance.classAttribute().isNumeric() && (Utils.sum(result) > 0))
      Utils.normalize(result);
    
    return result;
  }
  
  /**
   * Classifies a given instance using the Average of Probabilities 
   * combination rule.
   *
   * @param instance the instance to be classified
   * @return the distribution
   * @throws Exception if instance could not be classified
   * successfully
   */
  protected double[] distributionForInstanceAverage(Instance instance) throws Exception {

    double[] probs = getClassifier(0).distributionForInstance(instance);
    for (int i = 1; i < m_Classifiers.length; i++) {
      double[] dist = getClassifier(i).distributionForInstance(instance);
      for (int j = 0; j < dist.length; j++) {
    	  probs[j] += dist[j];
      }
    }
    for (int j = 0; j < probs.length; j++) {
      probs[j] /= (double)m_Classifiers.length;
    }
    return probs;
  }
  
  /**
   * Classifies a given instance using the Product of Probabilities 
   * combination rule.
   *
   * @param instance the instance to be classified
   * @return the distribution
   * @throws Exception if instance could not be classified
   * successfully
   */
  protected double[] distributionForInstanceProduct(Instance instance) throws Exception {

    double[] probs = getClassifier(0).distributionForInstance(instance);
    for (int i = 1; i < m_Classifiers.length; i++) {
      double[] dist = getClassifier(i).distributionForInstance(instance);
      for (int j = 0; j < dist.length; j++) {
    	  probs[j] *= dist[j];
      }
    }
    
    return probs;
  }
  
  /**
   * Classifies a given instance using the Majority Voting combination rule.
   *
   * @param instance the instance to be classified
   * @return the distribution
   * @throws Exception if instance could not be classified
   * successfully
   */
  protected double[] distributionForInstanceMajorityVoting(Instance instance) throws Exception {

    double[] probs = new double[instance.classAttribute().numValues()];
    double[] votes = new double[probs.length];
    
    for (int i = 0; i < m_Classifiers.length; i++) {
      probs = getClassifier(i).distributionForInstance(instance);
      int maxIndex = 0;
      for(int j = 0; j<probs.length; j++) {
          if(probs[j] > probs[maxIndex])
        	  maxIndex = j;
      }
      
      // Consider the cases when multiple classes happen to have the same probability
      for (int j=0; j<probs.length; j++) {
	if (probs[j] == probs[maxIndex])
	  votes[j]++;
      }
    }
    
    int tmpMajorityIndex = 0;
    for (int k = 1; k < votes.length; k++) {
      if (votes[k] > votes[tmpMajorityIndex])
	tmpMajorityIndex = k;
    }
    
    // Consider the cases when multiple classes receive the same amount of votes
    Vector<Integer> majorityIndexes = new Vector<Integer>();
    for (int k = 0; k < votes.length; k++) {
      if (votes[k] == votes[tmpMajorityIndex])
	majorityIndexes.add(k);
     }
    // Resolve the ties according to a uniform random distribution
    int majorityIndex = majorityIndexes.get(m_Random.nextInt(majorityIndexes.size()));
    
    //set probs to 0
    for (int k = 0; k<probs.length; k++)
      probs[k] = 0;
    probs[majorityIndex] = 1; //the class that have been voted the most receives 1
    
    return probs;
  }
  
  /**
   * Classifies a given instance using the Maximum Probability combination rule.
   *
   * @param instance the instance to be classified
   * @return the distribution
   * @throws Exception if instance could not be classified
   * successfully
   */
  protected double[] distributionForInstanceMax(Instance instance) throws Exception {

    double[] max = getClassifier(0).distributionForInstance(instance);
    for (int i = 1; i < m_Classifiers.length; i++) {
      double[] dist = getClassifier(i).distributionForInstance(instance);
      for (int j = 0; j < dist.length; j++) {
    	  if(max[j]<dist[j])
    		  max[j]=dist[j];
      }
    }
    
    return max;
  }
  
  /**
   * Classifies a given instance using the Minimum Probability combination rule.
   *
   * @param instance the instance to be classified
   * @return the distribution
   * @throws Exception if instance could not be classified
   * successfully
   */
  protected double[] distributionForInstanceMin(Instance instance) throws Exception {

    double[] min = getClassifier(0).distributionForInstance(instance);
    for (int i = 1; i < m_Classifiers.length; i++) {
      double[] dist = getClassifier(i).distributionForInstance(instance);
      for (int j = 0; j < dist.length; j++) {
    	  if(dist[j]<min[j])
    		  min[j]=dist[j];
      }
    }
    
    return min;
  } 
  
  /**
   * Returns the tip text for this property
   * 
   * @return 		tip text for this property suitable for
   * 			displaying in the explorer/experimenter gui
   */
  public String combinationRuleTipText() {
    return "The combination rule used.";
  }
  
  /**
   * Gets the combination rule used
   *
   * @return 		the combination rule used
   */
  public SelectedTag getCombinationRule() {
    return new SelectedTag(m_CombinationRule, TAGS_RULES);
  }

  /**
   * Sets the combination rule to use. Values other than
   *
   * @param newRule 	the combination rule method to use
   */
  public void setCombinationRule(SelectedTag newRule) {
    if (newRule.getTags() == TAGS_RULES)
      m_CombinationRule = newRule.getSelectedTag().getID();
  }
  
  /**
   * Output a representation of this classifier
   * 
   * @return a string representation of the classifier
   */
  public String toString() {

    if (m_Classifiers == null) {
      return "Vote: No model built yet.";
    }

    String result = "Vote combines";
    result += " the probability distributions of these base learners:\n";
    for (int i = 0; i < m_Classifiers.length; i++) {
      result += '\t' + getClassifierSpec(i) + '\n';
    }
    result += "using the '";
    
    switch (m_CombinationRule) {
      case AVERAGE_RULE:
	result += "Average of Probabilities";
	break;
	
      case PRODUCT_RULE:
	result += "Product of Probabilities";
	break;
	
      case MAJORITY_VOTING_RULE:
	result += "Majority Voting";
	break;
	
      case MIN_RULE:
	result += "Minimum Probability";
	break;
	
      case MAX_RULE:
	result += "Maximum Probability";
	break;
	
      case MEDIAN_RULE:
	result += "Median Probability";
	break;
	
      default:
	throw new IllegalStateException("Unknown combination rule '" + m_CombinationRule + "'!");
    }
    
    result += "' combination rule \n";

    return result;
  }

  /**
   * Main method for testing this class.
   *
   * @param argv should contain the following arguments:
   * -t training file [-T test file] [-c class index]
   */
  public static void main(String [] argv) {
    runClassifier(new Vote(), argv);
  }
}