naivebayes.java

为了下东西 随便发了个 datamining 的源代码

	}
	attIndex++;
      }
      m_ClassDistribution.addValue(instance.classValue(),
				   instance.weight());
    }
  }


  /**
   * Calculates the class membership probabilities for the given test 
   * instance.
   * 
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @exception Exception if there is a problem generating the prediction
   */
  public double [] distributionForInstance(Instance instance) 
  throws Exception { 
    
    if (m_UseDiscretization) {
      m_Disc.input(instance);
      instance = m_Disc.output();
    }
    double [] probs = new double[m_NumClasses];
    for (int j = 0; j < m_NumClasses; j++) {
      probs[j] = m_ClassDistribution.getProbability(j);
    }
    Enumeration emAtts = instance.emerateAttributes();
    int attIndex = 0;
    while (emAtts.hasMoreElements()) {
      Attribute attribute = (Attribute) emAtts.nextElement();
      if (!instance.isMissing(attribute)) {
	double temp, max = 0;
	for (int j = 0; j < m_NumClasses; j++) {
	  temp = Math.max(1e-75, m_Distributions[attIndex][j].
	  getProbability(instance.value(attribute)));
	  probs[j] *= temp;
	  if (probs[j] > max) {
	    max = probs[j];
	  }
	  if (Double.isNaN(probs[j])) {
	    throw new Exception("NaN returned from estimator for attribute "
				+ attribute.name() + ":\n"
				+ m_Distributions[attIndex][j].toString());
	  }
	}
	if ((max > 0) && (max < 1e-75)) { // Danger of probability underflow
	  for (int j = 0; j < m_NumClasses; j++) {
	    probs[j] *= 1e75;
	  }
	}
      }
      attIndex++;
    }

    // Display probabilities
    Utils.normalize(probs);
    return probs;
  }

  /**
   * Returns an enumeration describing the available options.
   *
   * @return an enumeration of all the available options.
   */
  public Enumeration<Option> listOptions() {

    Vector<Option> newVector = new Vector<Option>(2);

    newVector.addElement(
    new Option("\tUse kernel density estimator rather than normal\n"
	       +"\tdistribution for numeric attributes",
	       "K", 0,"-K"));
    newVector.addElement(
    new Option("\tUse supervised discretization to process numeric attributes\n",
	       "D", 0,"-D"));
    return newVector.elements();
  }

  /**
   * Parses a given list of options. Valid options are:<p>
   *
   * -K <br>
   * Use kernel estimation for modelling numeric attributes rather than
   * a single normal distribution.<p>
   *
   * -D <br>
   * Use supervised discretization to process numeric attributes.
   *
   * @param options the list of options as an array of strings
   * @exception Exception if an option is not supported
   */
  public void setOptions(String[] options) throws Exception {
    
    boolean k = Utils.getFlag('K', options);
    boolean d = Utils.getFlag('D', options);
    if (k && d) {
      throw new IllegalArgumentException("Can't use both kernel density " +
					 "estimation and discretization!");
    }
    setUseSupervisedDiscretization(d);
    setUseKernelEstimator(k);
    Utils.checkForRemainingOptions(options);
  }

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

    String [] options = new String [2];
    int current = 0;

    if (m_UseKernelEstimator) {
      options[current++] = "-K";
    }

    if (m_UseDiscretization) {
      options[current++] = "-D";
    }

    while (current < options.length) {
      options[current++] = "";
    }
    return options;
  }

  /**
   * Returns a description of the classifier.
   *
   * @return a description of the classifier as a string.
   */
  public String toString() {
    
    StringBuffer text = new StringBuffer();

    text.append("Naive Bayes Classifier");
    if (m_Instances == null) {
      text.append(": No model built yet.");
    } else {
      try {
	for (int i = 0; i < m_Distributions[0].length; i++) {
	  text.append("\n\nClass " + m_Instances.classAttribute().value(i) +
		      ": Prior probability = " + Utils.
		      doubleToString(m_ClassDistribution.getProbability(i),
				     4, 2) + "\n\n");
	  Enumeration emAtts = m_Instances.emerateAttributes();
	  int attIndex = 0;
	  while (emAtts.hasMoreElements()) {
	    Attribute attribute = (Attribute) emAtts.nextElement();
	    text.append(attribute.name() + ":  " 
			+ m_Distributions[attIndex][i]);
	    attIndex++;
	  }
	}
      } catch (Exception ex) {
	text.append(ex.getMessage());
      }
    }

    return text.toString();
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useKernelEstimatorTipText() {
    return "Use a kernel estimator for numeric attributes rather than a "
      +"normal distribution.";
  }
  /**
   * Gets if kernel estimator is being used.
   *
   * @return Value of m_UseKernelEstimatory.
   */
  public boolean getUseKernelEstimator() {
    
    return m_UseKernelEstimator;
  }
  
  /**
   * Sets if kernel estimator is to be used.
   *
   * @param v  Value to assign to m_UseKernelEstimatory.
   */
  public void setUseKernelEstimator(boolean v) {
    
    m_UseKernelEstimator = v;
    if (v) {
      setUseSupervisedDiscretization(false);
    }
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String useSupervisedDiscretizationTipText() {
    return "Use supervised discretization to convert numeric attributes to nominal "
      +"ones.";
  }

  /**
   * Get whether supervised discretization is to be used.
   *
   * @return true if supervised discretization is to be used.
   */
  public boolean getUseSupervisedDiscretization() {
    
    return m_UseDiscretization;
  }
  
  /**
   * Set whether supervised discretization is to be used.
   *
   * @param newblah true if supervised discretization is to be used.
   */
  public void setUseSupervisedDiscretization(boolean newblah) {
    
    m_UseDiscretization = newblah;
    if (newblah) {
      setUseKernelEstimator(false);
    }
  }
  
  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String [] argv) {

    try {
      System.out.println(Evaluation.evaluateModel(new NaiveBayes(), argv));
    } catch (Exception e) {
      e.printStackTrace();
      System.err.println(e.getMessage());
    }
  }
}