bayesnet.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

    catch (Exception e) {
      e.printStackTrace();
    }
  }

  /**
   * sets a specific option/value of the generator (option must be w/o
   * then '-')
   * @param option          the option to set
   * @param value           the new value for the option
   */
  protected void setGeneratorOption(String option, String value) {
    setGeneratorOption(getGenerator(), option, value);
  }

  /**
   * returns the default number of attributes
   * 
   * @return the default number of attributes
   */
  protected int defaultNumAttributes() {
    return 10;
  }

  /**
   * Sets the number of attributes the dataset should have.
   * @param numAttributes the new number of attributes
   */
  public void setNumAttributes(int numAttributes) {
    setGeneratorOption("N", "" + numAttributes);
  }

  /**
   * Gets the number of attributes that should be produced.
   * @return the number of attributes that should be produced
   */
  public int getNumAttributes() { 
    int       result;
    
    result = -1;
    try {
      result = Integer.parseInt(
          Utils.getOption('N', getGenerator().getOptions()));
    }
    catch (Exception e) {
      e.printStackTrace();
      result = -1;
    }

    return result;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String numAttributesTipText() {
    return "The number of attributes the generated data will contain (including class attribute), ie the number of nodes in the bayesian net.";
  }

  /**
   * returns the default cardinality
   * 
   * @return the default cardinality
   */
  protected int defaultCardinality() {
    return 2;
  }

  /**
   * Sets the cardinality of the attributes (incl class attribute)
   * @param value the cardinality
   */
  public void setCardinality(int value) { 
    setGeneratorOption("C", "" + value);
  }

  /**
   * Gets the cardinality of the attributes (incl class attribute)
   * @return the cardinality of the attributes
   */
  public int getCardinality() { 
    int       result;
    
    result = -1;
    try {
      result = Integer.parseInt(
          Utils.getOption('C', getGenerator().getOptions()));
    }
    catch (Exception e) {
      e.printStackTrace();
      result = -1;
    }

    return result;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String cardinalityTipText() {
    return "The cardinality of the attributes, incl the class attribute.";
  }

  /**
   * returns the default number of arcs
   * 
   * @return the default number of arcs
   */
  protected int defaultNumArcs() {
    return 20;
  }

  /**
   * Sets the number of arcs for the bayesian net
   * @param value the number of arcs
   */
  public void setNumArcs(int value) {
    int       nodes;
    int       minArcs;
    int       maxArcs;

    nodes   = getNumAttributes();
    minArcs = nodes - 1;
    maxArcs = nodes * (nodes - 1) / 2;
    
    if (value > maxArcs)
      throw new IllegalArgumentException(
          "Number of arcs should be at most nodes * (nodes - 1) / 2 = " 
          + maxArcs + " instead of " + value + " (nodes = numAttributes)!");
    else if (value < minArcs)
      throw new IllegalArgumentException(
          "Number of arcs should be at least (nodes - 1) = " + minArcs 
          + " instead of " + value + " (nodes = numAttributes)!");
    else
      setGeneratorOption("A", "" + value);
  }

  /**
   * Gets the number of arcs for the bayesian net
   * @return the number of arcs
   */
  public int getNumArcs() { 
    int       result;
    
    result = -1;
    try {
      result = Integer.parseInt(
          Utils.getOption('A', getGenerator().getOptions()));
    }
    catch (Exception e) {
      e.printStackTrace();
      result = -1;
    }

    return result;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String numArcsTipText() {
    return "The number of arcs in the bayesian net, at most: n * (n - 1) / 2 and at least: (n - 1); with n = numAttributes";
  }

  /**
   * Sets the number of examples, given by option.
   * @param numExamples the new number of examples
   */
  public void setNumExamples(int numExamples) { 
    super.setNumExamples(numExamples);
    setGeneratorOption("M", "" + numExamples);
  }

  /**
   * Gets the number of examples, given by option.
   * @return the number of examples, given by option 
   */
  public int getNumExamples() { 
    int       result;
    
    result = -1;
    try {
      result = Integer.parseInt(
          Utils.getOption('M', getGenerator().getOptions()));
    }
    catch (Exception e) {
      e.printStackTrace();
      result = -1;
    }

    return result;
  }

  /**
   * Return if single mode is set for the given data generator
   * mode depends on option setting and or generator type.
   * 
   * @return single mode flag
   * @throws Exception if mode is not set yet
   */
  public boolean getSingleModeFlag() throws Exception {
    return false;
  }

  /**
   * Initializes the format for the dataset produced. 
   * Must be called before the generateExample or generateExamples
   * methods are used.
   * Re-initializes the random number generator with the given seed.
   *
   * @return the format for the dataset 
   * @throws Exception if the generating of the format failed
   * @see  #getSeed()
   */
  public Instances defineDataFormat() throws Exception {
    BayesNetGenerator   bng;

    bng = new BayesNetGenerator();
    bng.setOptions(getGenerator().getOptions());
    setGeneratorOption(bng, "M", "1");
    bng.generateRandomNetwork();
    bng.generateInstances();
    bng.m_Instances.renameAttribute(0, "class");
    bng.m_Instances.setRelationName(getRelationNameToUse());
    
    return bng.m_Instances;
  }

  /**
   * Generates one example of the dataset. 
   *
   * @return the generated example
   * @throws Exception if the format of the dataset is not yet defined
   * @throws Exception if the generator only works with generateExamples
   * which means in non single mode
   */
  public Instance generateExample() throws Exception {
    throw new Exception("Cannot generate examples one-by-one!");
  }

  /**
   * Generates all examples of the dataset. Re-initializes the random number
   * generator with the given seed, before generating instances.
   *
   * @return the generated dataset
   * @throws Exception if the format of the dataset is not yet defined
   * @throws Exception if the generator only works with generateExample,
   * which means in single mode
   * @see   #getSeed()
   */
  public Instances generateExamples() throws Exception {
    getGenerator().setOptions(getGenerator().getOptions());
    getGenerator().generateRandomNetwork();
    getGenerator().generateInstances();
    getGenerator().m_Instances.renameAttribute(0, "class");
    getGenerator().m_Instances.setRelationName(getRelationNameToUse());
    
    return getGenerator().m_Instances;
  }

  /**
   * Generates a comment string that documentates the data generator.
   * By default this string is added at the beginning of the produced output
   * as ARFF file type, next after the options.
   * 
   * @return string contains info about the generated rules
   */
  public String generateStart () {
    return "";
  }

  /**
   * Generates a comment string that documentats the data generator.
   * By default this string is added at the end of theproduces output
   * as ARFF file type.
   * 
   * @return string contains info about the generated rules
   * @throws Exception if the generating of the documentaion fails
   */
  public String generateFinished() throws Exception {
    return "";
  }

  /**
   * Main method for executing this class.
   *
   * @param args should contain arguments for the data producer: 
   */
  public static void main(String[] args) {
    runDataGenerator(new BayesNet(), args);
  }
}