agrawal.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

   * <pre> -h
   *  Prints this help.</pre>
   * 
   * <pre> -o &lt;file&gt;
   *  The name of the output file, otherwise the generated data is
   *  printed to stdout.</pre>
   * 
   * <pre> -r &lt;name&gt;
   *  The name of the relation.</pre>
   * 
   * <pre> -d
   *  Whether to print debug informations.</pre>
   * 
   * <pre> -S
   *  The seed for random function (default 1)</pre>
   * 
   * <pre> -n &lt;num&gt;
   *  The number of examples to generate (default 100)</pre>
   * 
   * <pre> -F &lt;num&gt;
   *  The function to use for generating the data. (default 1)</pre>
   * 
   * <pre> -B
   *  Whether to balance the class.</pre>
   * 
   * <pre> -P &lt;num&gt;
   *  The perturbation factor. (default 0.05)</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 {
    String        tmpStr;

    super.setOptions(options);

    tmpStr = Utils.getOption('F', options);
    if (tmpStr.length() != 0)
      setFunction(new SelectedTag(Integer.parseInt(tmpStr), FUNCTION_TAGS));
    else
      setFunction(defaultFunction());

    setBalanceClass(Utils.getFlag('B', options));

    tmpStr = Utils.getOption('P', options);
    if (tmpStr.length() != 0)
      setPerturbationFraction(Double.parseDouble(tmpStr));
    else
      setPerturbationFraction(defaultPerturbationFraction());
  }

  /**
   * Gets the current settings of the datagenerator.
   *
   * @return an array of strings suitable for passing to setOptions
   */
  public String[] getOptions() {
    Vector        result;
    String[]      options;
    int           i;
    
    result  = new Vector();
    options = super.getOptions();
    for (i = 0; i < options.length; i++)
      result.add(options[i]);
    
    result.add("-F");
    result.add("" + m_Function);
    
    if (getBalanceClass())
      result.add("-B");
    
    result.add("-P");
    result.add("" + getPerturbationFraction());
    
    return (String[]) result.toArray(new String[result.size()]);
  }

  /**
   * returns the default function
   * 
   * @return the default function
   */
  protected SelectedTag defaultFunction() {
    return new SelectedTag(FUNCTION_1, FUNCTION_TAGS);
  }
  
  /**
   * Gets the function for generating the data.
   *
   * @return the function.
   * @see #FUNCTION_TAGS
   */
  public SelectedTag getFunction() {
    return new SelectedTag(m_Function, FUNCTION_TAGS);
  }
  
  /**
   * Sets the function for generating the data.
   *
   * @param value the function.
   * @see #FUNCTION_TAGS
   */
  public void setFunction(SelectedTag value) {
    if (value.getTags() == FUNCTION_TAGS)
      m_Function = value.getSelectedTag().getID();
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String functionTipText() {
    return "The function to use for generating the data.";
  }

  /**
   * returns the default for balancing the class
   * 
   * @return the default for balancing the class
   */
  protected boolean defaultBalanceClass() {
    return false;
  }
  
  /**
   * Gets whether the class is balanced.
   *
   * @return whether the class is balanced.
   */
  public boolean getBalanceClass() { 
    return m_BalanceClass; 
  }
  
  /**
   * Sets whether the class is balanced.
   *
   * @param value whether to balance the class.
   */
  public void setBalanceClass(boolean value) { 
    m_BalanceClass = value;
  }  
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String balanceClassTipText() {
    return "Whether to balance the class.";
  }

  /**
   * returns the default perturbation fraction
   * 
   * @return the default perturbation fraction
   */
  protected double defaultPerturbationFraction() {
    return 0.05;
  }
  
  /**
   * Gets the perturbation fraction.
   *
   * @return the perturbation fraction.
   */
  public double getPerturbationFraction() { 
    return m_PerturbationFraction; 
  }
  
  /**
   * Sets the perturbation fraction.
   *
   * @param value the perturbation fraction.
   */
  public void setPerturbationFraction(double value) { 
    if ( (value >= 0.0) && (value <= 1.0) )
      m_PerturbationFraction = value;
    else
      throw new IllegalArgumentException(
          "Perturbation fraction must be in [0,1] (provided: " + value + ")!");
  }  
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String perturbationFractionTipText() {
    return "The perturbation fraction: 0 <= fraction <= 1.";
  }

  /**
   * 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 true;
  }

  /**
   * 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 {
    FastVector      atts;
    FastVector      attValues;
    int             i;

    m_Random = new Random(getSeed());
    m_nextClassShouldBeZero = true;
    m_lastLabel             = Double.NaN;

    // number of examples is the same as given per option
    setNumExamplesAct(getNumExamples());

    // set up attributes
    atts = new FastVector();
    
    atts.addElement(new Attribute("salary"));
    
    atts.addElement(new Attribute("commission"));
    
    attValues = new FastVector();
    atts.addElement(new Attribute("age"));

    attValues = new FastVector();
    for (i = 0; i < 5; i++)
      attValues.addElement("" + i);
    atts.addElement(new Attribute("elevel", attValues));
    
    attValues = new FastVector();
    for (i = 1; i <= 20; i++)
      attValues.addElement("" + i);
    atts.addElement(new Attribute("car", attValues));
    
    attValues = new FastVector();
    for (i = 0; i < 9; i++)
      attValues.addElement("" + i);
    atts.addElement(new Attribute("zipcode", attValues));
    
    atts.addElement(new Attribute("hvalue"));
    
    atts.addElement(new Attribute("hyears"));
    
    atts.addElement(new Attribute("loan"));
    
    attValues = new FastVector();
    for (i = 0; i < 2; i++)
      attValues.addElement("" + i);
    atts.addElement(new Attribute("group", attValues));
    
    // dataset
    m_DatasetFormat = new Instances(getRelationNameToUse(), atts, 0);
    
    return m_DatasetFormat;
  }

  /**
   * perturbs the given value
   * 
   * @param val the value to perturb
   * @param min the minimum
   * @param max the maximum
   * @return the perturbed value
   */
  protected double perturbValue(double val, double min, double max) {
    return perturbValue(val, max - min, min, max);
  }

  /**
   * perturbs the given value
   * 
   * @param val the value to perturb
   * @param range the range for the perturbation
   * @param min the minimum
   * @param max the maximum
   * @return the perturbed value
   */
  protected double perturbValue(double val, double range, 
      double min, double max) {
    
    val += range * (2.0 * (getRandom().nextDouble() - 0.5)) 
                 * getPerturbationFraction();

    if (val < min)
      val = min;
    else if (val > max)
      val = max;
    
    return val;
  }

  /**
   * 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 {
    Instance      result;
    double        salary;
    double        commission;
    double        hvalue;
    double        loan;
    int           age;
    int           elevel;
    int           car;
    int           zipcode;
    int           hyears;
    boolean       desiredClassFound;
    double[]      atts;
    Random        random;
    ClassFunction classFunction;

    result = null;
    random = getRandom();

    if (m_DatasetFormat == null)
      throw new Exception("Dataset format not defined.");

    salary            = 0;
    commission        = 0;
    hvalue            = 0;
    loan              = 0;
    age               = 0;
    elevel            = 0;
    car               = 0;
    zipcode           = 0;
    hyears            = 0;
    desiredClassFound = false;
    classFunction     = builtInFunctions[m_Function - 1];

    while (!desiredClassFound) {
      // generate attributes
      salary     = 20000.0 + 130000.0 * random.nextDouble();
      commission = (salary >= 75000.0) ? 
                   0 : (10000.0 + 65000.0 * random.nextDouble());
      age        = 20 + random.nextInt(61);
      elevel     = random.nextInt(5);
      car        = 1 + random.nextInt(20);
      zipcode    = random.nextInt(9);
      hvalue     = (9.0 - (double) zipcode) * 100000.0
                   * (0.5 + random.nextDouble());
      hyears     = 1 + random.nextInt(30);
      loan       = random.nextDouble() * 500000.0;
      
      // determine class
      m_lastLabel = classFunction.determineClass(salary, commission, age,
          elevel, car, zipcode, hvalue, hyears, loan);
      if (!getBalanceClass()) {
        desiredClassFound = true;
      }
      else {
        // balance the classes
        if (    ( m_nextClassShouldBeZero && (m_lastLabel == 0))
             || (!m_nextClassShouldBeZero && (m_lastLabel == 1)) ) {
          desiredClassFound = true;
          m_nextClassShouldBeZero = !m_nextClassShouldBeZero;
        } // else keep searching
      }
    }
    
    // perturb values
    if (getPerturbationFraction() > 0.0) {
      salary = perturbValue(salary, 20000, 150000);
      if (commission > 0)
        commission = perturbValue(commission, 10000, 75000);
      age    = (int) Math.round(perturbValue(age, 20, 80));
      hvalue = perturbValue(
                  hvalue, (9.0 - (double) zipcode) * 100000.0, 0, 135000);
      hyears = (int) Math.round(perturbValue(hyears, 1, 30));
      loan   = perturbValue(loan, 0, 500000);
    }

    // create instance
    atts    = new double[m_DatasetFormat.numAttributes()];
    atts[0] = salary;
    atts[1] = commission;
    atts[2] = age;
    atts[3] = elevel;
    atts[4] = car - 1;
    atts[5] = zipcode;
    atts[6] = hvalue;
    atts[7] = hyears;
    atts[8] = loan;
    atts[9] = m_lastLabel;
    result  = new Instance(1.0, atts);
    result.setDataset(m_DatasetFormat);

    return result;
  }

  /**
   * 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 {
    Instances       result;
    int             i;

    result   = new Instances(m_DatasetFormat, 0);
    m_Random = new Random(getSeed());

    for (i = 0; i < getNumExamplesAct(); i++)
      result.add(generateExample());
    
    return result;
  }

  /**
   * 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 Agrawal(), args);
  }
}