mexicanhat.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

   * Gets the upper and lower boundary for the range of x
   *
   * @return the string containing the upper and lower boundary for
   *         the range of x, separated by ..
   */
  protected String getRange() {
    String fromTo = "" 
                    + Utils.doubleToString(getMinRange(), 2) + ".."
                    + Utils.doubleToString(getMaxRange(), 2);
    return fromTo;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  protected String rangeTipText() {
    return "The upper and lower boundary for the range x is drawn from randomly.";
  }

  /**
   * returns the default min range
   * 
   * @return the default min range
   */
  protected double defaultMinRange() {
    return -10;
  }

  /**
   * Sets the lower boundary for the range of x
   *
   * @param value the lower boundary
   */
  public void setMinRange(double value) {
    m_MinRange = value;
  }

  /**
   * Gets the lower boundary for the range of x
   *
   * @return the lower boundary for the range of x
   */
  public double getMinRange() {
    return m_MinRange;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String minRangeTipText() {
    return "The lower boundary for the range x is drawn from randomly.";
  }

  /**
   * returns the default max range
   * 
   * @return the default max range
   */
  protected double defaultMaxRange() {
    return 10;
  }

  /**
   * Sets the upper boundary for the range of x
   *
   * @param value the upper boundary
   */
  public void setMaxRange(double value) {
    m_MaxRange = value;
  }

  /**
   * Gets the upper boundary for the range of x
   *
   * @return the upper boundary for the range of x
   */
  public double getMaxRange() {
    return m_MaxRange;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String maxRangeTipText() {
    return "The upper boundary for the range x is drawn from randomly.";
  }

  /**
   * returns the default gaussian noise rate
   * 
   * @return the default gaussian noise rate
   */
  protected double defaultNoiseRate() {
    return 0.0;
  }

  /**
   * Gets the gaussian noise rate.
   *
   * @return the gaussian noise rate
   */
  public double getNoiseRate() { 
    return m_NoiseRate; 
  }
  
  /**
   * Sets the gaussian noise rate.
   *
   * @param value the gaussian noise rate
   */
  public void setNoiseRate(double value) {
    m_NoiseRate = value;
  }
  
  /**
   * Returns the tip text for this property
   * 
   * @return tip text for this property suitable for
   *         displaying in the explorer/experimenter gui
   */
  public String noiseRateTipText() {
    return "The gaussian noise rate to use.";
  }

  /**
   * returns the default variance of the noise rate
   * 
   * @return the default variance of the noise rate
   */
  protected double defaultNoiseVariance() {
    return 1.0;
  }

  /**
   * Gets the noise variance
   *
   * @return the noise variance
   */
  public double getNoiseVariance() { 
    return m_NoiseVariance; 
  }
  
  /**
   * Sets the noise variance
   *
   * @param value the noise variance
   */
  public void setNoiseVariance(double value) {
    if (value > 0)
      m_NoiseVariance = value;
    else
      throw new IllegalArgumentException(
          "Noise variance needs to be > 0 (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 noiseVarianceTipText() {
    return "The noise variance to use.";
  }

  /**
   * 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;

    m_Random      = new Random(getSeed());
    m_NoiseRandom = new Random(getSeed());

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

    // initialize dataset format
    atts = new FastVector();
    atts.addElement(new Attribute("x"));
    atts.addElement(new Attribute("y"));
    
    m_DatasetFormat = new Instances(getRelationNameToUse(), atts, 0);
    
    return m_DatasetFormat;
  }

  /**
   * 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;
    Random      rand;
    double      x;
    double      y;
    double[]    atts;

    result = null;
    rand   = getRandom();

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

    // generate attributes
    atts = new double[m_DatasetFormat.numAttributes()];
    
    // random x
    x = rand.nextDouble();
    // fit into range
    x = x * (getMaxRange() - getMinRange()) + getMinRange();
    
    // generate y
    if (Utils.eq(x, 0))
      y = getAmplitude();
    else
      y = getAmplitude() 
          * StrictMath.sin(StrictMath.abs(x)) / StrictMath.abs(x);
    // noise
    y = y + getAmplitude() 
            * m_NoiseRandom.nextGaussian() 
            * getNoiseRate() * getNoiseVariance();

    atts[0] = x;
    atts[1] = y;
    result = new Instance(1.0, atts);

    // dataset reference
    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 testing this class.
   *
   * @param args should contain arguments for the data producer: 
   */
  public static void main(String[] args) {
    runDataGenerator(new MexicanHat(), args);
  }
}