smo.java

MacroWeka扩展了著名数据挖掘工具weka

   */
  public void setExponent(double v) {
    
    if (v == 1.0) {
      m_featureSpaceNormalization = false;
      m_lowerOrder = false;
    }
    m_exponent = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String gammaTipText() {
    return "The value of the gamma parameter for RBF kernels.";
  }
  
  /**
   * Get the value of gamma. 
   *
   * @return Value of gamma.
   */
  public double getGamma() {
    
    return m_gamma;
  }
  
  /**
   * Set the value of gamma. 
   *
   * @param v  Value to assign to gamma.
   */
  public void setGamma(double v) {
    
    m_gamma = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String cTipText() {
    return "The complexity parameter C.";
  }
  
  /**
   * Get the value of C.
   *
   * @return Value of C.
   */
  public double getC() {
    
    return m_C;
  }
  
  /**
   * Set the value of C.
   *
   * @param v  Value to assign to C.
   */
  public void setC(double v) {
    
    m_C = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String toleranceParameterTipText() {
    return "The tolerance parameter (shouldn't be changed).";
  }
  
  /**
   * Get the value of tolerance parameter.
   * @return Value of tolerance parameter.
   */
  public double getToleranceParameter() {
    
    return m_tol;
  }
  
  /**
   * Set the value of tolerance parameter.
   * @param v  Value to assign to tolerance parameter.
   */
  public void setToleranceParameter(double v) {
    
    m_tol = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String epsilonTipText() {
    return "The epsilon for round-off error (shouldn't be changed).";
  }
  
  /**
   * Get the value of epsilon.
   * @return Value of epsilon.
   */
  public double getEpsilon() {
    
    return m_eps;
  }
  
  /**
   * Set the value of epsilon.
   * @param v  Value to assign to epsilon.
   */
  public void setEpsilon(double v) {
    
    m_eps = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String cacheSizeTipText() {
    return "The size of the kernel cache (should be a prime number). Use 0 for full cache.";
  }
  
  /**
   * Get the size of the kernel cache
   * @return Size of kernel cache.
   */
  public int getCacheSize() {
    
    return m_cacheSize;
  }
  
  /**
   * Set the value of the kernel cache.
   * @param v  Size of kernel cache.
   */
  public void setCacheSize(int v) {
    
    m_cacheSize = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String filterTypeTipText() {
    return "Determines how/if the data will be transformed.";
  }
  
  /**
   * Gets how the training data will be transformed. Will be one of
   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   *
   * @return the filtering mode
   */
  public SelectedTag getFilterType() {

    return new SelectedTag(m_filterType, TAGS_FILTER);
  }
  
  /**
   * Sets how the training data will be transformed. Should be one of
   * FILTER_NORMALIZE, FILTER_STANDARDIZE, FILTER_NONE.
   *
   * @param newType the new filtering mode
   */
  public void setFilterType(SelectedTag newType) {
    
    if (newType.getTags() == TAGS_FILTER) {
      m_filterType = newType.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 useRBFTipText() {
    return "Whether to use an RBF kernel instead of a polynomial one.";
  }

  /**
   * Check if the RBF kernel is to be used.
   * @return true if RBF
   */
  public boolean getUseRBF() {
    
    return m_useRBF;
  }
  
  /**
   * Set if the RBF kernel is to be used.
   * @param v  true if RBF
   */
  public void setUseRBF(boolean v) {

    if (v) {
      m_featureSpaceNormalization = false;
      m_lowerOrder = false;
    }
    m_useRBF = v;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String featureSpaceNormalizationTipText() {
    return "Whether feature-space normalization is performed (only "
      + "available for non-linear polynomial kernels).";
  }
  
  /**
   * Check whether feature spaces is being normalized.
   * @return true if feature space is normalized.
   */
  public boolean getFeatureSpaceNormalization() throws Exception {

    return m_featureSpaceNormalization;
  }
  
  /**
   * Set whether feature space is normalized.
   * @param v  true if feature space is to be normalized.
   */
  public void setFeatureSpaceNormalization(boolean v) throws Exception {
    
    if ((m_useRBF) || (m_exponent == 1.0)) {
      m_featureSpaceNormalization = false;
    } else {
      m_featureSpaceNormalization = v;
    }
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String lowerOrderTermsTipText() {
    return "Whether lower order polyomials are also used (only "
      + "available for non-linear polynomial kernels).";
  }
  
  /**
   * Check whether lower-order terms are being used.
   * @return Value of lowerOrder.
   */
  public boolean getLowerOrderTerms() {
    
    return m_lowerOrder;
  }
  
  /**
   * Set whether lower-order terms are to be used. Defaults
   * to false if a linear machine is built.
   * @param v  Value to assign to lowerOrder.
   */
  public void setLowerOrderTerms(boolean v) {
    
    if (m_exponent == 1.0 || m_useRBF) {
      m_lowerOrder = false;
    } else {
      m_lowerOrder = v;
    }
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String buildLogisticModelsTipText() {
    return "Whether to fit logistic models to the outputs (for proper "
      + "probability estimates).";
  }

  /**
   * Get the value of buildLogisticModels.
   *
   * @return Value of buildLogisticModels.
   */
  public boolean getBuildLogisticModels() {
    
    return m_fitLogisticModels;
  }
  
  /**
   * Set the value of buildLogisticModels.
   *
   * @param newbuildLogisticModels Value to assign to buildLogisticModels.
   */
  public void setBuildLogisticModels(boolean newbuildLogisticModels) {
    
    m_fitLogisticModels = newbuildLogisticModels;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String numFoldsTipText() {
    return "The number of folds for cross-validation used to generate "
      + "training data for logistic models (-1 means use training data).";
  }
  
  /**
   * Get the value of numFolds.
   *
   * @return Value of numFolds.
   */
  public int getNumFolds() {
    
    return m_numFolds;
  }
  
  /**
   * Set the value of numFolds.
   *
   * @param newnumFolds Value to assign to numFolds.
   */
  public void setNumFolds(int newnumFolds) {
    
    m_numFolds = newnumFolds;
  }
     
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String randomSeedTipText() {
    return "Random number seed for the cross-validation.";
  }
  
  /**
   * Get the value of randomSeed.
   *
   * @return Value of randomSeed.
   */
  public int getRandomSeed() {
    
    return m_randomSeed;
  }
  
  /**
   * Set the value of randomSeed.
   *
   * @param newrandomSeed Value to assign to randomSeed.
   */
  public void setRandomSeed(int newrandomSeed) {
    
    m_randomSeed = newrandomSeed;
  }
  
  /**
   * Prints out the classifier.
   *
   * @return a description of the classifier as a string
   */
  public String toString() {
    
    StringBuffer text = new StringBuffer();
    int printed = 0;
    
    if ((m_classAttribute == null)) {
      return "SMO: No model built yet.";
    }
    try {
      text.append("SMO\n\n");
      for (int i = 0; i < m_classAttribute.numValues(); i++) {
	for (int j = i + 1; j < m_classAttribute.numValues(); j++) {
	  text.append("Classifier for classes: " + 
		      m_classAttribute.value(i) + ", " +
		      m_classAttribute.value(j) + "\n\n");
	  text.append(m_classifiers[i][j]);
	  if (m_fitLogisticModels) {
	    text.append("\n\n");
	    if ( m_classifiers[i][j].m_logistic == null) {
	      text.append("No logistic model has been fit.\n");
	    } else {
	      text.append(m_classifiers[i][j].m_logistic);
	    }
	  }
	  text.append("\n\n");
	}
      }
    } catch (Exception e) {
      return "Can't print SMO classifier.";
    }
    
    return text.toString();
  }
  
  /**
   * Main method for testing this class.
   */
  public static void main(String[] argv) {

    Classifier scheme;

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