part.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

    // Pruning options
    m_unpruned = Utils.getFlag('U', options);
    m_reducedErrorPruning = Utils.getFlag('R', options);
    m_binarySplits = Utils.getFlag('B', options);
    String confidenceString = Utils.getOption('C', options);
    if (confidenceString.length() != 0) {
      if (m_reducedErrorPruning) {
	throw new Exception("Setting CF doesn't make sense " +
			    "for reduced error pruning.");
      } else {
	m_CF = (new Float(confidenceString)).floatValue();
	if ((m_CF <= 0) || (m_CF >= 1)) {
	  throw new Exception("CF has to be greater than zero and smaller than one!");
	} 
      }
    } else {
      m_CF = 0.25f;
    }
    String numFoldsString = Utils.getOption('N', options);
    if (numFoldsString.length() != 0) {
      if (!m_reducedErrorPruning) {
	throw new Exception("Setting the number of folds" +
			    " does only make sense for" +
			    " reduced error pruning.");
      } else {
	m_numFolds = Integer.parseInt(numFoldsString);
      }
    } else {
      m_numFolds = 3;
    }

    // Other options
    String minNumString = Utils.getOption('M', options);
    if (minNumString.length() != 0) {
      m_minNumObj = Integer.parseInt(minNumString);
    } else {
      m_minNumObj = 2;
    }
    String seedString = Utils.getOption('Q', options);
    if (seedString.length() != 0) {
      m_Seed = Integer.parseInt(seedString);
    } else {
      m_Seed = 1;
    }
  }

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

    String [] options = new String [11];
    int current = 0;

    if (m_unpruned) {
      options[current++] = "-U";
    }
    if (m_reducedErrorPruning) {
      options[current++] = "-R";
    }
    if (m_binarySplits) {
      options[current++] = "-B";
    }
    options[current++] = "-M"; options[current++] = "" + m_minNumObj;
    if (!m_reducedErrorPruning) {
      options[current++] = "-C"; options[current++] = "" + m_CF;
    }
    if (m_reducedErrorPruning) {
      options[current++] = "-N"; options[current++] = "" + m_numFolds;
    }
    options[current++] = "-Q"; options[current++] = "" + m_Seed;

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

  /**
   * Returns a description of the classifier
   * 
   * @return a string representation of the classifier
   */
  public String toString() {

    if (m_root == null) {
      return "No classifier built";
    }
    return "PART decision list\n------------------\n\n" + m_root.toString();
  }
  
  /**
   * Returns a superconcise version of the model
   * 
   * @return a concise version of the model
   */
  public String toSummaryString() {

    return "Number of rules: " + m_root.numRules() + "\n";
  }
  
  /**
   * Return the number of rules.
   * @return the number of rules
   */
  public double measureNumRules() {
    return m_root.numRules();
  }
  
  /**
   * Returns an enumeration of the additional measure names
   * @return an enumeration of the measure names
   */
  public Enumeration enumerateMeasures() {
    Vector newVector = new Vector(1);
    newVector.addElement("measureNumRules");
    return newVector.elements();
  }

  /**
   * Returns the value of the named measure
   * @param additionalMeasureName the name of the measure to query for its value
   * @return the value of the named measure
   * @throws IllegalArgumentException if the named measure is not supported
   */
  public double getMeasure(String additionalMeasureName) {
    if (additionalMeasureName.compareToIgnoreCase("measureNumRules") == 0) {
      return measureNumRules();
    } else {
      throw new IllegalArgumentException(additionalMeasureName 
			  + " not supported (PART)");
    }
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String confidenceFactorTipText() {
    return "The confidence factor used for pruning (smaller values incur "
      + "more pruning).";
  }

  /**
   * Get the value of CF.
   *
   * @return Value of CF.
   */
  public float getConfidenceFactor() {
    
    return m_CF;
  }
  
  /**
   * Set the value of CF.
   *
   * @param v  Value to assign to CF.
   */
  public void setConfidenceFactor(float v) {
    
    m_CF = v;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String minNumObjTipText() {
    return "The minimum number of instances per rule.";
  }

  /**
   * Get the value of minNumObj.
   *
   * @return Value of minNumObj.
   */
  public int getMinNumObj() {
    
    return m_minNumObj;
  }
  
  /**
   * Set the value of minNumObj.
   *
   * @param v  Value to assign to minNumObj.
   */
  public void setMinNumObj(int v) {
    
    m_minNumObj = v;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String reducedErrorPruningTipText() {
    return "Whether reduced-error pruning is used instead of C.4.5 pruning.";
  }

  /**
   * Get the value of reducedErrorPruning.
   *
   * @return Value of reducedErrorPruning.
   */
  public boolean getReducedErrorPruning() {
    
    return m_reducedErrorPruning;
  }
  
  /**
   * Set the value of reducedErrorPruning.
   *
   * @param v  Value to assign to reducedErrorPruning.
   */
  public void setReducedErrorPruning(boolean v) {
    
    m_reducedErrorPruning = v;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String unprunedTipText() {
    return "Whether pruning is performed.";
  }

  /**
   * Get the value of unpruned.
   *
   * @return Value of unpruned.
   */
  public boolean getUnpruned() {
    
    return m_unpruned;
  }
  
  /**
   * Set the value of unpruned.
   *
   * @param newunpruned Value to assign to unpruned.
   */
  public void setUnpruned(boolean newunpruned) {
    
    m_unpruned = newunpruned;
  }
  
  /**
   * 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 "Determines the amount of data used for reduced-error pruning. "
      + " One fold is used for pruning, the rest for growing the rules.";
  }

  /**
   * Get the value of numFolds.
   *
   * @return Value of numFolds.
   */
  public int getNumFolds() {
    
    return m_numFolds;
  }
  
  /**
   * Set the value of numFolds.
   *
   * @param v  Value to assign to numFolds.
   */
  public void setNumFolds(int v) {
    
    m_numFolds = v;
  }
  
  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String seedTipText() {
    return "The seed used for randomizing the data " +
      "when reduced-error pruning is used.";
  }

  /**
   * Get the value of Seed.
   *
   * @return Value of Seed.
   */
  public int getSeed() {
    
    return m_Seed;
  }
  
  /**
   * Set the value of Seed.
   *
   * @param newSeed Value to assign to Seed.
   */
  public void setSeed(int newSeed) {
    
    m_Seed = newSeed;
  }

  /**
   * Returns the tip text for this property
   * @return tip text for this property suitable for
   * displaying in the explorer/experimenter gui
   */
  public String binarySplitsTipText() {
    return "Whether to use binary splits on nominal attributes when "
      + "building the partial trees.";
  }
  
  /**
   * Get the value of binarySplits.
   *
   * @return Value of binarySplits.
   */
  public boolean getBinarySplits() {
    
    return m_binarySplits;
  }
  
  /**
   * Set the value of binarySplits.
   *
   * @param v  Value to assign to binarySplits.
   */
  public void setBinarySplits(boolean v) {
    
    m_binarySplits = v;
  }
  
  /**
   * Main method for testing this class.
   *
   * @param argv command line options 
   */
  public static void main(String [] argv){
    runClassifier(new PART(), argv);
  }
}