source: branches/MetisMQI/src/main/java/weka/classifiers/rules/ZeroR.java

/*
 *    This program is free software; you can redistribute it and/or modify
 *    it under the terms of the GNU General Public License as published by
 *    the Free Software Foundation; either version 2 of the License, or
 *    (at your option) any later version.
 *
 *    This program is distributed in the hope that it will be useful,
 *    but WITHOUT ANY WARRANTY; without even the implied warranty of
 *    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
 *    GNU General Public License for more details.
 *
 *    You should have received a copy of the GNU General Public License
 *    along with this program; if not, write to the Free Software
 *    Foundation, Inc., 675 Mass Ave, Cambridge, MA 02139, USA.
 */

/*
 *    ZeroR.java
 *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
 *
 */

package weka.classifiers.rules;

import weka.classifiers.Classifier;
import weka.classifiers.AbstractClassifier;
import weka.classifiers.Sourcable;
import weka.core.Attribute;
import weka.core.Capabilities;
import weka.core.Instance;
import weka.core.Instances;
import weka.core.RevisionUtils;
import weka.core.Utils;
import weka.core.WeightedInstancesHandler;
import weka.core.Capabilities.Capability;

import java.util.Enumeration;

/**
 <!-- globalinfo-start -->
 * Class for building and using a 0-R classifier. Predicts the mean (for a numeric class) or the mode (for a nominal class).
 * <p/>
 <!-- globalinfo-end -->
 *
 <!-- options-start -->
 * Valid options are: <p/>
 * 
 * <pre> -D
 *  If set, classifier is run in debug mode and
 *  may output additional info to the console</pre>
 * 
 <!-- options-end -->
 *
 * @author Eibe Frank (eibe@cs.waikato.ac.nz)
 * @version $Revision: 5928 $
 */
public class ZeroR 
  extends AbstractClassifier 
  implements WeightedInstancesHandler, Sourcable {

  /** for serialization */
  static final long serialVersionUID = 48055541465867954L;
  
  /** The class value 0R predicts. */
  private double m_ClassValue;

  /** The number of instances in each class (null if class numeric). */
  private double [] m_Counts;
  
  /** The class attribute. */
  private Attribute m_Class;
    
  /**
   * Returns a string describing classifier
   * @return a description suitable for
   * displaying in the explorer/experimenter gui
   */
  public String globalInfo() {
    return "Class for building and using a 0-R classifier. Predicts the mean " 
      + "(for a numeric class) or the mode (for a nominal class).";         
  }

  /**
   * Returns default capabilities of the classifier.
   *
   * @return      the capabilities of this classifier
   */
  public Capabilities getCapabilities() {
    Capabilities result = super.getCapabilities();
    result.disableAll();

    // attributes
    result.enable(Capability.NOMINAL_ATTRIBUTES);
    result.enable(Capability.NUMERIC_ATTRIBUTES);
    result.enable(Capability.DATE_ATTRIBUTES);
    result.enable(Capability.STRING_ATTRIBUTES);
    result.enable(Capability.RELATIONAL_ATTRIBUTES);
    result.enable(Capability.MISSING_VALUES);

    // class
    result.enable(Capability.NOMINAL_CLASS);
    result.enable(Capability.NUMERIC_CLASS);
    result.enable(Capability.DATE_CLASS);
    result.enable(Capability.MISSING_CLASS_VALUES);

    // instances
    result.setMinimumNumberInstances(0);
    
    return result;
  }

  /**
   * Generates the classifier.
   *
   * @param instances set of instances serving as training data 
   * @throws Exception if the classifier has not been generated successfully
   */
  public void buildClassifier(Instances instances) throws Exception {
    // can classifier handle the data?
    getCapabilities().testWithFail(instances);

    // remove instances with missing class
    instances = new Instances(instances);
    instances.deleteWithMissingClass();
    
    double sumOfWeights = 0;

    m_Class = instances.classAttribute();
    m_ClassValue = 0;
    switch (instances.classAttribute().type()) {
      case Attribute.NUMERIC:
        m_Counts = null;
        break;
      case Attribute.NOMINAL:
        m_Counts = new double [instances.numClasses()];
        for (int i = 0; i < m_Counts.length; i++) {
          m_Counts[i] = 1;
        }
        sumOfWeights = instances.numClasses();
        break;
    }
    Enumeration enu = instances.enumerateInstances();
    while (enu.hasMoreElements()) {
      Instance instance = (Instance) enu.nextElement();
      if (!instance.classIsMissing()) {
        if (instances.classAttribute().isNominal()) {
          m_Counts[(int)instance.classValue()] += instance.weight();
        } else {
          m_ClassValue += instance.weight() * instance.classValue();
        }
        sumOfWeights += instance.weight();
      }
    }
    if (instances.classAttribute().isNumeric()) {
      if (Utils.gr(sumOfWeights, 0)) {
        m_ClassValue /= sumOfWeights;
      }
    } else {
      m_ClassValue = Utils.maxIndex(m_Counts);
      Utils.normalize(m_Counts, sumOfWeights);
    }
  }

  /**
   * Classifies a given instance.
   *
   * @param instance the instance to be classified
   * @return index of the predicted class
   */
  public double classifyInstance(Instance instance) {

    return m_ClassValue;
  }

  /**
   * Calculates the class membership probabilities for the given test instance.
   *
   * @param instance the instance to be classified
   * @return predicted class probability distribution
   * @throws Exception if class is numeric
   */
  public double [] distributionForInstance(Instance instance) 
       throws Exception {
         
    if (m_Counts == null) {
      double[] result = new double[1];
      result[0] = m_ClassValue;
      return result;
    } else {
      return (double []) m_Counts.clone();
    }
  }

  /**
   * Returns a string that describes the classifier as source. The
   * classifier will be contained in a class with the given name (there may
   * be auxiliary classes),
   * and will contain a method with the signature:
   * <pre><code>
   * public static double classify(Object[] i);
   * </code></pre>
   * where the array <code>i</code> contains elements that are either
   * Double, String, with missing values represented as null. The generated
   * code is public domain and comes with no warranty.
   *
   * @param className the name that should be given to the source class.
   * @return the object source described by a string
   * @throws Exception if the souce can't be computed
   */
  public String toSource(String className) throws Exception {
    StringBuffer        result;
    
    result = new StringBuffer();
    
    result.append("class " + className + " {\n");
    result.append("  public static double classify(Object[] i) {\n");
    if (m_Counts != null)
      result.append("    // always predicts label '" + m_Class.value((int) m_ClassValue) + "'\n");
    result.append("    return " + m_ClassValue + ";\n");
    result.append("  }\n");
    result.append("}\n");
    
    return result.toString();
  }
 
  /**
   * Returns a description of the classifier.
   *
   * @return a description of the classifier as a string.
   */
  public String toString() {

    if (m_Class ==  null) {
      return "ZeroR: No model built yet.";
    }
    if (m_Counts == null) {
      return "ZeroR predicts class value: " + m_ClassValue;
    } else {
      return "ZeroR predicts class value: " + m_Class.value((int) m_ClassValue);
    }
  }
  
  /**
   * Returns the revision string.
   * 
   * @return            the revision
   */
  public String getRevision() {
    return RevisionUtils.extract("$Revision: 5928 $");
  }

  /**
   * Main method for testing this class.
   *
   * @param argv the options
   */
  public static void main(String [] argv) {
    runClassifier(new ZeroR(), argv);
  }
}