content: /*
   *    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.
   */
  
  /*
   *    Classifier.java
   *    Copyright (C) 1999 University of Waikato, Hamilton, New Zealand
   *
   */
  
  package weka.classifiers;
  
  import weka.core.Attribute;
  import weka.core.Capabilities;
  import weka.core.CapabilitiesHandler;
  import weka.core.Instance;
  import weka.core.Instances;
  import weka.core.Option;
  import weka.core.OptionHandler;
  import weka.core.RevisionHandler;
  import weka.core.RevisionUtils;
  import weka.core.SerializedObject;
  import weka.core.Utils;
  
  import java.io.Serializable;
  import java.util.Enumeration;
  import java.util.Set;
  import java.util.Vector;
  
  /**
   * Classifier interface. All schemes for numeric or nominal prediction in
   * Weka implement this interface. Note that a classifier MUST either implement
   * distributionForInstance() or classifyInstance().
   *
   * @author Eibe Frank (eibe@cs.waikato.ac.nz)
   * @author Len Trigg (trigg@cs.waikato.ac.nz)
   * @version $Revision: 6041 $
   */
  public interface Classifier {
  
    /**
     * Generates a classifier. Must initialize all fields of the classifier
     * that are not being set via options (ie. multiple calls of buildClassifier
     * must always lead to the same result). Must not change the dataset
     * in any way.
     *
     * @param data set of instances serving as training data
     * @exception Exception if the classifier has not been
     * generated successfully
     */
    public abstract void buildClassifier(Instances data) throws Exception;
  
    /**
     * Classifies the given test instance. The instance has to belong to a
     * dataset when it's being classified. Note that a classifier MUST
     * implement either this or distributionForInstance().
     *
     * @param instance the instance to be classified
     * @return the predicted most likely class for the instance or
     * Utils.missingValue() if no prediction is made
     * @exception Exception if an error occurred during the prediction
     */
    public double classifyInstance(Instance instance) throws Exception;
  
    /**
     * Predicts the class memberships for a given instance. If
     * an instance is unclassified, the returned array elements
     * must be all zero. If the class is numeric, the array
     * must consist of only one element, which contains the
     * predicted value. Note that a classifier MUST implement
     * either this or classifyInstance().
     *
     * @param instance the instance to be classified
     * @return an array containing the estimated membership
     * probabilities of the test instance in each class
     * or the numeric prediction
     * @exception Exception if distribution could not be
     * computed successfully
     */
    public double[] distributionForInstance(Instance instance) throws Exception;
  
    /**
     * Returns the Capabilities of this classifier. Maximally permissive
     * capabilities are allowed by default. Derived classifiers should
     * override this method and first disable all capabilities and then
     * enable just those capabilities that make sense for the scheme.
     *
     * @return            the capabilities of this object
     * @see               Capabilities
     */
    public Capabilities getCapabilities();
  }
  
  