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1   /*
2    * Copyright (C) 2003-2004 Christian Siefkes <christian@siefkes.net>.
3    * Development of this software is supported by the German Research Society,
4    * Berlin-Brandenburg Graduate School in Distributed Information Systems
5    * (DFG grant no. GRK 316).
6    *
7    * This library is free software; you can redistribute it and/or
8    * modify it under the terms of the GNU Lesser General Public
9    * License as published by the Free Software Foundation; either
10   * version 2.1 of the License, or (at your option) any later version.
11   *
12   * This library is distributed in the hope that it will be useful,
13   * but WITHOUT ANY WARRANTY; without even the implied warranty of
14   * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
15   * Lesser General Public License for more details.
16   *
17   * You should have received a copy of the GNU Lesser General Public
18   * License along with this library; if not, visit
19   * http://www.gnu.org/licenses/lgpl.html or write to the Free Software
20   * Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307, USA.
21   */
22  package de.fu_berlin.ties.classify;
23  
24  import java.util.Set;
25  
26  import de.fu_berlin.ties.ProcessingException;
27  import de.fu_berlin.ties.classify.feature.FeatureVector;
28  
29  /***
30   * Classes implementing this interface must be able to classify items
31   * represented by feature vectors.
32   *
33   * @author Christian Siefkes
34   * @version $Revision: 1.6 $, $Date: 2004/04/14 08:45:30 $, $Author: siefkes $
35   */
36  public interface Classifier {
37  
38      /***
39       * Base configuration key for classifiers.
40       */
41      String CONFIG_CLASSIFIER = "classifier";
42  
43      /***
44       * Classifies an item that is represented by a feature vector by choosing
45       * the most probable class among a set of candidate classes.
46       *
47       * @param features the feature vector to consider
48       * @param candidateClasses an set of classes that are allowed for this item
49       * @return the result of the classification; you can call
50       * {@link PredictionDistribution#best()} to get the most probably class
51       * @throws ProcessingException if an error occurs during classification
52       */
53      PredictionDistribution classify(final FeatureVector features,
54              final Set candidateClasses) throws ProcessingException;
55  
56  }