001/**
002 * Copyright (c) 2011, The University of Southampton and the individual contributors.
003 * All rights reserved.
004 *
005 * Redistribution and use in source and binary forms, with or without modification,
006 * are permitted provided that the following conditions are met:
007 *
008 *   *  Redistributions of source code must retain the above copyright notice,
009 *      this list of conditions and the following disclaimer.
010 *
011 *   *  Redistributions in binary form must reproduce the above copyright notice,
012 *      this list of conditions and the following disclaimer in the documentation
013 *      and/or other materials provided with the distribution.
014 *
015 *   *  Neither the name of the University of Southampton nor the names of its
016 *      contributors may be used to endorse or promote products derived from this
017 *      software without specific prior written permission.
018 *
019 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
020 * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
021 * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
022 * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR
023 * ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
024 * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
025 * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON
026 * ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
027 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
028 * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
029 */
030package org.openimaj.experiment.evaluation.classification;
031
032import java.util.Set;
033
034/**
035 * The result of a {@link Classifier}. The {@link ClassificationResult} models
036 * one or more assigned classes together with associated confidence values for
037 * each class.
038 * <p>
039 * Normally, confidence values are bounded between 0 and 1.
040 *
041 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
042 *
043 * @param <CLASS>
044 *            type of classes predicted
045 */
046public interface ClassificationResult<CLASS> {
047        /**
048         * Get the confidence associated with the given class. If the class is
049         * unknown, then this method should return 0.
050         * <p>
051         * Normally, confidence values are bounded between 0 and 1.
052         *
053         * @param clazz
054         *            the target class.
055         * @return the confidence in assigning the class.
056         */
057        public double getConfidence(CLASS clazz);
058
059        /**
060         * Get the set of classes predicted by this result. Usually, this is a
061         * subset of all classes; specifically those with a high confidence.
062         *
063         * @return the set of predicted classes.
064         */
065        public Set<CLASS> getPredictedClasses();
066}