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.ml.annotation;
031
032import java.util.List;
033
034import org.openimaj.data.dataset.GroupedDataset;
035import org.openimaj.data.dataset.ListDataset;
036import org.openimaj.ml.training.BatchTrainer;
037
038/**
039 * An {@link Annotator} that is trained in "batch" mode; all training examples
040 * are presented at once. Calling the {@link #train(List)} method more than once
041 * will cause the internal model to be re-initialised using the new data. If you
042 * want to implement an {@link Annotator} that can be updated, implement the
043 * {@link IncrementalAnnotator} interface instead.
044 *
045 * @author Jonathon Hare (jsh2@ecs.soton.ac.uk)
046 *
047 * @param <OBJECT>
048 *            Type of object being annotated
049 * @param <ANNOTATION>
050 *            Type of annotation
051 */
052public abstract class BatchAnnotator<OBJECT, ANNOTATION>
053                extends
054                AbstractAnnotator<OBJECT, ANNOTATION>
055                implements
056                BatchTrainer<Annotated<OBJECT, ANNOTATION>>
057{
058        /**
059         * Train the annotator with the given grouped dataset. Internally, the
060         * dataset is converted to a list containing exactly one reference to each
061         * object in the dataset with (potentially) multiple annotations.
062         *
063         * @param dataset
064         *            the dataset to train on
065         */
066        public void train(GroupedDataset<ANNOTATION, ? extends ListDataset<OBJECT>, OBJECT> dataset) {
067                train(AnnotatedObject.createList(dataset));
068        }
069}