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.demos.sandbox.ml.linear.learner.stream;
031
032import java.util.Map;
033
034import org.openimaj.ml.linear.learner.BilinearLearnerParameters;
035import org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner;
036import org.openimaj.util.function.Function;
037import org.openimaj.util.pair.IndependentPair;
038
039/**
040 * Consumes Y and X instances for an {@link IncrementalBilinearSparseOnlineLearner} which are used
041 * to measure loss of an underlying model, and then used to train the underlying model.
042 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
043 *
044 */
045public class IncrementalLearnerFunction implements Function<
046                IndependentPair<
047                        Map<String,Map<String,Double>>,
048                        Map<String,Double>
049                >,
050                IncrementalBilinearSparseOnlineLearner> {
051
052        final IncrementalBilinearSparseOnlineLearner learner;
053
054        /**
055         * Constructs the underlying learner to train
056         */
057        public IncrementalLearnerFunction() {
058                this.learner = new IncrementalBilinearSparseOnlineLearner();
059        }
060
061        /**
062         * Feeds the parameters to a new learner to train
063         * @param params
064         */
065        public IncrementalLearnerFunction(BilinearLearnerParameters params) {
066                this.learner = new IncrementalBilinearSparseOnlineLearner(params);
067        }
068        /**
069         * Takes an existing learner and continues training it.
070         * @param learner
071         */
072        public IncrementalLearnerFunction(IncrementalBilinearSparseOnlineLearner learner) {
073                this.learner = learner;
074        }
075
076
077
078        @Override
079        public IncrementalBilinearSparseOnlineLearner apply(IndependentPair<Map<String, Map<String, Double>>,Map<String, Double>> in)
080        {
081                learner.process(in.getFirstObject(),in.getSecondObject());
082                System.out.printf("Learner has learnt %d words\n",learner.getVocabulary().size());
083                System.out.printf("Learner has learnt %d users\n",learner.getUsers().size());
084                return this.learner;
085        }
086
087}
088