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.evaluation.BilinearEvaluator;
035import org.openimaj.ml.linear.learner.IncrementalBilinearSparseOnlineLearner;
036import org.openimaj.util.data.Context;
037import org.openimaj.util.function.Function;
038import org.openimaj.util.pair.IndependentPair;
039
040/**
041 * Given a new state from which to train an {@link IncrementalBilinearSparseOnlineLearner},
042 * This function:
043 *      - evaluates an old learner on the new state,
044 *      - selects important words from the new learner
045 *
046 * @author Sina Samangooei (ss@ecs.soton.ac.uk)
047 *
048 */
049public final class IncrementalLearnerWorldSelectingEvaluator
050                implements
051                        Function<Context,Context>
052{
053
054        BilinearEvaluator eval;
055        private IncrementalLearnerFunction func;
056        private IncrementalBilinearSparseOnlineLearner learner ;
057
058
059        /**
060         * The evaluation to apply before learning, the function to feed examples for learning
061         * @param eval
062         * @param func
063         */
064        public IncrementalLearnerWorldSelectingEvaluator(BilinearEvaluator eval, IncrementalLearnerFunction func) {
065                this.eval = eval;
066                this.func = func;
067                this.learner = null;
068        }
069        @Override
070        public Context apply(Context in)
071        {
072                ModelStats modelStats = null;
073                if(learner!=null){
074                        modelStats = new ModelStats(eval, learner, in);
075                }
076                Map<String,Map<String,Double>> x = in.getTyped("bagofwords");
077                Map<String,Double> y = in.getTyped("averageticks");
078                learner = func.apply(IndependentPair.pair(x, y));
079                if(modelStats == null) modelStats = new ModelStats();
080
081                in.put("modelstats",modelStats);
082                return in;
083        }
084
085}