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}