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