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.linear.learner.regul;
031
032import no.uib.cipr.matrix.Vector.Norm;
033
034import org.apache.log4j.Logger;
035import org.openimaj.math.matrix.CFMatrixUtils;
036
037import gov.sandia.cognition.math.matrix.Matrix;
038import gov.sandia.cognition.math.matrix.Vector;
039import gov.sandia.cognition.math.matrix.mtj.AbstractMTJMatrix;
040import gov.sandia.cognition.math.matrix.mtj.AbstractMTJVector;
041import gov.sandia.cognition.math.matrix.mtj.SparseMatrix;
042import gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ;
043import gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix;
044import gov.sandia.cognition.math.matrix.mtj.SparseVector;
045import gov.sandia.cognition.math.matrix.mtj.SparseVectorFactoryMTJ;
046
047public class L1L2Regulariser implements Regulariser{
048
049        private static final Logger logger = Logger.getLogger(L1L2Regulariser.class);
050
051        @Override
052        public Matrix prox(Matrix W, double lambda) {
053                int nrows = W.getNumRows();
054                Matrix ret = SparseMatrixFactoryMTJ.INSTANCE.createMatrix(W.getNumRows(), W.getNumColumns());
055                SparseRowMatrix Wrow = CFMatrixUtils.asSparseRow(W);
056//              Matrix Wrow = W;
057                ret = CFMatrixUtils.asSparseRow(ret);
058                
059                for (int r = 0; r < nrows; r++) {
060//                      Vector row = W.getRow(r);
061                        SparseVector row = Wrow.getRow(r);
062                        double rownorm = row.norm2();
063                        if(rownorm > lambda){
064                                double scal = (rownorm - lambda)/rownorm;
065                                Vector scaled = row.scale(scal);
066                                ret.setRow(r,scaled);
067                        }
068                }
069                return CFMatrixUtils.asSparseColumn(ret);
070        }
071
072}