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 gov.sandia.cognition.math.matrix.Matrix;
033import gov.sandia.cognition.math.matrix.Vector;
034import gov.sandia.cognition.math.matrix.mtj.SparseMatrixFactoryMTJ;
035import gov.sandia.cognition.math.matrix.mtj.SparseRowMatrix;
036import gov.sandia.cognition.math.matrix.mtj.SparseVector;
037
038import org.openimaj.math.matrix.CFMatrixUtils;
039
040public class L1L2Regulariser implements Regulariser {
041
042        @Override
043        public Matrix prox(Matrix W, double lambda) {
044                final int nrows = W.getNumRows();
045                Matrix ret = SparseMatrixFactoryMTJ.INSTANCE.createMatrix(W.getNumRows(), W.getNumColumns());
046                final SparseRowMatrix Wrow = CFMatrixUtils.asSparseRow(W);
047                // Matrix Wrow = W;
048                ret = CFMatrixUtils.asSparseRow(ret);
049
050                for (int r = 0; r < nrows; r++) {
051                        // Vector row = W.getRow(r);
052                        final SparseVector row = Wrow.getRow(r);
053                        final double rownorm = row.norm2();
054                        if (rownorm > lambda) {
055                                final double scal = (rownorm - lambda) / rownorm;
056                                final Vector scaled = row.scale(scal);
057                                ret.setRow(r, scaled);
058                        }
059                }
060                return CFMatrixUtils.asSparseColumn(ret);
061        }
062
063}