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.matlib.loss;
031
032import org.openimaj.math.matrix.MatlibMatrixUtils;
033
034import ch.akuhn.matrix.Matrix;
035import ch.akuhn.matrix.SparseMatrix;
036
037
038public class MatLossFunction extends LossFunction{
039        
040        private LossFunction f;
041        public MatLossFunction(LossFunction f) {
042                this.f = f;
043        }
044        
045        @Override
046        public void setX(Matrix X) {
047                super.setX(X);
048                f.setX(X);
049        }
050        
051        @Override
052        public void setY(Matrix Y) {
053                super.setY(Y);
054                f.setY(Y);
055        }
056        
057        @Override
058        public void setBias(Matrix bias) {
059                super.setBias(bias);
060                f.setBias(bias);
061        }
062        @Override
063        public Matrix gradient(Matrix W) {
064                SparseMatrix ret = SparseMatrix.sparse(W.rowCount(), W.columnCount());
065                int allRowsY = Y.rowCount()-1;
066                int allRowsW = W.rowCount()-1;
067                for (int i = 0; i < Y.columnCount(); i++) {
068                        this.f.setY(MatlibMatrixUtils.subMatrix(Y, 0, allRowsY, i, i));
069                        if(bias!=null) this.f.setBias(MatlibMatrixUtils.subMatrix(bias, 0, allRowsY, i, i));
070                        Matrix w = MatlibMatrixUtils.subMatrix(W, 0, allRowsW, i, i);
071                        Matrix submatrix = f.gradient(w);
072                        MatlibMatrixUtils.setSubMatrix(ret, 0, i, submatrix);
073                }
074                return ret;
075        }
076
077        @Override
078        public double eval(Matrix W) {
079                double total = 0;
080                f.setBias(this.bias);
081                total += f.eval(W);
082                return total;
083        }
084
085        @Override
086        public boolean isMatrixLoss() {
087                return true;
088        }
089
090}