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.loss;
031
032import gov.sandia.cognition.math.matrix.Matrix;
033
034import org.apache.log4j.Logger;
035
036public class SquareMissingLossFunction extends LossFunction {
037        Logger logger = Logger.getLogger(SquareMissingLossFunction.class);
038
039        @Override
040        public Matrix gradient(Matrix W) {
041                final Matrix resid = X.times(W).minus(Y);
042                if (this.bias != null)
043                        resid.plusEquals(this.bias);
044                for (int r = 0; r < Y.getNumRows(); r++) {
045                        final double yc = Y.getElement(r, 0);
046                        if (Double.isNaN(yc)) {
047                                resid.setElement(r, 0, 0);
048                        }
049                }
050                return X.transpose().times(resid);
051        }
052
053        @Override
054        public double eval(Matrix W) {
055                Matrix v;
056                if (W == null) {
057                        v = this.X;
058                }
059                else {
060                        v = X.times(W);
061                }
062                final Matrix vWithoutBias = v.clone();
063                if (this.bias != null)
064                        v.plusEquals(this.bias);
065                double sum = 0;
066                for (int r = 0; r < Y.getNumRows(); r++) {
067                        for (int c = 0; c < Y.getNumColumns(); c++) {
068                                final double yr = Y.getElement(r, c);
069                                if (!Double.isNaN(yr)) {
070                                        final double val = v.getElement(r, c);
071                                        final double valNoBias = vWithoutBias.getElement(r, c);
072                                        final double delta = yr - val;
073                                        logger.debug(
074                                                        String.format(
075                                                                        "yr=%d,y=%3.2f,v=%3.2f,v(no bias)=%2.5f,delta=%2.5f",
076                                                                        r, yr, val, valNoBias, delta
077                                                                        )
078                                                        );
079                                        sum += delta * delta;
080                                }
081                        }
082                }
083                return sum;
084        }
085
086        @Override
087        public boolean isMatrixLoss() {
088                return false;
089        }
090
091}