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.apache.logging.log4j.Logger; 033import org.apache.logging.log4j.LogManager; 034 035import org.openimaj.math.matrix.MatlibMatrixUtils; 036 037import ch.akuhn.matrix.Matrix; 038import ch.akuhn.matrix.Vector; 039 040 041public class MatSquareLossFunction extends LossFunction{ 042 Logger logger = LogManager.getLogger(MatSquareLossFunction.class); 043 public MatSquareLossFunction() { 044 } 045 @Override 046 public Matrix gradient(Matrix W) { 047 Matrix ret = W.newInstance(); 048 Matrix resid = MatlibMatrixUtils.dotProduct(X, W); 049 if(this.bias!=null) 050 { 051 MatlibMatrixUtils.plusInplace(resid, this.bias); 052 } 053 MatlibMatrixUtils.minusInplace(resid, Y); 054 for (int t = 0; t < resid.columnCount(); t++) { 055 Vector row = this.X.row(t); 056 row.times(resid.get(t, t)); 057 MatlibMatrixUtils.setSubMatrixCol(ret, 0, t, row); 058 } 059 return ret; 060 } 061 @Override 062 public double eval(Matrix W) { 063 Matrix resid = null; 064 if(W == null){ 065 resid = X; 066 } else { 067 resid = MatlibMatrixUtils.dotProduct(X,W); 068 } 069 Matrix vnobias = MatlibMatrixUtils.copy(X); 070 if(this.bias!=null) 071 { 072 MatlibMatrixUtils.plusInplace(resid, bias); 073 } 074 Matrix v = MatlibMatrixUtils.copy(resid); 075 MatlibMatrixUtils.minusInplace(resid,Y); 076 double retval = 0; 077 078 for (int t = 0; t < resid.columnCount(); t++) { 079 double loss = resid.get(t, t); 080 retval += loss * loss; 081 logger.debug( 082 String.format( 083 "yr=%d,y=%3.2f,v=%3.2f,v(no bias)=%2.5f,error=%2.5f,serror=%2.5f", 084 t, Y.get(t, t), v.get(t, t), vnobias.get(t,t), loss, loss*loss 085 ) 086 ); 087 } 088 return retval; 089 } 090 @Override 091 public boolean isMatrixLoss() { 092 return true; 093 } 094}