PLearn 0.1
MatrixOneHotSquaredLoss.cc
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00001 // -*- C++ -*-
00002 
00003 // PLearn (A C++ Machine Learning Library)
00004 // Copyright (C) 1998 Pascal Vincent
00005 // Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
00006 // Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
00007 // Copyright (C) 2002 Xiangdong Wang, Christian Dorion
00008 
00009 // Redistribution and use in source and binary forms, with or without
00010 // modification, are permitted provided that the following conditions are met:
00011 // 
00012 //  1. Redistributions of source code must retain the above copyright
00013 //     notice, this list of conditions and the following disclaimer.
00014 // 
00015 //  2. Redistributions in binary form must reproduce the above copyright
00016 //     notice, this list of conditions and the following disclaimer in the
00017 //     documentation and/or other materials provided with the distribution.
00018 // 
00019 //  3. The name of the authors may not be used to endorse or promote
00020 //     products derived from this software without specific prior written
00021 //     permission.
00022 // 
00023 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00024 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00025 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00026 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00027 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00028 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00029 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00030 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00031 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00032 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00033 // 
00034 // This file is part of the PLearn library. For more information on the PLearn
00035 // library, go to the PLearn Web site at www.plearn.org
00036 
00037 
00038 /* *******************************************************      
00039  * $Id: MatrixOneHotSquaredLoss.cc 4306 2005-10-23 02:42:13Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "MatrixOneHotSquaredLoss.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00051 PLEARN_IMPLEMENT_OBJECT(MatrixOneHotSquaredLoss,
00052                         "ONE LINE DESCR",
00053                         "NO HELP");
00054 
00055 MatrixOneHotSquaredLoss::MatrixOneHotSquaredLoss()
00056     : coldval_(0.0), hotval_(0.0)
00057 { }
00058   
00059 MatrixOneHotSquaredLoss::MatrixOneHotSquaredLoss(Variable* input1, Variable* input2, real coldval, real hotval)
00060     : inherited(input1,input2,input2->length(),input2->width()), coldval_(coldval), hotval_(hotval)
00061 {
00062     build_();
00063 }
00064 
00065 void
00066 MatrixOneHotSquaredLoss::build()
00067 {
00068     inherited::build();
00069     build_();
00070 }
00071 
00072 void
00073 MatrixOneHotSquaredLoss::build_()
00074 {
00075     if (input2 && !input2->isVec())
00076         PLERROR("In MatrixOneHotSquaredLoss: classnum must be a vector variable representing the indexs of netouts (typically some classnums)");
00077 }
00078 
00079 void
00080 MatrixOneHotSquaredLoss::declareOptions(OptionList &ol)
00081 {
00082     declareOption(ol, "coldval_", &MatrixOneHotSquaredLoss::coldval_, OptionBase::buildoption, "");
00083     declareOption(ol, "hotval_", &MatrixOneHotSquaredLoss::hotval_, OptionBase::buildoption, "");
00084     inherited::declareOptions(ol);
00085 }
00086 
00087 void MatrixOneHotSquaredLoss::recomputeSize(int& l, int& w) const
00088 {
00089     if (input2) {
00090         l = input2->length();
00091         w = input2->width();
00092     } else
00093         l = w = 0;
00094 }
00095 
00096 void MatrixOneHotSquaredLoss::fprop()
00097 {
00098     int n = input1->length();
00099     for (int k=0; k<length(); k++)
00100     {
00101         int classnum = (int) input2->valuedata[k];
00102         real res = 0.;
00103         for(int i=0; i<n; i++)
00104             res += square(input1->matValue[i][k] - (i==classnum ? hotval_ : coldval_));
00105         valuedata[k] = res;
00106     }
00107 }
00108 
00109 
00110 void MatrixOneHotSquaredLoss::bprop()
00111 {
00112     int n = input1->length();
00113     for(int k=0; k<length(); k++)
00114     {
00115         real gr = gradientdata[k];
00116         int classnum = (int) input2->valuedata[k];
00117         if (!fast_exact_is_equal(gr, 1.))
00118         {
00119             gr = gr+gr;
00120             for (int i=0; i<n; i++)
00121                 input1->matGradient[i][k] += gr*(input1->matValue[i][k] - (i==classnum ? hotval_ : coldval_));
00122         }
00123         else // specialised version for gr==1
00124         {
00125             for (int i=0; i<n; i++)
00126                 input1->matGradient[i][k] += two(input1->matValue[i][k] - (i==classnum ? hotval_ : coldval_));
00127         }
00128     }
00129 }
00130 
00131 
00132 void MatrixOneHotSquaredLoss::symbolicBprop()
00133 {
00134     PLERROR("MatrixOneHotSquaredLoss::symbolicBprop not implemented.");
00135 }
00136 
00137 
00138 
00139 } // end of namespace PLearn
00140 
00141 
00142 /*
00143   Local Variables:
00144   mode:c++
00145   c-basic-offset:4
00146   c-file-style:"stroustrup"
00147   c-file-offsets:((innamespace . 0)(inline-open . 0))
00148   indent-tabs-mode:nil
00149   fill-column:79
00150   End:
00151 */
00152 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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