PLearn 0.1
OneHotSquaredLoss.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: OneHotSquaredLoss.cc 4306 2005-10-23 02:42:13Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "OneHotSquaredLoss.h"
00044 #include "RowAtPositionVariable.h"
00045 #include "Var_operators.h"
00046 //#include "Var_utils.h"
00047 
00048 namespace PLearn {
00049 using namespace std;
00050 
00051 
00054 PLEARN_IMPLEMENT_OBJECT(OneHotSquaredLoss,
00055                         "Computes sum(square_i(netout[i]-(i==classnum ?hotval :coldval))",
00056                         "NO HELP");
00057 
00058 OneHotSquaredLoss::OneHotSquaredLoss()
00059     : coldval_(0.), hotval_(0.)
00060 { }
00061   
00062 OneHotSquaredLoss::OneHotSquaredLoss(Variable* netout, Variable* classnum, real coldval, real hotval)
00063     : inherited(netout,classnum,1,1), coldval_(coldval), hotval_(hotval)
00064 {
00065     build_();
00066 }
00067 
00068 void
00069 OneHotSquaredLoss::build()
00070 {
00071     inherited::build();
00072     build_();
00073 }
00074 
00075 void
00076 OneHotSquaredLoss::build_()
00077 {
00078     // input2 is classnum from constructor
00079     if(input2 && !input2->isScalar())
00080         PLERROR("In OneHotSquaredLoss: classnum must be a scalar variable representing an index of netout (typically a classnum)");
00081 }
00082 
00083 void
00084 OneHotSquaredLoss::declareOptions(OptionList &ol)
00085 {
00086     declareOption(ol, "coldval_", &OneHotSquaredLoss::coldval_, OptionBase::buildoption, "");
00087     declareOption(ol, "hotval_", &OneHotSquaredLoss::hotval_, OptionBase::buildoption, "");
00088     inherited::declareOptions(ol);
00089 }
00090 
00091 void OneHotSquaredLoss::recomputeSize(int& l, int& w) const
00092 { l=1, w=1; }
00093   
00094 void OneHotSquaredLoss::fprop()
00095 {
00096     real* netout = input1->valuedata;
00097     int n = input1->value.size();
00098     int classnum = (int) input2->valuedata[0];
00099     real res = 0.;
00100     for(int i=0; i<n; i++)
00101         res += square(*netout++ - (i==classnum ? hotval_ : coldval_));
00102     *valuedata = res;
00103 }
00104 
00105 
00106 void OneHotSquaredLoss::bprop()
00107 {
00108     real gr = *gradientdata;
00109     real* netout = input1->valuedata;
00110     int n = input1->value.size();
00111     int classnum = (int) input2->valuedata[0];
00112     real* input1grptr = input1->gradientdata;
00113     if(!fast_exact_is_equal(gr, 1.))
00114     {
00115         gr = gr+gr;
00116         for(int i=0; i<n; i++)
00117             *input1grptr++ += gr*(*netout++ - (i==classnum ? hotval_ : coldval_));
00118     }
00119     else // specialised version for gr==1
00120     {
00121         for(int i=0; i<n; i++)
00122             input1->gradientdata[i] += two(netout[i] - (i==classnum ? hotval_ : coldval_));        
00123     }
00124 }
00125 
00126 
00127 void OneHotSquaredLoss::symbolicBprop()
00128 {
00129     Var gi =  g*(input1 - coldval_);
00130     Var gindex = new RowAtPositionVariable(g*(coldval_-hotval_), input2, input1->length());
00131     Var ginput = gi + gindex;
00132     input1->accg(ginput+ginput); //2*gi
00133 }
00134 
00135 
00136 void OneHotSquaredLoss::rfprop()
00137 {
00138     int n = input1->value.size();
00139     int classnum = (int) input2->valuedata[0];
00140     real sum = 0;
00141     for (int i=0; i<n; i++)
00142         sum += 2 * input1->rvaluedata[i] * (input1->valuedata[i] - (i==classnum ? hotval_ : coldval_));
00143     rvaluedata[0] = sum;
00144 }
00145 
00146 
00147 
00148 } // end of namespace PLearn
00149 
00150 
00151 /*
00152   Local Variables:
00153   mode:c++
00154   c-basic-offset:4
00155   c-file-style:"stroustrup"
00156   c-file-offsets:((innamespace . 0)(inline-open . 0))
00157   indent-tabs-mode:nil
00158   fill-column:79
00159   End:
00160 */
00161 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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