MatrixOneHotSquaredLoss.cc

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// -*- C++ -*-

// PLearn (A C++ Machine Learning Library)
// Copyright (C) 1998 Pascal Vincent
// Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
// Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
// Copyright (C) 2002 Xiangdong Wang, Christian Dorion

// Redistribution and use in source and binary forms, with or without
// modification, are permitted provided that the following conditions are met:
// 
//  1. Redistributions of source code must retain the above copyright
//     notice, this list of conditions and the following disclaimer.
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/* *******************************************************      
   * $Id: MatrixOneHotSquaredLoss.cc,v 1.5 2004/04/27 16:03:35 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "MatrixOneHotSquaredLoss.h"

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(MatrixOneHotSquaredLoss,
                        "ONE LINE DESCR",
                        "NO HELP");

MatrixOneHotSquaredLoss::MatrixOneHotSquaredLoss(Variable* input1, Variable* input2, real coldval, real hotval)
  : inherited(input1,input2,input2->length(),input2->width()), coldval_(coldval), hotval_(hotval)
{
    build_();
}

void
MatrixOneHotSquaredLoss::build()
{
    inherited::build();
    build_();
}

void
MatrixOneHotSquaredLoss::build_()
{
    if (input2 && !input2->isVec())
        PLERROR("In MatrixOneHotSquaredLoss: classnum must be a vector variable representing the indexs of netouts (typically some classnums)");
}

void
MatrixOneHotSquaredLoss::declareOptions(OptionList &ol)
{
    declareOption(ol, "coldval_", &MatrixOneHotSquaredLoss::coldval_, OptionBase::buildoption, "");
    declareOption(ol, "hotval_", &MatrixOneHotSquaredLoss::hotval_, OptionBase::buildoption, "");
    inherited::declareOptions(ol);
}

void MatrixOneHotSquaredLoss::recomputeSize(int& l, int& w) const
{
    if (input2) {
        l = input2->length();
        w = input2->width();
    } else
        l = w = 0;
}

void MatrixOneHotSquaredLoss::fprop()
{
  int n = input1->length();
  for (int k=0; k<length(); k++)
   {
    int classnum = (int) input2->valuedata[k];
    real res = 0.;
    for(int i=0; i<n; i++)
        res += square(input1->matValue[i][k] - (i==classnum ? hotval_ : coldval_));
    valuedata[k] = res;
    }
}


void MatrixOneHotSquaredLoss::bprop()
{
  int n = input1->length();
  for(int k=0; k<length(); k++)
     {
     real gr = gradientdata[k];
     int classnum = (int) input2->valuedata[k];
     if (gr!=1.)
        {
        gr = gr+gr;
        for (int i=0; i<n; i++)
           input1->matGradient[i][k] += gr*(input1->matValue[i][k] - (i==classnum ? hotval_ : coldval_));
        }
        else // specialised version for gr==1
           {
           for (int i=0; i<n; i++)
               input1->matGradient[i][k] += two(input1->matValue[i][k] - (i==classnum ? hotval_ : coldval_));
           }
     }
}


void MatrixOneHotSquaredLoss::symbolicBprop()
{
  PLERROR("MatrixOneHotSquaredLoss::symbolicBprop not implemented.");
}



} // end of namespace PLearn

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