SquaredErrorCostFunction.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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//  2. Redistributions in binary form must reproduce the above copyright
//     notice, this list of conditions and the following disclaimer in the
//     documentation and/or other materials provided with the distribution.
// 
//  3. The name of the authors may not be used to endorse or promote
//     products derived from this software without specific prior written
//     permission.
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// THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
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/* *******************************************************      
   * $Id: SquaredErrorCostFunction.cc,v 1.4 2004/04/07 23:15:17 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "SelectedOutputCostFunction.h"
#include "SquaredErrorCostFunction.h"

namespace PLearn {
using namespace std;


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

real SquaredErrorCostFunction::evaluate(const Vec& output, const Vec& target) const
{
#ifdef BOUNDCHECK
  if(target.length()!=output.length() && classification==false)
    PLERROR("In SquaredErrorCostFunction::evaluate target.length() %d should be equal to output.length() %d",target.length(),output.length());
#endif

  real result = 0.0;
  if (targetindex>=0)
    result = square(output[targetindex]-target[targetindex]);
  else
    {
      real* outputdata = output.data();
      real* targetdata = target.data();
      if (classification) {
        if (target.length() != 1)
          PLERROR("In SquaredErrorCostFunction::evaluate target.length() %s should be 1", target.length());

        for (int i = 0; i < output.length(); ++i)
          if (i == targetdata[0])
            result += square(outputdata[i] - hotvalue);
          else
            result += square(outputdata[i] - coldvalue);
      } else {
        for(int i=0; i<output.length(); i++)
          result += square(outputdata[i]-targetdata[i]);
      }
    }
  return result;
}

void SquaredErrorCostFunction::declareOptions(OptionList &ol)
{
    declareOption(ol, "targetindex", &SquaredErrorCostFunction::targetindex, OptionBase::buildoption, "Index of target");
    declareOption(ol, "hotvalue", &SquaredErrorCostFunction::hotvalue, OptionBase::buildoption, "Hot value");
    declareOption(ol, "coldvalue", &SquaredErrorCostFunction::coldvalue, OptionBase::buildoption, "Cold value");
    declareOption(ol, "classification", &SquaredErrorCostFunction::classification, OptionBase::buildoption, "Used as classification cost");
    inherited::declareOptions(ol);
}

CostFunc squared_error(int singleoutputindex)
{ 
  if(singleoutputindex>=0)
    return new SelectedOutputCostFunction(new SquaredErrorCostFunction(),singleoutputindex); 
  else
    return new SquaredErrorCostFunction();
}

} // end of namespace PLearn

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