MiniBatchClassificationLossVariable.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

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// 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: MiniBatchClassificationLossVariable.cc,v 1.5 2004/04/27 15:58:16 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "MiniBatchClassificationLossVariable.h"

namespace PLearn {
using namespace std;


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

MiniBatchClassificationLossVariable::MiniBatchClassificationLossVariable(Variable* netout, Variable* classnum)
  : inherited(netout,classnum,classnum->length(),classnum->width())
{
    build_();
}

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

void
MiniBatchClassificationLossVariable::build_()
{
    // input2 is classnum from constructor
    if(input2 && !input2->isVec())
        PLERROR("In MiniBatchClassificationLossVariable: classnum must be a vector variable representing the indexs of netout (typically class numbers)");
}


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

void MiniBatchClassificationLossVariable::fprop()
{
  int n = input2->size();
  if(input1->length()==n)
    for (int i=0; i<n; i++)
      {
      int topscorepos = argmax(input1->matValue.row(i));
      int num = int(input2->valuedata[i]);
      valuedata[i] = (topscorepos==num ?0 :1);
      }
  else if(input1->width()==n)
    for (int i=0; i<n; i++)
      {
      int topscorepos = argmax(input1->matValue.column(i));
      int num = int(input2->valuedata[i]);
      valuedata[i] = (topscorepos==num ?0 :1);
      }
  else PLERROR("In MiniBatchClassificationLossVariable: The length or width of netout doesn't equal to the size of classnum");
}


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



} // end of namespace PLearn

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