LogVariable.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.
// 
//  2. Redistributions in binary form must reproduce the above copyright
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/* *******************************************************      
   * $Id: LogVariable.cc,v 1.10 2004/07/21 16:30:54 chrish42 Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "LogVariable.h"
#include <plearn/display/DisplayUtils.h>
#include "Var_operators.h"

namespace PLearn {
using namespace std;


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

LogVariable::LogVariable(Variable* input)
  : inherited(input, input->length(), input->width())
{}

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

void LogVariable::fprop()
{
  for(int i=0; i<nelems(); i++)
  {
    valuedata[i] = safeflog(input->valuedata[i]);
#ifdef BOUNDCHECK
    if (!finite(valuedata[i]) && finite(input->valuedata[i]))
    {
      //PLWARNING("LogVariable::fprop qqchose va pas");
      cout << "inputdata[i]= " << input->valuedata[i] << endl;
      cout << "valuedata[i]= " << valuedata[i] << endl;
      displayVarGraph(this, true, 250);
      PLERROR("LogVariable::fprop qqchose va pas");
    }
#endif
  }
}


void LogVariable::bprop()
{
  for(int i=0; i<nelems(); i++)
    if (input->valuedata[i]>0)
      input->gradientdata[i] += gradientdata[i]/input->valuedata[i];
}


void LogVariable::symbolicBprop()
{
  input->accg(g / input);
}


// R{log(x)} = 1/x R(x)
void LogVariable::rfprop()
{
  if (rValue.length()==0) resizeRValue();
  for(int i=0; i<nelems(); i++)
    rvaluedata[i] = input->rvaluedata[i] / input->valuedata[i];
}

} // end of namespace PLearn

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