ExpVariable.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
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//     products derived from this software without specific prior written
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/* *******************************************************      
   * $Id: ExpVariable.cc,v 1.6 2004/04/27 16:02:26 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "ExpVariable.h"
#include "Var_operators.h"
//#include "Var_utils.h"

namespace PLearn {
using namespace std;


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

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

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


void ExpVariable::fprop()
{
  for(int i=0; i<nelems(); i++)
    valuedata[i] = safeexp(input->valuedata[i]);
}


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


// void ExpVariable::bbprop()
// {
//   if (input->diaghessian.length()==0)
//     input->resizeDiagHessian();
//   for(int i=0; i<nelems(); i++)
//     {
//       real yi = valuedata[i];
//       input->diaghessiandata[i] += diaghessiandata[i] * yi*yi + gradientdata[i] * yi;
//     }
// }

void ExpVariable::symbolicBprop()
{
  input->accg(g * Var(this));
}


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



} // end of namespace PLearn

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