SquareRootVariable.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: SquareRootVariable.cc,v 1.5 2004/04/27 16:02:26 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "SquareRootVariable.h"

namespace PLearn {
using namespace std;



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

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

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

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


// dC/dx = dC/dy * 1/2 * 1/sqrt(x)
void SquareRootVariable::bprop()
{
  if (rValue.length()==0) resizeRValue();
  int n=nelems();
  for(int i=0; i<n; i++)
    {
      input->gradientdata[i] += 0.5/sqrt(input->valuedata[i]) * gradientdata[i];
      rvaluedata[i] = 2*input->valuedata[i]*input->rvaluedata[i];
    }
}

// void SquareRootVariable::bbprop()
// {
//   if (input->diaghessian.length()==0)
//     input->resizeDiagHessian();
//   int n=nelems();
//   for(int i=0; i<n; i++)
//   {
//     real input_i = input->valuedata[i];
//     input->diaghessiandata[i] += 1/(4*input_i) * diaghessiandata[i]
//                                  + (-0.25) * pow( i/sqrt(input_i), 3 ) * gradientdata[i];
//   }
// }


} // end of namespace PLearn

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