SquareVariable.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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/* *******************************************************      
   * $Id: SquareVariable.cc,v 1.6 2004/04/27 16:02:26 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

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

namespace PLearn {
using namespace std;


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

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

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


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


void SquareVariable::bprop()
{
  int n=nelems();
  for(int i=0; i<n; i++)
    input->gradientdata[i] += 2.0 * input->valuedata[i] * gradientdata[i];
}


void SquareVariable::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] += 4.0 * input_i * input_i * diaghessiandata[i]
      + 2.0 * gradientdata[i];
  }
}


void SquareVariable::symbolicBprop()
{
  input->accg(2. * (g * input));
}


void SquareVariable::rfprop()
{
  if (rValue.length()==0) resizeRValue();
  int n=nelems();
  for(int i=0; i<n; i++)
    rvaluedata[i] = 2*input->valuedata[i]*input->rvaluedata[i];
}



} // end of namespace PLearn

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