SubsampleVariable.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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/* *******************************************************      
   * $Id: SubsampleVariable.cc,v 1.5 2004/04/27 16:03:35 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "SubsampleVariable.h"

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(SubsampleVariable,
                        "A subsample var; equals subsample(input, the_subsamplefactor)",
                        "NO HELP");

SubsampleVariable::SubsampleVariable(Variable* input, int the_subsamplefactor) 
  : inherited(input, input->length()/the_subsamplefactor, input->width()/the_subsamplefactor), 
    subsamplefactor(the_subsamplefactor) 
{
    build_();
}

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

void
SubsampleVariable::build_()
{
    if (input) {
        if (input->length() % subsamplefactor != 0 || input->width() % subsamplefactor != 0)
            PLERROR("In SubsampleVariable constructor: Dimensions of input are not dividable by subsamplefactor");
    }
}

void
SubsampleVariable::declareOptions(OptionList &ol)
{
    declareOption(ol, "subsamplefactor", &SubsampleVariable::subsamplefactor, OptionBase::buildoption, "");
    inherited::declareOptions(ol);
}

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

void SubsampleVariable::fprop()
{
  subsample(input->matValue, subsamplefactor, matValue);
}


void SubsampleVariable::bprop()
{
  int norm = subsamplefactor * subsamplefactor;
  for(int i=0; i<length(); i++)
    for(int j=0; j<width(); j++)
      {
        real* inputgradientptr = input->matGradient[subsamplefactor*i]+subsamplefactor*j;
        real thisgradient = matGradient(i,j);
        for(int l=0; l<subsamplefactor; l++, inputgradientptr += input->matGradient.mod())
          for(int c=0; c<subsamplefactor; c++)
            {
              inputgradientptr[c] = thisgradient/norm;
            }
      }
}


void SubsampleVariable::symbolicBprop()
{ PLERROR("SubsampleVariable::symbolicBprop() not yet implemented"); }



} // end of namespace PLearn

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