TimesVariable.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
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/* *******************************************************      
   * $Id: TimesVariable.cc,v 1.6 2004/04/27 15:58:16 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

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

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(TimesVariable,
                        "Multiplies 2 matrix vars of same size elementwise", 
                        "NO HELP");

TimesVariable::TimesVariable(Variable* input1, Variable* input2)
  : inherited(input1, input2, input1->length(), input1->width())
{
    build_();
}

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

void
TimesVariable::build_()
{
    if (input1 && input2) {
        if(input1->length() != input2->length() || input1->width() != input2->width())
            PLERROR("In TimesVariable: input1 and input2 must have exactly the same size");
    }
}


void TimesVariable::recomputeSize(int& l, int& w) const
{
    if (input1 && input2) {
        l = input1->length();
        w = input1->width();
    } else
        l = w = 0;
}

void TimesVariable::fprop()
{
  for(int k=0; k<nelems(); k++)
    valuedata[k] = input1->valuedata[k]*input2->valuedata[k];
}


void TimesVariable::bprop()
{
  for(int k=0; k<nelems(); k++)
    {
      input1->gradientdata[k] += input2->valuedata[k]*gradientdata[k];
      input2->gradientdata[k] += input1->valuedata[k]*gradientdata[k];
    }
}


void TimesVariable::symbolicBprop()
{
  input1->accg(g*input2);
  input2->accg(g*input1);
}



} // end of namespace PLearn

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