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

#include "WeightedSumSquareVariable.h"
#include "Var_operators.h"

namespace PLearn {
using namespace std;

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

WeightedSumSquareVariable::WeightedSumSquareVariable(Variable* input, Variable* weights)
  : inherited(input,weights,1,1)
{
    build_();
}

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

void
WeightedSumSquareVariable::build_()
{
    if (input1 && input2) {
        // input1 and input2 are (respectively) input and weights from constructor
        if (input1->nelems() != input2->nelems())
            PLERROR("In WeightedSumSquareVariable: input and weights must be the same size;"
                    " input->nelems()=%d weights->nelems()=&d.",
                    input1->nelems(), input2->nelems());
    }
}


void WeightedSumSquareVariable::recomputeSize(int& l, int& w) const
{ l=1; w=1; }


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


void WeightedSumSquareVariable::bprop()
{
  int n=input1->nelems();
  for(int i=0; i<n; i++)
    {
      input1->gradientdata[i]+= 2.0 * input1->valuedata[i] * input2->valuedata[i] * *gradientdata;
      input2->gradientdata[i]+= input1->valuedata[i] * input1->valuedata[i] * *gradientdata;
    }
}


void WeightedSumSquareVariable::symbolicBprop()
{
  input1->accg(2.0 * (g*input1*input2));
  input2->accg(g*input1*input1);
}



} // end of namespace PLearn

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