DotProductVariable.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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// modification, are permitted provided that the following conditions are met:
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//  1. Redistributions of source code must retain the above copyright
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/* *******************************************************      
   * $Id: DotProductVariable.cc,v 1.6 2004/04/27 15:58:16 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

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

namespace PLearn {
using namespace std;


// Dot product between 2 matrices (or vectors) with same number of elements

PLEARN_IMPLEMENT_OBJECT(DotProductVariable,
                        "Dot product between 2 matrices (or vectors) with same number of elements",
                        "NO HELP");

DotProductVariable::DotProductVariable(Variable* input1, Variable* input2)
  : inherited(input1, input2, 1, 1)
{
    build_();
}

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

void
DotProductVariable::build_()
{
    if(input1 && input2 && (input1->nelems() != input2->nelems()))
        PLERROR("IN DotProductVariable input1 and input2 must have the same number of elements");
}

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








void DotProductVariable::fprop()
{
  real sum = 0.0;
  for (int k=0; k<input1->nelems(); k++)
    sum += input1->valuedata[k] * input2->valuedata[k];
  valuedata[0] = sum;
}


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


void DotProductVariable::bbprop()
{
  if (input1->diaghessian.length()==0)
    input1->resizeDiagHessian();
  if (input2->diaghessian.length()==0)
    input2->resizeDiagHessian();
  real h = diaghessiandata[0];
  for (int k=0; k<input1->nelems(); k++)
    {
      real in2v=input2->valuedata[k];
      input1->diaghessiandata[k] += in2v * in2v * h;
      real in1v=input1->valuedata[k];
      input2->diaghessiandata[k] += in1v * in1v * h;
    }
}


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


void DotProductVariable::rfprop()
{
  if (rValue.length()==0) resizeRValue(); 
  real sum = 0.0;
  for (int k=0; k<input1->nelems(); k++)
    sum += input1->rvaluedata[k] * input2->valuedata[k] + input1->valuedata[k] * input2->rvaluedata[k];
  rvaluedata[0] = sum;
}



} // end of namespace PLearn

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