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

#include "ColumnSumVariable.h"
#include "PlusRowVariable.h"
//#include "Var_utils.h"

namespace PLearn {
using namespace std;


PLEARN_IMPLEMENT_OBJECT(PlusRowVariable,
                        "Adds a single-row var to each row of a matrix var",
                        "NO HELP");

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

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

void
PlusRowVariable::build_()
{
    if (input1 && input2) {
        if(!input2->isRowVec())
            PLERROR("IN PlusRowVariable: input2 is not a row");
        if(input2->width() != input1->width())
            PLERROR("IN PlusRowVariable: input1 and input2 have a different width()");
    }
}


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


void PlusRowVariable::fprop()
{
  int k=0;
  for(int i=0; i<length(); i++)
    for(int j=0; j<width(); j++, k++)
      valuedata[k] = input1->valuedata[k] + input2->valuedata[j];
}


void PlusRowVariable::bprop()
{
  int k=0;
  for(int i=0; i<length(); i++)
    for(int j=0; j<width(); j++, k++)
      {
        input1->gradientdata[k] += gradientdata[k];
        input2->gradientdata[j] += gradientdata[k];
      }
}


void PlusRowVariable::bbprop()
{
  if (input1->diaghessian.length()==0)
    input1->resizeDiagHessian();
  if (input2->diaghessian.length()==0)
    input2->resizeDiagHessian();
  int k=0;
  for(int i=0; i<length(); i++)
    for(int j=0; j<width(); j++, k++)
      {
        input1->diaghessiandata[k] += diaghessiandata[k];
        input2->diaghessiandata[j] += diaghessiandata[k];
      }
}


void PlusRowVariable::symbolicBprop()
{
  input1->accg(g);
  input2->accg(columnSum(g));
}


void PlusRowVariable::rfprop()
{
  if (rValue.length()==0) resizeRValue();
  int k=0;
  for(int i=0; i<length(); i++)
    for(int j=0; j<width(); j++, k++)
      rvaluedata[k] = input1->rvaluedata[k] + input2->rvaluedata[j];
}



} // end of namespace PLearn

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