MinusScalarVariable.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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//  2. Redistributions in binary form must reproduce the above copyright
//     notice, this list of conditions and the following disclaimer in the
//     documentation and/or other materials provided with the distribution.
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/* *******************************************************      
   * $Id: MinusScalarVariable.cc,v 1.6 2004/04/27 15:58:16 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "MinusScalarVariable.h"
#include "SumVariable.h"
#include "Var_operators.h"
//#include "Var_utils.h"

namespace PLearn {
using namespace std;


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

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

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

void
MinusScalarVariable::build_()
{
    if(input2 && !input2->isScalar())
        PLERROR("IN MinusScalarVariable: input2 is not a scalar");
}

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

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


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


void MinusScalarVariable::bbprop()
{
  if (input1->diaghessian.length()==0)
    input1->resizeDiagHessian();
  if (input2->diaghessian.length()==0)
    input2->resizeDiagHessian();
  for(int k=0; k<nelems(); k++)
    {
      input1->diaghessiandata[k] += diaghessiandata[k];
      input2->diaghessiandata[0] -= diaghessiandata[k];
    }
}


void MinusScalarVariable::symbolicBprop()
{
  input1->accg(g);
  input2->accg(-sum(g));
}


// R(x1-x2) = R(x1)-R(x2)
void MinusScalarVariable::rfprop()
{
  if (rValue.length()==0) resizeRValue();
  real rscal = input2->rvaluedata[0];
  for(int k=0; k<nelems(); k++)
    rvaluedata[k] = input1->rvaluedata[k] - rscal;
}



} // end of namespace PLearn

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