MatrixAffineTransformVariable.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
//     notice, this list of conditions and the following disclaimer.
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/* *******************************************************      
   * $Id: MatrixAffineTransformVariable.cc,v 1.4 2004/04/27 16:02:26 morinf Exp $
   * This file is part of the PLearn library.
   ******************************************************* */

#include "MatrixAffineTransformVariable.h"
#include "SubMatVariable.h"
#include "ProductVariable.h"
#include "MatrixAffineTransformFeedbackVariable.h"

namespace PLearn {
using namespace std;


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

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


void MatrixAffineTransformVariable::fprop()
{
    Mat lintransform = input2->matValue.subMatRows(1,input2->length()-1);
    for (int i = 0; i < length(); i++)
        for (int j = 0; j < width(); j++)
             matValue[i][j] = input2->matValue[0][i];
    transposeProductAcc(matValue,lintransform, input1->matValue);
}


void MatrixAffineTransformVariable::bprop()
{
    Mat&  afftr = input2->matValue;
    int l = afftr.length();
    Mat lintr = afftr.subMatRows(1,l-1);

    Mat& afftr_g = input2->matGradient;
    Vec bias_g = afftr_g.firstRow();
    Mat lintr_g = afftr_g.subMatRows(1,l-1);

    for (int i = 0; i < length(); i++)
        for (int j = 0; j < width(); j++)
            {
            bias_g[i] += matGradient[i][j];
            }
    if(!input1->dont_bprop_here)      
      productAcc(input1->matGradient, lintr, matGradient);
    productTransposeAcc(lintr_g, input1->matValue, matGradient);
}


void MatrixAffineTransformVariable::symbolicBprop()
{
    Var lintr = new SubMatVariable(input2,0,0,input2->length()-1,input2->width());
    //Var bias = new SubMatVariable(input2,length()-1,0,1,width());

    if(!input1->dont_bprop_here)
      input1->accg(new ProductVariable(lintr,g));
    input2->accg(new MatrixAffineTransformFeedbackVariable(input1,g));
}



} // end of namespace PLearn

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