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

#include "ProductTransposeVariable.h"
#include "ProductVariable.h"
#include "TransposeProductVariable.h"
//#include "Var_utils.h"

namespace PLearn {
using namespace std;


// Matrix product

PLEARN_IMPLEMENT_OBJECT(ProductVariable,
                        "Matrix product",
                        "NO HELP");

ProductVariable::ProductVariable(Variable* m1, Variable* m2)
  : inherited(m1, m2, m1->length(), m2->width())
{
    build_();
}

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

void
ProductVariable::build_()
{
    if (input1 && input2) {
        // input1 and input2 are (respectively) m1 and m2 from constructor
        if (input1->width() != input2->length())
            PLERROR("In ProductVariable: the size of m1 and m2 are not compatible for a matrix product");
    }
}


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

void ProductVariable::fprop()
{
  // m[i,j] = sum_k input1[i,k] * input2[k,j]
  product(matValue, input1->matValue, input2->matValue);
}


void ProductVariable::bprop()
{
  // dC/dinput1[i,k] = sum_j dC/dm[i,j] input2[k,j]
  productTransposeAcc(input1->matGradient, matGradient, input2->matValue);
  // dC/dinput2[k,j] += sum_i dC/dm[i,j] input1[i,k]
  transposeProductAcc(input2->matGradient, input1->matValue, matGradient);
}


void ProductVariable::bbprop()
{
  if (input1->diaghessian.length()==0)
    input1->resizeDiagHessian();
  if (input2->diaghessian.length()==0)
    input2->resizeDiagHessian();
  // d^2C/dinput1[i,k]^2 = sum_j d^2C/dm[i,j]^2 input2[k,j]*input2[k,j]
  product2TransposeAcc(input1->matGradient, matGradient, input2->matValue);
  // dC/dinput2[k,j] += sum_i d^2C/dm[i,j]^2 input1[i,k]*input1[i,k]
  transposeProduct2Acc(input2->matGradient, input1->matValue, matGradient);
}


void ProductVariable::symbolicBprop()
{
  // dC/dinput1[i,k] = sum_j dC/dm[i,j] input2[k,j]
  input1->accg(productTranspose(g, input2));
  // dC/dinput2[k,j] += sum_i dC/dm[i,j] input1[i,k]
  input2->accg(transposeProduct(input1, g));
}


//R(x1x2)=R(x1)x2+x1R(x2)
void ProductVariable::rfprop()
{
  if (rValue.length()==0)
    resizeRValue();
  product(matRValue, input1->matValue, input2->matRValue);
  productAcc(matRValue,input1->matRValue, input2->matValue);
}



} // end of namespace PLearn

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