PLearn 0.1
TransposeProductVariable.cc
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00001 // -*- C++ -*-
00002 
00003 // PLearn (A C++ Machine Learning Library)
00004 // Copyright (C) 1998 Pascal Vincent
00005 // Copyright (C) 1999-2002 Pascal Vincent, Yoshua Bengio, Rejean Ducharme and University of Montreal
00006 // Copyright (C) 2001-2002 Nicolas Chapados, Ichiro Takeuchi, Jean-Sebastien Senecal
00007 // Copyright (C) 2002 Xiangdong Wang, Christian Dorion
00008 
00009 // Redistribution and use in source and binary forms, with or without
00010 // modification, are permitted provided that the following conditions are met:
00011 // 
00012 //  1. Redistributions of source code must retain the above copyright
00013 //     notice, this list of conditions and the following disclaimer.
00014 // 
00015 //  2. Redistributions in binary form must reproduce the above copyright
00016 //     notice, this list of conditions and the following disclaimer in the
00017 //     documentation and/or other materials provided with the distribution.
00018 // 
00019 //  3. The name of the authors may not be used to endorse or promote
00020 //     products derived from this software without specific prior written
00021 //     permission.
00022 // 
00023 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00024 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00025 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00026 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00027 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00028 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00029 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00030 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00031 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00032 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00033 // 
00034 // This file is part of the PLearn library. For more information on the PLearn
00035 // library, go to the PLearn Web site at www.plearn.org
00036 
00037 
00038 /* *******************************************************      
00039  * $Id: TransposeProductVariable.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "ProductVariable.h"
00044 #include "ProductTransposeVariable.h"
00045 #include "TransposeProductVariable.h"
00046 
00047 namespace PLearn {
00048 using namespace std;
00049 
00050 
00053 // Matrix product between transpose of matrix1 and matrix2
00054 
00055 PLEARN_IMPLEMENT_OBJECT(TransposeProductVariable,
00056                         "Matrix product between transpose of matrix1 and matrix2",
00057                         "NO HELP");
00058 
00059 TransposeProductVariable::TransposeProductVariable(Variable* m1, Variable* m2)
00060     : inherited(m1, m2, m1->width(), m2->width())
00061 {
00062     build_();
00063 }
00064 
00065 void
00066 TransposeProductVariable::build()
00067 {
00068     inherited::build();
00069     build_();
00070 }
00071 
00072 void
00073 TransposeProductVariable::build_()
00074 {
00075     if (input1 && input2) {
00076         // input1 and input2 are (respectively) m1 and m2 from constructor
00077         if (input1->length() != input2->length())
00078             PLERROR("In ProductVariable: the size of m1 and m2 are not compatible for a matrix product");
00079     }
00080 }
00081 
00082 void TransposeProductVariable::recomputeSize(int& l, int& w) const
00083 {
00084     if (input1 && input2) {
00085         l = input1->width();
00086         w = input2->width();
00087     } else
00088         l = w = 0;
00089 }
00090 
00091 void TransposeProductVariable::fprop()
00092 {
00093     // m[i,j] = sum_k input1[k,i] * input2[k,j]
00094     transposeProduct(matValue, input1->matValue,input2->matValue);
00095 }
00096 
00097 
00098 void TransposeProductVariable::bprop()
00099 {
00100     // dC/dinput1[k,i] += sum_j input2[k,j] dC/dm[i,j] 
00101     productTransposeAcc(input1->matGradient, input2->matValue,matGradient);
00102     // dC/dinput2[k,j] += sum_i input1[k,i] dC/dm[i,j] 
00103     productAcc(input2->matGradient, input1->matValue,matGradient);
00104 }
00105 
00106 
00107 void TransposeProductVariable::bbprop()
00108 {
00109     if (input1->diaghessian.length()==0)
00110         input1->resizeDiagHessian();
00111     if (input2->diaghessian.length()==0)
00112         input2->resizeDiagHessian();
00113     // d^2C/dinput1[k,i]^2 += sum_j input2[k,j]^2 dC/dm[i,j] 
00114     squareProductTransposeAcc(input1->matGradient, input2->matValue,matGradient);
00115     // d^2C/dinput2[k,j]^2 += sum_i input1[k,i]^2 dC/dm[i,j] 
00116     squareProductAcc(input2->matGradient, input1->matValue,matGradient);
00117 }
00118 
00119 
00120 void TransposeProductVariable::symbolicBprop()
00121 {
00122     // dC/dinput1[k,i] += sum_j input2[k,j] dC/dm[i,j] 
00123     input1->accg(productTranspose(input2,g));
00124     // dC/dinput2[k,j] += sum_i input1[k,i] dC/dm[i,j] 
00125     input2->accg(product(input1, g));
00126 }
00127 
00128 
00129 void TransposeProductVariable::rfprop()
00130 {
00131     if (rValue.length()==0) resizeRValue();
00132     // m[i,j] = sum_k input1[k,i] * input2[k,j]
00133     transposeProduct(matRValue, input1->matRValue,input2->matValue);
00134     transposeProductAcc(matRValue, input1->matValue,input2->matRValue);
00135 }
00136 
00137 
00138 
00139 } // end of namespace PLearn
00140 
00141 
00142 /*
00143   Local Variables:
00144   mode:c++
00145   c-basic-offset:4
00146   c-file-style:"stroustrup"
00147   c-file-offsets:((innamespace . 0)(inline-open . 0))
00148   indent-tabs-mode:nil
00149   fill-column:79
00150   End:
00151 */
00152 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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