PLearn 0.1
ProductVariable.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: ProductVariable.cc 8957 2008-05-08 19:19:31Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "ProductTransposeVariable.h"
00044 #include "ProductVariable.h"
00045 #include "TransposeProductVariable.h"
00046 //#include "Var_utils.h"
00047 
00048 namespace PLearn {
00049 using namespace std;
00050 
00051 
00054 // Matrix product
00055 
00056 PLEARN_IMPLEMENT_OBJECT(
00057         ProductVariable,
00058        "Matrix product.",
00059        "");
00060 
00061 ProductVariable::ProductVariable(Variable* m1, Variable* m2)
00062     : inherited(m1, m2, m1->length(), m2->width())
00063 {
00064     build_();
00065 }
00066 
00067 void
00068 ProductVariable::build()
00069 {
00070     inherited::build();
00071     build_();
00072 }
00073 
00074 void
00075 ProductVariable::build_()
00076 {
00077     if (input1 && input2) {
00078         // input1 and input2 are (respectively) m1 and m2 from constructor
00079         if (input1->width() != input2->length())
00080             PLERROR("In ProductVariable: the size of m1 and m2 are not compatible for a matrix product");
00081     }
00082 }
00083 
00084 
00085 void ProductVariable::recomputeSize(int& l, int& w) const
00086 {
00087     if (input1 && input2) {
00088         l = input1->length();
00089         w = input2->width();
00090     } else
00091         l = w = 0;
00092 }
00093 
00094 void ProductVariable::fprop()
00095 {
00096     // m[i,j] = sum_k input1[i,k] * input2[k,j]
00097     product(matValue, input1->matValue, input2->matValue);
00098 }
00099 
00100 
00101 void ProductVariable::bprop()
00102 {
00103     // dC/dinput1[i,k] = sum_j dC/dm[i,j] input2[k,j]
00104     productTransposeAcc(input1->matGradient, matGradient, input2->matValue);
00105     // dC/dinput2[k,j] += sum_i dC/dm[i,j] input1[i,k]
00106     transposeProductAcc(input2->matGradient, input1->matValue, matGradient);
00107 }
00108 
00109 
00110 void ProductVariable::bbprop()
00111 {
00112     if (input1->diaghessian.length()==0)
00113         input1->resizeDiagHessian();
00114     if (input2->diaghessian.length()==0)
00115         input2->resizeDiagHessian();
00116     // d^2C/dinput1[i,k]^2 = sum_j d^2C/dm[i,j]^2 input2[k,j]*input2[k,j]
00117     product2TransposeAcc(input1->matGradient, matGradient, input2->matValue);
00118     // dC/dinput2[k,j] += sum_i d^2C/dm[i,j]^2 input1[i,k]*input1[i,k]
00119     transposeProduct2Acc(input2->matGradient, input1->matValue, matGradient);
00120 }
00121 
00122 
00123 void ProductVariable::symbolicBprop()
00124 {
00125     // dC/dinput1[i,k] = sum_j dC/dm[i,j] input2[k,j]
00126     input1->accg(productTranspose(g, input2));
00127     // dC/dinput2[k,j] += sum_i dC/dm[i,j] input1[i,k]
00128     input2->accg(transposeProduct(input1, g));
00129 }
00130 
00131 
00132 //R(x1x2)=R(x1)x2+x1R(x2)
00133 void ProductVariable::rfprop()
00134 {
00135     if (rValue.length()==0)
00136         resizeRValue();
00137     product(matRValue, input1->matValue, input2->matRValue);
00138     productAcc(matRValue,input1->matRValue, input2->matValue);
00139 }
00140 
00141 
00142 
00143 } // end of namespace PLearn
00144 
00145 
00146 /*
00147   Local Variables:
00148   mode:c++
00149   c-basic-offset:4
00150   c-file-style:"stroustrup"
00151   c-file-offsets:((innamespace . 0)(inline-open . 0))
00152   indent-tabs-mode:nil
00153   fill-column:79
00154   End:
00155 */
00156 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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