PLearn 0.1
TimesScalarVariable.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: TimesScalarVariable.cc 8857 2008-04-21 20:58:25Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "DotProductVariable.h"
00044 #include "TimesScalarVariable.h"
00045 #include "Var_operators.h"
00046 
00047 
00048 namespace PLearn {
00049 using namespace std;
00050 
00053 PLEARN_IMPLEMENT_OBJECT(
00054         TimesScalarVariable,
00055        "Multiplies a matrix variable by a scalar variable.",
00056        "The first input is the matrix, while the second one is the scalar."
00057 );
00058 
00060 // TimesScalarVariable //
00062 TimesScalarVariable::TimesScalarVariable(Variable* input1, Variable* input2,
00063                                          bool call_build_):
00064     inherited(input1, input2, input1->length(), input1->width(), call_build_)
00065 {
00066     if (call_build_)
00067         build_();
00068 }
00069 
00071 // build //
00073 void TimesScalarVariable::build()
00074 {
00075     inherited::build();
00076     build_();
00077 }
00078 
00079 void
00080 TimesScalarVariable::build_()
00081 {
00082     if (input2 && !input2->isScalar())
00083         PLERROR("IN TimesScalarVariable: input2 is not a scalar");
00084 }
00085 
00086 void TimesScalarVariable::recomputeSize(int& l, int& w) const
00087 {
00088     if (input1) {
00089         l = input1->length();
00090         w = input1->width();
00091     } else
00092         l = w = 0;
00093 }
00094 
00095 void TimesScalarVariable::fprop()
00096 {
00097     real scal = input2->valuedata[0];
00098     for(int k=0; k<nelems(); k++)
00099         valuedata[k] = input1->valuedata[k] * scal;
00100 }
00101 
00102 
00103 void TimesScalarVariable::bprop()
00104 {
00105     for(int k=0; k<nelems(); k++)
00106     {
00107         input1->gradientdata[k] += input2->valuedata[0]*gradientdata[k];
00108         input2->gradientdata[0] += input1->valuedata[k]*gradientdata[k];
00109     }
00110 }
00111 
00112 
00113 void TimesScalarVariable::symbolicBprop()
00114 {
00115     input1->accg(g*input2);
00116     input2->accg(dot(g,input1));
00117 }
00118 
00119 
00120 //R(x1x2)=R(x1)x2+x1R(x2)
00121 void TimesScalarVariable::rfprop()
00122 {
00123     if (rValue.length()==0) resizeRValue();
00124     real scal = input2->valuedata[0];
00125     real rscal = input2->rvaluedata[0];
00126     for(int k=0; k<nelems(); k++)
00127         rvaluedata[k] = input1->rvaluedata[k] * scal + input1->valuedata[k] * rscal;
00128 }
00129 
00130 
00131 
00132 } // end of namespace PLearn
00133 
00134 
00135 /*
00136   Local Variables:
00137   mode:c++
00138   c-basic-offset:4
00139   c-file-style:"stroustrup"
00140   c-file-offsets:((innamespace . 0)(inline-open . 0))
00141   indent-tabs-mode:nil
00142   fill-column:79
00143   End:
00144 */
00145 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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