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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,v 1.6 2004/04/27 15:58:16 morinf Exp $ 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(TimesScalarVariable, 00054 "Multiplies a matrix var by a scalar var", 00055 "NO HELP"); 00056 00057 TimesScalarVariable::TimesScalarVariable(Variable* input1, Variable* input2) 00058 : inherited(input1, input2, input1->length(), input1->width()) 00059 { 00060 build_(); 00061 } 00062 00063 void 00064 TimesScalarVariable::build() 00065 { 00066 inherited::build(); 00067 build_(); 00068 } 00069 00070 void 00071 TimesScalarVariable::build_() 00072 { 00073 if (input2 && !input2->isScalar()) 00074 PLERROR("IN TimesScalarVariable: input2 is not a scalar"); 00075 } 00076 00077 void TimesScalarVariable::recomputeSize(int& l, int& w) const 00078 { 00079 if (input1) { 00080 l = input1->length(); 00081 w = input1->width(); 00082 } else 00083 l = w = 0; 00084 } 00085 00086 void TimesScalarVariable::fprop() 00087 { 00088 real scal = input2->valuedata[0]; 00089 for(int k=0; k<nelems(); k++) 00090 valuedata[k] = input1->valuedata[k] * scal; 00091 } 00092 00093 00094 void TimesScalarVariable::bprop() 00095 { 00096 for(int k=0; k<nelems(); k++) 00097 { 00098 input1->gradientdata[k] += input2->valuedata[0]*gradientdata[k]; 00099 input2->gradientdata[0] += input1->valuedata[k]*gradientdata[k]; 00100 } 00101 } 00102 00103 00104 void TimesScalarVariable::symbolicBprop() 00105 { 00106 input1->accg(g*input2); 00107 input2->accg(dot(g,input1)); 00108 } 00109 00110 00111 //R(x1x2)=R(x1)x2+x1R(x2) 00112 void TimesScalarVariable::rfprop() 00113 { 00114 if (rValue.length()==0) resizeRValue(); 00115 real scal = input2->valuedata[0]; 00116 real rscal = input2->rvaluedata[0]; 00117 for(int k=0; k<nelems(); k++) 00118 rvaluedata[k] = input1->rvaluedata[k] * scal + input1->valuedata[k] * rscal; 00119 } 00120 00121 00122 00123 } // end of namespace PLearn 00124 00125

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