PLearn 0.1
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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 :