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: TimesRowVariable.cc 8879 2008-04-24 14:21:04Z tihocan $ 00040 * This file is part of the PLearn library. 00041 ******************************************************* */ 00042 00043 #include "ColumnSumVariable.h" 00044 #include "TimesRowVariable.h" 00045 #include "Var_operators.h" 00046 00047 namespace PLearn { 00048 using namespace std; 00049 00050 00053 PLEARN_IMPLEMENT_OBJECT( 00054 TimesRowVariable, 00055 "Multiplies each row of a matrix elementwise with a row variable.", 00056 "More formally: result(i,j) = input1(i,j) * input2[j]." 00057 ); 00058 00060 // TimesRowVariable // 00062 TimesRowVariable::TimesRowVariable(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 TimesRowVariable::build() 00074 { 00075 inherited::build(); 00076 build_(); 00077 } 00078 00080 // build_ // 00082 void TimesRowVariable::build_() 00083 { 00084 if (input1 && input2) { 00085 PLCHECK_MSG(input2->isRowVec(), 00086 "input1 must be a row vector"); 00087 PLCHECK_MSG(input2->width() == input1->width(), 00088 "input1 and input2 must have same width"); 00089 } 00090 } 00091 00093 // recomputeSize // 00095 void TimesRowVariable::recomputeSize(int& l, int& w) const 00096 { 00097 if (input1) { 00098 l = input1->length(); 00099 w = input1->width(); 00100 } else 00101 l = w = 0; 00102 } 00103 00105 // fprop // 00107 void TimesRowVariable::fprop() 00108 { 00109 int k=0; 00110 for(int i=0; i<length(); i++) 00111 for(int j=0; j<width(); j++, k++) 00112 valuedata[k] = input1->valuedata[k] * input2->valuedata[j]; 00113 } 00114 00116 // bprop // 00118 void TimesRowVariable::bprop() 00119 { 00120 int k=0; 00121 for(int i=0; i<length(); i++) 00122 for(int j=0; j<width(); j++, k++) 00123 { 00124 input1->gradientdata[k] += input2->valuedata[j]*gradientdata[k]; 00125 input2->gradientdata[j] += input1->valuedata[k]*gradientdata[k]; 00126 } 00127 } 00128 00130 // symbolicBprop // 00132 void TimesRowVariable::symbolicBprop() 00133 { 00134 input1->accg(g*input2); 00135 input2->accg(columnSum(g*input1)); 00136 } 00137 00139 // rfprop // 00141 void TimesRowVariable::rfprop() 00142 { 00143 if (rValue.length()==0) resizeRValue(); 00144 int k=0; 00145 for(int i=0; i<length(); i++) 00146 for(int j=0; j<width(); j++, k++) 00147 rvaluedata[k] = input1->rvaluedata[k] * input2->valuedata[j] + input1->valuedata[k] * input2->rvaluedata[j]; 00148 } 00149 00150 00151 } // end of namespace PLearn 00152 00153 00154 /* 00155 Local Variables: 00156 mode:c++ 00157 c-basic-offset:4 00158 c-file-style:"stroustrup" 00159 c-file-offsets:((innamespace . 0)(inline-open . 0)) 00160 indent-tabs-mode:nil 00161 fill-column:79 00162 End: 00163 */ 00164 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :