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: SquareVariable.cc 8958 2008-05-08 19:20:25Z tihocan $ 00040 * This file is part of the PLearn library. 00041 ******************************************************* */ 00042 00043 #include "SquareVariable.h" 00044 #include "Var_operators.h" 00045 //#include "Var_utils.h" 00046 00047 namespace PLearn { 00048 using namespace std; 00049 00050 00053 PLEARN_IMPLEMENT_OBJECT( 00054 SquareVariable, 00055 "Element-wise square of the input matrix.", 00056 "" 00057 ); 00058 00060 // SquareVariable // 00062 SquareVariable::SquareVariable(Variable* input, bool call_build_): 00063 inherited(input, input->length(), input->width(), call_build_) 00064 { 00065 if (call_build_) 00066 build_(); 00067 } 00068 00070 // build // 00072 void SquareVariable::build() { 00073 inherited::build(); 00074 build_(); 00075 } 00076 00078 // build_ // 00080 void SquareVariable::build_() { 00081 // Nothing to do here. 00082 } 00083 00085 // recomputeSize // 00087 void SquareVariable::recomputeSize(int& l, int& w) const 00088 { 00089 if (input) { 00090 l = input->length(); 00091 w = input->width(); 00092 } else 00093 l = w = 0; 00094 } 00095 00096 00097 void SquareVariable::fprop() 00098 { 00099 int n=nelems(); 00100 for(int i=0; i<n; i++) 00101 valuedata[i] = input->valuedata[i]*input->valuedata[i]; 00102 } 00103 00104 00105 void SquareVariable::bprop() 00106 { 00107 int n=nelems(); 00108 for(int i=0; i<n; i++) 00109 input->gradientdata[i] += 2.0 * input->valuedata[i] * gradientdata[i]; 00110 } 00111 00112 00113 void SquareVariable::bbprop() 00114 { 00115 if (input->diaghessian.length()==0) 00116 input->resizeDiagHessian(); 00117 int n=nelems(); 00118 for(int i=0; i<n; i++) 00119 { 00120 real input_i = input->valuedata[i]; 00121 input->diaghessiandata[i] += 4.0 * input_i * input_i * diaghessiandata[i] 00122 + 2.0 * gradientdata[i]; 00123 } 00124 } 00125 00126 00127 void SquareVariable::symbolicBprop() 00128 { 00129 input->accg(2. * (g * input)); 00130 } 00131 00132 00133 void SquareVariable::rfprop() 00134 { 00135 if (rValue.length()==0) resizeRValue(); 00136 int n=nelems(); 00137 for(int i=0; i<n; i++) 00138 rvaluedata[i] = 2*input->valuedata[i]*input->rvaluedata[i]; 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 :