PLearn 0.1
SquareVariable.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: 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 :
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