PLearn 0.1
LogVariable.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: LogVariable.cc 6351 2006-10-25 19:05:45Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "LogVariable.h"
00044 #include <plearn/display/DisplayUtils.h>
00045 #include "Var_operators.h"
00046 
00047 namespace PLearn {
00048 using namespace std;
00049 
00050 
00054 PLEARN_IMPLEMENT_OBJECT(
00055     LogVariable,
00056     "Takes the natural logarithm of its input variable.",
00057     ""
00058 );
00059 
00060 LogVariable::LogVariable(Variable* input)
00061     : inherited(input, input->length(), input->width())
00062 {}
00063 
00065 // recomputeSize //
00067 void LogVariable::recomputeSize(int& l, int& w) const
00068 {
00069     if (input) {
00070         l = input->length();
00071         w = input->width();
00072     } else
00073         l = w = 0;
00074 }
00075 
00077 // fprop //
00079 void LogVariable::fprop()
00080 {
00081     PLASSERT( input && nelems() == input->nelems() );
00082     for(int i=0; i<nelems(); i++)
00083     {
00084         valuedata[i] = safeflog(input->valuedata[i]);
00085 #ifdef BOUNDCHECK
00086         real val = valuedata[i];
00087         real ival = input->valuedata[i];
00088         if (!finite(val) && finite(ival))
00089         {
00090             //PLWARNING("LogVariable::fprop something is wrong");
00091             pout << "inputdata[i]= " << input->valuedata[i] << endl;
00092             pout << "valuedata[i]= " << valuedata[i] << endl;
00093             displayVarGraph(this, true, 250);
00094             PLERROR("LogVariable::fprop Something is wrong");
00095         }
00096 #endif
00097     }
00098 }
00099 
00101 // bprop //
00103 void LogVariable::bprop()
00104 {
00105     PLASSERT( input && input->gradientdata == input->gradient.data() );
00106     for(int i=0; i<nelems(); i++)
00107         if (input->valuedata[i]>0)
00108             input->gradientdata[i] += gradientdata[i]/input->valuedata[i];
00109 }
00110 
00112 // symbolicBprop //
00114 void LogVariable::symbolicBprop()
00115 {
00116     input->accg(g / input);
00117 }
00118 
00119 
00121 // rfprop //
00123 void LogVariable::rfprop()
00124 // R{log(x)} = 1/x R(x)
00125 {
00126     if (rValue.length()==0) resizeRValue();
00127     for(int i=0; i<nelems(); i++)
00128         rvaluedata[i] = input->rvaluedata[i] / input->valuedata[i];
00129 }
00130 
00131 } // end of namespace PLearn
00132 
00133 
00134 /*
00135   Local Variables:
00136   mode:c++
00137   c-basic-offset:4
00138   c-file-style:"stroustrup"
00139   c-file-offsets:((innamespace . 0)(inline-open . 0))
00140   indent-tabs-mode:nil
00141   fill-column:79
00142   End:
00143 */
00144 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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