PLearn 0.1
SigmoidVariable.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: SigmoidVariable.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "SigmoidVariable.h"
00044 #include "Var_operators.h"
00045 
00046 namespace PLearn {
00047 using namespace std;
00048 
00049 
00052 PLEARN_IMPLEMENT_OBJECT(SigmoidVariable,
00053                         "ONE LINE DESCR",
00054                         "NO HELP");
00055 
00056 SigmoidVariable::SigmoidVariable(Variable* input) 
00057     : inherited(input, input->length(), input->width())
00058 {}
00059 
00060 void SigmoidVariable::recomputeSize(int& l, int& w) const
00061 {
00062     if (input) {
00063         l = input->length();
00064         w = input->width();
00065     } else
00066         l = w = 0;
00067 }
00068 
00069 void SigmoidVariable::fprop()
00070 {
00071     int l = nelems();
00072     real* valueptr = valuedata;
00073     real* inputvalueptr = input->valuedata;
00074     for(int i=0; i<l; i++)
00075         *valueptr++ = sigmoid(*inputvalueptr++);
00076 }
00077 
00078 
00079 void SigmoidVariable::bprop()
00080 {
00081     int l = nelems();
00082     real* inputgradientptr = input->gradientdata;
00083     real* gradientptr = gradientdata;
00084     real* valueptr = valuedata;
00085     for(int i=0; i<l; i++)
00086     {
00087         real val = *valueptr++;
00088         *inputgradientptr++ += *gradientptr++ * val*(1.0-val);
00089     }
00090 }
00091 
00092 
00093 void SigmoidVariable::bbprop()
00094 {
00095     if (input->diaghessian.length()==0)
00096         input->resizeDiagHessian();
00097     for(int i=0; i<nelems(); i++)
00098     {
00099         real yi = valuedata[i];
00100         real fprime = yi*(1-yi);
00101         input->gradientdata[i] += gradientdata[i] * fprime * fprime;
00102     }
00103 }
00104 
00105 
00106 void SigmoidVariable::symbolicBprop()
00107 {
00108     Var v(this);
00109     input->accg(g*v*(1. - v));
00110 }
00111 
00112 
00113 // R{sigmoid(x)} = f(x)(1-f(x))R(x)
00114 void SigmoidVariable::rfprop()
00115 {
00116     if (rValue.length()==0) resizeRValue();
00117     int l = nelems();
00118     real* inputptr = input->rvaluedata;
00119     real* inputvalueptr = valuedata;
00120     real* ptr = rvaluedata;
00121     for(int i=0; i<l; i++)
00122     {
00123         real val = *inputvalueptr++;
00124         *ptr++ = *inputptr++ * val * (1.0 - val);
00125     }
00126 }
00127 
00133 Var softmax(Var x1, Var x2, Var hardness)
00134 {
00135     Var w=sigmoid(hardness*(x1-x2));
00136     return x1*w + x2*(1-w);
00137 }
00138 
00139 
00140 } // end of namespace PLearn
00141 
00142 
00143 /*
00144   Local Variables:
00145   mode:c++
00146   c-basic-offset:4
00147   c-file-style:"stroustrup"
00148   c-file-offsets:((innamespace . 0)(inline-open . 0))
00149   indent-tabs-mode:nil
00150   fill-column:79
00151   End:
00152 */
00153 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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