PLearn 0.1
SubsampleVariable.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: SubsampleVariable.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "SubsampleVariable.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00051 PLEARN_IMPLEMENT_OBJECT(SubsampleVariable,
00052                         "A subsample var; equals subsample(input, the_subsamplefactor)",
00053                         "NO HELP");
00054 
00055 SubsampleVariable::SubsampleVariable(Variable* input, int the_subsamplefactor) 
00056     : inherited(input, input->length()/the_subsamplefactor, input->width()/the_subsamplefactor), 
00057       subsamplefactor(the_subsamplefactor) 
00058 {
00059     build_();
00060 }
00061 
00062 void
00063 SubsampleVariable::build()
00064 {
00065     inherited::build();
00066     build_();
00067 }
00068 
00069 void
00070 SubsampleVariable::build_()
00071 {
00072     if (input) {
00073         if (input->length() % subsamplefactor != 0 || input->width() % subsamplefactor != 0)
00074             PLERROR("In SubsampleVariable constructor: Dimensions of input are not dividable by subsamplefactor");
00075     }
00076 }
00077 
00078 void
00079 SubsampleVariable::declareOptions(OptionList &ol)
00080 {
00081     declareOption(ol, "subsamplefactor", &SubsampleVariable::subsamplefactor, OptionBase::buildoption, "");
00082     inherited::declareOptions(ol);
00083 }
00084 
00085 void SubsampleVariable::recomputeSize(int& l, int& w) const
00086 {
00087     if (input) {
00088         l = input->length() / subsamplefactor;
00089         w = input->width()/subsamplefactor;
00090     } else
00091         l = w = 0;
00092 }
00093 
00094 void SubsampleVariable::fprop()
00095 {
00096     subsample(input->matValue, subsamplefactor, matValue);
00097 }
00098 
00099 
00100 void SubsampleVariable::bprop()
00101 {
00102     int norm = subsamplefactor * subsamplefactor;
00103     for(int i=0; i<length(); i++)
00104         for(int j=0; j<width(); j++)
00105         {
00106             real* inputgradientptr = input->matGradient[subsamplefactor*i]+subsamplefactor*j;
00107             real thisgradient = matGradient(i,j);
00108             for(int l=0; l<subsamplefactor; l++, inputgradientptr += input->matGradient.mod())
00109                 for(int c=0; c<subsamplefactor; c++)
00110                 {
00111                     inputgradientptr[c] = thisgradient/norm;
00112                 }
00113         }
00114 }
00115 
00116 
00117 void SubsampleVariable::symbolicBprop()
00118 { PLERROR("SubsampleVariable::symbolicBprop() not yet implemented"); }
00119 
00120 
00121 
00122 } // end of namespace PLearn
00123 
00124 
00125 /*
00126   Local Variables:
00127   mode:c++
00128   c-basic-offset:4
00129   c-file-style:"stroustrup"
00130   c-file-offsets:((innamespace . 0)(inline-open . 0))
00131   indent-tabs-mode:nil
00132   fill-column:79
00133   End:
00134 */
00135 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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