PLearn 0.1
MiniBatchClassificationLossVariable.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: MiniBatchClassificationLossVariable.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "MiniBatchClassificationLossVariable.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00051 PLEARN_IMPLEMENT_OBJECT(MiniBatchClassificationLossVariable,
00052                         "ONE LINE DESCR",
00053                         "NO HELP");
00054 
00055 MiniBatchClassificationLossVariable::MiniBatchClassificationLossVariable(Variable* netout, Variable* classnum)
00056     : inherited(netout,classnum,classnum->length(),classnum->width())
00057 {
00058     build_();
00059 }
00060 
00061 void
00062 MiniBatchClassificationLossVariable::build()
00063 {
00064     inherited::build();
00065     build_();
00066 }
00067 
00068 void
00069 MiniBatchClassificationLossVariable::build_()
00070 {
00071     // input2 is classnum from constructor
00072     if(input2 && !input2->isVec())
00073         PLERROR("In MiniBatchClassificationLossVariable: classnum must be a vector variable representing the indexs of netout (typically class numbers)");
00074 }
00075 
00076 
00077 void MiniBatchClassificationLossVariable::recomputeSize(int& l, int& w) const
00078 {
00079     if (input2) {
00080         l = input2->length();
00081         w = input2->width();
00082     } else
00083         l = w = 0;
00084 }
00085 
00086 void MiniBatchClassificationLossVariable::fprop()
00087 {
00088     int n = input2->size();
00089     if(input1->length()==n)
00090         for (int i=0; i<n; i++)
00091         {
00092             int topscorepos = argmax(input1->matValue.row(i));
00093             int num = int(input2->valuedata[i]);
00094             valuedata[i] = (topscorepos==num ?0 :1);
00095         }
00096     else if(input1->width()==n)
00097         for (int i=0; i<n; i++)
00098         {
00099             int topscorepos = argmax(input1->matValue.column(i));
00100             int num = int(input2->valuedata[i]);
00101             valuedata[i] = (topscorepos==num ?0 :1);
00102         }
00103     else PLERROR("In MiniBatchClassificationLossVariable: The length or width of netout doesn't equal to the size of classnum");
00104 }
00105 
00106 
00107 void MiniBatchClassificationLossVariable::symbolicBprop()
00108 {
00109     PLERROR("MiniBatchClassificationLossVariable::symbolicBprop not implemented.");
00110 }
00111 
00112 
00113 
00114 } // end of namespace PLearn
00115 
00116 
00117 /*
00118   Local Variables:
00119   mode:c++
00120   c-basic-offset:4
00121   c-file-style:"stroustrup"
00122   c-file-offsets:((innamespace . 0)(inline-open . 0))
00123   indent-tabs-mode:nil
00124   fill-column:79
00125   End:
00126 */
00127 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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