PLearn 0.1
ManifoldParzenKernel.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: ManifoldParzenKernel.cc 3994 2005-08-25 13:35:03Z chapados $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "ManifoldParzenKernel.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00049 PLEARN_IMPLEMENT_OBJECT(ManifoldParzenKernel, 
00050                         "Kernel that uses the evaluate method of Manifold Parzen.", 
00051                         "");
00052 
00053 real ManifoldParzenKernel::evaluate(const Vec& x1, const Vec& x2) const
00054 { 
00055     real ret;
00056     if(is_symmetric)
00057         ret = mp->evaluate(x1,x2,scale) + mp->evaluate(x2,x1,scale);
00058     else
00059         ret = mp->evaluate(x1,x2,scale);
00060   
00061     return ret;
00062 }
00063 
00064 real ManifoldParzenKernel::evaluate_i_j(int i, int j) const
00065 { 
00066     real ret;
00067     if(is_symmetric)
00068         ret = mp->evaluate_i_j(i,j,scale) + mp->evaluate_i_j(j,i,scale);
00069     else
00070         ret = mp->evaluate_i_j(i,j,scale);
00071   
00072     return ret;
00073 }
00074 
00075 void ManifoldParzenKernel::declareOptions(OptionList& ol)
00076 {
00077     declareOption(ol, "scale", &ManifoldParzenKernel::scale, OptionBase::buildoption,
00078                   "The scale factor of the eigen values");
00079     declareOption(ol, "mp", &ManifoldParzenKernel::mp, OptionBase::buildoption,
00080                   "Manifold Parzen distribution");
00081     declareOption(ol, "train_mp", &ManifoldParzenKernel::train_mp, OptionBase::buildoption,
00082                   "Indication that the ManifoldParzen distribution should be trained");
00083     inherited::declareOptions(ol);
00084 }
00085 
00086 void ManifoldParzenKernel::setDataForKernelMatrix(VMat the_data)
00087 {
00088     inherited::setDataForKernelMatrix(the_data);
00089     if(train_mp && data)
00090     {
00091         mp->setTrainingSet(data);
00092         PP<VecStatsCollector> stats = new VecStatsCollector();
00093         mp->setTrainStatsCollector(stats);
00094         mp->train();
00095         stats->finalize();
00096     }
00097   
00098     if(!train_mp && data) PLWARNING("ManifoldParzenKernel::setDataForKernelMatrix: data of kernel is possibly different from data of ManifoldParzen distribution.");
00099   
00100 }
00101 
00102 void ManifoldParzenKernel::addDataForKernelMatrix(const Vec& newRow)
00103 {
00104     PLERROR("ManifoldParzenKernel::addDataForKernelMatrix: this method is currently not supported.");
00105   
00106 }
00107 
00108 
00109 } // end of namespace PLearn
00110 
00111 
00112 /*
00113   Local Variables:
00114   mode:c++
00115   c-basic-offset:4
00116   c-file-style:"stroustrup"
00117   c-file-offsets:((innamespace . 0)(inline-open . 0))
00118   indent-tabs-mode:nil
00119   fill-column:79
00120   End:
00121 */
00122 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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