PLearn 0.1
WeightedDistance.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: WeightedDistance.cc 4253 2005-10-18 19:02:25Z tihocan $
00040  * This file is part of the PLearn library.
00041  ******************************************************* */
00042 
00043 #include "WeightedDistance.h"
00044 
00045 namespace PLearn {
00046 using namespace std;
00047 
00048 
00049 PLEARN_IMPLEMENT_OBJECT(
00050     WeightedDistance,
00051     "Implements an weighted distance.",
00052     "Output is as follows:\n"
00053     "- k(x1,x2) = \\sum_i w[i]*(x1[i]-x2[i])^2\n"
00054 );
00055 
00057 // WeightedDistance //
00059 WeightedDistance::WeightedDistance()
00060 {
00061 }
00062 
00063 WeightedDistance::WeightedDistance(Vec the_weights)
00064  : weights(the_weights)
00065 {
00066 }
00067 
00069 // declareOptions //
00071 void WeightedDistance::declareOptions(OptionList& ol)
00072 {
00073 
00074     declareOption(ol, "weights", &WeightedDistance::weights, OptionBase::buildoption, 
00075                   "The vector of weights to apply to the distance computation.");
00076 
00077     inherited::declareOptions(ol);
00078 }
00079 
00081 // evaluate //
00083 real WeightedDistance::evaluate(const Vec& x1, const Vec& x2) const
00084 {
00085     if (weights.length() != x1.length()) PLERROR("In WeightedDistance: inconsistent length between weigths and x1");
00086     if (weights.length() != x2.length()) PLERROR("In WeightedDistance: inconsistent length between weigths and x2");
00087     real return_value = 0.0;
00088     for (int i = 0; i < weights.length(); i++)
00089     {
00090         return_value += weights[i] * pow(x1[i] - x2[i], 2);
00091     }
00092     return return_value;
00093 }
00094 
00096 // evaluate_i_j //
00098 real WeightedDistance::evaluate_i_j(int i, int j) const
00099 {
00100     PLERROR("In WeightedDistance: evaluate_i_j not implemented");
00101     return 0.0;
00102 }
00103 
00105 // setDataForKernelMatrix //
00107 void WeightedDistance::setDataForKernelMatrix(VMat the_data)
00108 {
00109     PLERROR("In WeightedDistance: setDataForKernelMatrix not implemented");
00110 }
00111 
00112 } // end of namespace PLearn
00113 
00114 
00115 /*
00116   Local Variables:
00117   mode:c++
00118   c-basic-offset:4
00119   c-file-style:"stroustrup"
00120   c-file-offsets:((innamespace . 0)(inline-open . 0))
00121   indent-tabs-mode:nil
00122   fill-column:79
00123   End:
00124 */
00125 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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