PLearn 0.1
LocallyWeightedDistribution.cc
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00001 // -*- C++ -*-
00002 
00003 // Distribution.cc
00004 // 
00005 // Copyright (C) 2002 Pascal Vincent
00006 // 
00007 // Redistribution and use in source and binary forms, with or without
00008 // modification, are permitted provided that the following conditions are met:
00009 // 
00010 //  1. Redistributions of source code must retain the above copyright
00011 //     notice, this list of conditions and the following disclaimer.
00012 // 
00013 //  2. Redistributions in binary form must reproduce the above copyright
00014 //     notice, this list of conditions and the following disclaimer in the
00015 //     documentation and/or other materials provided with the distribution.
00016 // 
00017 //  3. The name of the authors may not be used to endorse or promote
00018 //     products derived from this software without specific prior written
00019 //     permission.
00020 // 
00021 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00022 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00023 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00024 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00025 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00026 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00027 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00028 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00029 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00030 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00031 // 
00032 // This file is part of the PLearn library. For more information on the PLearn
00033 // library, go to the PLearn Web site at www.plearn.org
00034 
00035 /* *******************************************************      
00036  * $Id: LocallyWeightedDistribution.cc 9418 2008-09-02 15:33:46Z nouiz $ 
00037  ******************************************************* */
00038 
00041 #include "LocallyWeightedDistribution.h"
00042 #include <plearn/vmat/ConcatColumnsVMatrix.h>
00043 
00044 namespace PLearn {
00045 using namespace std;
00046 
00047 LocallyWeightedDistribution::LocallyWeightedDistribution() 
00048 {}
00049 
00050 
00051 PLEARN_IMPLEMENT_OBJECT(LocallyWeightedDistribution, "ONE LINE DESCR", "NO HELP");
00052 
00053 void LocallyWeightedDistribution::declareOptions(OptionList& ol)
00054 {
00055     declareOption(ol, "weighting_kernel", &LocallyWeightedDistribution::weighting_kernel, OptionBase::buildoption,
00056                   "The kernel that will be used to locally weigh the samples");
00057 
00058     declareOption(ol, "localdistr", &LocallyWeightedDistribution::localdistr, OptionBase::buildoption,
00059                   "The distribution that will be trianed with local weights");
00060 
00061     // Now call the parent class' declareOptions
00062     inherited::declareOptions(ol);
00063 }
00064 
00065 void LocallyWeightedDistribution::build_()
00066 {
00067     // ### This method should do the real building of the object,
00068     // ### according to set 'options', in *any* situation. 
00069     // ### Typical situations include:
00070     // ###  - Initial building of an object from a few user-specified options
00071     // ###  - Building of a "reloaded" object: i.e. from the complete set of all serialised options.
00072     // ###  - Updating or "re-building" of an object after a few "tuning" options have been modified.
00073     // ### You should assume that the parent class' build_() has already been called.
00074 
00075     if(weightsize()!=0 && weightsize()!=1)
00076         PLERROR("In LocallyWeightedDistribution::build_, weightsize must be 0 or 1");
00077 
00078     localdistr->inputsize_ = inputsize_;
00079     localdistr->weightsize_ = 1;
00080     localdistr->build();
00081 }
00082 
00083 // ### Nothing to add here, simply calls build_
00084 void LocallyWeightedDistribution::build()
00085 {
00086     inherited::build();
00087     build_();
00088 }
00089 
00090 
00091 void LocallyWeightedDistribution::train(VMat training_set)
00092 { 
00093     if(training_set.width() != inputsize()+weightsize())
00094         PLERROR("In LocallyWeightedDistribution::train width of training set is different from inputsize()+weightsize()");
00095     setTrainingSet(training_set);
00096 }
00097 
00098 
00099 void LocallyWeightedDistribution::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00100 {
00101     Distribution::makeDeepCopyFromShallowCopy(copies);
00102 
00103     // ### Call deepCopyField on all "pointer-like" fields 
00104     // ### that you wish to be deepCopied rather than 
00105     // ### shallow-copied.
00106     // ### ex:
00107     // deepCopyField(trainvec, copies);
00108 
00109     // ### Remove this line when you have fully implemented this method.
00110     PLERROR("LocallyWeightedDistribution::makeDeepCopyFromShallowCopy not fully (correctly) implemented yet!");
00111 }
00112 
00113 
00114 double LocallyWeightedDistribution::log_density(const Vec& x) const
00115 {
00116     int l = train_set.length();
00117     int w = inputsize();
00118     weights.resize(l);
00119     // 'weights' will contain the "localization" weights for the current test point.
00120     trainsample.resize(w+weightsize());
00121     Vec input = trainsample.subVec(0,w);
00122 
00123     for(int i=0; i<l; i++)
00124     {
00125         train_set->getRow(i,trainsample);
00126         real weight = weighting_kernel(x,input);
00127         if(weightsize()==1)
00128             weight *= trainsample[w];
00129         weights[i] = weight;
00130     }
00131   
00132     VMat weight_column(columnmatrix(weights));
00133 
00134     VMat weighted_trainset;
00135     if(weightsize()==0) // append weight column    
00136         weighted_trainset = hconcat(train_set, weight_column);
00137     else // replace last column by weight column
00138         weighted_trainset = hconcat(train_set.subMatColumns(0,inputsize()), weight_column);
00139 
00140     localdistr->forget();
00141     localdistr->train(weighted_trainset);
00142     return localdistr->log_density(x);
00143 }
00144 
00145 
00146 } // end of namespace PLearn
00147 
00148 
00149 /*
00150   Local Variables:
00151   mode:c++
00152   c-basic-offset:4
00153   c-file-style:"stroustrup"
00154   c-file-offsets:((innamespace . 0)(inline-open . 0))
00155   indent-tabs-mode:nil
00156   fill-column:79
00157   End:
00158 */
00159 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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