PLearn 0.1
KernelDensityEstimator.cc
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00001 // -*- C++ -*-
00002 
00003 // KernelDensityEstimator.cc
00004 //
00005 // Copyright (C) 2008 Dumitru Erhan
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 // Authors: Dumitru Erhan
00036 
00040 #include "KernelDensityEstimator.h"
00041 
00042 namespace PLearn {
00043 using namespace std;
00044 
00045 PLEARN_IMPLEMENT_OBJECT(
00046     KernelDensityEstimator,
00047     "Performs kernel density estimation ('Parzen Windows') with ANY given kernel",
00048     "Does not take into account the input weights!"
00049     );
00050 
00052 // KernelDensityEstimator //
00054 KernelDensityEstimator::KernelDensityEstimator() 
00055     : kernel_output_type("normal")
00056 {
00057 }
00058 
00060 // declareOptions //
00062 void KernelDensityEstimator::declareOptions(OptionList& ol)
00063 {
00064     declareOption(ol, "kernel", &KernelDensityEstimator::kernel,
00065                    OptionBase::buildoption,
00066                    "The kernel used at each point in the training set");
00067 
00068     declareOption(ol, "kernel_output_type", &KernelDensityEstimator::kernel_output_type,
00069                     OptionBase::buildoption,
00070                    "Specifies whether the output of our kernel is logarithmic in the distance (\"log\") or normal (anything else)");
00071     
00072     // Now call the parent class' declareOptions().
00073     inherited::declareOptions(ol);
00074 }
00075 
00077 // build //
00079 void KernelDensityEstimator::build()
00080 {
00081     // ### Nothing to add here, simply calls build_().
00082     inherited::build();
00083     build_();
00084 }
00085 
00087 // build_ //
00089 void KernelDensityEstimator::build_()
00090 {
00091 }
00092 
00094 // cdf //
00096 real KernelDensityEstimator::cdf(const Vec& y) const
00097 {
00098     PLERROR("cdf not implemented for KernelDensityEstimator"); return 0;
00099 }
00100 
00102 // expectation //
00104 void KernelDensityEstimator::expectation(Vec& mu) const
00105 {
00106     PLERROR("expectation not implemented for KernelDensityEstimator");
00107 }
00108 
00109 // ### Remove this method if your distribution does not implement it.
00111 // forget //
00113 void KernelDensityEstimator::forget()
00114 {
00115     inherited::forget();
00116 }
00117 
00119 // generate //
00121 void KernelDensityEstimator::generate(Vec& y) const
00122 {
00123     PLERROR("generate not implemented for KernelDensityEstimator");
00124 }
00125 
00126 // ### Default version of inputsize returns learner->inputsize()
00127 // ### If this is not appropriate, you should uncomment this and define
00128 // ### it properly here:
00129 // int KernelDensityEstimator::inputsize() const {}
00130 
00132 // log_density //
00134 real KernelDensityEstimator::log_density(const Vec& y) const
00135 {
00136     int numTrain = train_set.length();
00137     Vec input, target;
00138     real weight;
00139     real result = 0.0;
00140 
00141     // It can happen that for efficiency/numerical reasons, your kernel outputs
00142     // the logarithm of the actual value of the kernel at (input_i,y).
00143     if (kernel_output_type=="log") {
00144         real logprob = -INFINITY;
00145         for(int i=0; i<numTrain; i++) {
00146             train_set->getExample(i,input,target,weight);
00147             logprob = logadd(logprob,kernel->evaluate(input,y));
00148         }
00149         logprob -= pl_log(real(numTrain));
00150         result = logprob;
00151     }
00152     // Otherwise, it's just a log(\sum_i{k(input_i,y)} / numTrain)
00153     else if (kernel_output_type=="normal") {
00154         real sprob = 0.0;
00155         for(int i=0; i<numTrain; i++) {
00156             train_set->getExample(i,input,target,weight);
00157             sprob += kernel->evaluate(input,y);
00158         }
00159         sprob /= real(numTrain);
00160         result = pl_log(sprob);
00161     }
00162     else
00163         PLERROR("In KernelDensityEstimator::log_density kernel_output_type must be either \"log\" or \"normal\"");
00164 
00165     return result;
00166 
00167 }
00168 
00170 // makeDeepCopyFromShallowCopy //
00172 void KernelDensityEstimator::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00173 {
00174     inherited::makeDeepCopyFromShallowCopy(copies);
00175 }
00176 
00178 // resetGenerator //
00180 void KernelDensityEstimator::resetGenerator(long g_seed)
00181 {
00182     inherited::resetGenerator(g_seed);
00183 }
00184 
00186 // survival_fn //
00188 real KernelDensityEstimator::survival_fn(const Vec& y) const
00189 {
00190     PLERROR("survival_fn not implemented for KernelDensityEstimator"); return 0;
00191 }
00192 
00193 // ### Remove this method, if your distribution does not implement it.
00195 // train //
00197 void KernelDensityEstimator::train()
00198 {
00199     // PLERROR("train method not implemented for KernelDensityEstimator");
00200 }
00201 
00203 // variance //
00205 void KernelDensityEstimator::variance(Mat& covar) const
00206 {
00207     PLERROR("variance not implemented for KernelDensityEstimator");
00208 }
00209 
00210 } // end of namespace PLearn
00211 
00212 
00213 /*
00214   Local Variables:
00215   mode:c++
00216   c-basic-offset:4
00217   c-file-style:"stroustrup"
00218   c-file-offsets:((innamespace . 0)(inline-open . 0))
00219   indent-tabs-mode:nil
00220   fill-column:79
00221   End:
00222 */
00223 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
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