PLearn 0.1
ConditionalGaussianDistribution.cc
Go to the documentation of this file.
00001 
00002 
00003 // -*- C++ -*-
00004 
00005 // ConditionalGaussianDistribution.cc
00006 // 
00007 // Copyright (C) *YEAR* *AUTHOR(S)* 
00008 // ...
00009 // Copyright (C) *YEAR* *AUTHOR(S)* 
00010 // 
00011 // Redistribution and use in source and binary forms, with or without
00012 // modification, are permitted provided that the following conditions are met:
00013 // 
00014 //  1. Redistributions of source code must retain the above copyright
00015 //     notice, this list of conditions and the following disclaimer.
00016 // 
00017 //  2. Redistributions in binary form must reproduce the above copyright
00018 //     notice, this list of conditions and the following disclaimer in the
00019 //     documentation and/or other materials provided with the distribution.
00020 // 
00021 //  3. The name of the authors may not be used to endorse or promote
00022 //     products derived from this software without specific prior written
00023 //     permission.
00024 // 
00025 // THIS SOFTWARE IS PROVIDED BY THE AUTHORS ``AS IS'' AND ANY EXPRESS OR
00026 // IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES
00027 // OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN
00028 // NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
00029 // SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED
00030 // TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
00031 // PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
00032 // LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
00033 // NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
00034 // SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
00035 // 
00036 // This file is part of the PLearn library. For more information on the PLearn
00037 // library, go to the PLearn Web site at www.plearn.org
00038 
00039 #include "ConditionalGaussianDistribution.h"
00040 #include <plearn/math/plapack.h>
00041 #include <plearn/vmat/VMat_basic_stats.h>
00042 #include <plearn/math/TMat.h>
00043 
00044 namespace PLearn {
00045 using namespace std;
00046 
00047 ConditionalGaussianDistribution::ConditionalGaussianDistribution() 
00048     :inherited()
00049 {
00050 }
00051 
00052 
00053 PLEARN_IMPLEMENT_OBJECT(ConditionalGaussianDistribution,
00054                         "ConditionalGaussianDistribution is a gaussian distribution "
00055                         "in which the parameters could be learned or specified manually.", "");
00056 
00057 void ConditionalGaussianDistribution::declareOptions(OptionList& ol)
00058 {
00059     // ### Declare all of this object's options here
00060     // ### For the "flags" of each option, you should typically specify  
00061     // ### one of OptionBase::buildoption, OptionBase::learntoption or 
00062     // ### OptionBase::tuningoption. Another possible flag to be combined with
00063     // ### is OptionBase::nosave
00064   
00065     declareOption(ol, "mean", &ConditionalGaussianDistribution::mean, OptionBase::buildoption,
00066                   "The mean of the gaussian distribution \n"
00067                   "Could be learned on a training set or specified by calling setInput");
00068 
00069     declareOption(ol, "covariance", &ConditionalGaussianDistribution::covariance, OptionBase::buildoption,
00070                   "The covariance of the gaussian distribution \n"
00071                   "Could be learned on a training set or specified manually");
00072   
00073     // Now call the parent class' declareOptions
00074     inherited::declareOptions(ol);
00075 }
00076 
00077 void ConditionalGaussianDistribution::build()
00078 {
00079     inherited::build();
00080 }
00081 
00082 
00083 void ConditionalGaussianDistribution::train(VMat training_set)
00084 {
00085     mean.resize(training_set.width());
00086     covariance.resize(training_set.width(), training_set.width());
00087     computeMeanAndCovar(training_set, mean, covariance);
00088 }
00089 
00090 void ConditionalGaussianDistribution::makeDeepCopyFromShallowCopy(CopiesMap& copies)
00091 {
00092     Learner::makeDeepCopyFromShallowCopy(copies);
00093 }
00094 
00095 double ConditionalGaussianDistribution::log_density(const Vec& x) const
00096 { PLERROR("density not implemented yet for ConditionalGaussianDistribution"); return 0; }
00097 
00098 double ConditionalGaussianDistribution::density(const Vec& x) const
00099 { return exp(log_density(x)); }
00100   
00101 double ConditionalGaussianDistribution::survival_fn(const Vec& x) const
00102 { PLERROR("survival_fn not implemented yet for ConditionalGaussianDistribution"); return 0; }
00103 
00104 double ConditionalGaussianDistribution::cdf(const Vec& x) const
00105 { PLERROR("cdf not implemented yet for ConditionalGaussianDistribution"); return 0; }
00106 
00107 Vec ConditionalGaussianDistribution::expectation() const
00108 {
00109     return mean;
00110 }
00111 
00112 Mat ConditionalGaussianDistribution::variance() const
00113 {
00114     return covariance;
00115 }
00116 
00117 void ConditionalGaussianDistribution::generate(Vec& x) const
00118 {
00119     // WARNING! Function not found in lapack library for windows!!
00120 
00121 #ifdef WIN32
00122     PLERROR("multivariate_normal for Vec not found in lapack library for windows!");
00123 #else
00124     x = multivariate_normal(mean, covariance);
00125 #endif
00126 }
00127 
00128 void ConditionalGaussianDistribution::setInput(const Vec& input) const
00129 {
00130     mean.resize(input.size());
00131     mean << input;
00132 }
00133 
00134 } // end of namespace PLearn
00135 
00136 
00137 /*
00138   Local Variables:
00139   mode:c++
00140   c-basic-offset:4
00141   c-file-style:"stroustrup"
00142   c-file-offsets:((innamespace . 0)(inline-open . 0))
00143   indent-tabs-mode:nil
00144   fill-column:79
00145   End:
00146 */
00147 // vim: filetype=cpp:expandtab:shiftwidth=4:tabstop=8:softtabstop=4:encoding=utf-8:textwidth=79 :
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Defines