ConditionalGaussianDistribution.cc

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// -*- C++ -*-

// ConditionalGaussianDistribution.cc
// 
// Copyright (C) *YEAR* *AUTHOR(S)* 
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// Copyright (C) *YEAR* *AUTHOR(S)* 
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//  1. Redistributions of source code must retain the above copyright
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// This file is part of the PLearn library. For more information on the PLearn
// library, go to the PLearn Web site at www.plearn.org

#include "ConditionalGaussianDistribution.h"
#include <plearn/math/plapack.h>
#include <plearn/vmat/VMat_maths.h>
#include <plearn/math/TMat.h>

namespace PLearn {
using namespace std;

ConditionalGaussianDistribution::ConditionalGaussianDistribution() 
  :inherited()
{
}


PLEARN_IMPLEMENT_OBJECT(ConditionalGaussianDistribution,
                        "ConditionalGaussianDistribution is a gaussian distribution "
                        "in which the parameters could be learned or specified manually.", "");

void ConditionalGaussianDistribution::declareOptions(OptionList& ol)
{
  // ### Declare all of this object's options here
  // ### For the "flags" of each option, you should typically specify  
  // ### one of OptionBase::buildoption, OptionBase::learntoption or 
  // ### OptionBase::tuningoption. Another possible flag to be combined with
  // ### is OptionBase::nosave
  
  declareOption(ol, "mean", &ConditionalGaussianDistribution::mean, OptionBase::buildoption,
                "The mean of the gaussian distribution \n"
                "Could be learned on a training set or specified by calling setInput");

  declareOption(ol, "covariance", &ConditionalGaussianDistribution::covariance, OptionBase::buildoption,
                "The covariance of the gaussian distribution \n"
                "Could be learned on a training set or specified manually");
  
  // Now call the parent class' declareOptions
  inherited::declareOptions(ol);
}

  void ConditionalGaussianDistribution::build()
  {
    inherited::build();
  }


  void ConditionalGaussianDistribution::train(VMat training_set)
{
  mean.resize(training_set.width());
  covariance.resize(training_set.width(), training_set.width());
  computeMeanAndCovar(training_set, mean, covariance);
}

void ConditionalGaussianDistribution::makeDeepCopyFromShallowCopy(map<const void*, void*>& copies)
{
  Learner::makeDeepCopyFromShallowCopy(copies);
}

double ConditionalGaussianDistribution::log_density(const Vec& x) const
{ PLERROR("density not implemented yet for ConditionalGaussianDistribution"); return 0; }

double ConditionalGaussianDistribution::density(const Vec& x) const
{ return exp(log_density(x)); }
  
double ConditionalGaussianDistribution::survival_fn(const Vec& x) const
{ PLERROR("survival_fn not implemented yet for ConditionalGaussianDistribution"); return 0; }

double ConditionalGaussianDistribution::cdf(const Vec& x) const
{ PLERROR("cdf not implemented yet for ConditionalGaussianDistribution"); return 0; }

Vec ConditionalGaussianDistribution::expectation() const
{
  return mean;
}

Mat ConditionalGaussianDistribution::variance() const
{
  return covariance;
}

void ConditionalGaussianDistribution::generate(Vec& x) const
{
  // WARNING! Function not found in lapack library for windows!!

#ifdef WIN32
  PLERROR("multivariate_normal for Vec not found in lapack library for windows!");
#else
  x = multivariate_normal(mean, covariance);
#endif
}

void ConditionalGaussianDistribution::setInput(const Vec& input)
{
  mean.resize(input.size());
  mean << input;
}

} // end of namespace PLearn

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