diagonal_gaussian_distribution.hpp

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#ifndef MLPACK_CORE_DISTRIBUTIONS_DIAGONAL_GAUSSIAN_DISTRIBUTION_HPP
#define MLPACK_CORE_DISTRIBUTIONS_DIAGONAL_GAUSSIAN_DISTRIBUTION_HPP

#include <mlpack/prereqs.hpp>

namespace mlpack {
namespace distribution {

class DiagonalGaussianDistribution
{
 private:
  arma::vec mean;
  arma::vec covariance;
  arma::vec invCov;
  double logDetCov;

  static const constexpr double log2pi = 1.83787706640934533908193770912475883;

 public:
  DiagonalGaussianDistribution() : logDetCov(0.0) { /* nothing to do. */ }

  DiagonalGaussianDistribution(const size_t dimension) :
      mean(arma::zeros<arma::vec>(dimension)),
      covariance(arma::ones<arma::vec>(dimension)),
      invCov(arma::ones<arma::vec>(dimension)),
      logDetCov(0)
  { /* Nothing to do. */ }

  DiagonalGaussianDistribution(const arma::vec& mean,
                               const arma::vec& covariance);

  size_t Dimensionality() const { return mean.n_elem; }

  double Probability(const arma::vec& observation) const
  {
    return exp(LogProbability(observation));
  }

  double LogProbability(const arma::vec& observation) const;

  void Probability(const arma::mat& x, arma::vec& probabilities) const
  {
    arma::vec logProbabilities;
    LogProbability(x, logProbabilities);
    probabilities = arma::exp(logProbabilities);
  }

  void LogProbability(const arma::mat& observations,
                      arma::vec& logProbabilities) const;

  arma::vec Random() const;

  void Train(const arma::mat& observations);

  void Train(const arma::mat& observations,
             const arma::vec& probabilities);

  const arma::vec& Mean() const { return mean; }

  arma::vec& Mean() { return mean; }

  const arma::vec& Covariance() const { return covariance; }

  void Covariance(const arma::vec& covariance);

  void Covariance(arma::vec&& covariance);

  template<typename Archive>
  void serialize(Archive& ar, const uint32_t /* version */)
  {
    // We just need to serialize each of the members.
    ar(CEREAL_NVP(mean));
    ar(CEREAL_NVP(covariance));
    ar(CEREAL_NVP(invCov));
    ar(CEREAL_NVP(logDetCov));
  }
};

} // namespace distribution
} // namespace mlpack

#endif