gamma_distribution.hpp

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#ifndef _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_HPP
#define _MLPACK_CORE_DISTRIBUTIONS_GAMMA_DISTRIBUTION_HPP

#include <mlpack/prereqs.hpp>
#include <mlpack/core/math/random.hpp>
#include <boost/program_options.hpp>

namespace mlpack {
namespace distribution {

class GammaDistribution
{
  public:
    GammaDistribution(const size_t dimensionality = 0);

    GammaDistribution(const arma::mat& data, const double tol = 1e-8);

    GammaDistribution(const arma::vec& alpha, const arma::vec& beta);

    ~GammaDistribution() {};

    void Train(const arma::mat& rdata, const double tol = 1e-8);

    void Train(const arma::mat& observations,
               const arma::vec& probabilities,
               const double tol = 1e-8);

    void Train(const arma::vec& logMeanxVec,
               const arma::vec& meanLogxVec,
               const arma::vec& meanxVec,
               const double tol = 1e-8);


    void Probability(const arma::mat& observations,
                     arma::vec& Probabilities) const;

    /*
     * This is a shortcut to the Probability(arma::mat&, arma::vec&) function
     * for when we want to evaluate only the probability of one dimension of the
     * gamma.
     *
     * @param x The 1-dimensional observation.
     * @param dim The dimension for which to calculate the probability
     */
    double Probability(double x, size_t dim) const;

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

    arma::vec Random() const;

    // Access to Gamma distribution parameters.

    double Alpha(const size_t dim) const { return alpha[dim]; }
    double& Alpha(const size_t dim) { return alpha[dim]; }

    double Beta(const size_t dim) const { return beta[dim]; }
    double& Beta(const size_t dim) { return beta[dim]; }

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

  private:
    arma::vec alpha;
    arma::vec beta;

    inline bool Converged(const double aOld,
                          const double aNew,
                          const double tol);
};

} // namespace distributions.
} // namespace mlpack.

#endif