FastSupervisedEStep.hpp

#ifndef _LDAPLUSPLUS_EM_FASTSUPERVISEDESTEP_HPP_
#define _LDAPLUSPLUS_EM_FASTSUPERVISEDESTEP_HPP_

#include "ldaplusplus/em/AbstractEStep.hpp"

namespace ldaplusplus {
namespace em {


template<typename Scalar>
class FastSupervisedEStep : public AbstractEStep<Scalar>
{
    typedef Eigen::Matrix<Scalar, Eigen::Dynamic, Eigen::Dynamic> MatrixX;
    typedef Eigen::Matrix<Scalar, Eigen::Dynamic, 1> VectorX;

    public:
        enum CWeightType
        {
            Constant = 1,
            ExponentialDecay
        };

        FastSupervisedEStep(
            size_t e_step_iterations = 10,
            Scalar e_step_tolerance = 1e-2,
            Scalar C = 1,
            CWeightType weight_type = CWeightType::Constant,
            Scalar compute_likelihood = 1.0,
            int random_state = 0
        );

        std::shared_ptr<parameters::Parameters> doc_e_step(
            const std::shared_ptr<corpus::Document> doc,
            const std::shared_ptr<parameters::Parameters> parameters
        ) override;

        void e_step() override;

    private:
        Scalar get_weight();

        // The maximum number of iterations in expecation step.
        size_t e_step_iterations_;
        // The convergence tolerance for the maximazation of the ELBO w.r.t.
        // \f$\phi\f$ and \f$\gamma\f$ in expecation step.
        Scalar e_step_tolerance_;
        // A parameter weighting the supervised component in the variational
        // distribution.
        Scalar C_;
        // A parameter that is used to indicate whether to compute or not the
        // supervised likelihood at the end of each expectation step.
        Scalar compute_likelihood_;
        // The method used to update parameter C between consecutive expectation
        // steps.
        CWeightType weight_type_;
        // The epochs seen so far.
        int epochs_;
};

}  // namespace em
}  // namespace ldaplusplus

#endif   // _LDAPLUSPLUS_EM_FASTSUPERVISEDESTEP_HPP_