ldaplusplus::em::MultinomialSupervisedEStep< Scalar > Class Template Reference

Inheritance diagram for ldaplusplus::em::MultinomialSupervisedEStep< Scalar >:

Public Member Functions
	MultinomialSupervisedEStep (size_t e_step_iterations=10, Scalar e_step_tolerance=1e-2, Scalar mu=2, Scalar eta_weight=1, 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
Public Member Functions inherited from ldaplusplus::em::AbstractEStep< Scalar >
	AbstractEStep (int random_state)
virtual void	e_step () override
Public Member Functions inherited from ldaplusplus::events::EventDispatcherComposition
std::shared_ptr< EventDispatcherInterface >	get_event_dispatcher ()
void	set_event_dispatcher (std::shared_ptr< EventDispatcherInterface > dispatcher)

Additional Inherited Members
Protected Member Functions inherited from ldaplusplus::em::AbstractEStep< Scalar >
bool	converged (const Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > &gamma_old, const Eigen::Matrix< Scalar, Eigen::Dynamic, 1 > &gamma, Scalar tolerance)
PRNG &	get_prng ()

Constructor & Destructor Documentation

template<typename Scalar >

ldaplusplus::em::MultinomialSupervisedEStep< Scalar >::MultinomialSupervisedEStep	(	size_t	e_step_iterations = 10,
		Scalar	e_step_tolerance = 1e-2,
		Scalar	mu = 2,
		Scalar	eta_weight = 1,
		Scalar	compute_likelihood = 1.0,
		int	random_state = 0
	)

Parameters

e_step_iterations	The max number of times to alternate between maximizing for \(\gamma\) and for \(\phi\).
e_step_tolerance	The minimum relative change in the variational parameter \(\gamma\).
mu	The uniform Dirichlet prior of \(\eta\), practically is a smoothing parameter during the maximization of \(\eta\).
eta_weight	A weighting parameter that either increases or decreases the influence of the supervised part.
compute_likelihood	The percentage of documents to compute likelihood for (1.0 means compute for every document)
random_state	An initial seed value for any random numbers needed

Member Function Documentation

template<typename Scalar >

std::shared_ptr< parameters::Parameters > ldaplusplus::em::MultinomialSupervisedEStep< Scalar >::doc_e_step ( const std::shared_ptr< corpus::Document >  doc, const std::shared_ptr< parameters::Parameters >  parameters  )

overridevirtual

Maximize the ELBO w.r.t. \(\phi\) and \(\gamma\).

The following steps are the mathematics that are implemented where \(\beta\) are the over words topics distributions, \(\alpha\) is the Dirichlet prior, \(\eta\) are the logistic regression parameters, \(i\) is the topic subscript, \(n\) is the word subscript, \(\hat{y}\) is the class subscript, \(y\) is the document's class, \(w_n\) is n-th word vocabulary index, \(m \) is a weighting parameter used to adjust the influence of the supervised part and finally \(\Psi(\cdot)\) is the first derivative of the \(\log \Gamma\) function.

1. Repeat until convergence of \(\gamma\).
2. Compute \(\phi_{ni} \propto \beta_{iw_n}\eta_{yi}^m\exp\left( \Psi(\gamma_i)\right)\)
3. Compute \(\gamma_i = \alpha_i + \sum_n^N \phi_{ni} \)

Parameters

doc	A single document.
parameters	An instance of class Parameters, which contains all necessary model parameters for e-step's implementation.

Returns

The variational parameters for the current model, after e-step is completed.

Implements ldaplusplus::em::EStepInterface< Scalar >.

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The documentation for this class was generated from the following files:

• include/ldaplusplus/em/MultinomialSupervisedEStep.hpp
• src/ldaplusplus/em/MultinomialSupervisedEStep.cpp