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

#include <UnsupervisedEStep.hpp>

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

Public Member Functions
	UnsupervisedEStep (size_t e_step_iterations=10, Scalar e_step_tolerance=1e-2, Scalar compute_likelihood=1.0, int random_state=0)
virtual 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 ()

Detailed Description

template<typename Scalar>
class ldaplusplus::em::UnsupervisedEStep< Scalar >

UnsupervisedEStep implements the classic LDA expectation step.

For each document passed in UnsupervisedEStep::doc_e_step a factorized variational distribution is computed with Dirichlet parameter \(\gamma\) and multinomial parameters \(\phi\). The distribution is computed in such a way so that a lower bound of the probability of generating the document given the model parameters (the topics that is) is maximized.

See UnsupervisedEStep::doc_e_step for the mathematics.

[1] Blei, David M., Andrew Y. Ng, and Michael I. Jordan. "Latent dirichlet allocation." Journal of machine Learning research 3.Jan (2003): 993-1022.

Constructor & Destructor Documentation

template<typename Scalar >

ldaplusplus::em::UnsupervisedEStep< Scalar >::UnsupervisedEStep	(	size_t	e_step_iterations = 10,
		Scalar	e_step_tolerance = 1e-2,
		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 \(\gamma\).
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::UnsupervisedEStep< 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. to \(\phi\) and \(\gamma\).

The following steps are the mathematics that are implemented where \(\beta\) are the topics, \(i\) is the topic subscript, \(n\) is the word subscript, \(w_n\) is n-th word vocabulary index, \(\alpha\) is the Dirichlet prior and finally \(\Psi(\cdot)\) is the first derivative of the \(\log \Gamma\) function.

1. Repeat following steps until convergence
2. \(\phi_{ni} \propto \beta_{iw_n} \exp(\Psi(\gamma_i)) \)
3. \(\gamma_i = \alpha_i + \sum_n^N \phi_{ni} \)

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/UnsupervisedEStep.hpp
• src/ldaplusplus/em/UnsupervisedEStep.cpp