47#ifndef GUM_LEARNING_EM_APPROX_SCHEME_H
48#define GUM_LEARNING_EM_APPROX_SCHEME_H
gum::ApproximationSchemeListener header file.
This file contains general scheme for iteratively convergent algorithms.
ApproximationScheme(bool verbosity=false)
bool verbosity() const override
Returns true if verbosity is enabled.
void setMinDiffEpsilon(double eps)
sets the stopping criterion of EM as being the minimal difference between two consecutive log-likelih...
void setMinEpsilonRate(double rate) override
sets the stopping criterion of EM as being the minimal log-likelihood's evolution rate
~EMApproximationScheme() override=default
void setEpsilon(double eps) override
sets the stopping criterion of EM as being the minimal difference between two consecutive log-likelih...
EMApproximationScheme(bool verbosity=false)
initializes the EM parameter learning approximation with the min rate criterion
include the inlined functions if necessary
gum is the global namespace for all aGrUM entities