bob.trainer.EMTrainerLinear

class bob.trainer.EMTrainerLinear

Bases: Boost.Python.instance

The base python class for all EM-based trainers.

Raises an exception This class cannot be instantiated from Python

__init__()

Raises an exception This class cannot be instantiated from Python

Methods

__init__	Raises an exception This class cannot be instantiated from Python
compute_likelihood((EMTrainerLinear)self, …)	Computes the current log likelihood given the hidden variable distribution (or the sufficient statistics)
e_step((EMTrainerLinear)self, …)	Updates the hidden variable distribution (or the sufficient statistics) given the Machine parameters.
finalize((EMTrainerLinear)self, …)	This method is called at the end of the EM algorithm
initialize((EMTrainerLinear)self, …)	This method is called before the EM algorithm
m_step((EMTrainerLinear)self, …)	Updates the Machine parameters given the hidden variable distribution (or the sufficient statistics)
train((EMTrainerLinear)self, …)	Trains a machine using data

Attributes

compute_likelihood_variable	Indicates whether the log likelihood should be computed during EM or not
convergence_threshold	Convergence threshold
max_iterations	Max iterations
rng	The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.

compute_likelihood((EMTrainerLinear)self, (LinearMachine)machine) → float :

Computes the current log likelihood given the hidden variable distribution (or the sufficient statistics)

compute_likelihood_variable

Indicates whether the log likelihood should be computed during EM or not

convergence_threshold

Convergence threshold

e_step((EMTrainerLinear)self, (LinearMachine)machine, (object)data) → None :

Updates the hidden variable distribution (or the sufficient statistics) given the Machine parameters.

finalize((EMTrainerLinear)self, (LinearMachine)machine, (object)data) → None :

This method is called at the end of the EM algorithm

initialize((EMTrainerLinear)self, (LinearMachine)machine, (object)data) → None :

This method is called before the EM algorithm

m_step((EMTrainerLinear)self, (LinearMachine)machine, (object)data) → None :

Updates the Machine parameters given the hidden variable distribution (or the sufficient statistics)

max_iterations

Max iterations

rng

The Mersenne Twister mt19937 random generator used for the initialization of subspaces/arrays before the EM loop.

train((EMTrainerLinear)self, (LinearMachine)machine, (object)data) → None :

Trains a machine using data