bob.trainer.GMMTrainer¶

class bob.trainer.GMMTrainer¶

Bases: bob.trainer._trainer.EMTrainerGMM

This class implements the E-step of the expectation-maximisation algorithm for a GMM Machine. See Section 9.2.2 of Bishop, “Pattern recognition and machine learning”, 2006

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`((EMTrainerGMM)self, …)	Returns the likelihood.
`e_step`((EMTrainerGMM)self, …)	Update the hidden variable distribution (or the sufficient statistics) given the Machine parameters.
`finalize`((EMTrainerGMM)self, …)	This method is called after the EM algorithm
`initialize`((EMTrainerGMM)self, …)	This method is called before the EM algorithm
`m_step`((EMTrainerGMM)self, …)	Update the Machine parameters given the hidden variable distribution (or the sufficient statistics)
`train`((EMTrainerGMM)self, …)	Train a machine using data

Attributes

`convergence_threshold`	Convergence threshold
`gmm_statistics`	The internal GMM statistics.
`max_iterations`	Max iterations

compute_likelihood((EMTrainerGMM)self, (GMMMachine)machine) → float :¶: Returns the likelihood.

convergence_threshold¶: Convergence threshold

e_step((EMTrainerGMM)self, (GMMMachine)machine, (object)data) → None :¶: Update the hidden variable distribution (or the sufficient statistics) given the Machine parameters. Also, calculate the average output of the Machine given these parameters. Return the average output of the Machine across the dataset. The EM algorithm will terminate once the change in average_output is less than the convergence_threshold.

finalize((EMTrainerGMM)self, (GMMMachine)machine, (object)data) → None :¶: This method is called after the EM algorithm

gmm_statistics¶: The internal GMM statistics. Useful to parallelize the E-step.

initialize((EMTrainerGMM)self, (GMMMachine)machine, (object)data) → None :¶: This method is called before the EM algorithm

m_step((EMTrainerGMM)self, (GMMMachine)machine, (object)data) → None :¶: Update the Machine parameters given the hidden variable distribution (or the sufficient statistics)

max_iterations¶: Max iterations

train((EMTrainerGMM)self, (GMMMachine)machine, (object)data) → None :¶: Train a machine using data