mrftools.EM module

EM learner class.

class mrftools.EM.EM(inference_type)

Bases: mrftools.Learner.Learner

Objects that perform expectation maximization for learning with latent variables.

e_step(weights)

learn(weights, optimizer=<function ada_grad>, callback=None, opt_args=None)

Fit model parameters by alternating inference of latent variables and learning the best parameters to fit all variables. This method implements the variational expectation-maximization concept.

Parameters:

• weights – Initial weight vector. Can be used to warm start from a previous solution.
• optimizer – gradient-based optimization function, as defined in opt.py
• callback – callback function run during each iteration of the optimizer. The function receives the weights as input. Can be useful for diagnostics, live plotting, storing records, etc.
• opt_args – optimization arguments. Usually a dictionary of parameter values

Returns:

learned weights

m_step(weights, optimizer=<function ada_grad>, callback=None, opt_args=None)