fab.hme.learner.init package

Submodules

fab.hme.learner.init.init_hme_bern_gate_supervised_model module

class fab.hme.learner.init.init_hme_bern_gate_supervised_model.HMEBernGateModelRandomInitializer(comp_initializer, tree_depth)

Bases: HMESupervisedModelInitializer

Initializer for HMESupervisedModel with random initialization.

This class create an model in which any parameters are randomly initialized using specified scale parameter, input data and the depth of gating-tree.

Parameters:
comp_initializerSupervisedComponentInitializer

Component-initializer instance.

tree_depthint

Depth of gating-tree. The complete binary tree is considered.

Methods

init_model(data)

Creates an initialized model instance.

init_model(data)

Creates an initialized model instance.

Parameters:
dataHMESupervisedData

Input data to be used for calculating initial parameter values.

Returns:
modelHMESupervisedModel

Initialized model instance.

vposteriorHMEBinaryTreeVPosterior

Variational posterior for the model.

class fab.hme.learner.init.init_hme_bern_gate_supervised_model.HMEBernGateModelWithPosteriorInitializer(comp_initializer, posterior_prob, comp_ids)

Bases: HMESupervisedModelInitializer

Initializer for HMESupervisedModel with given initial posterior.

This class create an model in which any parameters are randomly initialized using specified scale parameter, input data. The structure of gating-tree is decided by a given list of component IDs (comp_ids).

Parameters:
comp_initializerSupervisedComponentInitializer

Component-initializer instance.

posterior_probnp.ndarray, shape = (num_samples, num_comps)

Initial posterior distribution. The number of columns must the same as length of comp_ids.

comp_idslist[int], size = (num_comps)

Component ID numbers. The IDs must be ones assigned by left-to-right order in a complete binary tree.

Methods

init_model(data)

Creates an initialized model instance.

init_model(data)

Creates an initialized model instance.

Parameters:
dataHMESupervisedData

Input data to be used for calculating initial parameter values.

Returns:
modelHMESupervisedModel

Initialized model instance.

vposteriorHMEBinaryTreeVPosterior

Variational posterior for the model.

fab.hme.learner.init.init_hme_logit_gate_supervised_model module

class fab.hme.learner.init.init_hme_logit_gate_supervised_model.HMELogitGateModelRandomInitializer(comp_initializer, tree_depth)

Bases: HMESupervisedModelInitializer

Initializer for HMESupervisedModel with random initialization.

This class create an model in which any parameters are randomly initialized using specified scale parameter, input data and the depth of gating-tree.

Parameters:
comp_initializerSupervisedComponentInitializer

Component-initializer instance.

tree_depthint

Depth of gating-tree. The complete binary tree is considered.

Methods

init_model(data)

Creates an initialized model instance.

init_model(data)

Creates an initialized model instance.

Parameters:
dataHMESupervisedData

Input data to be used for calculating initial parameter values.

Returns:
modelHMESupervisedModel

Initialized model instance.

vposteriorHMELogitGateVPosterior

Variational posterior for the model.

class fab.hme.learner.init.init_hme_logit_gate_supervised_model.HMELogitGateModelWithPosteriorInitializer(comp_initializer, posterior_prob, comp_ids)

Bases: HMESupervisedModelInitializer

Initializer for HMESupervisedModel with given initial posterior.

This class create an model in which any parameters are randomly initialized using specified scale parameter, input data. The structure of gating-tree is decided by a given list of component IDs (comp_ids).

Parameters:
comp_initializerSupervisedComponentInitializer

Component-initializer instance.

posterior_probnp.ndarray, shape = (num_samples, num_comps)

Initial posterior distribution. The number of columns must the same as length of comp_ids.

comp_idslist[int], size = (num_comps)

Component ID numbers. The IDs must be ones assigned by left-to-right order in a complete binary tree.

Methods

init_model(data)

Creates an initialized model instance.

init_model(data)

Creates an initialized model instance.

Parameters:
dataHMESupervisedData

Input data to be used for calculating initial parameter values.

Returns:
modelHMESupervisedModel

Initialized model instance.

vposteriorHMEBinaryTreeVPosterior

Variational posterior for the model.

fab.hme.learner.init.init_hme_model module

class fab.hme.learner.init.init_hme_model.HMESupervisedModelInitializer

Bases: object

An abstract class of initializer for HMESupervisedModel’s.

Concrete classes must implement the init_model() method that creates model instance using given data and parameters specified at __init__().

Methods

init_model(data)

Creates an initialized model instance.

abstract init_model(data)

Creates an initialized model instance.

Parameters:
dataHMESupervisedData

Input data to be used for calculating initial parameter values.

Returns:
modelHMESupervisedModel

Initialized model instance.

vposteriorHMEVPosterior

Variational posterior for the model.

class fab.hme.learner.init.init_hme_model.HMESupervisedModelWithModelInitializer(model_dict)

Bases: HMESupervisedModelInitializer

Initializer for HMESupervisedModel with given information on FAB/HME supervised model.

This class create model which is restored from model_dict.

Parameters:
model_dictdict

Information on FAB/HME supervised model.

Methods

init_model(data)

Creates an initialized model instance.

init_model(data)

Creates an initialized model instance.

Parameters:
dataHMESupervisedData

Input data to be used for calculating initial parameter values.

Returns:
modelHMESupervisedModel

Initialized model instance.

vposteriorHMEBinaryTreeVPosterior

Variational posterior for the model.

Module contents