Python API for bob.bio.gmm

Todo

Improve documentation of the functions and classes of bob.bio.gmm.

Generic functions

Miscellaneous functions

bob.bio.base.get_config()

Returns a string containing the configuration information.

Tools to run recognition experiments

Command line generation

Parallel GMM

Parallel ISV

Parallel I-Vector

Integration with bob.bio.video

Details

bob.bio.gmm.tools.add_jobs(args, submitter, local_job_adder)

Adds all (desired) jobs of the tool chain to the grid, or to the local list to be executed.

bob.bio.gmm.tools.add_parallel_gmm_options(parsers, sub_module=None)

Add the options for parallel UBM training to the given parsers.

bob.bio.gmm.tools.base(algorithm)

Returns the base algorithm, if it is a video extension, otherwise returns the algorithm itself

bob.bio.gmm.tools.gmm_estep(algorithm, extractor, iteration, indices, force=False)

Performs a single E-step of the GMM training (parallel).

bob.bio.gmm.tools.gmm_initialize(algorithm, extractor, limit_data=None, force=False)

Initializes the GMM calculation with the result of the K-Means algorithm (non-parallel). This might require a lot of memory.

bob.bio.gmm.tools.gmm_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False)

Performs a single M-step of the GMM training (non-parallel)

bob.bio.gmm.tools.gmm_project(algorithm, extractor, indices, force=False)

Performs GMM projection

bob.bio.gmm.tools.initialize_parallel_gmm(args, sub_module=None)

bob.bio.gmm.tools.is_video_extension(algorithm)

bob.bio.gmm.tools.ivector_estep(algorithm, iteration, indices, force=False)

Performs a single E-step of the IVector algorithm (parallel)

bob.bio.gmm.tools.ivector_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False)

Performs a single M-step of the IVector algorithm (non-parallel)

bob.bio.gmm.tools.ivector_project(algorithm, indices, force=False)

Performs IVector projection

bob.bio.gmm.tools.kmeans_estep(algorithm, extractor, iteration, indices, force=False)

Performs a single E-step of the K-Means algorithm (parallel)

bob.bio.gmm.tools.kmeans_initialize(algorithm, extractor, limit_data=None, force=False)

Initializes the K-Means training (non-parallel).

bob.bio.gmm.tools.kmeans_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False)

Performs a single M-step of the K-Means algorithm (non-parallel)

bob.bio.gmm.tools.lda_project(algorithm, indices, force=False)

Performs IVector projection

bob.bio.gmm.tools.read_feature(extractor, feature_file)

bob.bio.gmm.tools.save_projector(algorithm, force=False)

bob.bio.gmm.tools.train_isv(algorithm, force=False)

Finally, the UBM is used to train the ISV projector/enroller.

bob.bio.gmm.tools.train_lda(algorithm, force=False)

Train the feature projector with the extracted features of the world group.

bob.bio.gmm.tools.train_plda(algorithm, force=False)

Train the feature projector with the extracted features of the world group.

bob.bio.gmm.tools.train_wccn(algorithm, force=False)

Train the feature projector with the extracted features of the world group.

bob.bio.gmm.tools.train_whitener(algorithm, force=False)

Train the feature projector with the extracted features of the world group.

bob.bio.gmm.tools.wccn_project(algorithm, indices, force=False)

Performs IVector projection

bob.bio.gmm.tools.whitening_project(algorithm, indices, force=False)

Performs IVector projection