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¶
bob.bio.gmm.tools.add_parallel_gmm_options(parsers) |
Add the options for parallel UBM training to the given parsers. |
bob.bio.gmm.tools.initialize_parallel_gmm(args) |
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bob.bio.gmm.tools.add_jobs(args, submitter, …) |
Adds all (desired) jobs of the tool chain to the grid, or to the local list to be executed. |
Parallel GMM¶
bob.bio.gmm.tools.kmeans_initialize(…[, …]) |
Initializes the K-Means training (non-parallel). |
bob.bio.gmm.tools.kmeans_estep(algorithm, …) |
Performs a single E-step of the K-Means algorithm (parallel) |
bob.bio.gmm.tools.kmeans_mstep(algorithm, …) |
Performs a single M-step of the K-Means algorithm (non-parallel) |
bob.bio.gmm.tools.gmm_initialize(algorithm, …) |
Initializes the GMM calculation with the result of the K-Means algorithm (non-parallel). |
bob.bio.gmm.tools.gmm_estep(algorithm, …) |
Performs a single E-step of the GMM training (parallel). |
bob.bio.gmm.tools.gmm_mstep(algorithm, …) |
Performs a single M-step of the GMM training (non-parallel) |
bob.bio.gmm.tools.gmm_project(algorithm, …) |
Performs GMM projection |
Parallel ISV¶
bob.bio.gmm.tools.train_isv(algorithm[, …]) |
Finally, the UBM is used to train the ISV projector/enroller. |
Parallel I-Vector¶
bob.bio.gmm.tools.ivector_estep(algorithm, …) |
Performs a single E-step of the IVector algorithm (parallel) |
bob.bio.gmm.tools.ivector_mstep(algorithm, …) |
Performs a single M-step of the IVector algorithm (non-parallel) |
bob.bio.gmm.tools.ivector_project(algorithm, …) |
Performs IVector projection |
bob.bio.gmm.tools.train_whitener(algorithm) |
Train the feature projector with the extracted features of the world group. |
Integration with bob.bio.video¶
bob.bio.gmm.tools.is_video_extension(algorithm) |
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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.read_feature(extractor, …) |
Details¶
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bob.bio.gmm.tools.add_jobs(args, submitter, local_job_adder)[source]¶ Adds all (desired) jobs of the tool chain to the grid, or to the local list to be executed.
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bob.bio.gmm.tools.add_parallel_gmm_options(parsers, sub_module=None)[source]¶ Add the options for parallel UBM training to the given parsers.
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bob.bio.gmm.tools.base(algorithm)[source]¶ Returns the base algorithm, if it is a video extension, otherwise returns the algorithm itself
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bob.bio.gmm.tools.gmm_estep(algorithm, extractor, iteration, indices, force=False, allow_missing_files=False)[source]¶ Performs a single E-step of the GMM training (parallel).
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bob.bio.gmm.tools.gmm_initialize(algorithm, extractor, limit_data=None, force=False, allow_missing_files=False)[source]¶ Initializes the GMM calculation with the result of the K-Means algorithm (non-parallel). This might require a lot of memory.
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bob.bio.gmm.tools.gmm_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False)[source]¶ Performs a single M-step of the GMM training (non-parallel)
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bob.bio.gmm.tools.gmm_project(algorithm, extractor, indices, force=False, allow_missing_files=False)[source]¶ Performs GMM projection
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bob.bio.gmm.tools.ivector_estep(algorithm, iteration, indices, force=False, allow_missing_files=False)[source]¶ Performs a single E-step of the IVector algorithm (parallel)
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bob.bio.gmm.tools.ivector_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False)[source]¶ Performs a single M-step of the IVector algorithm (non-parallel)
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bob.bio.gmm.tools.ivector_project(algorithm, indices, force=False, allow_missing_files=False)[source]¶ Performs IVector projection
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bob.bio.gmm.tools.kmeans_estep(algorithm, extractor, iteration, indices, force=False, allow_missing_files=False)[source]¶ Performs a single E-step of the K-Means algorithm (parallel)
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bob.bio.gmm.tools.kmeans_initialize(algorithm, extractor, limit_data=None, force=False, allow_missing_files=False)[source]¶ Initializes the K-Means training (non-parallel).
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bob.bio.gmm.tools.kmeans_mstep(algorithm, iteration, number_of_parallel_jobs, force=False, clean=False)[source]¶ Performs a single M-step of the K-Means algorithm (non-parallel)
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bob.bio.gmm.tools.lda_project(algorithm, indices, force=False, allow_missing_files=False)[source]¶ Performs IVector projection
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bob.bio.gmm.tools.train_isv(algorithm, force=False, allow_missing_files=False)[source]¶ Finally, the UBM is used to train the ISV projector/enroller.
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bob.bio.gmm.tools.train_lda(algorithm, force=False, allow_missing_files=False)[source]¶ Train the feature projector with the extracted features of the world group.
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bob.bio.gmm.tools.train_plda(algorithm, force=False, allow_missing_files=False)[source]¶ Train the feature projector with the extracted features of the world group.
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bob.bio.gmm.tools.train_wccn(algorithm, force=False, allow_missing_files=False)[source]¶ Train the feature projector with the extracted features of the world group.
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bob.bio.gmm.tools.train_whitener(algorithm, force=False, allow_missing_files=False)[source]¶ Train the feature projector with the extracted features of the world group.