def __init__(self, setup):
"""Initializes the local UBM-GMM tool chain with the given file selector object"""
self.m_config = setup
self.m_ubm = None
UBMGMM.__init__(self, number_of_gaussians=self.m_config.n_gaussians)
if hasattr(self.m_config, 'scoring_function'):
self.m_scoring_function = self.m_config.scoring_function
self.m_normalize_before_k_means = self.m_config.norm_KMeans
#self.m_gaussians = self.m_config.n_gaussians
self.m_training_threshold = self.m_config.convergence_threshold
self.m_gmm_training_iterations = self.m_config.iterk
self.m_variance_threshold = self.m_config.variance_threshold
self.m_update_means = self.m_config.update_means
self.m_update_variances = self.m_config.update_variances
self.m_update_weights = self.m_config.update_weights
self.m_responsibility_threshold = self.m_config.responsibilities_threshold
self.m_relevance_factor = self.m_config.relevance_factor
self.m_gmm_enroll_iterations = self.m_config.iterg_enrol
self.use_unprojected_features_for_model_enrol = True
def __init__(self, setup):
"""Initializes the local UBM-GMM tool chain with the given file selector object"""
self.m_config = setup
self.m_ubm = None
UBMGMM.__init__(self, number_of_gaussians=self.m_config.n_gaussians)
if hasattr(self.m_config, 'scoring_function'):
self.m_scoring_function = self.m_config.scoring_function
self.m_normalize_before_k_means = self.m_config.norm_KMeans
#self.m_gaussians = self.m_config.n_gaussians
self.m_training_threshold = self.m_config.convergence_threshold
self.m_gmm_training_iterations = self.m_config.iterk
self.m_variance_threshold = self.m_config.variance_threshold
self.m_update_means = self.m_config.update_means
self.m_update_variances = self.m_config.update_variances
self.m_update_weights = self.m_config.update_weights
self.m_responsibility_threshold = self.m_config.responsibilities_threshold
self.m_relevance_factor = self.m_config.relevance_factor
self.m_gmm_enroll_iterations = self.m_config.iterg_enrol
self.use_unprojected_features_for_model_enrol = True