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src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/dct_ubm.py xbob.example.faceverify(Download)
kmeans_trainer.train(kmeans, training_set) [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(training_set) means = kmeans.means
src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/tools/ParaUBMGMM.py xbob.thesis.elshafey2014(Download)
gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1]) [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data) # Initializes the GMM
src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/script/ParallelUBMGMM.py xbob.spkrec(Download)
gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1]) [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data) # Initializes the GMM
src/b/o/bob.spear-1.1.2/spear/script/ParallelUBMGMM.py bob.spear(Download)
gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1]) [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data) # Initializes the GMM
src/f/a/facereclib-1.2.1/facereclib/tools/ParallelUBMGMM.py facereclib(Download)
gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1]) [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data) # Initializes the GMM
src/f/a/facereclib-HEAD/facereclib/tools/ParallelUBMGMM.py facereclib(Download)
gmm_machine = bob.machine.GMMMachine(self.m_tool.m_gaussians, data.shape[1]) [variances, weights] = kmeans_machine.get_variances_and_weights_for_each_cluster(data) # Initializes the GMM
src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/preprocessing/Energy.py xbob.spkrec(Download)
[variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_energy) means = kmeans.means if numpy.isnan(means[0]) or numpy.isnan(means[1]):
src/b/o/bob.spear-1.1.2/spear/preprocessing/Energy.py bob.spear(Download)
[variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_energy) means = kmeans.means if numpy.isnan(means[0]) or numpy.isnan(means[1]):
src/f/a/facereclib-1.2.1/facereclib/tools/UBMGMM.py facereclib(Download)
kmeans_trainer.train(kmeans, normalized_array) [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_array) means = kmeans.means
src/f/a/facereclib-HEAD/facereclib/tools/UBMGMM.py facereclib(Download)
kmeans_trainer.train(kmeans, normalized_array) [variances, weights] = kmeans.get_variances_and_weights_for_each_cluster(normalized_array) means = kmeans.means
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