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src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/dct_ubm.py   xbob.example.faceverify(Download)
  input_size = training_set.shape[1]
  # create the KMeans and UBM machine
  kmeans = bob.machine.KMeansMachine(number_of_gaussians, input_size)
  ubm = bob.machine.GMMMachine(number_of_gaussians, input_size)
 

src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/tools/ParaUBMGMM.py   xbob.thesis.elshafey2014(Download)
 
      # Perform KMeans initialization
      kmeans_machine = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1])
      # Creates the KMeansTrainer and call the initialization procedure
      kmeans_trainer = bob.trainer.KMeansTrainer()
      training_list = self.training_list()
      machine_file = self.m_configuration.kmeans_intermediate_file % self.m_args.iteration
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: KMeans E-Step from range(%d, %d)" % indices)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      t = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1]) # Temporary Kmeans machine required for trainer initialization
      kmeans_trainer.initialize(t, data)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      # Creates the KMeansMachine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(old_machine_file))
      kmeans_trainer.initialize(kmeans_machine, data)
 
 
      # load KMeans machine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(self.m_configuration.kmeans_file))
 
      # read features

src/f/a/facereclib-1.2.1/facereclib/tools/ParallelUBMGMM.py   facereclib(Download)
 
      # Perform KMeans initialization
      kmeans_machine = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1])
      # Creates the KMeansTrainer and call the initialization procedure
      kmeans_trainer = bob.trainer.KMeansTrainer()
      training_list = self.training_list()
      machine_file = self.m_configuration.kmeans_intermediate_file % self.m_args.iteration
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: KMeans E-Step from range(%d, %d)" % indices)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      t = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1]) # Temporary Kmeans machine required for trainer initialization
      kmeans_trainer.initialize(t, data)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      # Creates the KMeansMachine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(old_machine_file))
      kmeans_trainer.initialize(kmeans_machine, data)
 
 
      # load KMeans machine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(self.m_configuration.kmeans_file))
 
      # read features

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/script/ParallelUBMGMM.py   xbob.spkrec(Download)
 
      # Perform KMeans initialization
      kmeans_machine = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1])
      # Creates the KMeansTrainer and call the initialization procedure
      kmeans_trainer = bob.trainer.KMeansTrainer()
      training_list = self.m_file_selector.training_feature_list()
      machine_file = self.m_configuration.kmeans_intermediate_file % self.m_args.iteration
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: KMeans E-Step from range(%d, %d)" % indices)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      t = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1]) # Temporary Kmeans machine required for trainer initialization
      kmeans_trainer.initialize(t, data)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      # Creates the KMeansMachine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(old_machine_file))
      kmeans_trainer.initialize(kmeans_machine, data)
 
 
      # load KMeans machine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(self.m_configuration.kmeans_file))
 
      # read features

src/f/a/facereclib-HEAD/facereclib/tools/ParallelUBMGMM.py   facereclib(Download)
 
      # Perform KMeans initialization
      kmeans_machine = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1])
      # Creates the KMeansTrainer and call the initialization procedure
      kmeans_trainer = bob.trainer.KMeansTrainer()
      training_list = self.training_list()
      machine_file = self.m_configuration.kmeans_intermediate_file % self.m_args.iteration
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: KMeans E-Step from range(%d, %d)" % indices)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      t = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1]) # Temporary Kmeans machine required for trainer initialization
      kmeans_trainer.initialize(t, data)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      # Creates the KMeansMachine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(old_machine_file))
      kmeans_trainer.initialize(kmeans_machine, data)
 
 
      # load KMeans machine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(self.m_configuration.kmeans_file))
 
      # read features

src/b/o/bob.spear-1.1.2/spear/script/ParallelUBMGMM.py   bob.spear(Download)
 
      # Perform KMeans initialization
      kmeans_machine = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1])
      # Creates the KMeansTrainer and call the initialization procedure
      kmeans_trainer = bob.trainer.KMeansTrainer()
      training_list = self.m_file_selector.training_feature_list()
      machine_file = self.m_configuration.kmeans_intermediate_file % self.m_args.iteration
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: KMeans E-Step from range(%d, %d)" % indices)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      t = bob.machine.KMeansMachine(self.m_tool.m_gaussians, data.shape[1]) # Temporary Kmeans machine required for trainer initialization
      kmeans_trainer.initialize(t, data)
 
      kmeans_trainer = bob.trainer.KMeansTrainer()
      # Creates the KMeansMachine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(old_machine_file))
      kmeans_trainer.initialize(kmeans_machine, data)
 
 
      # load KMeans machine
      kmeans_machine = bob.machine.KMeansMachine(bob.io.HDF5File(self.m_configuration.kmeans_file))
 
      # read features

src/f/a/facereclib-1.2.1/facereclib/tools/UBMGMM.py   facereclib(Download)
    # Creates the machines (KMeans and GMM)
    utils.debug(" .... Creating machines")
    kmeans = bob.machine.KMeansMachine(self.m_gaussians, input_size)
    self.m_ubm = bob.machine.GMMMachine(self.m_gaussians, input_size)
 

src/f/a/facereclib-HEAD/facereclib/tools/UBMGMM.py   facereclib(Download)
    # Creates the machines (KMeans and GMM)
    utils.debug(" .... Creating machines")
    kmeans = bob.machine.KMeansMachine(self.m_gaussians, input_size)
    self.m_ubm = bob.machine.GMMMachine(self.m_gaussians, input_size)
 

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/preprocessing/Energy.py   xbob.spkrec(Download)
    normalized_energy = utils.normalize_std_array(energy_array)
 
    kmeans = bob.machine.KMeansMachine(2, 1)
 
    logger_propagate = logger.propagate

src/m/a/maskattack.study-1.0.0/maskattack/study/analyze/isv.py   maskattack.study(Download)
 
      print 'Training k-means..'
      kmeans = bob.machine.KMeansMachine(512,dct_size-1)
      ubm = bob.machine.GMMMachine(512,dct_size-1)
 

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