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src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/dct_ubm.py   xbob.example.faceverify(Download)
  # create the KMeans and UBM machine
  kmeans = bob.machine.KMeansMachine(number_of_gaussians, input_size)
  ubm = bob.machine.GMMMachine(number_of_gaussians, input_size)
 
  # create the KMeansTrainer
  enroll_set = numpy.vstack(model_features.values())
  # create a GMM from the UBM
  gmm = bob.machine.GMMMachine(ubm)
 
  # train the GMM

src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/tests/__init__.py   xbob.example.faceverify(Download)
 
    # load PCA reference and check that it is still similar
    ubm_ref = bob.machine.GMMMachine(bob.io.HDF5File(self.resource('dct_ubm.hdf5')))
    self.assertTrue(ubm_ref.is_similar_to(ubm))
 
      model.save(bob.io.HDF5File(self.resource('dct_model.hdf5'), 'w'))
 
    model_ref = bob.machine.GMMMachine(bob.io.HDF5File(self.resource('dct_model.hdf5')))
    self.assertTrue(model_ref.is_similar_to(model))
 

src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/utils/miris.py   xbob.thesis.elshafey2014(Download)
def _ubm_training_init():
  """Initializes a UBM GMM with predefined means and a ML trainer"""
  ubm = bob.machine.GMMMachine(2,2)
  ubm.means = numpy.array([[0.5,0.5],[2.5,2.5]]) # Initial means
  ml_trainer = bob.trainer.ML_GMMTrainer(True, True, True, 1e-6)
 
  ubm = _train_ubm(data)
  gmm = bob.machine.GMMMachine(2,2)
 
  # MAP 1
 
  ubm = _train_ubm(data)
  gmm = bob.machine.GMMMachine(2,2)
 
  isvbase = bob.machine.ISVBase(ubm, 1)
 
  ubm = _train_ubm(data)
  gmm = bob.machine.GMMMachine(2,2)
 
  jfabase = bob.machine.JFABase(ubm, 1, 1)
 
  ubm = _train_ubm(data)
  gmm = bob.machine.GMMMachine(2,2)
 
  ivecmachine = bob.machine.IVectorMachine(ubm, 2)

src/x/b/xbob.thesis.elshafey2014-0.0.1a0/xbob/thesis/elshafey2014/tools/ParaUBMGMM.py   xbob.thesis.elshafey2014(Download)
 
      # Create initial GMM Machine
      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)
      training_list = self.training_list()
      machine_file = self.m_configuration.gmm_intermediate_file % self.m_args.iteration
      gmm_machine = bob.machine.GMMMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: GMM E-Step from range(%d, %d)" % indices)
 
      # load the old gmm machine
      gmm_machine =  bob.machine.GMMMachine(bob.io.HDF5File(old_machine_file))
      # initialize the trainer
      gmm_trainer = bob.trainer.ML_GMMTrainer(self.m_tool.m_update_means, self.m_tool.m_update_variances, self.m_tool.m_update_weights)

src/f/a/facereclib-1.2.1/facereclib/tools/UBMGMM.py   facereclib(Download)
    utils.debug(" .... Creating machines")
    kmeans = bob.machine.KMeansMachine(self.m_gaussians, input_size)
    self.m_ubm = bob.machine.GMMMachine(self.m_gaussians, input_size)
 
    # Creates the KMeansTrainer
  def load_projector(self, projector_file):
    """Reads the UBM model from file"""
    # read UBM
    self.m_ubm = bob.machine.GMMMachine(bob.io.HDF5File(projector_file))
    self.m_ubm.set_variance_thresholds(self.m_variance_threshold)
  def _enroll_using_array(self, array):
    utils.debug(" .... Enrolling with %d feature vectors" % array.shape[0])
 
    gmm = bob.machine.GMMMachine(self.m_ubm)
    gmm.set_variance_thresholds(self.m_variance_threshold)
  def read_model(self, model_file):
    return bob.machine.GMMMachine(bob.io.HDF5File(model_file))
 
  def read_probe(self, probe_file):
    """Read the type of features that we require, namely GMM_Stats"""

src/f/a/facereclib-1.2.1/facereclib/tools/ParallelUBMGMM.py   facereclib(Download)
 
      # Create initial GMM Machine
      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)
      training_list = self.training_list()
      machine_file = self.m_configuration.gmm_intermediate_file % self.m_args.iteration
      gmm_machine = bob.machine.GMMMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: GMM E-Step from range(%d, %d)" % indices)
 
      # load the old gmm machine
      gmm_machine =  bob.machine.GMMMachine(bob.io.HDF5File(old_machine_file))
      # initialize the trainer
      gmm_trainer = bob.trainer.ML_GMMTrainer(self.m_tool.m_update_means, self.m_tool.m_update_variances, self.m_tool.m_update_weights)

src/f/a/facereclib-HEAD/facereclib/tools/UBMGMM.py   facereclib(Download)
    utils.debug(" .... Creating machines")
    kmeans = bob.machine.KMeansMachine(self.m_gaussians, input_size)
    self.m_ubm = bob.machine.GMMMachine(self.m_gaussians, input_size)
 
    # Creates the KMeansTrainer
  def load_projector(self, projector_file):
    """Reads the UBM model from file"""
    # read UBM
    self.m_ubm = bob.machine.GMMMachine(bob.io.HDF5File(projector_file))
    self.m_ubm.set_variance_thresholds(self.m_variance_threshold)
  def _enroll_using_array(self, array):
    utils.debug(" .... Enrolling with %d feature vectors" % array.shape[0])
 
    gmm = bob.machine.GMMMachine(self.m_ubm)
    gmm.set_variance_thresholds(self.m_variance_threshold)
  def read_model(self, model_file):
    return bob.machine.GMMMachine(bob.io.HDF5File(model_file))
 
  def read_probe(self, probe_file):
    """Read the type of features that we require, namely GMM_Stats"""

src/f/a/facereclib-HEAD/facereclib/tools/ParallelUBMGMM.py   facereclib(Download)
 
      # Create initial GMM Machine
      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)
      training_list = self.training_list()
      machine_file = self.m_configuration.gmm_intermediate_file % self.m_args.iteration
      gmm_machine = bob.machine.GMMMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: GMM E-Step from range(%d, %d)" % indices)
 
      # load the old gmm machine
      gmm_machine =  bob.machine.GMMMachine(bob.io.HDF5File(old_machine_file))
      # initialize the trainer
      gmm_trainer = bob.trainer.ML_GMMTrainer(self.m_tool.m_update_means, self.m_tool.m_update_variances, self.m_tool.m_update_weights)

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/script/ParallelUBMGMM.py   xbob.spkrec(Download)
 
      # Create initial GMM Machine
      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)
      training_list = self.m_file_selector.training_feature_list()
      machine_file = self.m_configuration.gmm_intermediate_file % self.m_args.iteration
      gmm_machine = bob.machine.GMMMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: GMM E-Step from range(%d, %d)" % indices)
 
      # load the old gmm machine
      gmm_machine =  bob.machine.GMMMachine(bob.io.HDF5File(old_machine_file))
      # initialize the trainer
      gmm_trainer = bob.trainer.ML_GMMTrainer(self.m_tool.m_update_means, self.m_tool.m_update_variances, self.m_tool.m_update_weights)

src/b/o/bob.spear-1.1.2/spear/script/ParallelUBMGMM.py   bob.spear(Download)
 
      # Create initial GMM Machine
      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)
      training_list = self.m_file_selector.training_feature_list()
      machine_file = self.m_configuration.gmm_intermediate_file % self.m_args.iteration
      gmm_machine = bob.machine.GMMMachine(bob.io.HDF5File(machine_file))
 
      utils.info("UBM training: GMM E-Step from range(%d, %d)" % indices)
 
      # load the old gmm machine
      gmm_machine =  bob.machine.GMMMachine(bob.io.HDF5File(old_machine_file))
      # initialize the trainer
      gmm_trainer = bob.trainer.ML_GMMTrainer(self.m_tool.m_update_means, self.m_tool.m_update_variances, self.m_tool.m_update_weights)

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