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src/x/b/xbob.example.faceverify-1.0.0/xbob/example/faceverify/tests/__init__.py   xbob.example.faceverify(Download)
 
    if regenerate_references:
      bob.io.save(model, self.resource('pca_model.hdf5'))
      bob.io.save(probe, self.resource('pca_probe.hdf5'))
 
 
    if regenerate_references:
      bob.io.save(model, self.resource('gabor_model.hdf5'))
      bob.io.save(probe, self.resource('gabor_probe.hdf5'))
 
    dct_feature = extract_feature(images[1])
    if regenerate_references:
      bob.io.save(dct_feature, self.resource('dct_feature.hdf5'))
 
    feature_ref = bob.io.load(self.resource('dct_feature.hdf5'))

src/x/b/xbob.spkrec-1.0.3/xbob/spkrec/toolchain/ToolChain.py   xbob.spkrec(Download)
    else:
      # this is most probably a numpy.ndarray that can be saved by bob.io.save
      bob.io.save(data, str(filename))
 
 
    else:
      # this is most probably a numpy.ndarray that can be saved by bob.io.save
      bob.io.save(data, str(filename))
 
  def __check_file__(self, filename, force, expected_file_size = 1):
      c_matrix_for_model = self.__c_matrix_split_for_model__(probe_objects_for_model, all_probe_objects, c_for_all)
      # Save C matrix to file
      bob.io.save(c_matrix_for_model, self.m_file_selector.c_file_for_model(model_id, group))
 
  # Function 15/
        d_same_value = numpy.vstack((d_same_value, tmp2))
    # Saves to files
    bob.io.save(d_for_all, self.m_file_selector.d_matrix_file(group))
    bob.io.save(d_same_value, self.m_file_selector.d_same_value_matrix_file(group))
 

src/b/o/bob.spear-1.1.2/spear/toolchain/ToolChain.py   bob.spear(Download)
    else:
      # this is most probably a numpy.ndarray that can be saved by bob.io.save
      bob.io.save(data, str(filename))
 
 
    else:
      # this is most probably a numpy.ndarray that can be saved by bob.io.save
      bob.io.save(data, str(filename))
 
  def __check_file__(self, filename, force, expected_file_size = 1):
      c_matrix_for_model = self.__c_matrix_split_for_model__(probe_objects_for_model, all_probe_objects, c_for_all)
      # Save C matrix to file
      bob.io.save(c_matrix_for_model, self.m_file_selector.c_file_for_model(model_id, group))
 
  # Function 15/
        d_same_value = numpy.vstack((d_same_value, tmp2))
    # Saves to files
    bob.io.save(d_for_all, self.m_file_selector.d_matrix_file(group))
    bob.io.save(d_same_value, self.m_file_selector.d_same_value_matrix_file(group))
 

src/f/a/facereclib-1.2.1/facereclib/toolchain/ToolChain.py   facereclib(Download)
        if compute_zt_norm:
          # write A matrix only when you want to compute zt norm afterwards
          bob.io.save(a, self.m_file_selector.a_file(model_id, group))
 
        # Save scores to text file
        else:
          b = self.__scores__(model, z_probe_files)
        bob.io.save(b, score_file)
 
  def __scores_c__(self, t_model_ids, group, force, preload_probes):
        else:
          c = self.__scores__(t_model, probe_files)
        bob.io.save(c, score_file)
 
  def __scores_d__(self, t_model_ids, group, force, preload_probes):
        else:
          d = self.__scores__(t_model, z_probe_files)
        bob.io.save(d, self.m_file_selector.d_file(t_model_id, group))
 
        t_client_id = [self.m_file_selector.client_id(t_model_id)]
        d_same_value_tm = bob.machine.ztnorm_same_value(t_client_id, z_probe_ids)
        bob.io.save(d_same_value_tm, score_file)

src/f/a/facereclib-HEAD/facereclib/toolchain/ToolChain.py   facereclib(Download)
        if compute_zt_norm:
          # write A matrix only when you want to compute zt norm afterwards
          bob.io.save(a, self.m_file_selector.a_file(model_id, group))
 
        # Save scores to text file
        else:
          b = self.__scores__(model, z_probe_files)
        bob.io.save(b, score_file)
 
  def __scores_c__(self, t_model_ids, group, force, preload_probes):
        else:
          c = self.__scores__(t_model, probe_files)
        bob.io.save(c, score_file)
 
  def __scores_d__(self, t_model_ids, group, force, preload_probes):
        else:
          d = self.__scores__(t_model, z_probe_files)
        bob.io.save(d, self.m_file_selector.d_file(t_model_id, group))
 
        t_client_id = [self.m_file_selector.client_id(t_model_id)]
        d_same_value_tm = bob.machine.ztnorm_same_value(t_client_id, z_probe_ids)
        bob.io.save(d_same_value_tm, score_file)

src/f/a/facereclib-1.2.1/facereclib/tools/Dummy.py   facereclib(Download)
  def train_projector(self, train_files, projector_file):
    """Does not train the projector, but writes some file"""
    utils.debug("DummyTool: Training projector %s with %d training files" % (projector_file, len(train_files)))
    # save something
    bob.io.save(self.m_test_value, projector_file)
  def train_enroller(self, train_files, enroller_file):
    """Does not train the projector, but writes some file"""
    utils.debug("DummyTool: Training enroller %s using %d features" % (enroller_file, len(train_files)))
    # save something
    bob.io.save(self.m_test_value, enroller_file)
  def save_feature(self, feature, feature_file):
    """Saves the given feature to the given file"""
    utils.debug("DummyTool: Saving feature of length %d to file %s" % (feature.shape[0], feature_file))
    bob.io.save(feature, feature_file)
 
  def save_model(self, model, model_file):
    """Writes the model to the given model file"""
    utils.debug("DummyTool: Saving model of length %d to file %s" % (model.shape[0], model_file))
    bob.io.save(model, model_file)
 

src/f/a/facereclib-HEAD/facereclib/tools/Dummy.py   facereclib(Download)
  def train_projector(self, train_files, projector_file):
    """Does not train the projector, but writes some file"""
    utils.debug("DummyTool: Training projector %s with %d training files" % (projector_file, len(train_files)))
    # save something
    bob.io.save(self.m_test_value, projector_file)
  def train_enroller(self, train_files, enroller_file):
    """Does not train the projector, but writes some file"""
    utils.debug("DummyTool: Training enroller %s using %d features" % (enroller_file, len(train_files)))
    # save something
    bob.io.save(self.m_test_value, enroller_file)
  def save_feature(self, feature, feature_file):
    """Saves the given feature to the given file"""
    utils.debug("DummyTool: Saving feature of length %d to file %s" % (feature.shape[0], feature_file))
    bob.io.save(feature, feature_file)
 
  def save_model(self, model, model_file):
    """Writes the model to the given model file"""
    utils.debug("DummyTool: Saving model of length %d to file %s" % (model.shape[0], model_file))
    bob.io.save(model, model_file)
 

src/f/a/facereclib-1.2.1/facereclib/tools/Tool.py   facereclib(Download)
      feature.save(bob.io.HDF5File(feature_file, "w"))
    else:
      bob.io.save(feature, feature_file)
 
 
      model.save(bob.io.HDF5File(model_file, "w"))
    else:
      bob.io.save(model, model_file)
 
 

src/f/a/facereclib-HEAD/facereclib/tools/Tool.py   facereclib(Download)
      feature.save(bob.io.HDF5File(feature_file, "w"))
    else:
      bob.io.save(feature, feature_file)
 
 
      model.save(bob.io.HDF5File(model_file, "w"))
    else:
      bob.io.save(model, model_file)
 
 

src/f/a/facereclib-1.2.1/facereclib/preprocessing/Preprocessor.py   facereclib(Download)
      image.save(bob.io.HDF5File(data_file, "w"))
    else:
      bob.io.save(data, data_file)
 
 

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