def backward_attributes(dictionary):
"""
add new attributes to old instance
or update attribute types
"""
if "name" not in dictionary:
dictionary["name"] = "default_gp"
if "per_atom_par" not in dictionary:
dictionary["per_atom_par"] = True
if "optimization_algorithm" not in dictionary:
dictionary["opt_algorithm"] = "L-BFGS-B"
if "hyps_mask" not in dictionary:
dictionary["hyps_mask"] = None
if "parallel" not in dictionary:
dictionary["parallel"] = False
if "component" not in dictionary:
dictionary["component"] = "mc"
if "training_structures" not in dictionary:
# Environments of each structure
dictionary["training_structures"] = []
dictionary["energy_labels"] = [] # Energies of training structures
dictionary["energy_labels_np"] = np.empty(0, )
if "training_labels" not in dictionary:
dictionary["training_labels"] = []
dictionary["training_labels_np"] = np.empty(0, )
if "energy_noise" not in dictionary:
dictionary["energy_noise"] = 0.01
if not isinstance(dictionary["cutoffs"], dict):
dictionary["cutoffs"] = Parameters.cutoff_array_to_dict(
dictionary["cutoffs"])
dictionary["hyps_mask"] = Parameters.backward(
dictionary["kernels"], deepcopy(dictionary["hyps_mask"]))
if "logger_name" not in dictionary:
dictionary["logger_name"] = None
def backward_attributes(dictionary):
"""
add new attributes to old instance
or update attribute types
"""
if 'name' not in dictionary:
dictionary['name'] = 'default_gp'
if 'per_atom_par' not in dictionary:
dictionary['per_atom_par'] = True
if 'optimization_algorithm' not in dictionary:
dictionary['opt_algorithm'] = 'L-BFGS-B'
if 'hyps_mask' not in dictionary:
dictionary['hyps_mask'] = None
if 'parallel' not in dictionary:
dictionary['parallel'] = False
if 'component' not in dictionary:
dictionary['component'] = 'mc'
if 'training_structures' not in dictionary:
# Environments of each structure
dictionary['training_structures'] = []
dictionary['energy_labels'] = [] # Energies of training structures
dictionary['energy_labels_np'] = np.empty(0, )
if 'training_labels' not in dictionary:
dictionary['training_labels'] = []
dictionary['training_labels_np'] = np.empty(0, )
if 'energy_noise' not in dictionary:
dictionary['energy_noise'] = 0.01
if not isinstance(dictionary['cutoffs'], dict):
dictionary['cutoffs'] = Parameters.cutoff_array_to_dict(
dictionary['cutoffs'])
dictionary['hyps_mask'] = Parameters.backward(
dictionary['kernels'], deepcopy(dictionary['hyps_mask']))
if 'logger_name' not in dictionary:
dictionary['logger_name'] = None
