def predictE(atoms_train,
atoms_predict,
featureCalculator,
feature_filename,
eta=1):
Etrain = np.array([a.get_potential_energy() for a in atoms_train])
try:
features = np.load(feature_filename + '.npy')
except IOError:
features = featureCalculator.get_featureMat(atoms_train,
show_progress=True)
np.save(feature_filename, features)
# Apply eta
Nbins1 = featureCalculator.Nbins1
Nbondtypes_2body = len(featureCalculator.bondtypes_2body)
if featureCalculator.use_angular:
features[:, Nbondtypes_2body * Nbins1:] *= eta
# Set up KRR-model
comparator = gaussComparator()
krr = krr_class(comparator=comparator, featureCalculator=featureCalculator)
# Perform training
GSkwargs = {'reg': [1e-5], 'sigma': np.logspace(0, 2, 10)}
FVU, params = krr.train(data_values=Etrain,
featureMat=features,
add_new_data=False,
k=5,
**GSkwargs)
# Predict
Npredict = len(atoms_predict)
Epred = np.zeros(Npredict)
Epred_error = np.zeros(Npredict)
theta0 = np.zeros(Npredict)
for i, a in enumerate(atoms_predict):
Epred[i], Epred_error[i], theta0[i] = krr.predict_energy(
atoms=a, return_error=True)
return Epred, Epred_error, theta0
Rc2 = 4
Nbins2 = 30
sigma2 = 0.2
gamma = 1
eta = 5
use_angular = True
"""
featureCalculator = Angular_Fingerprint(a, Rc1=Rc1, Rc2=Rc2, binwidth1=binwidth1, Nbins2=Nbins2, sigma1=sigma1, sigma2=sigma2, gamma=gamma, eta=eta, use_angular=use_angular)
# Set up KRR-model
comparator = gaussComparator(featureCalculator=featureCalculator)
delta_function = deltaFunc(atoms=a, rcut=6)
krr = krr_class(comparator=comparator,
featureCalculator=featureCalculator,
delta_function=delta_function)
# Savefile setup
savefiles_path = sys.argv[1]
try:
run_num = sys.argv[2]
except IndexError:
run_num = ''
savefiles_namebase = savefiles_path + 'global' + run_num + '_'
try:
start_pop_file = sys.argv[3]
start_pop = read(start_pop_file, index=':')
except IndexError: