def search_lower_bound(self, GP):
"""
If the lower bound is set to be 'auto', search the minimal interatomic
distances in the training set of GP.
"""
upper_bound = Parameters.get_cutoff(self.kernel_name, self.species,
GP.hyps_mask)
lower_bound = np.min(upper_bound)
training_data = _global_training_data[GP.name]
for env in training_data:
if len(env.bond_array_2) == 0:
continue
min_dist = env.bond_array_2[0][0]
if min_dist < lower_bound:
lower_bound = min_dist
training_struc = _global_training_structures[GP.name]
for struc in training_struc:
for env in struc:
if len(env.bond_array_2) == 0:
continue
min_dist = env.bond_array_2[0][0]
if min_dist < lower_bound:
lower_bound = min_dist
return lower_bound
def update_bounds(self, GP):
rebuild_container = False
# double check the container and the GP is consistent
if not Parameters.compare_dict(GP.hyps_mask, self.hyps_mask):
rebuild_container = True
lower_bound = self.bounds[0]
min_dist = self.search_lower_bound(GP)
# change lower bound only when there appears a smaller distance
if lower_bound is None or min_dist < np.max(lower_bound):
lower_bound = np.max((min_dist - self.lower_bound_relax, 0.0))
rebuild_container = True
warnings.warn(
"The minimal distance in training data is lower than "
f"the current lower bound, will reset lower bound to {lower_bound}"
)
upper_bound = self.bounds[1]
if self.auto_upper or upper_bound is None:
gp_cutoffs = Parameters.get_cutoff(self.kernel_name, self.species,
GP.hyps_mask)
if upper_bound is None or np.any(gp_cutoffs > upper_bound):
upper_bound = gp_cutoffs
rebuild_container = True
if rebuild_container:
self.set_bounds(lower_bound, upper_bound)
self.build_map_container()
