sampled_points = np.array(sampled_points)
sampled_energies = np.array(sampled_energies)
if len(sampled_points) > 0 and len(sampled_energies) >= len(sampled_points[0]):
weights = np.linalg.lstsq(sampled_points, sampled_energies)[0]
else:
weights = None
# Submit new candidates until enough are running
while (not pbs_run.enough_jobs_running()
and len(population.get_current_population()) > 2):
a1, a2 = population.get_two_candidates()
# Selecting the "worst" parent energy
# which the child should be compared to
ce_a1 = da.get_atoms(a1.info['relax_id']).get_potential_energy()
ce_a2 = da.get_atoms(a2.info['relax_id']).get_potential_energy()
comparison_energy = min(ce_a1, ce_a2)
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
if should_we_skip(a3, comparison_energy, weights):
continue
da.add_unrelaxed_candidate(a3, description=desc)
if random() < mutation_probability:
a3_mut, desc_mut = mutations.get_new_individual([a3])
if (a3_mut is not None
and not should_we_skip(a3_mut, comparison_energy, weights)):
da.add_unrelaxed_step(a3_mut, desc_mut)
sampled_points = np.array(sampled_points)
sampled_energies = np.array(sampled_energies)
if len(sampled_points) > 0 and len(sampled_energies) >= len(sampled_points[0]):
weights = np.linalg.lstsq(sampled_points, sampled_energies)[0]
else:
weights = None
# Submit new candidates until enough are running
while (not pbs_run.enough_jobs_running() and
len(population.get_current_population()) > 2):
a1, a2 = population.get_two_candidates()
# Selecting the "worst" parent energy
# which the child should be compared to
ce_a1 = da.get_atoms(a1.info['relax_id']).get_potential_energy()
ce_a2 = da.get_atoms(a2.info['relax_id']).get_potential_energy()
comparison_energy = min(ce_a1, ce_a2)
a3, desc = pairing.get_new_individual([a1, a2])
if a3 is None:
continue
if should_we_skip(a3, comparison_energy, weights):
continue
da.add_unrelaxed_candidate(a3, description=desc)
if random() < mutation_probability:
a3_mut, desc_mut = mutations.get_new_individual([a3])
if (a3_mut is not None and
not should_we_skip(a3_mut, comparison_energy, weights)):
da.add_unrelaxed_step(a3_mut, desc_mut)
