def get_gp(
bodies,
kernel_type="mc",
multihyps=True,
cellabc=[1, 1, 1.5],
force_only=False,
noa=5,
) -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
print("\nSetting up...\n")
# params
cell = np.diag(cellabc)
unique_species = [1, 2]
ntwobody = 0
nthreebody = 0
prefix = bodies
if "2" in bodies or "two" in bodies:
ntwobody = 1
if "3" in bodies or "three" in bodies:
nthreebody = 1
hyps, hm, _ = generate_hm(ntwobody,
nthreebody,
nmanybody=0,
multihyps=multihyps)
cutoffs = hm["cutoffs"]
kernels = hm["kernels"]
hl = hm["hyp_labels"]
# create test structure
test_structure, forces = get_random_structure(cell, unique_species, noa)
energy = 3.14
# test update_db
gaussian = GaussianProcess(
kernels=kernels,
component=kernel_type,
hyps=hyps,
hyp_labels=hl,
cutoffs=cutoffs,
hyps_mask=hm,
parallel=False,
n_cpus=1,
)
if force_only:
gaussian.update_db(test_structure, forces)
else:
gaussian.update_db(test_structure, forces, energy=energy)
gaussian.check_L_alpha()
# print(gaussian.alpha)
return gaussian
def get_gp(bodies, kernel_type='mc', multihyps=True) -> GaussianProcess:
"""Returns a GP instance with a two-body numba-based kernel"""
print("\nSetting up...\n")
# params
cell = np.diag(np.array([1, 1, 1.5]))
unique_species = [2, 1]
cutoffs = np.array([0.8, 0.8])
noa = 5
nbond = 0
ntriplet = 0
prefix = bodies
if ('2' in bodies or 'two' in bodies):
nbond = 1
if ('3' in bodies or 'three' in bodies):
ntriplet = 1
hyps, hm, _ = generate_hm(nbond, ntriplet, multihyps=multihyps)
# create test structure
test_structure, forces = get_random_structure(cell, unique_species, noa)
hl = hm['hyps_label']
if (multihyps is False):
hm = None
# test update_db
gaussian = \
GaussianProcess(kernel_name=f'{prefix}{kernel_type}',
hyps=hyps,
hyp_labels=hl,
cutoffs=cutoffs, multihyps=multihyps, hyps_mask=hm,
parallel=False, n_cpus=1)
gaussian.update_db(test_structure, forces)
gaussian.check_L_alpha()
return gaussian
