def test_set_L_alpha(two_body_gp, params):
# params
cell = np.eye(3)
unique_species = [2, 1]
noa = 2
# create test structure
test_structure, forces = get_random_structure(cell, unique_species, noa)
# set gp model
kernel = en.two_plus_three_body
kernel_grad = en.two_plus_three_body_grad
hyps = np.array([
2.23751151e-01, 8.19990316e-01, 1.28421842e-04, 1.07467158e+00,
5.50677932e-02
])
cutoffs = np.array([5.4, 5.4])
hyp_labels = ['sig2', 'ls2', 'sig3', 'ls3', 'noise']
energy_force_kernel = en.two_plus_three_force_en
energy_kernel = en.two_plus_three_en
opt_algorithm = 'BFGS'
# test update_db
gaussian = \
GaussianProcess(kernel, kernel_grad, hyps, cutoffs, hyp_labels,
energy_force_kernel, energy_kernel,
opt_algorithm,
par=True, no_cpus=2)
gaussian.update_db(test_structure, forces)
gaussian.set_L_alpha()
def methanol_gp():
the_gp = GaussianProcess(
kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06,
1.53990678e-02,
2.50624782e-05,
5.07884426e-01,
1.70172923e-03,
]),
cutoffs=np.array([5, 3]),
hyp_labels=["l2", "s2", "l3", "s3", "n0"],
maxiter=1,
opt_algorithm="L-BFGS-B",
)
with open(path.join(TEST_FILE_DIR, "methanol_envs.json"), "r") as f:
dicts = [loads(s) for s in f.readlines()]
for cur_dict in dicts:
force = cur_dict["forces"]
env = AtomicEnvironment.from_dict(cur_dict)
the_gp.add_one_env(env, force)
the_gp.set_L_alpha()
return the_gp
def methanol_gp():
the_gp = GaussianProcess(kernel_name="2+3_mc",
hyps=np.array([
3.75996759e-06, 1.53990678e-02,
2.50624782e-05, 5.07884426e-01, 1.70172923e-03
]),
cutoffs=np.array([7, 7]),
hyp_labels=['l2', 's2', 'l3', 's3', 'n0'],
maxiter=1,
opt_algorithm='L-BFGS-B')
with open('./test_files/methanol_envs.json') as f:
dicts = [loads(s) for s in f.readlines()]
for cur_dict in dicts:
force = cur_dict['forces']
env = AtomicEnvironment.from_dict(cur_dict)
the_gp.add_one_env(env, force)
the_gp.set_L_alpha()
return the_gp
