def test_efs_kern_vec(params, ihyps):
name, cutoffs, hyps_mask_list, _ = params
np.random.seed(10)
test_point = get_tstp()
size1 = len(flare.gp_algebra._global_training_data[name])
size2 = len(flare.gp_algebra._global_training_structures[name])
hyps_mask = hyps_mask_list[ihyps]
hyps = hyps_mask["hyps"]
kernel = str_to_kernel_set(hyps_mask["kernels"], "mc", hyps_mask)
test_point = get_tstp()
energy_vector, force_array, stress_array = efs_kern_vec(
name, kernel[5], kernel[4], test_point, hyps, cutoffs, hyps_mask)
energy_vector_par, force_array_par, stress_array_par = efs_kern_vec(
name,
kernel[5],
kernel[4],
test_point,
hyps,
cutoffs,
hyps_mask,
n_cpus=2,
n_sample=100,
)
assert np.equal(energy_vector, energy_vector_par).all()
assert np.equal(force_array, force_array_par).all()
assert np.equal(stress_array, stress_array_par).all()
def test_en_kern_vec(params, ihyps):
name, cutoffs, hyps_mask_list, _ = params
hyps_mask = hyps_mask_list[ihyps]
hyps = hyps_mask["hyps"]
kernel = str_to_kernel_set(hyps_mask["kernels"], "mc", hyps_mask)
np.random.seed(10)
test_point = get_tstp()
size1 = len(flare.gp_algebra._global_training_data[name])
size2 = len(flare.gp_algebra._global_training_structures[name])
# test the parallel implementation for multihyps
vec = en_kern_vec(name, kernel[3], kernel[2], test_point, hyps, cutoffs,
hyps_mask)
vec_par = en_kern_vec(
name,
kernel[3],
kernel[2],
test_point,
hyps,
cutoffs,
hyps_mask,
n_cpus=2,
n_sample=100,
)
assert all(np.equal(vec, vec_par)), "parallel implementation is wrong"
assert vec.shape[0] == size1 * 3 + size2
def test_kernel_vector(params, ihyps):
name, cutoffs, hyps_mask_list, _ = params
np.random.seed(10)
test_point = get_tstp()
size1 = len(flare.gp_algebra._global_training_data[name])
size2 = len(flare.gp_algebra._global_training_structures[name])
hyps_mask = hyps_mask_list[ihyps]
hyps = hyps_mask["hyps"]
kernel = str_to_kernel_set(hyps_mask["kernels"], "mc", hyps_mask)
# test the parallel implementation for multihyps
vec = get_kernel_vector(name, kernel[0], kernel[3], test_point, 1, hyps,
cutoffs, hyps_mask)
vec_par = get_kernel_vector(
name,
kernel[0],
kernel[3],
test_point,
1,
hyps,
cutoffs,
hyps_mask,
n_cpus=2,
n_sample=100,
)
assert np.isclose(vec, vec_par,
rtol=1e-4).all(), "parallel implementation is wrong"
assert vec.shape[0] == size1 * 3 + size2