def test_slatm_local_representation():
files = ["qm7/0001.xyz",
"qm7/0002.xyz",
"qm7/0003.xyz",
"qm7/0004.xyz",
"qm7/0005.xyz",
"qm7/0006.xyz",
"qm7/0007.xyz",
"qm7/0008.xyz",
"qm7/0009.xyz",
"qm7/0010.xyz"]
path = os.path.dirname(os.path.realpath(__file__))
mols = []
for xyz_file in files:
mol = qml.data.Compound(xyz=path + "/" + xyz_file)
mols.append(mol)
mbtypes = get_slatm_mbtypes(np.array([mol.nuclear_charges for mol in mols]))
for i, mol in enumerate(mols):
mol.generate_slatm(mbtypes, local=True)
X_qml = []
for i, mol in enumerate(mols):
for rep in mol.representation:
X_qml.append(rep)
X_qml = np.asarray(X_qml)
X_ref = np.loadtxt(path + "/data/slatm_local_representation.txt")
assert np.allclose(X_qml, X_ref), "Error in SLATM generation"
def get_slatm_representations(atoms_list, coordinates_list):
charge_list = []
for atoms in atoms_list:
charges = [get_atom(atom) for atom in atoms]
charges = np.array(charges)
charge_list.append(charges)
mbtypes = qml_representations.get_slatm_mbtypes(charge_list)
replist = [
qml_representations.generate_slatm(coordinates,
atoms,
mbtypes,
local=False)
for coordinates, atoms in zip(coordinates_list, charge_list)
]
replist = np.array(replist)
return replist
def _get_slatm_mbtypes(self, arr):
from qml.representations import get_slatm_mbtypes
return get_slatm_mbtypes(arr)
# Make training and test sets
n_test = 60
n_train = 140
training = mols[:n_train]
test = mols[-n_test:]
sigma = 100.0
llambda = 1e-10
# List of properties
Y = np.array([mol.properties for mol in training])
Ys = np.array([mol.properties for mol in test])
# List of representations
mbtypes = get_slatm_mbtypes(np.array([mol.nuclear_charges
for mol in mols]))
for i, mol in enumerate(training):
mol.generate_slatm(mbtypes, local=False, rpower=6)
for i, mol in enumerate(test):
mol.generate_slatm(mbtypes, local=False, rpower=6)
X = np.array([mol.representation for mol in training])
Xs = np.array([mol.representation for mol in test])
# Generate training Kernel
K = gaussian_kernel(X, X, sigma)
Ks = gaussian_kernel(X, Xs, sigma)
K[np.diag_indices_from(K)] += llambda
alpha = cho_solve(K, Y)
