def test():
# Default multi-Slater wave function
mol = gto.M(atom="Li 0. 0. 0.; H 0. 0. 1.5",
basis="cc-pvtz",
unit="bohr",
spin=0)
mf = scf.RHF(mol).run()
mc = mcscf.CASCI(mf, ncas=2, nelecas=(1, 1))
mc.kernel()
wf, to_opt = default_msj(mol, mf, mc)
old_parms = wf.parameters
lt = LinearTransform(wf.parameters, to_opt)
# Test serialize parameters
x0 = lt.serialize_parameters(wf.parameters)
x0 += np.random.normal(size=x0.shape)
wf.parameters = lt.deserialize(x0)
assert wf.parameters["wf1det_coeff"][0] == old_parms["wf1det_coeff"][0]
assert np.sum(wf.parameters["wf2bcoeff"][0] -
old_parms["wf2bcoeff"][0]) == 0
# Test serialize gradients
configs = OpenConfigs(np.random.randn(10, 4, 3))
wf.recompute(configs)
pgrad = wf.pgradient()
pgrad_serial = lt.serialize_gradients(pgrad)
assert np.sum(pgrad_serial[:, :3] - pgrad["wf1det_coeff"][:, 1:4]) == 0
def test_transform():
""" Just prints things out;
TODO: figure out a thing to test.
"""
from pyscf import gto, scf
r = 1.54 / 0.529177
mol = gto.M(
atom="H 0. 0. 0.; H 0. 0. %g" % r,
ecp="bfd",
basis="bfd_vtz",
unit="bohr",
verbose=1,
)
mf = scf.RHF(mol).run()
wf, to_opt = pyqmc.default_sj(mol, mf)
enacc = pyqmc.EnergyAccumulator(mol)
print(list(wf.parameters.keys()))
transform = LinearTransform(wf.parameters)
x = transform.serialize_parameters(wf.parameters)
nconfig = 10
configs = pyqmc.initial_guess(mol, nconfig)
wf.recompute(configs)
pgrad = wf.pgradient()
gradtrans = transform.serialize_gradients(pgrad)
assert gradtrans.shape[1] == len(x)
assert gradtrans.shape[0] == nconfig
def test_transform():
""" Just prints things out;
TODO: figure out a thing to test.
"""
from pyscf import gto, scf
import pyqmc
r = 1.54 / .529177
mol = gto.M(atom='H 0. 0. 0.; H 0. 0. %g' % r,
ecp='bfd',
basis='bfd_vtz',
unit='bohr',
verbose=1)
mf = scf.RHF(mol).run()
wf = pyqmc.slater_jastrow(mol, mf)
enacc = pyqmc.EnergyAccumulator(mol)
print(list(wf.parameters.keys()))
transform = LinearTransform(wf.parameters)
x = transform.serialize_parameters(wf.parameters)
nconfig = 10
configs = pyqmc.initial_guess(mol, nconfig)
wf.recompute(configs)
pgrad = wf.pgradient()
gradtrans = transform.serialize_gradients(pgrad)
assert gradtrans.shape[1] == len(x)
assert gradtrans.shape[0] == nconfig
def test_constraints(H2_ccecp_casci_s0):
mol, mf, mc = H2_ccecp_casci_s0
wf, to_opt = pyq.generate_wf(mol, mf, mc=mc)
old_parms = copy.deepcopy(wf.parameters)
lt = LinearTransform(wf.parameters, to_opt)
# Test serialize parameters
x0 = lt.serialize_parameters(wf.parameters)
x0 += np.random.normal(size=x0.shape)
for k, it in lt.deserialize(wf, x0).items():
assert wf.parameters[k].shape == it.shape
wf.parameters[k] = it
# to_opt is supposed to be false for both of these.
assert wf.parameters["wf1det_coeff"][0] == old_parms["wf1det_coeff"][0]
assert np.sum(wf.parameters["wf2bcoeff"][0] -
old_parms["wf2bcoeff"][0]) == 0
# While this one is supposed to change.
assert np.sum(wf.parameters["wf2bcoeff"][1] -
old_parms["wf2bcoeff"][1]) != 0
# Test serialize gradients
configs = pyq.initial_guess(mol, 10)
wf.recompute(configs)
pgrad = wf.pgradient()
pgrad_serial = lt.serialize_gradients(pgrad)
# Pgrad should be walkers, configs
assert pgrad_serial.shape[1] == x0.shape[0]
def test_transform(LiH_sto3g_rhf):
"""Tests that the shapes are ok"""
mol, mf = LiH_sto3g_rhf
wf, to_opt = pyq.generate_wf(mol, mf)
transform = LinearTransform(wf.parameters)
x = transform.serialize_parameters(wf.parameters)
nconfig = 10
configs = pyq.initial_guess(mol, nconfig)
wf.recompute(configs)
pgrad = wf.pgradient()
gradtrans = transform.serialize_gradients(pgrad)
assert gradtrans.shape[1] == len(x)
assert gradtrans.shape[0] == nconfig