wf = HarmOsc1D(nelec=1, ndim=1)
sampler = Metropolis(nwalkers=1000,
nstep=1000,
step_size=3,
nelec=1,
ndim=1,
domain={
'min': -2,
'max': 2
})
# sampler = PYMC3(nwalkers=1000,ndim=1)
optimizer = Minimize(method='bfgs', maxiter=25, tol=1E-4)
# optimizer = Swarm( maxiter=25)
# VMC solver
vmc = VMC(wf=wf, sampler=sampler, optimizer=optimizer)
# single point
opt_param = [0.5]
pos, e, s = vmc.single_point(opt_param)
print('Energy : ', e)
print('Variance : ', s)
vmc.plot_density(pos)
# optimization
init_param = [1.]
vmc.optimize(init_param)
vmc.plot_history()
def test_vmc_opt(self):
vmc = VMC(wf=self.wf, sampler=self.sampler, optimizer=self.optimizer)
init_param = [1.25]
vmc.optimize(init_param)
vf = vmc.history['variance'][-1]
assert np.allclose([vf], [0.0], atol=1E-3)
return 0
if __name__ == "__main__":
# wf = HarmOsc2D(nelec=1, ndim=2)
wf = PYSCF_WF(atom='O 0 0 0; H 0 0 1; H 0 1 0', basis='dzp')
# sampler = Metropolis(nwalkers=100, nstep=100, step_size = 3,
# nelec=wf.nelec, ndim=wf.ndim, domain = {'min':-5,'max':5})
sampler = PYMC3(nwalkers=100, ndim=wf.nelec * wf.ndim)
optimizer = Minimize(method='bfgs', maxiter=25, tol=1E-4)
optlin = Linear(wf=wf, maxiter=25, tol=1E-6)
# VMS solver
vmc = VMC(wf=wf, sampler=sampler, optimizer=None)
pos = vmc.sample([])
vmc.plot_density(pos)
e = vmc.wf.energy([], pos)
# single point
# opt_param = [0.5]
# pos,e,s = vmc.single_point(opt_param)
# print('Energy : ', e)
# print('Variance : ', s)
# vmc.plot_density(pos)
# optimization
# init_param = [1.25]
# vmc.optimize(init_param)
# vmc.plot_history()
def test_vmc_single_point(self):
vmc = VMC(wf=self.wf, sampler=self.sampler, optimizer=None)
opt_param = [0.5]
_, e, s = vmc.single_point(opt_param)
assert np.allclose([e, s], [0.5, 0], atol=1E-3)
if __name__ == "__main__":
wf = H2(nelec=2, ndim=3)
sampler = Metropolis(nwalkers=1000,
nstep=1000,
step_size=3,
nelec=2,
ndim=3,
domain={
'min': -5,
'max': 5
})
# sampler = PYMC3(nwalkers=100,ndim=6)
# sampler = Hamiltonian(nwalkers=1000, nstep=1000, step_size = 3, nelec=1, ndim=3)
# optimizer = Minimize(method='bfgs', maxiter=25, tol=1E-4)
# VMS solver
vmc = VMC(wf=wf, sampler=sampler, optimizer=None)
# single point
opt_param = [1.]
pos, e, s = vmc.single_point(opt_param)
print('Energy : ', e)
print('Variance : ', s)
vmc.plot_density(pos)
# # optimization
# init_param = [0.5]
# vmc.optimize(init_param)
# vmc.plot_history()