def test_vmc_construction_vstate():
ha, sx, ma, sa, driver = _setup_vmc()
op = nk.optimizer.Sgd(learning_rate=0.05)
driver = nk.Vmc(ha, op, sa, nk.models.RBM(), n_samples=1000, seed=SEED)
driver.run(1)
assert driver.step_count == 1
with raises(TypeError):
ha2 = nk.operator.LocalOperator(ha.hilbert * ha.hilbert)
driver = nk.Vmc(ha2, op, variational_state=driver.state)
def _setup_vmc(n_samples=200,
diag_shift=0,
use_iterative=False,
lsq_solver=None,
**kwargs):
L = 8
nk.random.seed(SEED)
hi = nk.hilbert.Spin(s=0.5)**L
ma = nk.machine.RbmSpin(hilbert=hi, alpha=1)
ma.init_random_parameters(sigma=0.01, seed=SEED)
ha = nk.operator.Ising(hi, nk.graph.Hypercube(length=L, n_dim=1), h=1.0)
sa = nk.sampler.MetropolisLocal(machine=ma)
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
sr = nk.optimizer.SR(ma,
use_iterative=use_iterative,
diag_shift=diag_shift,
lsq_solver=lsq_solver)
vmc = nk.Vmc(ha, sa, op, n_samples=n_samples, **kwargs)
# Add custom observable
X = [[0, 1], [1, 0]]
sx = nk.operator.LocalOperator(hi, [X] * 8, [[i] for i in range(8)])
return ma, vmc, sx
def _vmc(n_iter=20):
hi = nk.hilbert.Spin(s=0.5) ** L
ma = nk.models.RBM(alpha=1)
ha = nk.operator.Ising(hi, nk.graph.Hypercube(length=L, n_dim=1), h=1.0)
sa = nk.sampler.MetropolisLocal(hi)
vs = nk.vqs.MCState(sa, ma, n_samples=500, seed=SEED)
op = nk.optimizer.Sgd(learning_rate=0.1)
return nk.Vmc(hamiltonian=ha, variational_state=vs, optimizer=op)
def test_mps(diag):
L = 6
g = nk.graph.Hypercube(length=L, n_dim=1)
hi = nk.hilbert.Spin(s=0.5, N=g.n_nodes)
ma = nk.models.MPSPeriodic(hilbert=hi, graph=g, bond_dim=2, diag=diag)
sa = nk.sampler.MetropolisLocal(hilbert=hi, n_chains=16)
vs = nk.vqs.MCState(sa, ma, n_samples=1000)
ha = nk.operator.Ising(hi, graph=g, h=1.0)
op = nk.optimizer.Sgd(learning_rate=0.05)
driver = nk.Vmc(ha, op, variational_state=vs)
driver.run(3)
def run(h, j, l, n_iterations, a, n_samp):
# 1D Lattice
g = nk.graph.Hypercube(length=l, n_dim=1, pbc=True)
# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=1 / 2, N=g.n_nodes)
# Ising spin hamiltonian
ha = nk.operator.Ising(h=h, hilbert=hi, graph=g, J=j)
# RBM Spin Machine
ma = nk.machine.RbmSpin(alpha=a,
hilbert=hi,
use_visible_bias=False,
use_hidden_bias=False)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(ma, n_chains=32)
# Optimizer
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
# Stochastic Reconfiguration
sr = nk.optimizer.SR(ma, diag_shift=0.05)
# Create the optimization driver
gs = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=n_samp,
n_discard=20)
# Run the optimization for 300 iterations
gs.run(n_iter=n_iterations, out="DataMATRIX5_J1_1D_H_{:.1f}".format(h))
s = ma.to_array()
normalised = s.real / (sum(s.real))
#normalised = np.sqrt(s.real)
#normalised = 1
#plt.plot(normalised)
params = ma.parameters.imag
#weights = params[:-24]
print(np.shape(params))
return normalised, params
def reset(self):
comm = MPI.COMM_WORLD
rank = comm.Get_rank()
self._set_graph()
if rank == 0:
sys.stdout.write("Graph one the {:d} vertices.\n".format(
self.graph.n_sites))
sys.stdout.flush()
self.n_spins = self.graph.n_sites
self.hilbert = nk.hilbert.Spin(graph=self.graph, s=0.5)
self.machine = nk.machine.RbmSpin(self.hilbert, alpha=4)
self.machine.init_random_parameters(seed=42, sigma=1.0e-2)
self.sampler = nk.sampler.MetropolisLocal(self.machine)
self._set_operator()
self.optimizer = nk.optimizer.RmsProp()
use_cholesky = self.machine.n_par < 10000
self.vmc = nk.Vmc(
hamiltonian=self.hamiltonian,
sampler=self.sampler,
optimizer=self.optimizer,
n_samples=max([2000, self.n_spins * 50]),
sr=nk.optimizer.SR(
lsq_solver="LLT",
diag_shift=1.0e-2,
use_iterative=not use_cholesky,
is_holomorphic=self.sampler.machine.is_holomorphic,
),
)
if rank == 0:
sys.stdout.write("RBM with {:d} params.\n".format(
self.machine.n_par))
sys.stdout.write(self.vmc.info())
sys.stdout.write("\n")
sys.stdout.flush()
self.corr_operators = {}
for i in range(self.n_spins):
for j in range(self.n_spins):
self.corr_operators["{:d}-{:d}".format(
i, j)] = sigmaz(self.hilbert, i) * sigmaz(self.hilbert, j)
self.correlations = []
def _vmc(n_iter=20):
nk.random.seed(SEED)
hi = nk.hilbert.Spin(s=0.5)**L
ma = nk.machine.RbmSpin(hilbert=hi, alpha=1)
ma.init_random_parameters(sigma=0.01, seed=SEED)
ha = nk.operator.Ising(hi, nk.graph.Hypercube(length=L, n_dim=1), h=1.0)
sa = nk.sampler.MetropolisLocal(machine=ma)
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
return nk.Vmc(hamiltonian=ha, sampler=sa, optimizer=op, n_samples=500)
st = time.time()
vmc.run(n_iter, callback=callbacks)
runtime = time.time() - st
return vmc.step_count, runtime
def _setup_vmc(lsq_solver=None):
L = 4
g = nk.graph.Hypercube(length=L, n_dim=1)
hi = nk.hilbert.Spin(s=0.5, N=g.n_nodes)
ma = nk.machine.RbmSpin(hilbert=hi, alpha=1)
ma.init_random_parameters(sigma=0.01)
ha = nk.operator.Ising(hi, graph=g, h=1.0)
sa = nk.sampler.ExactSampler(machine=ma, sample_size=16)
op = nk.optimizer.Sgd(ma, learning_rate=0.05)
# Add custom observable
X = [[0, 1], [1, 0]]
sx = nk.operator.LocalOperator(hi, [X] * L, [[i] for i in range(8)])
sr = nk.optimizer.SR(ma, use_iterative=False, lsq_solver=lsq_solver)
driver = nk.Vmc(ha, sa, op, 1000, sr=sr)
return ha, sx, ma, sa, driver
def run_vmc(steps, step_size, diag_shift, n_samples):
opt = jaxopt.sgd(step_size)
# opt = nk.optimizer.Sgd(step_size)
sr = nk.optimizer.SR(lsq_solver="BDCSVD", diag_shift=diag_shift)
sr.store_rank_enabled = False # not supported by BDCSVD
sr.store_covariance_matrix_enabled = True
vmc = nk.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=opt,
n_samples=n_samples,
n_discard=min(n_samples // 10, 200),
sr=sr,
)
if mpi_rank == 0:
print(vmc.info())
print(HEADER_STRING)
for step in vmc.iter(steps, 1):
output(vmc, step)
def _setup_vmc(dtype=np.float32, sr=True):
L = 4
g = nk.graph.Hypercube(length=L, n_dim=1)
hi = nk.hilbert.Spin(s=0.5, N=g.n_nodes)
ma = nk.models.RBM(alpha=1, dtype=dtype)
sa = nk.sampler.ExactSampler(hilbert=hi, n_chains=16)
vs = nk.variational.MCState(sa, ma, n_samples=1000, seed=SEED)
ha = nk.operator.Ising(hi, graph=g, h=1.0)
op = nk.optimizer.Sgd(learning_rate=0.05)
# Add custom observable
X = [[0, 1], [1, 0]]
sx = nk.operator.LocalOperator(hi, [X] * L, [[i] for i in range(8)])
if sr:
sr_config = nk.optimizer.SR()
else:
sr_config = None
driver = nk.Vmc(ha, op, variational_state=vs, sr=sr_config)
return ha, sx, vs, sa, driver
def load(L=__L__,
alpha=__alpha__,
sr=None,
dataname=None,
path='run',
machine_name='JaxRBM',
sampler='Local',
hamiltonian_name='transformed_Heisenberg',
n_samples=10000,
n_iterations=20):
"""Method to load a pretrained machine and continue the training.
A hamiltonian and sampler can be chosen. The machine is defined and trained for the hamiltonian.
Args:
L (int) : The number of sites of the lattice
alpha (int) : A factor to define the size of different machines
sr (float) : The parameter for stochastic reconfiguration method. If it is None, stochastic reconfiguration is not used
dataname (str) : The dataname. If None, an automatic dataname is chosen
path (str) : The directory, where the results are saved. If None, the directory is 'run'
machine_name (str) A string to choose the machine. Possible inputs: See get_machine in my_machines.py
sampler (str) : A string to choose the sampler: Recommended: 'Local' (this works with every machine)
hamiltonian_name (str) : A string to choose the hamiltonian. Possible inputs: see get_hamiltonian in my_models.py
n_samples (int) : The number of samples used in every iteration step
n_iterations (int) : The number of iterations (training steps)
"""
if (dataname == None):
dataname = ''.join(('L', str(L)))
dataname = functions.create_path(dataname, path=path)
ha, hi, g = models.get_hamiltonian(hamiltonian_name, L)
print('uses', hamiltonian_name, 'hamiltonian')
sys.stdout.flush()
print('load the machine: ', dataname)
generate_machine = machines.get_machine(machine_name)
ma, op, sa, machine_name = generate_machine(hilbert=hi,
hamiltonian=ha,
alpha=alpha)
ma.load(''.join((dataname, '.wf')))
op, sa = machines.load_machine(machine=ma,
hamiltonian=ha,
optimizer='Adamax',
lr=0.001,
sampler=sampler)
#observables = functions.get_operator(hilbert=hi, L=L, operator='FerroCorr', symmetric=True)
#observables = {**functions.get_operator(hilbert=hi, L=L, operator='FerroCorr', symmetric=False), **functions.get_operator(hilbert=hi, L=L, operator='FerroCorr', symmetric=True)}
if (sr == None):
gs2 = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=n_samples) #, n_discard=5000)
else:
sr = nk.optimizer.SR(ma, diag_shift=sr)
gs2 = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=n_samples,
sr=sr) #, n_discard=5000)
functions.create_machinefile(machine_name, L, alpha, dataname, sr)
start = time.time()
#gs2.run(n_iter=n_iterations, out=''.join((dataname, '_load')), obs=observables, write_every=4, save_params_every=4)
gs2.run(n_iter=n_iterations,
out=dataname,
write_every=10,
save_params_every=10)
end = time.time()
with open(''.join((dataname, '.time')), 'a') as reader:
reader.write(str(end - start))
print('Time', end - start)
sys.stdout.flush()
def test_deprecated_vmc_name():
ha, sx, ma, sa, driver = _setup_vmc()
op = nk.optimizer.Sgd(learning_rate=0.05)
with pytest.warns(FutureWarning):
driver = nk.Vmc(ha, op, sa, nk.models.RBM(), n_samples=1000, seed=SEED)
######################################
# Initialize RBM with lattice symmetry
######################################
n_autom = len(g.automorphisms())
ma = nk.machine.RbmSpinSymm(hilbert=hi,
alpha=alpha_sym * n_autom // N) # Machine
ma.init_random_parameters(seed=1234, sigma=0.01)
assert (ma._ws.shape == (N, alpha_sym))
opt = nk.optimizer.Sgd(ma, learning_rate=sgd_lr) # Optimizer
sa = nk.sampler.MetropolisLocal(machine=ma) # Metropolis Local Sampling
sr = nk.optimizer.SR(ma, diag_shift=0.1) # Stochastic Reconfiguration
gs = nk.Vmc( # VMC object
hamiltonian=ha,
sampler=sa,
optimizer=opt,
n_samples=1000,
sr=sr)
################################################
# Optimize RBM parameters to obtain ground state
################################################
if args.direct_calc:
assert os.path.isfile("data/{}_opt_{}.log".format(pyfile, str_params))
output(">>>> RBMSym optimization skipped\n", logfile)
else:
output(">>>> RBMSym optimization\n", logfile)
start = time.time()
gs.run(out="data/{}_opt_{}".format(pyfile, str_params),
# 1D Lattice
g = nk.graph.Hypercube(length=20, n_dim=1, pbc=True)
# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=0.5, graph=g)
# Ising spin hamiltonian
ha = nk.operator.Ising(h=1.0, hilbert=hi)
# RBM Spin Machine
ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(ma, n_chains=32)
# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)
# Stochastic reconfiguration
sr = nk.optimizer.SR(diag_shift=0.1)
gs = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=10,
sr=sr,
n_discard=5)
gs.run(n_iter=3)
# Hilbert space of spins on the graph
# with total Sz equal to 0
hi = nk.hilbert.Spin(s=1 / 2, N=g.n_nodes, total_sz=0)
# Heisenberg hamiltonian
ha = nk.operator.Heisenberg(hilbert=hi)
# Symmetric RBM Spin Machine
ma = nk.machine.JastrowSymm(hilbert=hi, automorphisms=g, dtype=float)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Metropolis Exchange Sampling
# Notice that this sampler exchanges two neighboring sites
# thus preservers the total magnetization
sa = nk.sampler.MetropolisExchange(machine=ma)
# Optimizer
op = nk.optimizer.Sgd(ma, learning_rate=0.05)
# Stochastic reconfiguration
gs = nk.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=1000,
sr=nk.optimizer.SR(diag_shift=0.1, lsq_solver="QR"),
)
gs.run(out="test", n_iter=300)
# 1D Lattice
L = 20
g = nk.graph.Hypercube(length=L, n_dim=1, pbc=True)
# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=0.5, graph=g)
ha = nk.operator.Ising(h=1.0, hilbert=hi)
alpha = 1
ma = nk.machine.JaxRbm(hi, alpha, dtype=float)
ma.init_random_parameters(seed=1232)
# Jax Sampler
sa = nk.sampler.MetropolisLocal(machine=ma, n_chains=2)
# Using Sgd
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
# Create the optimization driver
gs = nk.Vmc(
hamiltonian=ha, sampler=sa, optimizer=op, n_samples=1000, sr=None, n_discard=None
)
# The first iteration is slower because of start-up jit times
gs.run(out="test", n_iter=2)
gs.run(out="test", n_iter=300)
# Spin based Hilbert Space
hi = nk.hilbert.Spin(s=0.5, total_sz=0.0, graph=g)
# Custom Hamiltonian operator
ha = nk.operator.LocalOperator(hi)
for mat, site in zip(mats, sites):
ha += nk.operator.LocalOperator(hi, mat, site)
# Restricted Boltzmann Machine
ma = nk.machine.RbmSpin(hi, alpha=1, symmetry=True)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Exchange Sampler randomly exchange up to next-to-nearest neighbours
sa = nk.sampler.MetropolisExchange(machine=ma, n_chains=16, d_max=2)
# Optimizer
opt = nk.optimizer.Sgd(ma, learning_rate=0.02)
# Stochastic reconfiguration
sr = nk.optimizer.SR(ma, diag_shift=0.1)
# Variational Monte Carlo
gs = nk.Vmc(hamiltonian=op,
sampler=sa,
optimizer=opt,
sr=sr,
n_samples=4000,
n_discard=5)
vmc.run(n_iter=300, out="test")
def Dimer_RBM(h, V, length, alpha, n_iter, n_samples, n_chains, n_discard,
sweep_size):
kernel = 1
n_iter = 300
# sweep_size = 200
decay_factor = 'sigmoid decay' # or 'sigmoid decay'
n_jobs = 12
n_discard = 300
name = 'h={}V={}l={}'.format(h, V, length)
g = nk.graph.Graph(nodes=[i for i in range(length[0] * length[1] * 2)])
hi = nk.hilbert.Spin(s=0.5, graph=g)
ham = f.dimer_hamiltonian(V=V, h=h, length=np.array(length))
op_transition1 = f.dimer_flip1(length=np.array(length))
hex_ = nk.machine.new_hex(np.array(length))
ma = nk.machine.RbmDimer(hi,
hex_,
alpha=alpha,
symmetry=True,
use_hidden_bias=False,
use_visible_bias=False,
dtype=float,
reverse=True)
ma.init_random_parameters(seed=1234)
sa_mul = nk.sampler.DimerMetropolisLocal_multi(machine=ma,
op=op_transition1,
length=length,
n_chains=n_chains,
sweep_size=sweep_size,
kernel=1,
n_jobs=n_jobs)
sr = nk.optimizer.SR(ma, diag_shift=0)
opt = nk.optimizer.Sgd(ma,
learning_rate=0.05,
decay_factor=decay_factor,
N=n_iter)
gs = nk.Vmc(
hamiltonian=ham,
sampler=sa_mul,
optimizer=opt,
n_samples=n_samples,
sr=sr,
n_discard=n_discard,
)
gs.run(n_iter=n_iter, out=parentdir + '/log/' + name)
# slight modification with large sample and large seep_size
sweep_size = sweep_size * 3
n_samples = n_samples * 3
n_iter = 100
n_discard = 600
sa_mul = nk.sampler.DimerMetropolisLocal_multi(machine=ma,
op=op_transition1,
length=length,
n_chains=n_chains,
sweep_size=sweep_size,
kernel=1,
n_jobs=n_jobs)
sr = nk.optimizer.SR(ma, diag_shift=0)
opt = nk.optimizer.Sgd(ma, learning_rate=0.01, decay_factor=1, N=n_iter)
gs = nk.Vmc(
hamiltonian=ham,
sampler=sa_mul,
optimizer=opt,
n_samples=n_samples,
sr=sr,
n_discard=n_discard,
)
gs.run(n_iter=n_iter, out=parentdir + '/log/' + name + '2')
ma.save(parentdir + '/save/ma/' + name)
# Ising spin hamiltonian
ha = nk.operator.Ising(h=1.0, hilbert=hi)
input_size = hi.size
alpha = 1
model = torch.nn.Sequential(
torch.nn.Linear(input_size, alpha * input_size),
torch.nn.ReLU(),
torch.nn.Linear(alpha * input_size, 2),
torch.nn.ReLU(),
)
ma = nk.machine.Torch(model, hilbert=hi)
ma.parameters = 0.1 * (np.random.randn(ma.n_par))
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma, n_chains=8)
# Optimizer
op = nk.optimizer.Sgd(0.1)
# Stochastic reconfiguration
sr = nk.optimizer.SR(diag_shift=0.1, use_iterative=True)
# Driver
gs = nk.Vmc(hamiltonian=ha, sampler=sa, optimizer=op, n_samples=500, sr=sr)
gs.run(n_iter=300, out="test")
g = nk.graph.Hypercube(length=8, n_dim=1, pbc=True)
# Boson Hilbert Space
hi = nk.hilbert.Boson(N=g.n_nodes, n_max=3, n_bosons=8)
# Bose Hubbard Hamiltonian
ha = nk.operator.BoseHubbard(hilbert=hi, graph=g, U=4.0)
# RBM Machine with one-hot encoding, real parameters, and symmetries
ma = nk.machine.RbmMultiVal(hilbert=hi, alpha=1, dtype=float, automorphisms=g)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Sampler using Hamiltonian moves, thus preserving the total number of particles
sa = nk.sampler.MetropolisHamiltonian(machine=ma,
hamiltonian=ha,
batch_size=16)
# Stochastic gradient descent optimization
op = nk.optimizer.Sgd(ma, 0.05)
# Variational Monte Carlo
sr = nk.optimizer.SR(ma, diag_shift=0.1)
vmc = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=4000,
n_discard=0,
sr=sr)
vmc.run(n_iter=300, out="test")
# 2D Lattice
g = nk.graph.Hypercube(length=5, n_dim=2, pbc=True)
# Hilbert space of spins on the graph
hi = nk.hilbert.Spin(s=0.5, graph=g)
# Ising spin hamiltonian at the critical point
ha = nk.operator.Ising(h=3.0, hilbert=hi)
# RBM Spin Machine
ma = nk.machine.RbmSpin(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma)
# Optimizer
op = nk.optimizer.Sgd(ma, learning_rate=0.1)
# Stochastic Reconfiguration
sr = nk.optimizer.SR(ma, diag_shift=0.1)
# Stochastic reconfiguration
gs = nk.Vmc(hamiltonian=ha, sampler=sa, optimizer=op, sr=sr, n_samples=1000)
# Create a JSON output file, and overwrite if file exists
logger = nk.logging.JsonLog("test", "w")
# Run the optimization
gs.run(n_iter=1000, out=logger)
# Ising spin hamiltonian
ha = nk.operator.Ising(h=1.0, hilbert=hi)
# RBM Spin Machine
ma = nk.machine.RbmSpinReal(alpha=1, hilbert=hi)
ma.init_random_parameters(seed=1234, sigma=0.01)
# Metropolis Local Sampling
sa = nk.sampler.MetropolisLocal(machine=ma)
# Optimizer
op = nk.optimizer.Sgd(learning_rate=0.1)
# Stochastic reconfiguration
gs = nk.Vmc(
hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=1000,
sr=nk.optimizer.SR(ma, diag_shift=0.1),
)
# Adding an observable
# The sum of sigma_x on all sites
X = [[0, 1], [1, 0]]
sx = nk.operator.LocalOperator(hi, [X] * L, [[i] for i in range(L)])
obs = {"SigmaX": sx}
gs.run(n_iter=300, out="test", obs=obs)
def run(L=__L__,
alpha=__alpha__,
sr=None,
dataname=None,
path='run',
machine_name='JaxRBM',
sampler='Local',
hamiltonian_name='transformed_Heisenberg',
n_samples=__number_samples__,
n_iterations=__number_iterations__):
"""Method to train a machine.
A hamiltonian and sampler can be chosen. The machine is defined and trained for the hamiltonian.
Args:
L (int) : The number of sites of the lattice.
alpha (int) : A factor to define the size of different machines.
sr (float) : The parameter for stochastic reconfiguration method. If it is None, stochastic reconfiguration is not used.
dataname (str) : The dataname. If None, an automatic dataname is chosen
path (str) : The directory, where the results are saved. If None, the directory is 'run'
machine_name (str) A string to choose the machine. Possible inputs: See get_machine in my_machines.py
sampler (str) : A string to choose the sampler: Recommended: 'Local' (this works with every machine)
hamiltonian_name (str) : A string to choose the hamiltonian. Possible inputs: see get_hamiltonian in my_models.py
n_samples (int) : The number of samples used in every iteration step
n_iterations (int) : The number of iterations (training steps)
"""
ha, hi, g = models.get_hamiltonian(hamiltonian_name, L)
print('uses', hamiltonian_name, 'hamiltonian')
sys.stdout.flush()
generate_machine = machines.get_machine(machine_name)
ma, op, sa, machine_name = generate_machine(hilbert=hi,
hamiltonian=ha,
alpha=alpha,
optimizer='Adamax',
lr=0.005,
sampler=sampler)
if (sr == None):
gs = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=n_samples)
else:
sr = nk.optimizer.SR(ma, diag_shift=sr)
gs = nk.Vmc(hamiltonian=ha,
sampler=sa,
optimizer=op,
n_samples=n_samples,
sr=sr)
if (dataname == None):
dataname = ''.join(('L', str(L)))
dataname = functions.create_path(dataname, path=path)
print('')
functions.create_machinefile(machine_name, L, alpha, dataname, sr)
start = time.time()
gs.run(n_iter=int(n_iterations), out=dataname)
end = time.time()
with open(''.join((dataname, '.time')), 'w') as reader:
reader.write(str(end - start))
print('Time', end - start)
sys.stdout.flush()