def hubo_simulated_annealing(bhom: BinaryHigherOrderModel, state: list,
schedule: list, var_type):
adj_dict = bhom.adj_dict()
state = np.array(state)
if SPIN == cast_var_type(var_type):
for beta, mc_steps in schedule:
for _ in range(mc_steps):
for i in bhom.indices:
de = -2 * state[i] * np.sum([
d * np.prod(state[_inds]) for _inds, d in adj_dict[i]
])
de += -2 * state[i] * bhom.interactions[0][i]
if de < 0 or np.random.uniform(0, 1) < np.exp(-beta * de):
state[i] *= -1
return state
elif BINARY == cast_var_type(var_type):
for beta, mc_steps in schedule:
for _ in range(mc_steps):
for i in bhom.indices:
de = (1 - 2 * state[i]) * np.sum([
d * np.prod(state[_inds]) for _inds, d in adj_dict[i]
])
de += (1 - 2 * state[i]) * bhom.interactions[0][i]
if de <= 0 or np.random.uniform(0, 1) < np.exp(-beta * de):
state[i] = 0 if state[i] == 1 else 1
return state
else:
raise ValueError("var_type should be SPIN or BINARY")
def sample_hubo(self,
interactions: list,
var_type,
beta_min=None,
beta_max=None,
schedule=None,
num_sweeps=100,
num_reads=1,
init_state=None,
seed=None):
"""sampling from higher order unconstrainted binary optimization
Args:
interactions (list of dict): ordered by degree of interaction. [linear, quadratic, ...]
var_type (str, openjij.VarType): "SPIN" or "BINARY"
beta_min (float, optional): Minimum beta (initial inverse temperature). Defaults to None.
beta_max (float, optional): Maximum beta (final inverse temperature). Defaults to None.
schedule (list, optional): [description]. Defaults to None.
num_sweeps (int, optional): [description]. Defaults to 100.
num_reads (int, optional): [description]. Defaults to 1.
init_state (list, optional): initial state. Defaults to None.
seed (int, optional): [description]. Defaults to None.
Returns:
[type]: [description]
"""
self._setting_overwrite(beta_min=beta_min,
beta_max=beta_max,
num_sweeps=num_sweeps,
num_reads=num_reads)
bhom = BinaryHigherOrderModel(interactions)
if schedule or self.schedule:
self._schedule = self._convert_validation_schedule(
schedule if schedule else self.schedule)
self.schedule_info = {'schedule': 'custom schedule'}
else:
schedule = default_schedule(
bhom,
beta_min=self._schedule_setting['beta_min'],
beta_max=self._schedule_setting['beta_max'],
num_sweeps=self._schedule_setting['num_sweeps'])
self.schedule_info = {
'beta_max': schedule[-1][0],
'beta_min': schedule[0][0],
'num_sweeps': self._schedule_setting['num_sweeps']
}
response = hubo_sa_sampling(
bhom,
var_type,
schedule=schedule,
schedule_info=self.schedule_info,
num_sweeps=self._schedule_setting['num_sweeps'],
num_reads=self._schedule_setting['num_reads'],
init_state=init_state,
seed=seed)
return response
def sample_hubo(self,
interactions: list,
var_type,
beta_min=None,
beta_max=None,
schedule=None,
num_sweeps=100,
num_reads=1,
init_state=None,
seed=None):
"""sampling from higher order unconstrainted binary optimization
Args:
interactions (list of dict): ordered by degree of interaction. [linear, quadratic, ...]
var_type (str, openjij.VarType): "SPIN" or "BINARY"
beta_min (float, optional): Minimum beta (initial inverse temperature). Defaults to None.
beta_max (float, optional): Maximum beta (final inverse temperature). Defaults to None.
schedule (list, optional): [description]. Defaults to None.
num_sweeps (int, optional): [description]. Defaults to 100.
num_reads (int, optional): [description]. Defaults to 1.
init_state (list, optional): initial state. Defaults to None.
seed (int, optional): [description]. Defaults to None.
Returns:
[type]: [description]
"""
self._setting_overwrite(beta_min=beta_min,
beta_max=beta_max,
num_sweeps=num_sweeps,
num_reads=num_reads)
bhom = BinaryHigherOrderModel(interactions)
if schedule or self.schedule:
self._schedule = self._convert_validation_schedule(
schedule if schedule else self.schedule)
self.schedule_info = {'schedule': 'custom schedule'}
else:
schedule = default_schedule(
bhom,
beta_min=self._schedule_setting['beta_min'],
beta_max=self._schedule_setting['beta_max'],
num_sweeps=self._schedule_setting['num_sweeps'])
self.schedule_info = {
'beta_max': schedule[-1][0],
'beta_min': schedule[0][0],
'num_sweeps': self._schedule_setting['num_sweeps']
}
init_state = init_state if init_state else np.random.choice(
[1, -1], len(bhom.indices))
response = openjij.Response(var_type=var_type, indices=bhom.indices)
execution_time = []
@measure_time
def exec_sampling():
for _ in range(num_reads):
_exec_time, state = measure_time(hubo_simulated_annealing)(
bhom, init_state, schedule, var_type=var_type)
execution_time.append(_exec_time)
response.states.append(state)
response.energies.append(bhom.calc_energy(state))
sampling_time, _ = exec_sampling()
response.info['sampling_time'] = sampling_time * 10**6 # micro sec
response.info['execution_time'] = np.mean(
execution_time) * 10**6 # micro sec
response.info['list_exec_times'] = np.array(
execution_time) * 10**6 # micro sec
response.update_ising_states_energies(response.states,
response.energies)
response.info['schedule'] = self.schedule_info
return response
def sample_hubo(self,
interactions: list,
var_type,
beta_min=None,
beta_max=None,
schedule=None,
num_sweeps=100,
num_reads=1,
init_state=None,
seed=None):
"""sampling from higher order unconstrainted binary optimization.
Args:
interactions (list): ordered by degree of interaction.
var_type (str, openjij.VarType): "SPIN" or "BINARY"
beta_min (float, optional): Minimum beta (initial inverse temperature). Defaults to None.
beta_max (float, optional): Maximum beta (final inverse temperature). Defaults to None.
schedule (list, optional): schedule list. Defaults to None.
num_sweeps (int, optional): number of sweeps. Defaults to 100.
num_reads (int, optional): number of reads. Defaults to 1.
init_state (list, optional): initial state. Defaults to None.
seed (int, optional): [description]. Defaults to None.
Returns:
:class:`openjij.sampler.response.Response`: results
Examples::
>>> sampler = oj.SASampler()
>>> h = {0: -1}
>>> J = {(0, 1): -1}
>>> K = {(0, 1, 2): 1}
>>> response = sampler.sample_hubo([h, J, K], var_type="SPIN")
"""
self._setting_overwrite(beta_min=beta_min,
beta_max=beta_max,
num_sweeps=num_sweeps,
num_reads=num_reads)
bhom = BinaryHigherOrderModel(interactions)
if schedule or self.schedule:
self._schedule = self._convert_validation_schedule(
schedule if schedule else self.schedule)
self.schedule_info = {'schedule': 'custom schedule'}
else:
schedule = default_schedule(
bhom,
beta_min=self._schedule_setting['beta_min'],
beta_max=self._schedule_setting['beta_max'],
num_sweeps=self._schedule_setting['num_sweeps'])
self.schedule_info = {
'beta_max': schedule[-1][0],
'beta_min': schedule[0][0],
'num_sweeps': self._schedule_setting['num_sweeps']
}
response = hubo_sa_sampling(
bhom,
var_type,
schedule=schedule,
schedule_info=self.schedule_info,
num_sweeps=self._schedule_setting['num_sweeps'],
num_reads=self._schedule_setting['num_reads'],
init_state=init_state,
seed=seed)
return response