def sample(self, bqm, num_reads=10, flux_biases=[], **kwargs):
# we are altering the bqm if flux_biases given
info = dict(problem_id=str(uuid4()))
label = kwargs.get('label')
if label is not None:
info.update(problem_label=label)
if not flux_biases:
ss = SimulatedAnnealingSampler().sample(bqm, num_reads=num_reads)
ss.info.update(info)
return ss
new_bqm = bqm.copy()
for v, fbo in enumerate(flux_biases):
self.flux_biases_flag = True
new_bqm.add_variable(v, 1000. * fbo) # add the bias
response = SimulatedAnnealingSampler().sample(new_bqm, num_reads=num_reads)
# recalculate the energies with the old bqm
return dimod.SampleSet.from_samples_bqm([{v: sample[v] for v in bqm.variables}
for sample in response.samples()],
bqm, info=info)
def sample(self, bqm, num_reads=10, flux_biases=[]):
# we are altering the bqm
new_bqm = bqm.copy()
for v, fbo in enumerate(flux_biases):
self.flux_biases_flag = True
new_bqm.add_variable(v, 1000. * fbo) # add the bias
response = SimulatedAnnealingSampler().sample(new_bqm,
num_reads=num_reads)
energies = [
bqm.energy(sample) for sample in response.samples(sorted_by=None)
]
return dimod.Response.from_samples(response.samples(sorted_by=None),
{'energy': energies}, {},
bqm.vartype)
def sample(self, bqm, num_reads=10, flux_biases=[]):
# we are altering the bqm
if not flux_biases:
return SimulatedAnnealingSampler().sample(bqm, num_reads=num_reads)
new_bqm = bqm.copy()
for v, fbo in enumerate(flux_biases):
self.flux_biases_flag = True
new_bqm.add_variable(v, 1000. * fbo) # add the bias
response = SimulatedAnnealingSampler().sample(new_bqm,
num_reads=num_reads)
# recalculate the energies with the old bqm
return dimod.SampleSet.from_samples_bqm(
[{v: sample[v]
for v in bqm} for sample in response.samples()], bqm)
