def chain_break(self, sampleset, embedding):
if as_action(self.action) is IGNORE:
return
ground = sampleset.lowest()
variables = list(embedding)
chains = [embedding[v] for v in variables]
broken = broken_chains(ground, chains)
if not (len(sampleset) and broken.any()):
return
for nc, chain in enumerate(chains):
for row in range(broken.shape[0]):
if not broken[row, nc]:
continue
self.issue(
"Lowest-energy samples contain a broken chain",
category=ChainBreakWarning,
level=logging.ERROR,
data=dict(target_variables=chain,
source_variables=[variables[nc]],
sample_index=row),
)
A = dwave_sampler.edgelist
Adj = dwave_sampler.adjacency
embedding = find_embedding(Q, A)
print(embedding)
bqm = BinaryQuadraticModel.from_qubo(Q)
# Cannot use a Composite to get the broken chains, so do the embedding
# directly
bqm_embedded = embed_bqm(bqm, embedding, Adj, chain_strength=chainstrength)
response = DWaveSampler().sample(bqm_embedded, num_reads=numruns)
# We need to get the chains directly, as a list
chains = [embedding[v] for v in list(bqm)]
# Obtain the broken chains
broken = broken_chains(response, chains)
# Interpret the results in terms of the embedding. Be sure to
# tell the method to compute the chain_break_frequency.
print(
unembed_sampleset(response,
embedding,
source_bqm=bqm,
chain_break_fraction=True), broken)
# Use NumPy method to obtain the indices of the broken chains
w = np.where(broken == True)
indices = list(zip(*w))
print(indices)