def next(self, states):
"""ICM between two first samples in the first two input states."""
if len(states) > 2:
raise ValueError("exactly two input states required")
inp1, inp2 = states
bqm = inp1.problem
ss1 = inp1.samples.change_vartype(dimod.BINARY, inplace=False)
ss2 = inp2.samples.change_vartype(dimod.BINARY, inplace=False)
# sanity check: we operate on the same set of variables
if ss1.variables ^ ss2.variables:
raise ValueError(
"input samples not over the same set of variables")
# reorder variables, if necessary
# (use sequence comparison, not set)
variables = list(ss1.variables)
if ss2.variables != variables:
reorder = [ss2.variables.index(v) for v in variables]
record = ss2.record[:, reorder]
ss2 = dimod.SampleSet(record, variables, ss2.info, ss2.vartype)
# samples' symmetric difference (XOR)
# (form clusters of input variables with opposite values)
sample1 = ss1.record.sample[0]
sample2 = ss2.record.sample[0]
symdiff = sample1 ^ sample2
# for cluster detection we'll use a reduced problem graph
graph = bqm.to_networkx_graph()
# note: instead of numpy mask indexing of `notcluster`, we enumerate
# non-cluster variables manually to avoid conversion of potentially
# unhashable variable names to numpy types
notcluster = [v for v, d in zip(variables, symdiff) if d == 0]
graph.remove_nodes_from(notcluster)
# pick a random variable that belongs to a cluster, then select the cluster
node = self.random.choice(list(graph.nodes))
cluster = nx.node_connected_component(graph, node)
# flip variables from `cluster` in both input samples
flipper = np.array([1 if v in cluster else 0 for v in variables])
ss1.record.sample[0] ^= flipper
ss2.record.sample[0] ^= flipper
# change vartype back to input's type
ss1.change_vartype(inp1.samples.vartype)
ss2.change_vartype(inp2.samples.vartype)
# update sampleset's energies
ss1.record.energy = bqm.energies(ss1)
ss2.record.energy = bqm.energies(ss2)
return States(inp1.updated(samples=ss1), inp2.updated(samples=ss2))
def test_embedding_superset(self):
# source graph in the embedding is a superset of the bqm
response = dimod.SampleSet(np.rec.array([([-1, 1, -1, 1, -1, 1, -1, 1], -1.4, 1),
([-1, 1, -1, -1, -1, 1, -1, -1], -1.4, 1),
([+1, -1, -1, -1, 1, -1, -1, -1], -1.6, 1),
([+1, -1, -1, -1, 1, -1, -1, -1], -1.6, 1)],
dtype=[('sample', 'i1', (8,)), ('energy', '<f8'), ('num_occurrences', '<i8')]),
[0, 1, 2, 3, 4, 5, 6, 7], {}, 'SPIN')
embedding = {0: {0, 4}, 1: {1, 5}, 2: {2, 6}, 3: {3, 7}}
bqm = dimod.BinaryQuadraticModel.from_ising([.1, .2], {(0, 1): 1.5}, 0.0)
unembedded = dwave.embedding.unembed_sampleset(response, embedding, source_bqm=bqm)
arr = np.rec.array([([-1, 1], -1.4, 1), ([-1, 1], -1.4, 1), ([+1, -1], -1.6, 1), ([+1, -1], -1.6, 1)],
dtype=[('sample', 'i1', (2,)), ('energy', '<f8'), ('num_occurrences', '<i8')])
np.testing.assert_array_equal(arr, unembedded.record)
def next(self, state, **runopts):
beta = runopts.get('beta', self.beta)
beta = state.get('beta', beta)
if beta is None:
raise ValueError(
'beta must be given on construction or during run-time')
ss = state.samples
# calculate weights
w = np.exp(-beta * ss.record.energy)
p = w / sum(w)
# resample
idx = self.random.choice(len(ss), len(ss), p=p)
record = ss.record[idx]
info = ss.info.copy()
info.update(beta=beta)
new_samples = dimod.SampleSet(record, ss.variables, info, ss.vartype)
return state.updated(samples=new_samples)
def next(self, state, **runopts):
delta_beta = runopts.get('delta_beta', self.delta_beta)
delta_beta = state.get('delta_beta', delta_beta)
if delta_beta is None:
raise ValueError(
'delta_beta must be given on construction or during run-time')
ss = state.samples
# calculate weights (note: to avoid overflow, we offset energy, as it
# cancels out during probability calc)
min_energy = ss.record.energy.min()
w = np.exp(-delta_beta * (ss.record.energy - min_energy))
p = w / sum(w)
# resample
idx = self.random.choice(len(ss), len(ss), p=p)
record = ss.record[idx]
info = ss.info.copy()
info.update(beta=state.beta, delta_beta=delta_beta)
new_samples = dimod.SampleSet(record, ss.variables, info, ss.vartype)
return state.updated(samples=new_samples)
