def __init__(self, raw_results, answer, problem, all_vars):
# Store our arguments.
self.raw_results = raw_results
self.answer = answer
self.problem = problem
# Unembed the solutions. Fix rather than discard invalid solutions.
if self.problem.embedding == {}:
fixed_answer = self.answer.copy()
else:
fixed_answer = unembed_sampleset(self.answer, self.problem.embedding,
self.problem.logical.bqm,
chain_break_method=chain_breaks.majority_vote)
# Construct one Solution object per solution.
self.solutions = []
energies = fixed_answer.record.energy
tallies = fixed_answer.record.num_occurrences
fixed_solns = fixed_answer.record.sample
raw_solns = self.answer.record.sample
sym2col, num2col = self._map_to_column(fixed_answer,
self.problem.embedding,
self.problem.qmasm.sym_map)
for i in range(len(fixed_solns)):
sset = self.answer.slice(i, i + 1)
self.solutions.append(Solution(self.problem, sym2col, num2col, sset,
fixed_solns[i], tallies[i], energies[i],
all_vars))
# Store the frequency of chain breaks across the entire SampleSet.
self.chain_breaks = chain_break_frequency(self.answer, self.problem.embedding)
dwave_sampler.adjacency,
chain_strength=2.0)
reads = 1000
fixed_response = dwave_sampler.sample_ising(embedded_h,
embedded_J,
num_reads=reads)
energies = fixed_response.record.energy
print("QPU call complete using",
fixed_response.info['timing']['qpu_access_time'] / 1000000.0,
"seconds of QPU time.\n")
# Next we will look at the frequency that chains broke in our samples.
# This will be our metric to explore the advantages of `VirtualGraphComposite`.
from dwave.embedding import chain_break_frequency
chain_break_frequency = chain_break_frequency(fixed_response, embedding)
for key, val in chain_break_frequency.items():
print("Chain", key, "broke in", val * 100, "percent of samples.")
# Let's visualize these results by plotting each chain against the percentage of samples in which it broke.
get_ipython().run_line_magic('matplotlib', 'inline')
import matplotlib.pyplot as plt
x_vals = range(S_size)
x_pos = np.arange(len(chain_break_frequency.items()))
y_vals = [-1 for _ in x_vals]
for key, val in chain_break_frequency.items():
y_vals[key] = val * 100
f = plt.figure(figsize=(10, 3))