def chainlength_rundown(n=100, m=8):
from dwave_sapi2.embedding import find_embedding as find_embedding_dws2
C = Chimera(m)
K = Clique(4 * m)
nodes = set(x for e in C for x in e)
trans = {x: i for i, x in enumerate(nodes)}
C = [(trans[x], trans[y]) for x, y in C]
def trial(f):
t0 = time.clock()
stats = [f() for _ in range(n)]
t = time.clock() - t0
stats = filter(None, stats)
stats = [max(map(len, e)) for e in stats]
print("successes %d, best maxchain %d, avg maxchain %.02f, time %.02fs" % (
len(stats), min(stats), sum(stats) / float(len(stats)), t))
return t
print("sapi fast embedding:", end='')
trial(lambda: find_embedding_dws2(K, C, tries=1, fast_embedding=True))
print("sapi slow embedding:", end='')
basetime = trial(lambda: find_embedding_dws2(
K, C, tries=1, fast_embedding=False))
patience = 0
while 1:
print("minorminer, chainlength_patience %d:" % patience, end='')
t = trial(lambda: find_embedding_orig(K, C, tries=1,
chainlength_patience=patience).values())
if t > basetime:
break
patience += 1
def chainlength_diagnostic(n=100,
old=False,
chainlength_argument=0,
verbose=0,
m=8):
C = Chimera(m)
K = Clique(4 * m)
if old:
from dwave_sapi2.embedding import find_embedding as find_embedding_dws2
nodes = set(x for e in C for x in e)
trans = {x: i for i, x in enumerate(nodes)}
C = [(trans[x], trans[y]) for x, y in C]
assert 0 <= chainlength_argument <= 1, "sapi2 only supports a chainlength argument of 0 or 1"
embs = [
find_embedding_dws2(K,
C,
tries=1,
fast_embedding=chainlength_argument,
verbose=verbose) for _ in range(n)
]
else:
embs = [
find_embedding_orig(K,
C,
tries=1,
chainlength_patience=chainlength_argument,
verbose=verbose).values() for _ in range(n)
]
return sorted(max(map(len, e)) if e else None for e in embs)
