def test_runner_multiprocessing_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latents = [model.initialize(defn, views, prng)
for _ in xrange(mp.cpu_count())]
kc = [('assign', range(len(domains)))]
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=10000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_iter():
r.run(r=prng, niters=10)
for latent in r.get_latents():
key = tuple(tuple(permutation_canonical(latent.assignments(i)))
for i in xrange(len(domains)))
yield idmap[key]
ref = [None]
def sample_fn():
if ref[0] is None:
ref[0] = sample_iter()
try:
return next(ref[0])
except StopIteration:
ref[0] = None
return sample_fn()
assert_discrete_dist_approx(sample_fn, posterior, ntries=100, kl_places=2)
def test_runner_default_kernel_config_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latent = model.initialize(defn, views, prng)
r = runner.runner(defn, views, latent, [('assign', range(len(domains)))])
r.run(r=prng, niters=1000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_fn():
r.run(r=prng, niters=10)
new_latent = r.get_latent()
key = tuple(tuple(permutation_canonical(new_latent.assignments(i)))
for i in xrange(len(domains)))
return idmap[key]
assert_discrete_dist_approx(sample_fn, posterior, ntries=100)
def test_runner_default_kernel_config_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latent = model.initialize(defn, views, prng)
r = runner.runner(defn, views, latent, [('assign', range(len(domains)))])
r.run(r=prng, niters=1000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_fn():
r.run(r=prng, niters=10)
new_latent = r.get_latent()
key = tuple(
tuple(permutation_canonical(new_latent.assignments(i)))
for i in xrange(len(domains)))
return idmap[key]
assert_discrete_dist_approx(sample_fn, posterior, ntries=100)
def test_runner_multiprocessing_convergence():
domains = [4]
defn = model_definition(domains, [((0, 0), bb)])
prng = rng()
relations, posterior = data_with_posterior(defn, prng)
views = map(numpy_dataview, relations)
latents = [
model.initialize(defn, views, prng) for _ in xrange(mp.cpu_count())
]
kc = [('assign', range(len(domains)))]
runners = [runner.runner(defn, views, latent, kc) for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=10000) # burnin
product_assignments = tuple(map(list, map(permutation_iter, domains)))
idmap = {C: i for i, C in enumerate(it.product(*product_assignments))}
def sample_iter():
r.run(r=prng, niters=10)
for latent in r.get_latents():
key = tuple(
tuple(permutation_canonical(latent.assignments(i)))
for i in xrange(len(domains)))
yield idmap[key]
ref = [None]
def sample_fn():
if ref[0] is None:
ref[0] = sample_iter()
try:
return next(ref[0])
except StopIteration:
ref[0] = None
return sample_fn()
assert_discrete_dist_approx(sample_fn, posterior, ntries=100, kl_places=2)