def _test_convergence_bb_cxx(N,
D,
kernel,
preprocess_data_fn=None,
nonconj=False,
burnin_niters=10000,
skip=10,
ntries=50,
nsamples=1000,
kl_places=2):
r = rng()
cluster_hp = {'alpha': 2.0}
feature_hps = [{'alpha': 1.0, 'beta': 1.0}] * D
defn = model_definition(N, [bb] * D)
nonconj_defn = model_definition(N, [bbnc] * D)
Y, posterior = data_with_posterior(
defn, cluster_hp, feature_hps, preprocess_data_fn)
data = numpy_dataview(Y)
s = initialize(nonconj_defn if nonconj else defn,
data,
cluster_hp=cluster_hp,
feature_hps=feature_hps,
r=r)
bs = bind(s, data)
wrapped_kernel = lambda s: kernel(s, r)
_test_convergence(bs,
posterior,
wrapped_kernel,
burnin_niters,
skip,
ntries,
nsamples,
kl_places)
def _test_convergence_bb_cxx(N,
D,
kernel,
preprocess_data_fn=None,
nonconj=False,
burnin_niters=10000,
skip=10,
ntries=50,
nsamples=1000,
kl_places=2):
r = rng()
cluster_hp = {'alpha': 2.0}
feature_hps = [{'alpha': 1.0, 'beta': 1.0}] * D
defn = model_definition(N, [bb] * D)
nonconj_defn = model_definition(N, [bbnc] * D)
Y, posterior = data_with_posterior(defn, cluster_hp, feature_hps,
preprocess_data_fn)
data = numpy_dataview(Y)
s = initialize(nonconj_defn if nonconj else defn,
data,
cluster_hp=cluster_hp,
feature_hps=feature_hps,
r=r)
bs = bind(s, data)
wrapped_kernel = lambda s: kernel(s, r)
_test_convergence(bs, posterior, wrapped_kernel, burnin_niters, skip,
ntries, nsamples, kl_places)
def test_runner_multiprocessing_convergence():
N, D = 4, 5
defn = model_definition(N, [bb] * D)
prng = rng()
Y, posterior = data_with_posterior(defn, r=prng)
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng)
for _ in xrange(mp.cpu_count())]
runners = [runner.runner(defn, view, latent, ['assign'])
for latent in latents]
r = parallel.runner(runners)
r.run(r=prng, niters=1000) # burnin
idmap = {C: i for i, C in enumerate(permutation_iter(N))}
def sample_iter():
r.run(r=prng, niters=10)
for latent in r.get_latents():
yield idmap[tuple(permutation_canonical(latent.assignments()))]
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_convergence():
N, D = 4, 5
defn = model_definition(N, [bb] * D)
prng = rng()
Y, posterior = data_with_posterior(defn, r=prng)
view = numpy_dataview(Y)
latent = model.initialize(defn, view, prng)
r = runner.runner(defn, view, latent, ['assign'])
r.run(r=prng, niters=1000) # burnin
idmap = {C: i for i, C in enumerate(permutation_iter(N))}
def sample_fn():
r.run(r=prng, niters=10)
new_latent = r.get_latent()
return idmap[tuple(permutation_canonical(new_latent.assignments()))]
assert_discrete_dist_approx(sample_fn, posterior, ntries=100)
