def test_posterior_predictive_statistic():
N, D = 10, 4 # D needs to be even
defn = model_definition(N, [bb] * D)
Y = toy_dataset(defn)
prng = rng()
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng) for _ in xrange(10)]
q = ma.masked_array(
np.array([(False,) * D], dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)])
statistic = query.posterior_predictive_statistic(q, latents, prng)
assert_equals(statistic.shape, (1,))
assert_equals(len(statistic.dtype), D)
statistic = query.posterior_predictive_statistic(
q, latents, prng, merge='mode')
assert_equals(statistic.shape, (1,))
assert_equals(len(statistic.dtype), D)
statistic = query.posterior_predictive_statistic(
q, latents, prng, merge=['mode', 'mode', 'avg', 'avg'])
assert_equals(statistic.shape, (1,))
assert_equals(len(statistic.dtype), D)
q = ma.masked_array(
np.array([(False,) * D] * 3, dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)] * 3)
statistic = query.posterior_predictive_statistic(q, latents, prng)
assert_equals(statistic.shape, (3,))
assert_equals(len(statistic.dtype), D)
def test_zmatrix():
N, D = 10, 4
defn = model_definition(N, [bb] * D)
Y = toy_dataset(defn)
prng = rng()
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng) for _ in xrange(10)]
zmat = query.zmatrix(latents)
assert_equals(zmat.shape, (N, N))
def _test_copy_state(defn, initialize_fn, bind_fn):
Y = toy_dataset(defn)
view = cxx_numpy_dataview(Y)
r = rng()
state = initialize_fn(defn, view, r)
state_shallow = copy.copy(state)
state_deep = copy.deepcopy(state)
assert_is_not(state, state_shallow)
assert_is_not(state, state_deep)
_assert_copy(state, state_shallow, bind_fn, view, r)
_assert_copy(state, state_deep, bind_fn, view, r)
def _test_copy_state(defn, initialize_fn, bind_fn):
Y = toy_dataset(defn)
view = cxx_numpy_dataview(Y)
r = rng()
state = initialize_fn(defn, view, r)
state_shallow = copy.copy(state)
state_deep = copy.deepcopy(state)
assert_is_not(state, state_shallow)
assert_is_not(state, state_deep)
_assert_copy(state, state_shallow, bind_fn, view, r)
_assert_copy(state, state_deep, bind_fn, view, r)
def test_runner_multyvac():
defn = model_definition(10, [bb, nich, niw(3)])
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, view, prng)
for _ in xrange(2)]
runners = [runner.runner(defn, view, latent, kc) for latent in latents]
r = parallel.runner(runners, backend='multyvac', layer='perf', core='f2')
r.run(r=prng, niters=1000)
r.run(r=prng, niters=1000)
def test_runner_multiprocessing():
defn = model_definition(10, [bb, nich, niw(3)])
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = runner.default_kernel_config(defn)
prng = rng()
latents = [model.initialize(defn, view, prng)
for _ in xrange(mp.cpu_count())]
runners = [runner.runner(defn, view, latent, kc) for latent in latents]
r = parallel.runner(runners)
# check it is restartable
r.run(r=prng, niters=10)
r.run(r=prng, niters=10)
def test_posterior_predictive():
N, D = 10, 4 # D needs to be even
defn = model_definition(N, [bb] * D)
Y = toy_dataset(defn)
prng = rng()
view = numpy_dataview(Y)
latents = [model.initialize(defn, view, prng) for _ in xrange(10)]
q = ma.masked_array(
np.array([(False,) * D], dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)])
samples = query.posterior_predictive(q, latents, prng)
assert_equals(samples.shape, (1, len(latents)))
q = ma.masked_array(
np.array([(False,) * D] * 3, dtype=[('', bool)] * D),
mask=[(False,) * (D / 2) + (True,) * (D / 2)] * 3)
samples = query.posterior_predictive(q, latents, prng)
assert_equals(samples.shape, (3, len(latents)))
def _test_runner_kernel_config(kc_fn, models):
defn = model_definition(10, models)
Y = toy_dataset(defn)
view = numpy_dataview(Y)
kc = kc_fn(defn)
prng = rng()
ntries = 5
while ntries:
latent = model.initialize(defn, view, prng)
assignments = latent.assignments()
r = runner.runner(defn, view, latent, kc)
r.run(r=prng, niters=10)
assignments1 = r.get_latent().assignments()
# XXX: it should be very unlikely the assignments are all equal
if assignments == assignments1:
ntries -= 1
else:
return # success
assert_true(False) # exceeded ntries
