def test_slice_theta_irm():
N = 10
defn = model_definition([N], [((0, 0), bbnc)])
data = np.random.random(size=(N, N)) < 0.8
view = numpy_dataview(data)
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
s = initialize(
defn,
[view],
r=r,
cluster_hps=[{'alpha': 2.0}],
relation_hps=[prior],
domain_assignments=[[0] * N])
bs = bind(s, 0, [view])
params = {0: {'p': 0.05}}
heads = len([1 for y in data.flatten() if y])
tails = len([1 for y in data.flatten() if not y])
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, [0, 0])['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
def test_slice_theta_mm():
N = 100
data = np.array([(np.random.random() < 0.8, ) for _ in xrange(N)],
dtype=[('', bool)])
defn = model_definition(N, [bbnc])
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
view = numpy_dataview(data)
s = initialize(defn,
view,
cluster_hp={
'alpha': 1.,
'beta': 9.
},
feature_hps=[prior],
r=r,
assignment=[0] * N)
heads = len([1 for y in data if y[0]])
tails = N - heads
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
bs = bind(s, view)
params = {0: {'p': 0.05}}
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, 0)['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
def test_slice_theta_mm():
N = 100
data = np.array(
[(np.random.random() < 0.8,) for _ in xrange(N)],
dtype=[('', bool)])
defn = model_definition(N, [bbnc])
r = rng()
prior = {'alpha': 1.0, 'beta': 9.0}
view = numpy_dataview(data)
s = initialize(
defn,
view,
cluster_hp={'alpha': 1., 'beta': 9.},
feature_hps=[prior],
r=r,
assignment=[0] * N)
heads = len([1 for y in data if y[0]])
tails = N - heads
alpha1 = prior['alpha'] + heads
beta1 = prior['beta'] + tails
bs = bind(s, view)
params = {0: {'p': 0.05}}
def sample_fn():
theta(bs, r, tparams=params)
return s.get_suffstats(0, 0)['p']
rv = beta(alpha1, beta1)
assert_1d_cont_dist_approx_sps(sample_fn, rv, nsamples=50000)
