def test_wald(self):
# Cannot do anything too exciting as scipy wald is a
# location-scale model of the *standard* wald with mu=1 and lam=1
pymc3_random(Wald, {
'mu': Domain([1., 1., 1.]),
'lam': Domain([1., 1., 1.]),
'alpha': Rplus
},
ref_rand=lambda size, mu=None, lam=None, alpha=None: st.
wald.rvs(size=size, loc=alpha))
def pymc3_random_discrete(dist, paramdomains,
valuedomain=Domain([0]), ref_rand=None,
size=100000, alpha=0.05, fails=20):
model = build_model(dist, valuedomain, paramdomains)
domains = paramdomains.copy()
for pt in product(domains):
pt = Point(pt, model=model)
p = alpha
# Allow Chisq test to fail (i.e., the samples be different)
# a certain number of times.
f = fails
while p <= alpha and f > 0:
o = model.named_vars['value'].random(size=size, point=pt)
e = ref_rand(size=size, **pt)
o = np.atleast_1d(o).flatten()
e = np.atleast_1d(e).flatten()
observed = dict(zip(*np.unique(o, return_counts=True)))
expected = dict(zip(*np.unique(e, return_counts=True)))
for e in expected.keys():
expected[e] = (observed.get(e, 0), expected[e])
k = np.array([v for v in expected.values()])
if np.all(k[:, 0] == k[:, 1]):
p = 1.
else:
_chi, p = st.chisquare(k[:, 0], k[:, 1])
f -= 1
assert p > alpha, str(pt)
def test_mv_t(self):
for n in [2, 3]:
pymc3_random(MvStudentT, {'nu': Domain([5, 10, 25, 50]), 'Sigma': PdMatrix(n),
'mu':Vector(R,n)}, size=100,
valuedomain=Vector(R,n),
ref_rand=(lambda nu=None, Sigma=None, mu=None, size=None:
mu + np.sqrt(nu)
* (st.multivariate_normal.rvs(cov=Sigma, size=size).T
/ st.chi2.rvs(df=nu, size=size)).T))
def pymc3_random(dist, paramdomains,
ref_rand=None, valuedomain=Domain([0]),
size=10000, alpha=0.05, fails=10):
model = build_model(dist, valuedomain, paramdomains)
domains = paramdomains.copy()
for pt in product(domains):
pt = Point(pt, model=model)
p = alpha
# Allow KS test to fail (i.e., the samples be different)
# a certain number of times. Crude, but necessary.
f = fails
while p <= alpha and f > 0:
s0 = model.named_vars['value'].random(size=size, point=pt)
s1 = ref_rand(size=size, **pt)
_, p = st.ks_2samp(np.atleast_1d(s0).flatten(),
np.atleast_1d(s1).flatten())
f -= 1
assert p > alpha, str(pt)
from pymc3.tests.test_transforms import *
import ContinuousTimeMarkovModel.transforms as ctrans
from pymc3.tests.test_distributions import Domain
min_rate_matrix = np.zeros(shape=(5, 5))
max_rate_matrix = np.zeros(shape=(5, 5)) + np.inf
max_rate_matrix = np.fill_diagonal(max_rate_matrix, -np.inf)
rate_matrix_test = np.array([[-6, 2, 2, 1, 1], [1, -4, 0, 1, 2],
[1, 0, -4, 2, 1], [2, 1, 0, -3, 0],
[1, 1, 1, 1, -4]])
RateMatrixDomain = Domain([min_rate_matrix, rate_matrix_test, max_rate_matrix])
def check_matrix_transform_identity(transform, domain):
return check_transform_identity(transform,
domain,
t.dmatrix,
test=rate_matrix_test)
def test_rate_matrix():
check_matrix_transform_identity(ctrans.rate_matrix(0, 100),
RateMatrixDomain)
min_rate_matrix = np.zeros(shape=(6, 6))
max_rate_matrix = np.zeros(shape=(6, 6)) + np.inf
max_rate_matrix = np.fill_diagonal(max_rate_matrix, -np.inf)
rate_matrix_test = np.array(
[[-0.631921, 0.631921, 0.0000000, 0.0000000, 0.0000000, 0.0000000],