def testMakeLogJointConditional(self):
"""Test `make_log_joint` works on conditional model."""
def linear_regression(features, prior_precision):
beta = ed.norm.rvs(loc=0.,
scale=1. / np.sqrt(prior_precision),
size=features.shape[1],
name='beta')
loc = np.einsum('ij,j->i', features, beta)
y = ed.norm.rvs(loc=loc, scale=1., name='y')
return y
log_joint = ed.make_log_joint_fn(linear_regression)
features = np.random.normal(size=[3, 2])
prior_precision = 0.5
beta = np.random.normal(size=[2])
y = np.random.normal(size=[3])
true_value = np.sum(
ed.norm.logpdf(beta, loc=0., scale=1. / np.sqrt(prior_precision)))
loc = np.einsum('ij,j->i', features, beta)
true_value += np.sum(ed.norm.logpdf(y, loc=loc, scale=1.))
# Test args as input.
value = log_joint(features, prior_precision, beta, y)
self.assertAlmostEqual(value, true_value)
# Test kwargs as input.
value = log_joint(features, prior_precision, y=y, beta=beta)
self.assertAlmostEqual(value, true_value)
def testMakeLogJointUnconditional(self):
"""Test `make_log_joint` works on unconditional model."""
def normal_normal_model():
loc = ed.norm.rvs(loc=0., scale=1., name='loc')
x = ed.norm.rvs(loc=loc, scale=0.5, size=5, name='x')
return x
log_joint = ed.make_log_joint_fn(normal_normal_model)
x = np.random.normal(size=5)
loc = 0.3
value = log_joint(loc=loc, x=x)
true_value = np.sum(ed.norm.logpdf(loc, loc=0., scale=1.))
true_value += np.sum(ed.norm.logpdf(x, loc=loc, scale=0.5))
self.assertAlmostEqual(value, true_value)