def test_init(self):
"""
Test the creation of Bernoulli nodes.
"""
# Some simple initializations
X = Bernoulli(0.5)
X = Bernoulli(Beta([2, 3]))
# Check that plates are correct
X = Bernoulli(0.7, plates=(4, 3))
self.assertEqual(X.plates, (4, 3))
X = Bernoulli(0.7 * np.ones((4, 3)))
self.assertEqual(X.plates, (4, 3))
X = Bernoulli(Beta([4, 3], plates=(4, 3)))
self.assertEqual(X.plates, (4, 3))
# Invalid probability
self.assertRaises(ValueError, Bernoulli, -0.5)
self.assertRaises(ValueError, Bernoulli, 1.5)
# Inconsistent plates
self.assertRaises(ValueError,
Bernoulli,
0.5 * np.ones(4),
plates=(3, ))
# Explicit plates too small
self.assertRaises(ValueError,
Bernoulli,
0.5 * np.ones(4),
plates=(1, ))
pass
def test_observed(self):
"""
Test observation of Bernoulli node
"""
Z = Bernoulli(0.3)
Z.observe(2 < 3)
pass
def test_observed(self):
"""
Test observation of Bernoulli node
"""
Z = Bernoulli(0.3)
Z.observe(2 < 3)
pass
def test_moments(self):
"""
Test the moments of Bernoulli nodes.
"""
# Simple test
X = Bernoulli(0.7)
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0], 0.7)
# Test plates in p
p = np.random.rand(3)
X = Bernoulli(p)
u = X._message_to_child()
self.assertAllClose(u[0], p)
# Test with beta prior
P = Beta([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Bernoulli(P)
u = X._message_to_child()
self.assertAllClose(u[0], p0)
# Test with broadcasted plates
P = Beta([7, 3], plates=(10,))
X = Bernoulli(P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)), p0 * np.ones(10))
pass
def test_random(self):
"""
Test random sampling in Bernoulli node
"""
p = [1.0, 0.0]
with np.errstate(divide='ignore'):
Z = Bernoulli(p, plates=(3, 2)).random()
self.assertArrayEqual(Z, np.ones((3, 2)) * p)
def test_moments(self):
"""
Test the moments of Bernoulli nodes.
"""
# Simple test
X = Bernoulli(0.7)
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0], 0.7)
# Test plates in p
p = np.random.rand(3)
X = Bernoulli(p)
u = X._message_to_child()
self.assertAllClose(u[0], p)
# Test with beta prior
P = Beta([7, 3])
logp = P._message_to_child()[0]
p0 = np.exp(logp[0]) / (np.exp(logp[0]) + np.exp(logp[1]))
X = Bernoulli(P)
u = X._message_to_child()
self.assertAllClose(u[0], p0)
# Test with broadcasted plates
P = Beta([7, 3], plates=(10, ))
X = Bernoulli(P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)), p0 * np.ones(10))
pass
