def test_observed(self):
"""
Test observation of Bernoulli node
"""
Z = Binomial(10, 0.3)
Z.observe(10)
u = Z._message_to_child()
self.assertAllClose(u[0], 10)
Z = Binomial(10, 0.9)
Z.observe(2)
u = Z._message_to_child()
self.assertAllClose(u[0], 2)
pass
def test_random(self):
"""
Test random sampling in Binomial node
"""
N = [[5], [50]]
p = [1.0, 0.0]
with np.errstate(divide='ignore'):
Z = Binomial(N, p, plates=(3, 2, 2)).random()
self.assertArrayEqual(Z, np.ones((3, 2, 2)) * N * p)
def test_observed(self):
"""
Test observation of Bernoulli node
"""
Z = Binomial(10, 0.3)
Z.observe(10)
u = Z._message_to_child()
self.assertAllClose(u[0], 10)
Z = Binomial(10, 0.9)
Z.observe(2)
u = Z._message_to_child()
self.assertAllClose(u[0], 2)
pass
def test_init(self):
"""
Test the creation of binomial nodes.
"""
# Some simple initializations
X = Binomial(10, 0.5)
X = Binomial(10, Beta([2, 3]))
# Check that plates are correct
X = Binomial(10, 0.7, plates=(4, 3))
self.assertEqual(X.plates, (4, 3))
X = Binomial(10, 0.7 * np.ones((4, 3)))
self.assertEqual(X.plates, (4, 3))
n = np.ones((4, 3), dtype=np.int)
X = Binomial(n, 0.7)
self.assertEqual(X.plates, (4, 3))
X = Binomial(10, Beta([4, 3], plates=(4, 3)))
self.assertEqual(X.plates, (4, 3))
# Invalid probability
self.assertRaises(ValueError, Binomial, 10, -0.5)
self.assertRaises(ValueError, Binomial, 10, 1.5)
# Invalid number of trials
self.assertRaises(ValueError, Binomial, -1, 0.5)
self.assertRaises(ValueError, Binomial, 8.5, 0.5)
# Inconsistent plates
self.assertRaises(ValueError,
Binomial,
10,
0.5 * np.ones(4),
plates=(3, ))
# Explicit plates too small
self.assertRaises(ValueError,
Binomial,
10,
0.5 * np.ones(4),
plates=(1, ))
pass
def test_moments(self):
"""
Test the moments of binomial nodes.
"""
# Simple test
X = Binomial(1, 0.7)
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0], 0.7)
# Test n
X = Binomial(10, 0.7)
u = X._message_to_child()
self.assertAllClose(u[0], 10 * 0.7)
# Test plates in p
n = np.random.randint(1, 10)
p = np.random.rand(3)
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0], p * n)
# Test plates in n
n = np.random.randint(1, 10, size=(3, ))
p = np.random.rand()
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0], p * n)
# Test plates in p and n
n = np.random.randint(1, 10, size=(4, 1))
p = np.random.rand(3)
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0], p * n)
# 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 = Binomial(1, P)
u = X._message_to_child()
self.assertAllClose(u[0], p0)
# Test with broadcasted plates
P = Beta([7, 3], plates=(10, ))
X = Binomial(5, P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
5 * p0 * np.ones(10))
pass
def test_moments(self):
"""
Test the moments of binomial nodes.
"""
# Simple test
X = Binomial(1, 0.7)
u = X._message_to_child()
self.assertEqual(len(u), 1)
self.assertAllClose(u[0],
0.7)
# Test n
X = Binomial(10, 0.7)
u = X._message_to_child()
self.assertAllClose(u[0],
10*0.7)
# Test plates in p
n = np.random.randint(1, 10)
p = np.random.rand(3)
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test plates in n
n = np.random.randint(1, 10, size=(3,))
p = np.random.rand()
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# Test plates in p and n
n = np.random.randint(1, 10, size=(4,1))
p = np.random.rand(3)
X = Binomial(n, p)
u = X._message_to_child()
self.assertAllClose(u[0],
p*n)
# 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 = Binomial(1, P)
u = X._message_to_child()
self.assertAllClose(u[0],
p0)
# Test with broadcasted plates
P = Beta([7, 3], plates=(10,))
X = Binomial(5, P)
u = X._message_to_child()
self.assertAllClose(u[0] * np.ones(X.get_shape(0)),
5*p0*np.ones(10))
pass
