def test_marginalise(self):
var = set('c')
b = self.potential.marginalise(var)
var2 = set(['c', 'a'])
c = self.potential.marginalise(var2)
# extended test
a = DiscretePotential('a b c d e f'.split(), [2, 3, 4, 5, 6, 7], \
numpy.arange(7*6*5*4*3*2))
aa = a.marginalise('c f a'.split())
self.assertEqual(b.names, self.potential.names - var)
self.assertEqual(b[0, 1], numpy.sum(self.potential[0, 1]))
self.assertEqual(c.names, self.potential.names - var2)
self.assert_(numpy.alltrue(c.cpt.flat ==
numpy.sum(numpy.sum(self.potential.cpt,
axis=2),
axis=0)))
self.assertEqual(aa.shape, (3, 5, 6))
self.assertEqual(aa.names_list, 'b d e'.split())
self.assertEqual(aa[2, 4, 3], numpy.sum(a[:, 2, :, 4, 3, :].flat))
class DiscretePotentialTestCase(unittest.TestCase):
"""
This test the discrete potential
"""
def setUp(self):
names = ('a', 'b', 'c')
shape = (2, 3, 4)
self.potential = DiscretePotential(names, shape, numpy.arange(24))
self.names = names
self.shape = shape
def test_marginalise(self):
var = set('c')
b = self.potential.marginalise(var)
var2 = set(['c', 'a'])
c = self.potential.marginalise(var2)
# extended test
a = DiscretePotential('a b c d e f'.split(), [2, 3, 4, 5, 6, 7], \
numpy.arange(7*6*5*4*3*2))
aa = a.marginalise('c f a'.split())
self.assertEqual(b.names, self.potential.names - var)
self.assertEqual(b[0, 1], numpy.sum(self.potential[0, 1]))
self.assertEqual(c.names, self.potential.names - var2)
self.assert_(numpy.alltrue(c.cpt.flat ==
numpy.sum(numpy.sum(self.potential.cpt,
axis=2),
axis=0)))
self.assertEqual(aa.shape, (3, 5, 6))
self.assertEqual(aa.names_list, 'b d e'.split())
self.assertEqual(aa[2, 4, 3], numpy.sum(a[:, 2, :, 4, 3, :].flat))
def test_add(self):
d = DiscretePotential(['b', 'c'], [3, 4], numpy.arange(12))
self.assertEqual(self.potential + d,
self.potential.marginalise(['a']))
def test_int_eq_index(self):
self.potential[1, 1, 1] = -2
self.potential[self.potential == -2] = -3
self.assertEquals(self.potential[1, 1, 1], -3)
def test_all(self):
""" this is actually the Water-sprinkler example """
c = DiscretePotential(['c'], [2], [0.5, 0.5])
s = DiscretePotential(['s', 'c'], [2, 2], [0.5, 0.9, 0.5, 0.1])
r = DiscretePotential(['r', 'c'], [2, 2], [0.8, 0.2, 0.2, 0.8])
w = DiscretePotential(['w', 's', 'r'], [2, 2, 2])
w[:, 0, 0] = [0.99, 0.01]
w[:, 0, 1] = [0.1, 0.9]
w[:, 1, 0] = [0.1, 0.9]
w[:, 1, 1] = [0.0, 1.0]
cr = c * r # Pr(C,R) = Pr(R|C) * Pr(C)
crs = cr * s # Pr(C,S,R) = Pr(S|C) * Pr(C,R)
crsw = crs * w # Pr(C,S,R,W) = Pr(W|S,R) * Pr(C,R,S)
# this can be verified using any bayesian network software
# check the result for the multiplication and marginalisation
self.assert_(numpy.allclose(crsw.marginalise('s r w'.split()).cpt,
[0.5, 0.5]))
self.assert_(numpy.allclose(crsw.marginalise('c r w'.split()).cpt,
[0.7, 0.3]))
self.assert_(numpy.allclose(crsw.marginalise('c s w'.split()).cpt,
[0.5, 0.5]))
self.assert_(numpy.allclose(crsw.marginalise('c s r'.split()).cpt,
[0.349099, 0.6509]))