def test_observed_complex(self):
s1 = DiscreteVariableNode('s1', ['a', 'b'])
s2 = DiscreteVariableNode('s2', ['a', 'b'])
f = DiscreteFactorNode(
'f',
Factor(['s1', 's2'], {
's1': ['a', 'b'],
's2': ['a', 'b'],
}, {
('a', 'a'): 1,
('a', 'b'): 0.5,
('b', 'a'): 0,
('b', 'b'): 0.5
}))
s1.connect(f)
s2.connect(f)
s2.observed({('a', ): 0.7, ('b', ): 0.3})
s1.send_messages()
s2.send_messages()
f.update()
f.normalize()
f.send_messages()
s1.update()
s1.normalize()
self.assertClose(s1.belief[('a', )], 0.85)
self.assertClose(s1.belief[('b', )], 0.15)
self.assertClose(s2.belief[('a', )], 0.7)
def test_parameter_simple(self):
alpha = DirichletParameterNode(
'theta',
Factor(['X0', 'X1'], {
'X0': ['x0_0', 'x0_1'],
'X1': ['x1_0'],
}, {
('x0_0', 'x1_0'): 3,
('x0_1', 'x1_0'): 1,
}))
factor = DirichletFactorNode('factor')
x0 = DiscreteVariableNode('X0', ['x0_0', 'x0_1'])
x1 = DiscreteVariableNode('X1', ['x1_0'])
x1.observed({('x1_0', ): 1})
factor.connect(alpha)
factor.connect(x0, parent=False)
factor.connect(x1, parent=True)
x0.message_to(factor)
x1.message_to(factor)
factor.update()
self.assertAlmostEqual(factor.belief[('x0_0', 'x1_0')], 0.5)
factor.message_to(x0)
factor.message_to(x1)
x0.update()
self.assertAlmostEqual(x0.belief[('x0_0', )], 3.0 / 4)
factor.message_to(alpha)
def test_parameter(self):
alpha = DirichletParameterNode('theta', Factor(
['X0', 'X1'],
{
'X0': ['x0_0', 'x0_1'],
'X1': ['x1_0', 'x1_1', 'x1_2'],
},
{
('x0_0', 'x1_0'): 1,
('x0_0', 'x1_1'): 8,
('x0_0', 'x1_2'): 1,
('x0_1', 'x1_0'): 1,
('x0_1', 'x1_1'): 2,
('x0_1', 'x1_2'): 1,
}
))
factor = DirichletFactorNode('factor')
x0 = DiscreteVariableNode('X0', ['x0_0', 'x0_1'])
x1 = DiscreteVariableNode('X1', ['x1_0', 'x1_1', 'x1_2'])
x0.observed({('x0_0',): 1})
x1.observed({('x1_0',): 0.7, ('x1_1',): 0.2, ('x1_2',): 0.1})
factor.connect(alpha)
factor.connect(x0, parent=False)
factor.connect(x1, parent=True)
x0.message_to(factor)
x1.message_to(factor)
factor.update()
factor.message_to(x0)
factor.message_to(x1)
x0.update()
factor.message_to(alpha)
print alpha.alpha
alpha.message_to(factor)
factor.update()
factor.message_to(alpha)
print alpha.alpha
def test_observed_complex(self):
s1 = DiscreteVariableNode('s1', ['a', 'b'])
s2 = DiscreteVariableNode('s2', ['a', 'b'])
f = DiscreteFactorNode('f', Factor(
['s1', 's2'],
{
's1': ['a', 'b'],
's2': ['a', 'b'],
},
{
('a', 'a'): 1,
('a', 'b'): 0.5,
('b', 'a'): 0,
('b', 'b'): 0.5
}))
s1.connect(f)
s2.connect(f)
s2.observed({
('a',): 0.7,
('b',): 0.3
})
s1.send_messages()
s2.send_messages()
f.update()
f.normalize()
f.send_messages()
s1.update()
s1.normalize()
self.assertClose(s1.belief[('a',)], 0.85)
self.assertClose(s1.belief[('b',)], 0.15)
self.assertClose(s2.belief[('a',)], 0.7)
def test_parameter_simple(self):
alpha = DirichletParameterNode('theta', Factor(
['X0', 'X1'],
{
'X0': ['x0_0', 'x0_1'],
'X1': ['x1_0'],
},
{
('x0_0', 'x1_0'): 3,
('x0_1', 'x1_0'): 1,
}
))
factor = DirichletFactorNode('factor')
x0 = DiscreteVariableNode('X0', ['x0_0', 'x0_1'])
x1 = DiscreteVariableNode('X1', ['x1_0'])
x1.observed({('x1_0',): 1})
factor.connect(alpha)
factor.connect(x0, parent=False)
factor.connect(x1, parent=True)
x0.message_to(factor)
x1.message_to(factor)
factor.update()
self.assertAlmostEqual(factor.belief[('x0_0', 'x1_0')], 0.5)
factor.message_to(x0)
factor.message_to(x1)
x0.update()
self.assertAlmostEqual(x0.belief[('x0_0',)], 3.0/4)
factor.message_to(alpha)
print alpha.alpha
def test_network(self):
# Create network.
hid = DiscreteVariableNode("hid", ["save", "del"])
obs = DiscreteVariableNode("obs", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode("fact_h1_o1", Factor(
['hid', 'obs'],
{
"hid": ["save", "del"],
"obs": ["osave", "odel"]
},
{
("save", "osave"): 0.3,
("save", "odel"): 0.6,
("del", "osave"): 0.7,
("del", "odel"): 0.4
}))
# Add edges.
obs.connect(fact_h1_o1)
fact_h1_o1.connect(hid)
# 1. Without observations, send_messages used.
obs.send_messages()
fact_h1_o1.send_messages()
hid.update()
hid.normalize()
self.assertClose(hid.belief[("save",)], 0.45)
# 2. Observed value, message_to and update_belief used.
obs.observed({("osave",): 1})
obs.message_to(fact_h1_o1)
fact_h1_o1.update()
fact_h1_o1.message_to(hid)
hid.update()
hid.normalize()
self.assertClose(hid.belief[("save",)], 0.3)
# 3. Without observations, send_messages used.
obs.observed(None)
obs.send_messages()
fact_h1_o1.send_messages()
hid.update()
hid.normalize()
self.assertClose(hid.belief[("save",)], 0.45)
