def test_ep_tight(self):
theta = DirichletParameterNode(
'theta',
Factor(['X', 'ZDummy'], {
'X': ['same', 'diff'],
'ZDummy': ['dummy']
}, {
('same', 'dummy'): 1,
('diff', 'dummy'): 1,
},
logarithmetic=False))
X = DiscreteVariableNode('X', ['same', 'diff'], logarithmetic=False)
D = DiscreteVariableNode('ZDummy', ['dummy'], logarithmetic=False)
f = DirichletFactorNode('f')
X.observed({('same', ): 0.50001, ('diff', ): 0.49999})
f.connect(theta)
f.connect(X, parent=False)
f.connect(D)
# Add nodes to lbp.
lbp = LBP(strategy='tree')
lbp.add_nodes([f, X, D, theta])
for i in range(50):
lbp.run()
#print theta.alpha.pretty_print(precision=5)
lbp.init_messages()
print theta.alpha.pretty_print(precision=5)
def test_ep_tight(self):
theta = DirichletParameterNode('theta', Factor(
['X', 'ZDummy'],
{
'X': ['same', 'diff'],
'ZDummy': ['dummy']
},
{
('same', 'dummy'): 1,
('diff', 'dummy'): 1,
}, logarithmetic=False))
X = DiscreteVariableNode('X', ['same', 'diff'], logarithmetic=False)
D = DiscreteVariableNode('ZDummy', ['dummy'], logarithmetic=False)
f = DirichletFactorNode('f')
X.observed({('same',): 0.50001, ('diff',): 0.49999})
f.connect(theta)
f.connect(X, parent=False)
f.connect(D)
# Add nodes to lbp.
lbp = LBP(strategy='tree')
lbp.add_nodes([
f, X, D, theta
])
for i in range(50):
lbp.run()
#print theta.alpha.pretty_print(precision=5)
lbp.init_messages()
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_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_dir_tight(self):
theta = DirichletParameterNode('theta', Factor(
['X', 'ZDummy'],
{
'X': ['same', 'diff'],
'ZDummy': ['dummy']
},
{
('same', 'dummy'): 1,
('diff', 'dummy'): 1,
},
logarithmetic=False
))
X = DiscreteVariableNode('X', ['same', 'diff'], logarithmetic=False)
D = DiscreteVariableNode('ZDummy', ['dummy'], logarithmetic=False)
f = DirichletFactorNode('f')
X.observed({('same',): 0.8, ('diff',): 0.2})
f.connect(theta)
f.connect(X, parent=False)
f.connect(D)
X.message_to(f)
D.message_to(f)
f.update()
f.message_to(theta)
print theta.alpha
theta.message_to(f)
X.observed({('same',): 0.5, ('diff',): 0.7})
X.message_to(f)
f.update()
f.message_to(theta)
print theta.alpha
def test_two_factors_one_theta2(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,
}
))
f1 = DirichletFactorNode('f1', aliases={'X0': 'X0_a', 'X1': 'X1_a'})
x0 = DiscreteVariableNode('X0_a', ['x0_0', 'x0_1'])
x1 = DiscreteVariableNode('X1_a', ['x1_0', 'x1_1', 'x1_2'])
f2 = DirichletFactorNode('f2', aliases={'X0': 'X0_b', 'X1': 'X1_b'})
x2 = DiscreteVariableNode('X0_b', ['x0_0', 'x0_1'])
x3 = DiscreteVariableNode('X1_b', ['x1_0', 'x1_1', 'x1_2'])
f1.connect(x0, parent=False)
f1.connect(x1)
f2.connect(x2, parent=False)
f2.connect(x3)
f1.connect(alpha)
f2.connect(alpha)
alpha.aliases = {'X0_a': 'X0', 'X0_b': 'X0', 'X1_a': 'X1', 'X1_b': 'X1'}
x0.observed({('x0_0',): 1})
x1.observed({('x1_0',): 1})
x2.observed({('x0_1',): 1})
x3.observed({('x1_0',): 1})
x0.message_to(f1)
x1.message_to(f1)
x2.message_to(f2)
x3.message_to(f2)
f1.update()
f2.update()
f1.message_to(alpha)
f2.message_to(alpha)
print alpha.alpha
self.assertAlmostEqual(alpha.alpha[('x0_0', 'x1_0')], 2, places=5)
self.assertAlmostEqual(alpha.alpha[('x0_1', 'x1_0')], 2, places=5)
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)
def test_layers(self):
f_h_o = {
("save", "osave"): 0.8,
("del", "osave"): 0.2,
("save", "odel"): 0.2,
("del", "odel"): 0.8,
}
f_h_h = {
("save", "save"): 0.9,
("del", "save"): 0.1,
("save", "del"): 0.1,
("del", "del"): 0.9
}
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode(
"fact_h1_o1",
Factor(['hid1', 'obs1'], {
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
}, f_h_o))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DiscreteFactorNode(
"fact_h2_o2",
Factor(['hid2', 'obs2'], {
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
}, f_h_o))
fact_h1_h2 = DiscreteFactorNode(
"fact_h1_h2",
Factor(['hid1', 'hid2'], {
"hid1": ["save", "del"],
"hid2": ["save", "del"],
}, f_h_h))
hid3 = DiscreteVariableNode("hid3", ["save", "del"])
obs3 = DiscreteVariableNode("obs3", ["osave", "odel"])
fact_h3_o3 = DiscreteFactorNode(
"fact_h3_o3",
Factor(['hid3', 'obs3'], {
"hid3": ["save", "del"],
"obs3": ["osave", "odel"]
}, f_h_o))
fact_h2_h3 = DiscreteFactorNode(
"fact_h2_h3",
Factor(['hid2', 'hid3'], {
"hid2": ["save", "del"],
"hid3": ["save", "del"],
}, f_h_h))
# Connect nodes.
obs1.connect(fact_h1_o1)
obs2.connect(fact_h2_o2)
obs3.connect(fact_h3_o3)
fact_h1_o1.connect(hid1)
fact_h2_o2.connect(hid2)
fact_h3_o3.connect(hid3)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
hid2.connect(fact_h2_h3)
hid3.connect(fact_h2_h3)
# Set observations.
obs1.observed({('osave', ): 1})
# Add nodes to lbp.
lbp = LBP(strategy='layers')
lbp.add_layers([
[obs1, hid1, fact_h1_o1],
[obs2, hid2, fact_h2_o2, fact_h1_h2],
[obs3, hid3, fact_h3_o3, fact_h2_h3],
])
lbp.run()
self.assertAlmostEqual(hid1.belief[('save', )], 0.8)
self.assertAlmostEqual(hid2.belief[('save', )],
0.8 * 0.9 + 0.2 * 0.1,
places=6)
# @DM: The original test that fails at my computer.
# self.assertAlmostEqual(hid3.belief[('save',)], hid2.belief[('save',)] * 0.9 + hid2.belief[('del',)] * 0.1)
# MK: This one passes.
self.assertAlmostEqual(hid3.belief[('save', )],
hid2.belief[('save', )] * 0.9 +
hid2.belief[('del', )] * 0.1,
places=6)
lbp = LBP(strategy='layers')
lbp.add_layers([[obs1, hid1, fact_h1_o1]])
lbp.run()
self.assertAlmostEqual(hid1.belief[('save', )], 0.8)
lbp.add_layer([obs2, hid2, fact_h2_o2, fact_h1_h2])
lbp.run(from_layer=0)
self.assertAlmostEqual(hid2.belief[('save', )],
0.8 * 0.9 + 0.2 * 0.1,
places=6)
lbp.add_layers([[obs3, hid3, fact_h3_o3, fact_h2_h3]])
lbp.run(from_layer='last')
self.assertAlmostEqual(
hid3.belief[('save', )],
hid2.belief[('save', )] * 0.9 + hid2.belief[('del', )] * 0.1)
def test_dir_tight(self):
theta = DirichletParameterNode(
'theta',
Factor(['X', 'ZDummy'], {
'X': ['same', 'diff'],
'ZDummy': ['dummy']
}, {
('same', 'dummy'): 1,
('diff', 'dummy'): 1,
},
logarithmetic=False))
X = DiscreteVariableNode('X', ['same', 'diff'], logarithmetic=False)
D = DiscreteVariableNode('ZDummy', ['dummy'], logarithmetic=False)
f = DirichletFactorNode('f')
X.observed({('same', ): 0.8, ('diff', ): 0.2})
f.connect(theta)
f.connect(X, parent=False)
f.connect(D)
X.message_to(f)
D.message_to(f)
f.update()
f.message_to(theta)
theta.message_to(f)
X.observed({('same', ): 0.5, ('diff', ): 0.7})
X.message_to(f)
f.update()
f.message_to(theta)
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)
def test_ep(self):
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DirichletFactorNode("fact_h1_o1")
theta_h1_o1 = DirichletParameterNode('theta_h1_o1', Factor(
['hid1', 'obs1'],
{
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
},
{
("save", "osave"): 1,
("save", "odel"): 1,
("del", "osave"): 1,
("del", "odel"): 1,
}))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DirichletFactorNode("fact_h2_o2")
theta_h2_o2 = DirichletParameterNode('theta_h2_o2', Factor(
['hid2', 'obs2'],
{
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
},
{
("save", "osave"): 1,
("save", "odel"): 1,
("del", "osave"): 1,
("del", "odel"): 1,
}))
fact_h1_h2 = DiscreteFactorNode("fact_h1_h2", Factor(
['hid1', 'hid2'],
{
"hid1": ["save", "del"],
"hid2": ["save", "del"],
},
{
("save", "save"): 0.9,
("save", "del"): 0.1,
("del", "save"): 0,
("del", "del"): 1
}))
# Connect nodes.
obs1.connect(fact_h1_o1, parent=False)
fact_h1_o1.connect(hid1)
fact_h1_o1.connect(theta_h1_o1)
obs2.connect(fact_h2_o2, parent=False)
fact_h2_o2.connect(hid2)
fact_h2_o2.connect(theta_h2_o2)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
# Add nodes to lbp.
lbp = LBP(strategy='tree')
lbp.add_nodes([
obs1, obs2,
fact_h1_o1, fact_h2_o2,
theta_h1_o1, theta_h2_o2,
hid1, hid2,
fact_h1_h2
])
obs1.observed({('osave',): 1})
obs2.observed({('osave',): 1})
hid1.observed({('save',): 1})
hid2.observed({('save',): 1})
for i in range(100):
lbp.run()
#print theta_h1_o1.alpha.pretty_print(precision=5)
lbp.init_messages()
def test_layers(self):
f_h_o = {
("save", "osave"): 0.8,
("del", "osave"): 0.2,
("save", "odel"): 0.2,
("del", "odel"): 0.8,
}
f_h_h = {
("save", "save"): 0.9,
("del", "save"): 0.1,
("save", "del"): 0.1,
("del", "del"): 0.9
}
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode("fact_h1_o1", Factor(
['hid1', 'obs1'],
{
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
},
f_h_o))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DiscreteFactorNode("fact_h2_o2", Factor(
['hid2', 'obs2'],
{
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
},
f_h_o))
fact_h1_h2 = DiscreteFactorNode("fact_h1_h2", Factor(
['hid1', 'hid2'],
{
"hid1": ["save", "del"],
"hid2": ["save", "del"],
},
f_h_h))
hid3 = DiscreteVariableNode("hid3", ["save", "del"])
obs3 = DiscreteVariableNode("obs3", ["osave", "odel"])
fact_h3_o3 = DiscreteFactorNode("fact_h3_o3", Factor(
['hid3', 'obs3'],
{
"hid3": ["save", "del"],
"obs3": ["osave", "odel"]
},
f_h_o))
fact_h2_h3 = DiscreteFactorNode("fact_h2_h3", Factor(
['hid2', 'hid3'],
{
"hid2": ["save", "del"],
"hid3": ["save", "del"],
},
f_h_h))
# Connect nodes.
obs1.connect(fact_h1_o1)
obs2.connect(fact_h2_o2)
obs3.connect(fact_h3_o3)
fact_h1_o1.connect(hid1)
fact_h2_o2.connect(hid2)
fact_h3_o3.connect(hid3)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
hid2.connect(fact_h2_h3)
hid3.connect(fact_h2_h3)
# Set observations.
obs1.observed({('osave',): 1})
# Add nodes to lbp.
lbp = LBP(strategy='layers')
lbp.add_layers([
[obs1, hid1, fact_h1_o1],
[obs2, hid2, fact_h2_o2, fact_h1_h2],
[obs3, hid3, fact_h3_o3, fact_h2_h3],
])
lbp.run()
self.assertAlmostEqual(hid1.belief[('save',)], 0.8)
self.assertAlmostEqual(hid2.belief[('save',)], 0.8 * 0.9 + 0.2 * 0.1, places=6)
# @DM: The original test that fails at my computer.
# self.assertAlmostEqual(hid3.belief[('save',)], hid2.belief[('save',)] * 0.9 + hid2.belief[('del',)] * 0.1)
# MK: This one passes.
self.assertAlmostEqual(
hid3.belief[('save',)],
hid2.belief[('save',)] * 0.9 + hid2.belief[('del',)] * 0.1,
places=6)
lbp = LBP(strategy='layers')
lbp.add_layers([
[obs1, hid1, fact_h1_o1]
])
lbp.run()
self.assertAlmostEqual(hid1.belief[('save',)], 0.8)
lbp.add_layer([obs2, hid2, fact_h2_o2, fact_h1_h2])
lbp.run(from_layer=0)
self.assertAlmostEqual(hid2.belief[('save',)], 0.8 * 0.9 + 0.2 * 0.1, places=6)
lbp.add_layers([
[obs3, hid3, fact_h3_o3, fact_h2_h3]
])
lbp.run(from_layer='last')
self.assertAlmostEqual(hid3.belief[('save',)], hid2.belief[('save',)] * 0.9 + hid2.belief[('del',)] * 0.1)
def test_network(self):
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode("fact_h1_o1", Factor(
['hid1', 'obs1'],
{
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
},
{
("save", "osave"): 0.8,
("save", "odel"): 0.3,
("del", "osave"): 0.2,
("del", "odel"): 0.7,
}))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DiscreteFactorNode("fact_h2_o2", Factor(
['hid2', 'obs2'],
{
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
},
{
("save", "osave"): 0.8,
("save", "odel"): 0.2,
("del", "osave"): 0.2,
("del", "odel"): 0.8,
}))
fact_h1_h2 = DiscreteFactorNode("fact_h1_h2", Factor(
['hid1', 'hid2'],
{
"hid1": ["save", "del"],
"hid2": ["save", "del"],
},
{
("save", "save"): 0.9,
("save", "del"): 0.1,
("del", "save"): 0,
("del", "del"): 1
}))
# Connect nodes.
obs1.connect(fact_h1_o1)
fact_h1_o1.connect(hid1)
obs2.connect(fact_h2_o2)
fact_h2_o2.connect(hid2)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
# Add nodes to lbp.
lbp = LBP()
lbp.add_nodes([
obs1, fact_h1_o1, hid1,
obs2, fact_h2_o2, hid2,
fact_h1_h2
])
obs1.observed({('osave',): 1})
lbp.run(n_iterations=1)
self.assertAlmostEqual(hid1.belief[('save',)], 0.8)
obs2.observed({('odel',): 1})
lbp.run(n_iterations=1)
self.assertAlmostEqual(hid1.belief[('save',)], 0.56521738)
def test_single_linked(self):
f_h_o = {
("save", "osave"): 0.8,
("del", "osave"): 0.2,
("save", "odel"): 0.2,
("del", "odel"): 0.8,
}
f_h_h = {
("save", "save"): 0.9,
("del", "save"): 0.1,
("save", "del"): 0.1,
("del", "del"): 0.9
}
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode(
"fact_h1_o1",
Factor(['hid1', 'obs1'], {
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
}, f_h_o))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DiscreteFactorNode(
"fact_h2_o2",
Factor(['hid2', 'obs2'], {
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
}, f_h_o))
fact_h1_h2 = DiscreteFactorNode(
"fact_h1_h2",
Factor(['hid1', 'hid2'], {
"hid1": ["save", "del"],
"hid2": ["save", "del"],
}, f_h_h))
hid3 = DiscreteVariableNode("hid3", ["save", "del"])
obs3 = DiscreteVariableNode("obs3", ["osave", "odel"])
fact_h3_o3 = DiscreteFactorNode(
"fact_h3_o3",
Factor(['hid3', 'obs3'], {
"hid3": ["save", "del"],
"obs3": ["osave", "odel"]
}, f_h_o))
fact_h2_h3 = DiscreteFactorNode(
"fact_h2_h3",
Factor(['hid2', 'hid3'], {
"hid2": ["save", "del"],
"hid3": ["save", "del"],
}, f_h_h))
# Connect nodes.
obs1.connect(fact_h1_o1)
obs2.connect(fact_h2_o2)
obs3.connect(fact_h3_o3)
fact_h1_o1.connect(hid1)
fact_h2_o2.connect(hid2)
fact_h3_o3.connect(hid3)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
hid2.connect(fact_h2_h3)
hid3.connect(fact_h2_h3)
# Add nodes to lbp.
lbp = LBP(strategy='tree')
lbp.add_nodes([
obs1, obs2, obs3, fact_h1_o1, fact_h2_o2, fact_h3_o3, hid1, hid2,
hid3, fact_h1_h2, fact_h2_h3
])
obs1.observed({('osave', ): 1})
lbp.run()
self.assertAlmostEqual(hid1.belief[('save', )], 0.8)
self.assertAlmostEqual(hid2.belief[('save', )],
0.8 * 0.9 + 0.2 * 0.1,
places=6)
self.assertAlmostEqual(hid3.belief[('save', )],
hid2.belief[('save', )] * 0.9 +
hid2.belief[('del', )] * 0.1,
places=6)
def test_ep(self):
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DirichletFactorNode("fact_h1_o1")
theta_h1_o1 = DirichletParameterNode(
'theta_h1_o1',
Factor(['hid1', 'obs1'], {
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
}, {
("save", "osave"): 1,
("save", "odel"): 1,
("del", "osave"): 1,
("del", "odel"): 1,
}))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DirichletFactorNode("fact_h2_o2")
theta_h2_o2 = DirichletParameterNode(
'theta_h2_o2',
Factor(['hid2', 'obs2'], {
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
}, {
("save", "osave"): 1,
("save", "odel"): 1,
("del", "osave"): 1,
("del", "odel"): 1,
}))
fact_h1_h2 = DiscreteFactorNode(
"fact_h1_h2",
Factor(['hid1', 'hid2'], {
"hid1": ["save", "del"],
"hid2": ["save", "del"],
}, {
("save", "save"): 0.9,
("save", "del"): 0.1,
("del", "save"): 0,
("del", "del"): 1
}))
# Connect nodes.
obs1.connect(fact_h1_o1, parent=False)
fact_h1_o1.connect(hid1)
fact_h1_o1.connect(theta_h1_o1)
obs2.connect(fact_h2_o2, parent=False)
fact_h2_o2.connect(hid2)
fact_h2_o2.connect(theta_h2_o2)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
# Add nodes to lbp.
lbp = LBP(strategy='tree')
lbp.add_nodes([
obs1, obs2, fact_h1_o1, fact_h2_o2, theta_h1_o1, theta_h2_o2, hid1,
hid2, fact_h1_h2
])
obs1.observed({('osave', ): 1})
obs2.observed({('osave', ): 1})
hid1.observed({('save', ): 1})
hid2.observed({('save', ): 1})
for i in range(100):
lbp.run()
print theta_h1_o1.alpha.pretty_print(precision=5)
lbp.init_messages()
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)
alpha.message_to(factor)
factor.update()
factor.message_to(alpha)
def test_single_linked(self):
f_h_o = {
("save", "osave"): 0.8,
("del", "osave"): 0.2,
("save", "odel"): 0.2,
("del", "odel"): 0.8,
}
f_h_h = {
("save", "save"): 0.9,
("del", "save"): 0.1,
("save", "del"): 0.1,
("del", "del"): 0.9
}
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode("fact_h1_o1", Factor(
['hid1', 'obs1'],
{
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
},
f_h_o))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DiscreteFactorNode("fact_h2_o2", Factor(
['hid2', 'obs2'],
{
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
},
f_h_o))
fact_h1_h2 = DiscreteFactorNode("fact_h1_h2", Factor(
['hid1', 'hid2'],
{
"hid1": ["save", "del"],
"hid2": ["save", "del"],
},
f_h_h))
hid3 = DiscreteVariableNode("hid3", ["save", "del"])
obs3 = DiscreteVariableNode("obs3", ["osave", "odel"])
fact_h3_o3 = DiscreteFactorNode("fact_h3_o3", Factor(
['hid3', 'obs3'],
{
"hid3": ["save", "del"],
"obs3": ["osave", "odel"]
},
f_h_o))
fact_h2_h3 = DiscreteFactorNode("fact_h2_h3", Factor(
['hid2', 'hid3'],
{
"hid2": ["save", "del"],
"hid3": ["save", "del"],
},
f_h_h))
# Connect nodes.
obs1.connect(fact_h1_o1)
obs2.connect(fact_h2_o2)
obs3.connect(fact_h3_o3)
fact_h1_o1.connect(hid1)
fact_h2_o2.connect(hid2)
fact_h3_o3.connect(hid3)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
hid2.connect(fact_h2_h3)
hid3.connect(fact_h2_h3)
# Add nodes to lbp.
lbp = LBP(strategy='tree')
lbp.add_nodes([
obs1, obs2, obs3,
fact_h1_o1, fact_h2_o2, fact_h3_o3,
hid1, hid2, hid3,
fact_h1_h2, fact_h2_h3
])
obs1.observed({('osave',): 1})
lbp.run()
self.assertAlmostEqual(hid1.belief[('save',)], 0.8)
self.assertAlmostEqual(hid2.belief[('save',)], 0.8 * 0.9 + 0.2 * 0.1, places=6)
self.assertAlmostEqual(hid3.belief[('save',)],
hid2.belief[('save',)] * 0.9 + hid2.belief[('del',)] * 0.1,
places=6)
def test_two_factors_one_theta2(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,
}))
f1 = DirichletFactorNode('f1', aliases={'X0': 'X0_a', 'X1': 'X1_a'})
x0 = DiscreteVariableNode('X0_a', ['x0_0', 'x0_1'])
x1 = DiscreteVariableNode('X1_a', ['x1_0', 'x1_1', 'x1_2'])
f2 = DirichletFactorNode('f2', aliases={'X0': 'X0_b', 'X1': 'X1_b'})
x2 = DiscreteVariableNode('X0_b', ['x0_0', 'x0_1'])
x3 = DiscreteVariableNode('X1_b', ['x1_0', 'x1_1', 'x1_2'])
f1.connect(x0, parent=False)
f1.connect(x1)
f2.connect(x2, parent=False)
f2.connect(x3)
f1.connect(alpha)
f2.connect(alpha)
alpha.aliases = {
'X0_a': 'X0',
'X0_b': 'X0',
'X1_a': 'X1',
'X1_b': 'X1'
}
x0.observed({('x0_0', ): 1})
x1.observed({('x1_0', ): 1})
x2.observed({('x0_1', ): 1})
x3.observed({('x1_0', ): 1})
x0.message_to(f1)
x1.message_to(f1)
x2.message_to(f2)
x3.message_to(f2)
f1.update()
f2.update()
f1.message_to(alpha)
f2.message_to(alpha)
self.assertAlmostEqual(alpha.alpha[('x0_0', 'x1_0')], 2, places=5)
self.assertAlmostEqual(alpha.alpha[('x0_1', 'x1_0')], 2, places=5)
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_network(self):
# Create nodes.
hid1 = DiscreteVariableNode("hid1", ["save", "del"])
obs1 = DiscreteVariableNode("obs1", ["osave", "odel"])
fact_h1_o1 = DiscreteFactorNode(
"fact_h1_o1",
Factor(['hid1', 'obs1'], {
"hid1": ["save", "del"],
"obs1": ["osave", "odel"]
}, {
("save", "osave"): 0.8,
("save", "odel"): 0.3,
("del", "osave"): 0.2,
("del", "odel"): 0.7,
}))
hid2 = DiscreteVariableNode("hid2", ["save", "del"])
obs2 = DiscreteVariableNode("obs2", ["osave", "odel"])
fact_h2_o2 = DiscreteFactorNode(
"fact_h2_o2",
Factor(['hid2', 'obs2'], {
"hid2": ["save", "del"],
"obs2": ["osave", "odel"]
}, {
("save", "osave"): 0.8,
("save", "odel"): 0.2,
("del", "osave"): 0.2,
("del", "odel"): 0.8,
}))
fact_h1_h2 = DiscreteFactorNode(
"fact_h1_h2",
Factor(['hid1', 'hid2'], {
"hid1": ["save", "del"],
"hid2": ["save", "del"],
}, {
("save", "save"): 0.9,
("save", "del"): 0.1,
("del", "save"): 0,
("del", "del"): 1
}))
# Connect nodes.
obs1.connect(fact_h1_o1)
fact_h1_o1.connect(hid1)
obs2.connect(fact_h2_o2)
fact_h2_o2.connect(hid2)
hid1.connect(fact_h1_h2)
hid2.connect(fact_h1_h2)
# Add nodes to lbp.
lbp = LBP()
lbp.add_nodes(
[obs1, fact_h1_o1, hid1, obs2, fact_h2_o2, hid2, fact_h1_h2])
obs1.observed({('osave', ): 1})
lbp.run(n_iterations=1)
self.assertAlmostEqual(hid1.belief[('save', )], 0.8)
obs2.observed({('odel', ): 1})
lbp.run(n_iterations=1)
self.assertAlmostEqual(hid1.belief[('save', )], 0.56521738)