def main4(self):
values = [
# A G F E C B D
[True, False, True, True, 0.1, False, True],
[True, True, True, True, 0.2, True, True],
[True, False, True, True, 0.5, False, False],
[False, True, True, False, -0.3, False, True],
[True, False, True, False, -0.0, True, True],
[True, True, True, False, -0.2, False, True],
[True, False, False, True, 0.6, False, True],
[False, True, False, True, 0.8, False, True],
[True, True, False, True, 0.1, True, True],
[False, False, False, False, -0.8, True, False],
[False, True, False, False, -0.2, False, True],
[False, True, False, False, -0.3, True, True],
[False, True, False, False, -0.5, False, True]
]
a = CPT("A", [])
b = CPT("B", [])
c = GDT("C", ["B"]) # Gaussian continuous
d = CPT("D", ["A"])
e = CPT("E", ["A", "B"])
f = CPT("F", ["D", "E"])
g = CPT("G", ["F"])
# put them all into the BN
bn = BNet()
bn.add(a)
bn.add(b)
bn.add(c)
bn.add(d)
bn.add(e)
bn.add(f)
bn.add(g)
# train it using EM
bn.train(["A", "G", "F", "E", "C", "B", "D"], values, [], 9)
# bn.train( ["A", "G", "F", "E"], values, ["B", "D"], 9)
print a
print b
print c
print d
print e
print f
print g
c.instance = 0.5
a.instance = True
test = bn.infer_naive(["E"])
test.normalize()
print test
def main(self):
values = [
# A G F E C
[True, False, True, True, 0.1],
[True, True, True, True, 0.2],
[True, False, True, True, 0.5],
[False, True, True, False, -0.3],
[True, False, True, False, -0.0],
[True, True, True, False, -0.2],
[True, False, False, True, 0.6],
[False, True, False, True, 0.8],
[True, True, False, True, 0.1],
[False, False, False, False, -0.8],
[False, True, False, False, -0.2],
[False, True, False, False, -0.3],
[False, True, False, False, -0.5]
]
# construct a BN with two root nodes
a = CPT("A", [])
b = CPT("B", [])
c = GDT("C", ["B"]) # Gaussian continuous
d = CPT("D", ["A"])
e = CPT("E", ["A", "B"])
f = CPT("F", ["D", "E"])
g = CPT("G", ["F"])
# put them all into the BN
bn = BNet()
bn.add(a)
bn.add(b)
bn.add(c)
bn.add(d)
bn.add(e)
bn.add(f)
bn.add(g)
# train it using EM
bn.train(["A", "G", "F", "E", "C"], values, ["B", "D"], 9)
print a
print b
print c
print d
print e
print f
print g
c.instance = 0.5
a.instance = True
test = bn.infer_naive(["E"])
test.normalize()
print test
def main2(self):
values = [
# A G F E
[True, False, True, True],
[True, True, True, True],
[True, False, True, True],
[False, True, True, False],
[True, False, True, False],
[True, True, True, False],
[True, False, False, True],
[False, True, False, True],
[True, True, False, True],
[False, False, False, False],
[False, True, False, False],
[False, True, False, False],
[False, True, False, False]
]
# construct a BN with two root nodes
a = CPT("A", [])
b = CPT("B", [])
d = CPT("D", ["A"])
e = CPT("E", ["A", "B"])
f = CPT("F", ["D", "E"])
g = CPT("G", ["F"])
# put them all into the BN
bn = BNet()
bn.add(a)
bn.add(b)
bn.add(d)
bn.add(e)
bn.add(f)
bn.add(g)
# train it using EM
bn.train(["A", "G", "F", "E"], values, ["B", "D"], 9)
print a
print b
print d
print e
print f
print g
a.instance = True
test = bn.infer_naive(["E"])
test.normalize()
print test
def testtrain1(self):
"""
\n****Testing training the large train network,
two unknown, one GDT****
"""
values=[
# A G F E C
[True, False, True, True, 0.1],
[True, True, True, True, 0.2],
[True, False, True, True, 0.5],
[False, True, True, False, -0.3],
[True, False, True, False, -0.0],
[True, True, True, False, -0.2],
[True, False, False, True, 0.6],
[False, True, False, True, 0.8],
[True, True, False, True, 0.1],
[False, False, False, False, -0.8],
[False, True, False, False, -0.2],
[False, True, False, False, -0.3],
[False, True, False, False, -0.5]]
a = CPT("A", [])
b = CPT("B", [])
c = GDT("C", ["B"]) # Gaussian continuous
d = CPT("D", ["A"])
e = CPT("E", ["A", "B"])
f = CPT("F", ["D", "E"])
g = CPT("G", ["F"])
bn = BNet()
bn.add(a)
bn.add(b)
bn.add(c)
bn.add(d)
bn.add(e)
bn.add(f)
bn.add(g)
bn.train( ["A", "G", "F", "E", "C"], values, ["B", "D"], 9)
c.instance = 0.5
a.instance = True
test = bn.infer_naive(["E"])
test.normalize()
print test
def main3(self):
values = [[-0.1, None], [-0.5, None], [-0.3, True], [0.3, None],
[0.4, None], [0.1, None], [-0.4, None], [-0.2, None],
[-0.3, None], [0.1, None], [0.4, False], [0.2, None]]
# construct a BN with one boolean root and one continuous child
a = CPT("A", [])
b = GDT("B", ["A"])
#b.setTieVariances(True)
bn = BNet()
bn.add(a)
bn.add(b)
#bn.EM_PRINT_STATUS=True
# train it using EM
# bn.train(["B","A"], values, [], 5)
bn.train(["B"], values, ["A"], 5)
print a
print b
b.instance = 0.0
test = bn.infer_naive(["A"])
test.normalize()
print test