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parents = ['Burglary', 'Earthquake']
event = {'Burglary': True, 'Earthquake': True}
event = {'Burglary': False, 'Earthquake': True}
# assert BoolCPT({T: 0.2, F: 0.625}).p(False, ['Burglary'], event) == 0.375
# assert BoolCPT(0.75).p(False, [], {}) == 0.25
# cpt = BoolCPT({True: 0.2, False: 0.7})
# assert cpt.rand(['A'], {'A': True}) in [True, False]
# cpt = BoolCPT({(True, True): 0.1, (True, False): 0.3,
# (False, True): 0.5, (False, False): 0.7})
# assert cpt.rand(['A', 'B'], {'A': True, 'B': False}) in [True, False]
# #enumeration_ask('Earthquake', {}, burglary)
s = {'A': True, 'B': False, 'C': True, 'D': False}
assert consistent_with(s, {})
assert consistent_with(s, s)
assert not consistent_with(s, {'A': False})
assert not consistent_with(s, {'D': True})
random.seed(21)
p = rejection_sampling('Earthquake', {}, burglary, 1000)
assert p[True], p[False] == (0.001, 0.999)
random.seed(71)
p = likelihood_weighting('Earthquake', {}, burglary, 1000)
assert p[True], p[False] == (0.002, 0.998)
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def test_probdist_basic():
P = ProbDist('Flip')
P['H'], P['T'] = 0.25, 0.75;
assert P['H'] == 0.25
def test_probdist_frequency():
P = ProbDist('X', {'lo': 125, 'med': 375, 'hi': 500})
assert (P['lo'], P['med'], P['hi']) == (0.125, 0.375, 0.5)
def test_probdist_normalize():
P = ProbDist('Flip')
P['H'], P['T'] = 35, 65
P = P.normalize()
assert (P.prob['H'], P.prob['T']) == (0.350, 0.650)
def test_jointprob():
P = JointProbDist(['X', 'Y'])
P[1, 1] = 0.25
assert P[1, 1] == 0.25
P[dict(X=0, Y=1)] = 0.5
assert P[dict(X=0, Y=1)] == 0.5
def test_event_values():
assert event_values ({'A': 10, 'B': 9, 'C': 8}, ['C', 'A']) == (8, 10)
assert event_values ((1, 2), ['C', 'A']) == (1, 2)
def test_enumerate_joint_ask():
P = JointProbDist(['X', 'Y'])
P[0,0] = 0.25
P[0,1] = 0.5
P[1,1] = P[2,1] = 0.125
assert enumerate_joint_ask('X', dict(Y=1),
P).show_approx() == '0: 0.667, 1: 0.167, 2: 0.167'
def test_bayesnode_p():
bn = BayesNode('X', 'Burglary', {T: 0.2, F: 0.625})
assert bn.p(False, {'Burglary': False, 'Earthquake': True}) == 0.375
def test_enumeration_ask():
assert enumeration_ask('Burglary',
dict(JohnCalls=T, MaryCalls=T), burglary).show_approx() == 'False: 0.716, True: 0.284'
def test_elemination_ask():
elimination_ask('Burglary', dict(JohnCalls=T, MaryCalls=T),
burglary).show_approx() == 'False: 0.716, True: 0.284'
def test_rejection_sampling():
random.seed(47)
rejection_sampling('Burglary', dict(JohnCalls=T, MaryCalls=T),
burglary, 10000).show_approx() == 'False: 0.7, True: 0.3'
def test_likelihood_weighting():
random.seed(1017)
assert likelihood_weighting('Burglary', dict(JohnCalls=T, MaryCalls=T),
burglary, 10000).show_approx() == 'False: 0.702, True: 0.298'