'''
@author: Ian Park, for CS344, Lab05
@version March 7, 2020
'''
from aima.probability import BayesNet, enumeration_ask, elimination_ask, gibbs_ask
# Utility variables
T, F = True, False
cancer = BayesNet([
('Cancer', '', 0.01),
('Test1', 'Cancer', {
T: 0.90,
F: 0.20
}),
('Test2', 'Cancer', {
T: 0.90,
F: 0.20
}),
])
# P(Cancer | positive results on both tests)
print(enumeration_ask('Cancer', dict(Test1=T, Test2=T), cancer).show_approx())
# P(Cancer | Test1 ∧ Test2) = alpha * <P(Cancer) * P(Test1 | Cancer) * P(Test2 | Cancer), P(¬Cancer) * P(Test1 | ¬Cancer) * P(Test2 | ¬Cancer)>
# = alpha * <0.9 * 0.9 * 0.01, 0.2 * 0.2 * 0.99>
# = alpha * <0.0081, 0.0396>
# = 1 / 0.0477 * <0.0081, 0.0396>
# = <0.1698, 0.8302>
# P(Cancer | a positive result on test 1, but a negative result on test 2)
@version March 7, 2020
'''
from aima.probability import BayesNet, enumeration_ask, elimination_ask, gibbs_ask
# Utility variables
T, F = True, False
# From AIMA code (probability.py) - Fig. 14.2 - burglary example
burglary = BayesNet([('Burglary', '', 0.001), ('Earthquake', '', 0.002),
('Alarm', 'Burglary Earthquake', {
(T, T): 0.95,
(T, F): 0.94,
(F, T): 0.29,
(F, F): 0.001
}), ('JohnCalls', 'Alarm', {
T: 0.90,
F: 0.05
}), ('MaryCalls', 'Alarm', {
T: 0.70,
F: 0.01
})])
# P(Alarm | burglary ∧ ¬earthquake)
print(
enumeration_ask('Alarm', dict(Burglary=T, Earthquake=F),
burglary).show_approx())
# P(John | burglary ∧ ¬earthquake)
print(
enumeration_ask('JohnCalls', dict(Burglary=T, Earthquake=F),
It's taken from the AIMA Python code.
@author: kvlinden
@version Jan 2, 2013
'''
from aima.probability import BayesNet, enumeration_ask, elimination_ask, gibbs_ask
# Utility variables
T, F = True, False
# From AIMA code (probability.py) - Fig. 14.2 - burglary example
cancer = BayesNet([('Cancer', '', 0.01), ('Test1', 'Cancer', {
T: 0.9,
F: 0.2
}), ('Test2', 'Cancer', {
T: 0.9,
F: 0.2
})])
# Exercise 5.2
# P(Cancer | positive results on both tests)
print(enumeration_ask('Cancer', dict(Test1=T, Test2=T),
cancer).show_approx()) # False: 0.83, True: 0.17
print(elimination_ask('Cancer', dict(Test1=T, Test2=T),
cancer).show_approx()) # False: 0.83, True: 0.17
print(gibbs_ask('Cancer', dict(Test1=T, Test2=T),
cancer).show_approx()) # False: 0.837, True: 0.163
# P(Cancer | a positive result on test 1, but negative result on test2)
print(enumeration_ask('Cancer', dict(Test1=T, Test2=F),
@author: kvlinden
@version Jan 2, 2013
'''
from aima.probability import BayesNet, enumeration_ask, elimination_ask, gibbs_ask
# Utility variables
T, F = True, False
# From AIMA code (probability.py) - Fig. 14.2 - burglary example
emotion = BayesNet([
('Sunny', '', 0.7),
('Raise', '', 0.01),
('Happy', 'Sunny Raise', {
(T, T): 1.0,
(T, F): 0.7,
(F, T): 0.9,
(F, F): 0.1
}),
])
# Exercise 5.3 a. in respective order
print(enumeration_ask('Raise', dict(Sunny=T),
emotion).show_approx()) # False: 0.99, True: 0.01
print(elimination_ask('Raise', dict(Sunny=T),
emotion).show_approx()) # False: 0.99, True: 0.01
print(gibbs_ask('Raise', dict(Sunny=T),
emotion).show_approx()) # False: 0.996, True: 0.004
print(enumeration_ask('Raise', dict(Happy=T, Sunny=T),
emotion).show_approx()) # False: 0.986, True: 0.0142
'''
@author: Ian Park, for CS344, Lab05
@version March 7, 2020
'''
from aima.probability import BayesNet, enumeration_ask, elimination_ask, gibbs_ask
# Utility variables
T, F = True, False
happiness = BayesNet([
('Sunny', '', 0.70),
('Raise', '', 0.01),
('Happy', 'Sunny Raise', {
(T, T): 1.0,
(T, F): 0.7,
(F, T): 0.9,
(F, F): 0.1
}),
])
# P(Raise | Sunny)
print(enumeration_ask('Raise', dict(Sunny=T), happiness).show_approx())
# P(Raise | Sunny) = alpha * <P(Raise) * P(Sunny), P(¬Raise) * P(Sunny)>
# = alpha * P(Sunny) * <0.01, 0.99>
# = <0.01, 0.99>
# The probability of raise does not depend on sunny. So, since we have the probability of a raise, we can easily find the probability.
# P(Raise | Happy ∧ Sunny)
print(
enumeration_ask('Raise', dict(Happy=T, Sunny=T), happiness).show_approx())