T, F = True, False
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
})])
burglary.label = 'Burglary Example, Fig. 14.2'
storm = BayesNet([
('Thunderstorm', '', 0.005),
('Earthquake', '', 0.002),
('Rain', '', 0.02),
('Loud', 'Thunderstorm Earthquake Rain', {
(T, T, T): 0.99,
(T, F, T): 0.65,
(T, T, F): 0.95,
(T, F, F): 0.85,
(F, T, T): 0.80,
(F, T, F): 0.50,
(F, F, T): 0.20,
(F, F, F): 0.05,
}),
F: 0.05
}),
('Outside', 'Sleeping Sad', {
(T, T): 0.005,
(T, F): 0.01,
(F, T): 0.28,
(F, F): 0.43
}),
('Exercise', 'Outside Moving', {
(T, T): 0.6,
(T, F): 0.0,
(F, T): 0.34,
(F, F): 0.0
}),
])
depressedStudent.label = 'Depressed Student: is he sleeping, moving, drooling or outside?'
examples = {
depressedStudent: [
{
'variable': 'Sad',
'evidence': {
'Outside': T,
'Drooling': T
},
},
{
'variable': 'Homework',
'evidence': {
'Outside': F,
'Moving': F
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
})])
burglary.label = 'Burglary Example, Fig. 14.2'
famous = BayesNet([
# will you get famous?
# statistics recevied from https://nypost.com/2009/07/25/you-are-not-going-to-be-famous/ and
('liveInAmerica', '', 0.04),
('liveInNashville', '', 0.002),
('Famous', 'liveInAmerica liveInNashville', {
(T, T): 0.000000024,
(T, F): 0.000000024,
(F, T): 0.0000000012,
(F, F): 0.0000006
}),
('Actor', 'Famous', {
T: 0.00041,
F: 0.9
T, F = True, False
death = BayesNet([('Overweight', '', 0.339), ('HighStress', '', 0.72),
('HeartAttack', 'Overweight HighStress', {
(T, T): 0.72,
(T, F): 0.15,
(F, T): 0.12,
(F, F): 0.003
}), ('OldAge', '', 0.147),
('Death', 'HeartAttack OldAge', {
(T, T): 0.749,
(T, F): 0.25,
(F, T): 0.0009,
(F, F): 0.001
})])
death.label = 'Death and Heart Attacks'
examples = {
death: [
{
'variable': 'Overweight',
'evidence': {
'Death': T,
},
},
{
'variable': 'Death',
'evidence': {
'HeartAttack': T,
'OldAge': F
},
(F, T, T): 0.62,
(F, T, F): 0.9,
(F, F, T): 0.7,
(F, F, F): 0.2
}),
# my reaction to my cat sitting on me
('Pets', 'CatSits', {
T: 0.7,
F: 0.03
}),
('Feeds', 'CatSits', {
T: .2,
F: 0.85
})
])
cat.label = "Interactions with my cat"
examples = {
cat: [{
'variable': 'HW',
'evidence': {
'Pets': T,
'Feeds': F
}
}, {
'variable': 'HW',
'evidence': {
'Pets': F,
'Feeds': F
}
}, {
from probability import BayesNet
T, F = True, False
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})
])
burglary.label = 'Burglary Example, Fig. 14.2'
examples = {
burglary: [
{'variable': 'Burglary',
'evidence': {'JohnCalls':T, 'MaryCalls':T},
},
{'variable': 'Burglary',
'evidence': {'JohnCalls':F, 'MaryCalls':T},
},
{'variable': 'Earthquake',
'evidence': {'JohnCalls':T, 'MaryCalls':T},
},
],
}
('Female', '', 0.51),
('LGB', '', 0.054),
('Smoker', 'LGB Female',
{(T, T): 0.3047,
(T, F): 0.3046,
(F, T): 0.1885,
(F, F): 0.2202}),
('LungCancer', 'Smoker Female',
{(T, T): 0.095,
(T, F): 0.159,
(F, T): 0.004,
(F, F): 0.002}),
('PrematureDeath', 'LungCancer', {T: 0.86, F: 0.0134}),
# ('Smoker', 'LGB', {T: 0.205, F: 0.153})
])
smoking.label = 'Smoking Example'
examples = {
smoking: [
{'variable': 'LungCancer',
'evidence': {'LGB':T},
},
{'variable': 'LGB',
'evidence': {'PrematureDeath':F},
},
{'variable': 'PrematureDeath',
'evidence': {'Female':T, 'LGB':T},
},
{'variable': 'Smoker',
'evidence': {'Female':F, 'PrematureDeath':T},
},
(F, F, T): 0.75,
(F, F, F): 0.09
}),
('CantFindSemiColon', 'CompileError', {
T: 0.7,
F: 0.3
}), ('ArrayIsNull', 'CompileError', {
T: 0.3,
F: 0.85
}),
('OhYeahForgotThat', 'CompileError', {
T: 0.8,
F: 0.1
})])
compile_error.label = 'Programming Compile Error Correlation With Cause (Hypothetical)'
examples = {
compile_error: [{
'variable': 'ExtraSemicolon',
'evidence': {
'CantFindSemiColon': T,
'OhYeahForgotThat': T
}
}, {
'variable': 'MissingSemicolon',
'evidence': {
'ArrayIsNull': F,
'OhYeahForgotThat': T
}
}, {
from probability import BayesNet
T, F = True, False
nashvilleWeather = BayesNet([
('Rain', '', 0.36),
('Freezing', '', 0.27),
('CarAccident', 'Rain Freezing',
{(T, T): 0.95,
(T, F): 0.79,
(F, T): 0.29,
(F, F): 0.001}),
('InjuriesReported', 'CarAccident', {T: 0.89, F: 0.09}),
('DeathReported', 'CarAccident', {T: 0.95, F: 0.02})
])
nashvilleWeather.label = 'Nashville Weather Correlation With Accidents (Hypothetical)'
examples = {
nashvilleWeather: [
{'variable': 'Rain',
'evidence': {'InjuriesReported': T, 'DeathReported': T},
},
{'variable': 'Freezing',
'evidence': {'InjuriesReported': T, 'DeathReported': T},
},
{'variable': 'CarAccident',
'evidence': {'InjuriesReported': T, 'Rain': F},
},
{'variable': 'DeathReported',
'evidence': {'Rain': T, 'Freezing': T}
}
('Sleep', '', 0.6),
('PlayGames', '', 0.1),
('Pass', 'Study Sleep PlayGames',
{(T, T, T): 0.85,
(T, T, F): 0.98,
(F, T, T): 0.1,
(F, F, T): 0.001,
(F, F, F): 0.01,
(T, F, T): 0.51,
(F, T, F): 0.21,
(T, F, F): 0.78}),
('BadDream', 'Pass', {T: 0.4, F: 0.6}),
('BeatGame', 'Pass', {T: 0.01, F: 0.2}),
('GoodDream', 'Pass', {T: 0.70, F: 0.1})
])
test.label = 'Pass Test'
examples = {
test: [
{'variable': 'Study',
'evidence': {'BadDream':T, 'GoodDream':T},
},
{'variable': 'Study',
'evidence': {'BadDream':F, 'GoodDream':T},
},
{'variable': 'PlayGames',
'evidence': {'BeatGame':F, 'GoodDream':F},
},
{'variable': 'PlayGames',
'evidence': {'BeatGame':T, 'GoodDream':T},
},
T, F = True, False
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
})])
burglary.label = 'Burglary Example, Fig. 14.2'
Bball = BayesNet([('StephPlays', '', 0.96), ('KevinPlays', '', 0.76),
('DraymondPlays', '', .92),
('StephScores+25', 'StephPlays', {
T: .48,
F: 0.
}), ('KevinScores+25', 'KevinPlays', {
(T): .56,
(F): 0.
}),
('Lose', 'KevinScores+25 StephScores+25', {
(T, T): .02,
(T, F): .06,
(F, T): .06,
(F, F): .96
}),
death = BayesNet([
('Overweight', '', 0.339),
('HighStress', '', 0.72),
('HeartAttack', 'Overweight HighStress',
{(T, T): 0.72,
(T, F): 0.15,
(F, T): 0.12,
(F, F): 0.003}),
('OldAge', '', 0.147),
('Death', 'HeartAttack OldAge',
{(T, T): 0.749,
(T, F): 0.25,
(F, T): 0.0009,
(F, F): 0.001})
])
death.label = 'Death and Heart Attacks'
examples = {
death: [
{'variable': 'Overweight',
'evidence': {'Death':T,},
},
{'variable': 'Death',
'evidence': {'HeartAttack':T, 'OldAge':F},
},
{'variable': 'HighStress',
'evidence': {'Death':F, },
},
{'variable': 'OldAge',
'evidence': {'HeartAttack':T, },
},
('LegDay', '', .33),
('ArmsDay', '', 0.33),
('Cardio', '', 0.33),
('Tired', 'LegDay ArmDay', 'Cardio',
{(T, T, T): 0.1,
(T, T, F): 0.1,
(T, F, T): 0.7,
(T, F, F): 0.8,
(F, T, T): 0.7,
(F, T, F): 0.9,
(F, F, T): 0.9,
(F, F, F): 0.5}),
('Quit', 'Tired', {T: 0.70, F: 0.01}),
('Push', 'Tired', {T: 0.90, F: 0.10})
])
gym.label = 'Gym Day'
examples = {
gym: [
{'variable': 'Legday',
'evidence': {'Quit': T, 'Push': F}
},
{'variable': 'Legday',
'evidence': {'Quit': F, 'Push': F}
},
{'variable': 'Armday',
'evidence': {'Quit': T, 'Push': T}
},
{'variable': 'Cardio',
'evidence': {'Quit': F, 'Push': T}
}
T, F = True, False
burglary = BayesNet([('Pollution', '', 0.1), ('Smoker', '', 0.3),
('Cancer', 'Pollution Smoker', {
(T, T): 0.05,
(T, F): 0.02,
(F, T): 0.03,
(F, F): 0.001
}), ('XRay', 'Cancer', {
T: 0.90,
F: 0.20
}), ('Dyspnoea', 'Cancer', {
T: 0.65,
F: 0.30
})])
burglary.label = 'Lung Cancer Probability'
examples = {
burglary: [
{
'variable': 'Cancer',
'evidence': {
'Dyspnoea': T,
'Smoker': T
},
},
{
'variable': 'Smoker',
'evidence': {
'Xray': T,
'Cancer': F
from probability import BayesNet
T, F = True, False
coffee = BayesNet([
('Tired', '', 0.24),
('Working', '', 0.45),
('Coffee', 'Tired Working',
{(T, T): 0.97,
(T, F): 0.38,
(F, T): 0.29,
(F, F): 0.05}),
('Energy', 'Coffee', {T: 0.83, F: 0.21}),
('Jitters', 'Coffee', {T: 0.19, F: 0.01})
])
coffee.label = 'Probability of getting coffee'
examples = {
coffee: [
{'variable': 'Tired',
'evidence': {'Energy':T, 'Jitters':T},
},
{'variable': 'Tired',
'evidence': {'Energy':F, 'Jitters':T},
},
{'variable': 'Working',
'evidence': {'Energy':T, 'Jitters':T},
},
],
}
from probability import BayesNet
T, F = True, False
snowDay = BayesNet([
('snow', '', 0.022),
('windChill', '', 0.017),
('advisory', 'snow windChill',
{(T, T): 0.62,
(T, F): 0.48,
(F, T): 0.41,
(F, F): 0.0001}),
('noSchool', 'advisory', {T: 0.70, F: 0.005}),
('extremeWarning', 'advisory', {T: 0.16, F: 0.002})
])
snowDay.label = 'snowDay shows various weather probabilities that effect the chances of a snow day in Buffalo, New York.'
examples = {
snowDay: [
{'variable': 'snow',
'evidence': {'noSchool':T, 'extremeWarning':T},
},
{'variable': 'windChill',
'evidence': {'noSchool': F, 'advisory': T},
},
{'variable': 'advisory',
'evidence': {'noSchool': T, 'windChill': F},
},
{'variable': 'extremeWarning',
'evidence': {'snow': T, 'windChill': T},
},
(F, F, T): 0.89,
(F, F, F): 0.01}),
('Moving', 'Sleeping', {T: 0.5, F: 0.87}),
('Drooling', 'Sleeping', {T: 0.56, F: 0.05}),
('Outside', 'Sleeping Sad',
{(T, T): 0.005,
(T, F): 0.01,
(F, T): 0.28,
(F, F): 0.43}),
('Exercise', 'Outside Moving',
{(T, T): 0.6,
(T, F): 0.0,
(F, T): 0.34,
(F, F): 0.0}),
])
depressedStudent.label = 'Depressed Student: is he sleeping, moving, drooling or outside?'
examples = {
depressedStudent: [
{'variable': 'Sad',
'evidence': {'Outside':T, 'Drooling':T},
},
{'variable': 'Homework',
'evidence': {'Outside':F, 'Moving':F},
},
{'variable': 'Tired',
'evidence': {'Outside':F, 'Drooling':T},
},
{'variable': 'Sleeping',
'evidence': {'Outside':T, 'Drooling':F},
},
T, F = True, False
cancer = BayesNet([
('Pollution', '', 0.10),
('Smoker','', 0.90),
('LungCancer', 'Pollution Smoker',
{(T, T): 0.987,
(T, F): 0.10,
(F, T): 0.87,
(F, F): 0.09}),
('XRay', 'LungCancer', {T: 0.90, F: 0.10}),
('Dyspnoea', 'LungCancer', {T: 0.70, F: 0.30}),
('Death', 'LungCancer', {T: 0.87, F: 0.13}),
])
cancer.label = 'Lung Cancer Example'
examples = {
cancer: [
{'variable': 'LungCancer',
'evidence': {'Death':T, 'Pollution':T},
},
{'variable': 'Death',
'evidence': {'LungCancer':F, 'Smoker':T},
},
{'variable': 'Smoker',
'evidence': {'LungCancer':T, 'Pollution':T},
},
{'variable': 'Pollution',
'evidence': {'LungCancer':T, 'Xray':T},
},
(F, F): 0.09
}),
('XRay', 'LungCancer', {
T: 0.90,
F: 0.10
}),
('Dyspnoea', 'LungCancer', {
T: 0.70,
F: 0.30
}),
('Death', 'LungCancer', {
T: 0.87,
F: 0.13
}),
])
cancer.label = 'Lung Cancer Example'
examples = {
cancer: [{
'variable': 'LungCancer',
'evidence': {
'Death': T,
'Pollution': T
},
}, {
'variable': 'Death',
'evidence': {
'LungCancer': F,
'Smoker': T
},
}, {
grass = BayesNet([('Raining', '', 0.1), ('Sprinklers', '', 0.4),
('GrassWet', 'Raining Sprinklers', {
(T, T): 0.99,
(T, F): 0.82,
(F, T): 0.94,
(F, F): 0.001
}), ('DadIsHappy', 'GrassWet', {
T: 0.90,
F: 0.05
}),
('AquaphobicNeighborIsNotHappy', 'GrassWet', {
T: 0.70,
F: 0.01
})])
grass.label = 'WetGrass'
examples = {
grass: [
{
'variable': 'Raining',
'evidence': {
'DadIsHappy': T,
'AquaphobicNeighborIsNotHappy': T
},
},
{
'variable': 'Sprinklers',
'evidence': {
'DadIsHappy': F,
'AquaphobicNeighborIsNotHappy': T
# ('HitCar', 'AccidentB', {T: 0.60, F: 0.40}),
# ('HitGuardRail', 'AccidentB', {T: 0.80, F: 0.20}),
#
# # ('HitCar', 'AccidentC', {T: 0.60, F: 0.40}),
# ('HitGuardRail', 'AccidentC', {T: 0.80, F: 0.20}),
#
# ('HitCar', 'AccidentD', {T: 0.60, F: 0.40}),
# ('HitGuardRail', 'AccidentD', {T: 0.80, F: 0.20}),
#
# ('HitCar', 'AccidentE', {T: 0.60, F: 0.40}),
# ('HitGuardRail', 'AccidentE', {T: 0.80, F: 0.20}),
#
# ('HitCar', 'AccidentF', {T: 0.60, F: 0.40}),
# ('HitGuardRail', 'AccidentF', {T: 0.80, F: 0.20})
])
CarAccidents.label = 'Car Accident Example, Fig. 1'
examples = {
CarAccidents: [
# Texting
{
'variable': 'Texting',
'evidence': {
'HitCar': T,
'HitGuardRail': T
},
},
{
'variable': 'Texting',
'evidence': {
'HitCar': F,
{
(T, T, T): 0.2,
(T, T, F): 0.5,
(T, F, T): 0.1,
(T, F, F): 0.7,
(F, T, T): 0.1,
(F, T, F): 0.4,
(F, F, T): 0.75,
(F, F, F): 0.09
}),
('CantFindSemiColon', 'CompileError', {T: 0.7, F: 0.3}),
('ArrayIsNull', 'CompileError', {T: 0.3, F: 0.85}),
('OhYeahForgotThat', 'CompileError', {T: 0.8, F: 0.1})
])
compile_error.label = 'Programming Compile Error Correlation With Cause (Hypothetical)'
examples = {
compile_error: [
{'variable': 'ExtraSemicolon',
'evidence': {'CantFindSemiColon': T, 'OhYeahForgotThat': T}
},
{'variable': 'MissingSemicolon',
'evidence': {'ArrayIsNull': F, 'OhYeahForgotThat': T}
},
{'variable': 'IndexOutOfRange',
'evidence': {'ArrayIsNull': T, 'MissingSemicolon': F}
},
{'variable': 'ArrayIsNull',
'evidence': {'IndexOutOfRange': T}
},
(F, F, T): 0.001,
(F, F, F): 0.01,
(T, F, T): 0.51,
(F, T, F): 0.21,
(T, F, F): 0.78
}), ('BadDream', 'Pass', {
T: 0.4,
F: 0.6
}), ('BeatGame', 'Pass', {
T: 0.01,
F: 0.2
}), ('GoodDream', 'Pass', {
T: 0.70,
F: 0.1
})])
test.label = 'Pass Test'
examples = {
test: [
{
'variable': 'Study',
'evidence': {
'BadDream': T,
'GoodDream': T
},
},
{
'variable': 'Study',
'evidence': {
'BadDream': F,
'GoodDream': T
(T, T, T): 0.90,
(T, T, F): 0.13,
(T, F, T): 0.85,
(T, F, F): 0.07,
(F, T, T): 0.0,
(F, T, F): 0.0,
(F, F, T): 0.99,
(F, F, F): 0.95
}),
('Jason', 'Dead', {T: 0.01, F: 0.90}),
('Lauren', 'Dead', {T: 0.20, F: 0.50}),
('Zach', 'Dead', {T: 0.49, F: 0.11})
])
Aids.label = 'Probability of Death with HIV/AIDS'
examples = {
Aids: [
{'variable': 'HIV',
'evidence': {'Jason':T, 'Lauren':T, 'Zach':T},
},
{'variable': 'HIV',
'evidence': {'Jason':F, 'Lauren':T, 'Zach':T},
},
{'variable': 'HIV',
'evidence': {'Jason':F, 'Lauren':F, 'Zach':T},
},
{'variable': 'HIV',
'evidence': {'Jason':F, 'Lauren':F, 'Zach':F},
},
