def conditional_cow_framework():
language = [
HP, not_HP, HOC, not_HOC, CCA, not_CCA, FM, not_FM, cond_JN,
cond_neg_JN, JF, CM
]
rules = [
Semantics.Rule(HP, [HOC, CCA, not_FM]),
Semantics.Rule(HOC, []),
Semantics.Rule(FM, [CM]),
Semantics.Rule(CCA, [cond_neg_JN])
]
assumptions = [not_HP, not_HOC, not_CCA, not_FM]
contraries = {
not_HP: Semantics.Contrary(not_HP, HP),
not_HOC: Semantics.Contrary(not_HOC, HOC),
not_CCA: Semantics.Contrary(not_CCA, CCA),
not_FM: Semantics.Contrary(not_FM, FM)
}
random_variables = [cond_JN, JF, CM]
BN = Bayesian.BayesianNetwork({
cond_JN.symbol:
Bayesian.ConditionalProbability(cond_JN.symbol, [JF], {
"JF": 0.8,
"~JF": 0.0
}),
JF.symbol:
0.25,
CM.symbol:
0.1
})
return Semantics.BABA(language, rules, assumptions, contraries,
random_variables, BN)
def cow_framework():
JN = Semantics.Sentence('JN')
not_JN = Semantics.Sentence('not_JN')
language = [
HP, not_HP, HOC, not_HOC, CCA, not_CCA, FM, not_FM, JN, not_JN, JF, CM
]
rules = [
Semantics.Rule(HP, [HOC, CCA, not_FM]),
Semantics.Rule(HOC, []),
Semantics.Rule(FM, [CM]),
Semantics.Rule(CCA, [not_JN]),
Semantics.Rule(JN, [JF])
]
assumptions = [not_HP, not_HOC, not_CCA, not_FM, not_JN]
contraries = {
not_HP: Semantics.Contrary(not_HP, HP),
not_HOC: Semantics.Contrary(not_HOC, HOC),
not_CCA: Semantics.Contrary(not_CCA, CCA),
not_FM: Semantics.Contrary(not_FM, FM),
not_JN: Semantics.Contrary(not_JN, JN),
HP: Semantics.Contrary(HP, not_HP),
HOC: Semantics.Contrary(HOC, not_HOC),
CCA: Semantics.Contrary(CCA, not_CCA),
FM: Semantics.Contrary(FM, not_FM),
JN: Semantics.Contrary(JN, not_JN)
}
random_variables = [JF, CM]
BN = Bayesian.BayesianNetwork({CM.symbol: 0.1, JF.symbol: 0.8})
return Semantics.BABA(language, rules, assumptions, contraries,
random_variables, BN)
def invalid_non_flat_framework():
language = [a, b, c]
rules = [Semantics.Rule(a, [b, c])]
assumptions = [a, b, c]
contraries = {}
random_variables = []
return Semantics.BABA(language, rules, assumptions, contraries,
random_variables, None)
def valid_BABA_framework():
f = Semantics.Sentence('f', random_variable=True)
language = [a, b, c, d, e, f]
rules = [Semantics.Rule(a, [b, c])]
assumptions = [b, c]
contraries = {b: Semantics.Contrary(b, d), c: Semantics.Contrary(c, e)}
random_variables = [f]
return Semantics.BABA(language, rules, assumptions, contraries,
random_variables, None)
def with_chaining():
language = [a, b, c, d, e, f, g]
rules = [
Semantics.Rule(a, [b, c]),
Semantics.Rule(c, [d, e]),
Semantics.Rule(e, [f]),
Semantics.Rule(g, [c])
]
assumptions = [b, d, f]
return Semantics.BABA(language, rules, assumptions, {}, [], None)
def larger_framework():
language = [a, b, c, d, e, f, g, h, i, j]
rules = [
Semantics.Rule(a, [b]),
Semantics.Rule(a, [e, f]),
Semantics.Rule(c, [d, e, f]),
Semantics.Rule(d, [g, h]),
Semantics.Rule(d, [i]),
Semantics.Rule(j, [])
]
assumptions = [b, e, f, g, h, i]
contraries = {b: Semantics.Contrary(b, c)}
return Semantics.BABA(language, rules, assumptions, contraries, [], None)
def with_contraries():
language = [a, b, c, d, e, f, g, h, i]
rules = [
Semantics.Rule(a, [d, e]),
Semantics.Rule(b, [f, g]),
Semantics.Rule(c, [h, i])
]
assumptions = [d, e, f, g, h, i]
contraries = {
b: Semantics.Contrary(b, d),
d: Semantics.Contrary(d, b),
c: Semantics.Contrary(c, e),
e: Semantics.Contrary(e, c)
}
return Semantics.BABA(language, rules, assumptions, contraries, [], None)
def ideal_framework():
language = [a, _a, b, _b, c, _c, d, _d]
rules = [
Semantics.Rule(_a, [b]),
Semantics.Rule(_b, [c]),
Semantics.Rule(_b, [d]),
Semantics.Rule(_c, [d]),
Semantics.Rule(_d, [c])
]
assumptions = [a, b, c, d]
contraries = {
a: Semantics.Contrary(a, _a),
b: Semantics.Contrary(b, _b),
c: Semantics.Contrary(c, _c),
d: Semantics.Contrary(d, _d)
}
return Semantics.BABA(language, rules, assumptions, contraries, [], None)
def r_framework():
language = [a, _a, b, _b, c, _c, j, t, s]
rules = [
Semantics.Rule(j, [a]),
Semantics.Rule(_a, [b, s]),
Semantics.Rule(_b, [c, t])
]
assumptions = [a, b, c]
contraries = {
a: Semantics.Contrary(a, _a),
b: Semantics.Contrary(b, _b),
c: Semantics.Contrary(c, _c)
}
random_variables = [s, t]
bayes_net = Bayesian.BayesianNetwork({s.symbol: 0.6, t.symbol: 0.4})
return Semantics.BABA(language, rules, assumptions, contraries,
random_variables, bayes_net)
def parse_program(self, program):
self.reset_program_elements()
rules = [] # Parse rules at the end
for line in program:
if matches_rule_declaration(line):
rules.append(line)
elif matches_assumption_declaration(line):
assumption = extract_assumption(line)
self.assumptions.append(assumption)
self.language.append(assumption)
elif matches_contrary_declaration(line):
contrary = extract_contrary(line)
self.contraries[contrary.assumption] = contrary
self.language.append(contrary.contrary)
elif matches_random_variable_declaration(line):
rv, probability = extract_random_variable(line)
self.random_variables.append(rv)
self.language.append(rv)
self.bayesian_network[rv.symbol] = probability
elif matches_conditional_random_variable_declaration(line):
rv, probability = extract_conditional_random_variable(line)
self.random_variables.append(rv)
self.language.append(rv)
self.bayesian_network[rv.symbol] = probability
for rule in rules:
extracted_rule = extract_rule(rule, self.random_variables)
rule_elements = extracted_rule.body + [extracted_rule.head]
for sentence in rule_elements:
if sentence not in self.language:
self.language.append(sentence)
self.rules.append(extracted_rule)
return Semantics.BABA(set(self.language), self.rules,
set(self.assumptions), self.contraries,
set(self.random_variables),
Bayesian.BayesianNetwork(self.bayesian_network))
def s_framework():
language = [a, _a, b, _b, c, _c, d, _d, e, _e, f, _f]
rules = [
Semantics.Rule(_a, [b]),
Semantics.Rule(_b, [a]),
Semantics.Rule(_c, [b]),
Semantics.Rule(_d, [c]),
Semantics.Rule(_e, [d]),
Semantics.Rule(_d, [e])
]
assumptions = [a, b, c, d, e, f]
contraries = {
a: Semantics.Contrary(a, _a),
b: Semantics.Contrary(b, _b),
c: Semantics.Contrary(c, _c),
d: Semantics.Contrary(d, _d),
e: Semantics.Contrary(e, _e),
f: Semantics.Contrary(f, _f)
}
return Semantics.BABA(language, rules, assumptions, contraries, [], None)
def with_no_contraries():
language = [a, b, c, d, e, f]
rules = [Semantics.Rule(a, [b, c])]
assumptions = [b, c]
return Semantics.BABA(language, rules, assumptions, {}, [], None)
