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)