matmod/generate_model.py

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"""
File which generates all the models
"""
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from common import set_to_str
from logic import Logic, Operation, Rule, get_operations_from_term, PropositionalVariable
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from model import ModelValue, Model, satisfiable, ModelFunction
from itertools import combinations, chain, product
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from typing import Set
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def possible_designations(iterable):
"""Powerset without the empty and complete set"""
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(1, len(s)))
def possible_functions(operation, carrier_set):
arity = operation.arity
inputs = list(product(*(carrier_set for _ in range(arity))))
possible_outputs = product(*(carrier_set for _ in range(len(inputs))))
for outputs in possible_outputs:
assert len(inputs) == len(outputs)
new_function = dict()
for input, output in zip(inputs, outputs):
new_function[input] = output
yield ModelFunction(new_function, operation.symbol)
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def only_rules_with(rules: Set[Rule], operation: Operation) -> Set[Rule]:
result_rules = []
for rule in rules:
is_valid = True
for t in (rule.premises | {rule.conclusion,}):
t_operations = get_operations_from_term(t)
if len(t_operations) > 1:
is_valid = False
break
if len(t_operations) == 0:
continue
t_operation = next(iter(t_operations))
if t_operation != operation:
is_valid = False
break
if is_valid:
result_rules.append(rule)
return result_rules
def possible_interpretations(
logic: Logic, carrier_set: Set[ModelValue],
designated_values: Set[ModelValue]):
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operations = []
model_functions = []
for operation in logic.operations:
operations.append(operation)
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candidate_functions = list(possible_functions(operation, carrier_set))
passed_functions = []
"""
Only consider functions that at least pass
in the rules with the operation by itself.
"""
restricted_rules = only_rules_with(logic.rules, operation)
if len(restricted_rules) > 0:
small_logic = Logic({operation,}, restricted_rules)
for f in candidate_functions:
small_model = Model(carrier_set, {f,}, designated_values)
interp = {operation: f}
if satisfiable(small_logic, small_model, interp):
passed_functions.append(f)
else:
passed_functions = candidate_functions
if len(passed_functions) == 0:
raise Exception("No interpretation satisfies the axioms for the operation " + str(operation))
else:
print(
f"Operation {operation.symbol} has {len(passed_functions)} candidate functions"
)
model_functions.append(passed_functions)
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functions_choice = product(*model_functions)
for functions in functions_choice:
assert len(operations) == len(functions)
interpretation = dict()
for operation, function in zip(operations, functions):
interpretation[operation] = function
yield interpretation
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def generate_model(logic: Logic, number_elements: int, num_solutions: int = -1, print_model=False):
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carrier_set = {
ModelValue("a" + str(i)) for i in range(number_elements)
}
possible_designated_values = possible_designations(carrier_set)
satisfied_models = []
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for designated_values in possible_designated_values:
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designated_values = set(designated_values)
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print("Considering models for designated values", set_to_str(designated_values))
possible_interps = possible_interpretations(logic, carrier_set, designated_values)
for interpretation in possible_interps:
is_valid = True
model = Model(carrier_set, set(interpretation.values()), designated_values)
# Iteratively test possible interpretations
# by adding one axiom at a time
for rule in logic.rules:
small_logic = Logic(logic.operations, {rule,})
if not satisfiable(small_logic, model, interpretation):
is_valid = False
break
if is_valid:
satisfied_models.append(model)
if print_model:
print(model, flush=True)
if num_solutions >= 0 and len(satisfied_models) >= num_solutions:
return satisfied_models
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return satisfied_models