mirror of
https://github.com/Brandon-Rozek/matmod.git
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166 lines
5.3 KiB
Python
166 lines
5.3 KiB
Python
"""
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Defining what it means to be a model
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"""
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from common import set_to_str
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from logic import (
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PropositionalVariable, get_propostional_variables, Logic, Term,
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Operation
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)
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from typing import Set, List, Dict, Tuple, Optional
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from itertools import product
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from functools import lru_cache
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__all__ = ['ModelValue', 'ModelFunction', 'Model']
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class ModelValue:
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def __init__(self, name):
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self.name = name
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self.hashed_value = hash(self.name)
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def immutable(self, name, value):
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raise Exception("Model values are immutable")
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self.__setattr__ = immutable
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def __str__(self):
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return self.name
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def __hash__(self):
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return self.hashed_value
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def __eq__(self, other):
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return isinstance(other, ModelValue) and self.name == other.name
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def __lt__(self, other):
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assert isinstance(other, ModelValue)
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return ModelOrderConstraint(self, other)
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class ModelFunction:
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def __init__(self, mapping, operation_name = ""):
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self.operation_name = operation_name
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# Correct input to always be a tuple
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corrected_mapping = dict()
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for k, v in mapping.items():
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if isinstance(k, tuple):
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corrected_mapping[k] = v
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elif isinstance(k, list):
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corrected_mapping[tuple(k)] = v
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else: # Assume it's atomic
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corrected_mapping[(k,)] = v
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self.mapping = corrected_mapping
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def __str__(self):
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str_dict = dict()
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for k, v in self.mapping.items():
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inputstr = "(" + ", ".join(str(ki) for ki in k) + ")"
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str_dict[inputstr] = str(v)
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return str(str_dict)
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def __call__(self, *args):
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return self.mapping[args]
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# def __eq__(self, other):
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# return isinstance(other, ModelFunction) and self.name == other.name and self.arity == other.arity
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class ModelOrderConstraint:
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# a < b
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def __init__(self, a: ModelValue, b: ModelValue):
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self.a = a
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self.b = b
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def __hash__(self):
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return hash(self.a) * hash(self.b)
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def __eq__(self, other):
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return isinstance(other, ModelOrderConstraint) and \
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self.a == other.a and self.b == other.b
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class Model:
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def __init__(
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self,
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carrier_set: Set[ModelValue],
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logical_operations: Set[ModelFunction],
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designated_values: Set[ModelValue],
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ordering: Optional[Set[ModelOrderConstraint]] = None
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):
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assert designated_values <= carrier_set
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self.carrier_set = carrier_set
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self.logical_operations = logical_operations
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self.designated_values = designated_values
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self.ordering = ordering if ordering is not None else set()
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# TODO: Make sure ordering is "valid"
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# That is: transitive, etc.
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def __str__(self):
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result = f"""Carrier Set: {set_to_str(self.carrier_set)}
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Designated Values: {set_to_str(self.designated_values)}
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"""
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for function in self.logical_operations:
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result += f"{str(function)}\n"
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return result
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def evaluate_term(t: Term, f: Dict[PropositionalVariable, ModelValue], interpretation: Dict[Operation, ModelFunction]) -> ModelValue:
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if isinstance(t, PropositionalVariable):
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return f[t]
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model_function = interpretation[t.operation]
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model_arguments = []
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for logic_arg in t.arguments:
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model_arg = evaluate_term(logic_arg, f, interpretation)
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model_arguments.append(model_arg)
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return model_function(*model_arguments)
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def all_model_valuations(
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pvars: Tuple[PropositionalVariable],
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mvalues: Tuple[ModelValue]):
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possible_valuations = [mvalues for _ in pvars]
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all_possible_values = product(*possible_valuations)
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for valuation in all_possible_values:
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mapping: Dict[PropositionalVariable, ModelValue] = dict()
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assert len(pvars) == len(valuation)
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for pvar, value in zip(pvars, valuation):
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mapping[pvar] = value
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yield mapping
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@lru_cache
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def all_model_valuations_cached(
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pvars: Tuple[PropositionalVariable],
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mvalues: Tuple[ModelValue]):
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return list(all_model_valuations(pvars, mvalues))
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def satisfiable(logic: Logic, model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
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pvars = tuple(get_propostional_variables(tuple(logic.rules)))
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mappings = all_model_valuations_cached(pvars, tuple(model.carrier_set))
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"""
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TODO: Make sure that ordering for conjunction and disjunction
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at the model function level.
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"""
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for mapping in mappings:
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for rule in logic.rules:
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premise_met = True
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premise_ts = set()
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for premise in rule.premises:
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premise_t = evaluate_term(premise, mapping, interpretation)
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if premise_t not in model.designated_values:
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premise_met = False
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break
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premise_ts.add(premise_t)
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if not premise_met:
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continue
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consequent_t = evaluate_term(rule.conclusion, mapping, interpretation)
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if consequent_t not in model.designated_values:
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return False
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# Make sure ordering constraint is met
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for premise_t in premise_ts:
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if consequent_t < premise_t in model.ordering:
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return False
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return True
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