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https://github.com/Brandon-Rozek/matmod.git
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Code cleanup and documentation
This commit is contained in:
parent
81a2d17965
commit
6b4d5828c8
3 changed files with 118 additions and 108 deletions
25
R.py
25
R.py
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@ -2,17 +2,17 @@
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Modeling the logic R
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"""
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from logic import (
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Conjunction,
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Disjunction,
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Implication,
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Logic,
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Negation,
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PropositionalVariable,
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Rule,
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Logic,
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Implication,
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Conjunction,
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Negation,
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Disjunction,
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Rule,
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)
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from model import Model, ModelFunction, ModelValue, has_vsp, satisfiable
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from model import Model, ModelFunction, ModelValue, satisfiable
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from generate_model import generate_model
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from vsp import has_vsp
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# ===================================================
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@ -63,7 +63,7 @@ R_logic = Logic(operations, logic_rules)
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# ===============================
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# Example Model of R
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# Example 2-element Model of R
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a0 = ModelValue("a0")
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@ -87,14 +87,14 @@ mconjunction = ModelFunction(2, {
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(a0, a0): a0,
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(a0, a1): a0,
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(a1, a0): a0,
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(a1, a1): a1
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(a1, a1): a1
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})
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mdisjunction = ModelFunction(2, {
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(a0, a0): a0,
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(a0, a1): a1,
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(a1, a0): a1,
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(a1, a1): a1
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(a1, a1): a1
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})
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@ -123,13 +123,14 @@ solutions = generate_model(R_logic, model_size, print_model=True)
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print(f"There are {len(solutions)} satisfiable models of element length {model_size}")
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for model, interpretation in solutions:
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print("Has VSP?", has_vsp(model, interpretation))
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print(has_vsp(model, interpretation))
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print("-" * 5)
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######
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# Smallest model for R that has the variable sharing property
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# This has 6 elements
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a0 = ModelValue("a0")
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a1 = ModelValue("a1")
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@ -299,4 +300,4 @@ print(R_model_6)
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print("Satisfiable", satisfiable(R_logic, R_model_6, interpretation))
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print("Has VSP?", has_vsp(R_model_6, interpretation))
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print(has_vsp(R_model_6, interpretation))
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149
model.py
149
model.py
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@ -1,21 +1,20 @@
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"""
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Defining what it means to be a model
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Matrix model semantics and satisfiability of
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a given logic.
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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, Conjunction, Disjunction, Implication
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get_propostional_variables, Logic,
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Operation, PropositionalVariable, Term
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)
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from typing import Set, Dict, Tuple, Optional
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from collections import defaultdict
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from functools import lru_cache
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from itertools import combinations, chain, product, permutations
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from copy import deepcopy
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from itertools import combinations_with_replacement, permutations, product
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from typing import Dict, List, Set, Tuple
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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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@ -29,10 +28,7 @@ class ModelValue:
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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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def __deepcopy__(self, memo):
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def __deepcopy__(self, _):
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return ModelValue(self.name)
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@ -41,8 +37,9 @@ class ModelFunction:
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self.operation_name = operation_name
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self.arity = arity
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# Correct input to always be a tuple
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corrected_mapping = dict()
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# Transform the mapping such that the
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# key is always a tuple of model values
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corrected_mapping: Dict[Tuple[ModelValue], ModelValue] = {}
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for k, v in mapping.items():
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if isinstance(k, tuple):
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assert len(k) == arity
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@ -66,35 +63,17 @@ class ModelFunction:
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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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@ -106,12 +85,22 @@ Designated Values: {set_to_str(self.designated_values)}
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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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def evaluate_term(
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t: Term, f: Dict[PropositionalVariable, ModelValue],
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interpretation: Dict[Operation, ModelFunction]) -> ModelValue:
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"""
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Given a term in a logic, mapping
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between terms and model values,
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as well as an interpretation
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of operations to model functions,
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return the evaluated model value.
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"""
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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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model_arguments: List[ModelValue] = []
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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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@ -121,11 +110,15 @@ def evaluate_term(t: Term, f: Dict[PropositionalVariable, ModelValue], interpret
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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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"""
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Given propositional variables and model values,
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produce every possible mapping between the two.
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"""
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all_possible_values = product(mvalues, repeat=len(pvars))
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for valuation in all_possible_values:
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mapping: Dict[PropositionalVariable, ModelValue] = dict()
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mapping: Dict[PropositionalVariable, ModelValue] = {}
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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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@ -137,98 +130,92 @@ def all_model_valuations_cached(
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mvalues: Tuple[ModelValue]):
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return list(all_model_valuations(pvars, mvalues))
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def rule_ordering_satisfied(model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
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"""
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Currently testing whether this function helps with runtime...
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"""
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if Conjunction in interpretation:
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possible_inputs = ((a, b) for (a, b) in product(model.carrier_set, model.carrier_set))
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for a, b in possible_inputs:
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output = interpretation[Conjunction](a, b)
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if a < b in model.ordering and output != a:
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print("RETURNING FALSE")
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return False
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if b < a in model.ordering and output != b:
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print("RETURNING FALSE")
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return False
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if Disjunction in interpretation:
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possible_inputs = ((a, b) for (a, b) in product(model.carrier_set, model.carrier_set))
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for a, b in possible_inputs:
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output = interpretation[Disjunction](a, b)
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if a < b in model.ordering and output != b:
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print("RETURNING FALSE")
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return False
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if b < a in model.ordering and output != a:
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print("RETURNING FALSE")
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return False
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return True
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def satisfiable(logic: Logic, model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
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"""
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Determine whether a model satisfies a logic
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given an interpretation.
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"""
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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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# NOTE: Does not look like rule ordering is helping for finding
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# models of R...
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if not rule_ordering_satisfied(model, interpretation):
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return False
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for mapping in mappings:
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# Make sure that the model satisfies each of the rules
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for rule in logic.rules:
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# The check only applies if the premises are designated
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premise_met = True
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premise_ts = set()
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premise_ts: Set[ModelValue] = 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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# As soon as one premise is not designated,
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# move to the next rule.
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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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# If designated, keep track of the evaluated term
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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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# With the premises designated, make sure the consequent is designated
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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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return True
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from itertools import combinations_with_replacement
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from collections import defaultdict
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def model_closure(initial_set: Set[ModelValue], mfunctions: Set[ModelFunction]):
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"""
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Given an initial set of model values and a set of model functions,
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compute the complete set of model values that are closed
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under the operations.
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"""
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closure_set: Set[ModelValue] = initial_set
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last_new = initial_set
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changed = True
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last_new: Set[ModelValue] = initial_set
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changed: bool = True
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while changed:
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changed = False
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new_elements = set()
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old_closure = closure_set - last_new
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new_elements: Set[ModelValue] = set()
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old_closure: Set[ModelValue] = closure_set - last_new
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# arity -> args
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cached_args = defaultdict(list)
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# Pass elements into each model function
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for mfun in mfunctions:
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# Use cached args if this arity was looked at before
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# If a previous function shared the same arity,
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# we'll use the same set of computed arguments
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# to pass into the model functions.
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if mfun.arity in cached_args:
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for args in cached_args[mfun.arity]:
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# Compute the new elements
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# given the cached arguments.
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element = mfun(*args)
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if element not in closure_set:
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new_elements.add(element)
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# Move onto next function
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# We don't need to compute the arguments
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# thanks to the cache, so move onto the
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# next function.
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continue
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# Iterate over how many new elements would be within the arguments
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# NOTE: To not repeat work, there must be at least one new element
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# At this point, we don't have cached arguments, so we need
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# to compute this set.
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# Each argument must have at least one new element to not repeat
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# work. We'll range over the number of new model values within our
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# argument.
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for num_new in range(1, mfun.arity + 1):
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new_args = combinations_with_replacement(last_new, r=num_new)
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old_args = combinations_with_replacement(old_closure, r=mfun.arity - num_new)
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# Determine every possible ordering of the concatenated
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# new and old model values.
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for new_arg, old_arg in product(new_args, old_args):
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for args in permutations(new_arg + old_arg):
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cached_args[mfun.arity].append(args)
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52
vsp.py
52
vsp.py
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@ -3,40 +3,62 @@ Check to see if the model has the variable
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sharing property.
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"""
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from itertools import chain, combinations, product
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from typing import Dict, Set, Tuple, List
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from typing import Dict, List, Optional, Set, Tuple
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from model import (
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Model, ModelFunction, ModelValue, model_closure
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Model, model_closure, ModelFunction, ModelValue
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)
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from logic import Implication, Operation
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def preseed(initial_set: Set[ModelValue], cache:List[Tuple[Set[ModelValue], Set[ModelValue]]]):
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def preseed(
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initial_set: Set[ModelValue],
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cache:List[Tuple[Set[ModelValue], Set[ModelValue]]]):
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"""
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Cache contains caches of model closure calls:
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Ex:
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{1, 2, 3} -> {....}
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Given a cache of previous model_closure calls,
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use this to compute an initial model closure
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set based on the initial set.
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Basic Idea:
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Let {1, 2, 3} -> X be in the cache.
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If {1,2,3} is a subset of initial set,
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then {....} is the subset of the output of model_closure.
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then X is the subset of the output of model_closure.
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We'll use the output to speed up the saturation procedure
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This is used to speed up subsequent calls to model_closure
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"""
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candidate_preseed: Tuple[Set[ModelValue], int] = (None, None)
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for i, o in cache:
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if i < initial_set:
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cost = len(initial_set - i)
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# If i is a subset with less missing elements than
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# the previous candidate, then it's the new candidate.
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if candidate_preseed[1] is None or cost < candidate_preseed[1]:
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candidate_preseed = o, cost
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same_set = candidate_preseed[1] == 0
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return candidate_preseed[0], same_set
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def has_vsp(model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
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"""
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Tells you whether a model has the
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variable sharing property.
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"""
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class VSP_Result:
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def __init__(
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self, has_vsp: bool, subalgebra1: Optional[Set[ModelValue]] = None,
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subalgebra2: Optional[Set[ModelValue]] = None, x: Optional[ModelValue] = None,
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y: Optional[ModelValue] = None):
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self.has_vsp = has_vsp
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self.subalgebra1 = subalgebra1
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self.subalgebra2 = subalgebra2
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self.x = x
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self.y = y
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def __str__(self):
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if self.has_vsp:
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return "Model has the variable sharing property."
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else:
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return "Model does not have the variable sharing property."
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def has_vsp(model: Model, interpretation: Dict[Operation, ModelFunction]) -> VSP_Result:
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"""
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Checks whether a model has the variable
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sharing property.
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"""
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impfunction = interpretation[Implication]
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# Compute I the set of tuples (x, y) where
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break
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if falsified:
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return True
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return VSP_Result(True, carrier_set_left, carrier_set_right, x2, y2)
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return False
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return VSP_Result(False)
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