Code cleanup and documentation

R.py

@@ -2,17 +2,17 @@
 Modeling the logic R
 """
 from logic import (
+    Conjunction,
+    Disjunction,
+    Implication,
+    Logic,
+    Negation,
     PropositionalVariable,
     Rule,
-    Logic,
-    Implication,
-    Conjunction,
-    Negation,
-    Disjunction,
-    Rule,
 )
-from model import Model, ModelFunction, ModelValue, has_vsp, satisfiable
+from model import Model, ModelFunction, ModelValue, satisfiable
 from generate_model import generate_model
+from vsp import has_vsp
 
 
 # ===================================================
@@ -63,7 +63,7 @@ R_logic = Logic(operations, logic_rules)
 
 # ===============================
 
-# Example Model of R
+# Example 2-element Model of R
 
 
 a0 = ModelValue("a0")
@@ -123,13 +123,14 @@ solutions = generate_model(R_logic, model_size, print_model=True)
 print(f"There are {len(solutions)} satisfiable models of element length {model_size}")
 
 for model, interpretation in solutions:
-    print("Has VSP?", has_vsp(model, interpretation))
+    print(has_vsp(model, interpretation))
 
 print("-" * 5)
 
 ######
 
 # Smallest model for R that has the variable sharing property
+# This has 6 elements
 
 a0 = ModelValue("a0")
 a1 = ModelValue("a1")
@@ -299,4 +300,4 @@ print(R_model_6)
 
 print("Satisfiable", satisfiable(R_logic, R_model_6, interpretation))
 
-print("Has VSP?", has_vsp(R_model_6, interpretation))
+print(has_vsp(R_model_6, interpretation))

model.py

@@ -1,21 +1,20 @@
 """
-Defining what it means to be a model
+Matrix model semantics and satisfiability of
+a given logic.
 """
 from common import set_to_str
 from logic import (
-PropositionalVariable, get_propostional_variables, Logic, Term,
+    get_propostional_variables, Logic,
-Operation, Conjunction, Disjunction, Implication
+    Operation, PropositionalVariable, Term
 )
-from typing import Set, Dict, Tuple, Optional
+from collections import defaultdict
 from functools import lru_cache
-from itertools import combinations, chain, product, permutations
+from itertools import combinations_with_replacement, permutations, product
-from copy import deepcopy
+from typing import Dict, List, Set, Tuple
 
 
 __all__ = ['ModelValue', 'ModelFunction', 'Model']
 
-
-
 class ModelValue:
     def __init__(self, name):
         self.name = name
@@ -29,10 +28,7 @@ class ModelValue:
         return self.hashed_value
     def __eq__(self, other):
         return isinstance(other, ModelValue) and self.name == other.name
-    def __lt__(self, other):
+    def __deepcopy__(self, _):
-        assert isinstance(other, ModelValue)
-        return ModelOrderConstraint(self, other)
-    def __deepcopy__(self, memo):
         return ModelValue(self.name)
 
 
@@ -41,8 +37,9 @@ class ModelFunction:
         self.operation_name = operation_name
         self.arity = arity
 
-        # Correct input to always be a tuple
+        # Transform the mapping such that the
-        corrected_mapping = dict()
+        # key is always a tuple of model values
+        corrected_mapping: Dict[Tuple[ModelValue], ModelValue] = {}
         for k, v in mapping.items():
             if isinstance(k, tuple):
                 assert len(k) == arity
@@ -66,35 +63,17 @@ class ModelFunction:
     def __call__(self, *args):
         return self.mapping[args]
 
-    # def __eq__(self, other):
-    #     return isinstance(other, ModelFunction) and self.name == other.name and self.arity == other.arity
-
-class ModelOrderConstraint:
-    # a < b
-    def __init__(self, a: ModelValue, b: ModelValue):
-        self.a = a
-        self.b = b
-    def __hash__(self):
-        return hash(self.a) * hash(self.b)
-    def __eq__(self, other):
-        return isinstance(other, ModelOrderConstraint) and \
-            self.a == other.a and self.b == other.b
-
 class Model:
     def __init__(
             self,
             carrier_set: Set[ModelValue],
             logical_operations: Set[ModelFunction],
             designated_values: Set[ModelValue],
-            ordering: Optional[Set[ModelOrderConstraint]] = None
     ):
         assert designated_values <= carrier_set
         self.carrier_set = carrier_set
         self.logical_operations = logical_operations
         self.designated_values = designated_values
-        self.ordering = ordering  if ordering is not None else set()
-        # TODO: Make sure ordering is "valid"
-        # That is: transitive, etc.
 
     def __str__(self):
         result = f"""Carrier Set: {set_to_str(self.carrier_set)}
@@ -106,12 +85,22 @@ Designated Values: {set_to_str(self.designated_values)}
         return result
 
 
-def evaluate_term(t: Term, f: Dict[PropositionalVariable, ModelValue], interpretation: Dict[Operation, ModelFunction]) -> ModelValue:
+def evaluate_term(
+        t: Term, f: Dict[PropositionalVariable, ModelValue],
+        interpretation: Dict[Operation, ModelFunction]) -> ModelValue:
+    """
+    Given a term in a logic, mapping
+    between terms and model values,
+    as well as an interpretation
+    of operations to model functions,
+    return the evaluated model value.
+    """
+
     if isinstance(t, PropositionalVariable):
         return f[t]
 
     model_function = interpretation[t.operation]
-    model_arguments = []
+    model_arguments: List[ModelValue] = []
     for logic_arg in t.arguments:
         model_arg = evaluate_term(logic_arg, f, interpretation)
         model_arguments.append(model_arg)
@@ -121,11 +110,15 @@ def evaluate_term(t: Term, f: Dict[PropositionalVariable, ModelValue], interpret
 def all_model_valuations(
         pvars: Tuple[PropositionalVariable],
         mvalues: Tuple[ModelValue]):
+    """
+    Given propositional variables and model values,
+    produce every possible mapping between the two.
+    """
 
     all_possible_values = product(mvalues, repeat=len(pvars))
 
     for valuation in all_possible_values:
-        mapping: Dict[PropositionalVariable, ModelValue] = dict()
+        mapping: Dict[PropositionalVariable, ModelValue] = {}
         assert len(pvars) == len(valuation)
         for pvar, value in zip(pvars, valuation):
             mapping[pvar] = value
@@ -137,98 +130,92 @@ def all_model_valuations_cached(
         mvalues: Tuple[ModelValue]):
     return list(all_model_valuations(pvars, mvalues))
 
-def rule_ordering_satisfied(model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
-    """
-    Currently testing whether this function helps with runtime...
-    """
-    if Conjunction in interpretation:
-        possible_inputs = ((a, b) for (a, b) in product(model.carrier_set, model.carrier_set))
-        for a, b in possible_inputs:
-            output = interpretation[Conjunction](a, b)
-            if a < b in model.ordering and output != a:
-                print("RETURNING FALSE")
-                return False
-            if b < a in model.ordering and output != b:
-                print("RETURNING FALSE")
-                return False
-
-    if Disjunction in interpretation:
-        possible_inputs = ((a, b) for (a, b) in product(model.carrier_set, model.carrier_set))
-        for a, b in possible_inputs:
-            output = interpretation[Disjunction](a, b)
-            if a < b in model.ordering and output != b:
-                print("RETURNING FALSE")
-                return False
-            if b < a in model.ordering and output != a:
-                print("RETURNING FALSE")
-                return False
-
-    return True
-
-
 
 def satisfiable(logic: Logic, model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
+    """
+    Determine whether a model satisfies a logic
+    given an interpretation.
+    """
     pvars = tuple(get_propostional_variables(tuple(logic.rules)))
     mappings = all_model_valuations_cached(pvars, tuple(model.carrier_set))
 
-    # NOTE: Does not look like rule ordering is helping for finding
-    # models of R...
-    if not rule_ordering_satisfied(model, interpretation):
-        return False
-
     for mapping in mappings:
+        # Make sure that the model satisfies each of the rules
         for rule in logic.rules:
+            # The check only applies if the premises are designated
             premise_met = True
-            premise_ts = set()
+            premise_ts: Set[ModelValue] = set()
+
             for premise in rule.premises:
                 premise_t = evaluate_term(premise, mapping, interpretation)
+                # As soon as one premise is not designated,
+                # move to the next rule.
                 if premise_t not in model.designated_values:
                     premise_met = False
                     break
+                # If designated, keep track of the evaluated term
                 premise_ts.add(premise_t)
 
             if not premise_met:
                 continue
 
+            # With the premises designated, make sure the consequent is designated
             consequent_t = evaluate_term(rule.conclusion, mapping, interpretation)
-
             if consequent_t not in model.designated_values:
                 return False
 
     return True
 
-from itertools import combinations_with_replacement
+
-from collections import defaultdict
 
 def model_closure(initial_set: Set[ModelValue], mfunctions: Set[ModelFunction]):
+    """
+    Given an initial set of model values and a set of model functions,
+    compute the complete set of model values that are closed
+    under the operations.
+    """
     closure_set: Set[ModelValue] = initial_set
-    last_new = initial_set
+    last_new: Set[ModelValue] = initial_set
-    changed = True
+    changed: bool = True
 
     while changed:
         changed = False
-        new_elements = set()
+        new_elements: Set[ModelValue] = set()
-        old_closure = closure_set - last_new
+        old_closure: Set[ModelValue] = closure_set - last_new
 
         # arity -> args
         cached_args = defaultdict(list)
 
+        # Pass elements into each model function
         for mfun in mfunctions:
 
-            # Use cached args if this arity was looked at before
+            # If a previous function shared the same arity,
+            # we'll use the same set of computed arguments
+            # to pass into the model functions.
             if mfun.arity in cached_args:
                 for args in cached_args[mfun.arity]:
+                    # Compute the new elements
+                    # given the cached arguments.
                     element = mfun(*args)
                     if element not in closure_set:
                         new_elements.add(element)
-                # Move onto next function
+
+                # We don't need to compute the arguments
+                # thanks to the cache, so move onto the
+                # next function.
                 continue
 
-            # Iterate over how many new elements would be within the arguments
+            # At this point, we don't have cached arguments, so we need
-            # NOTE: To not repeat work, there must be at least one new element
+            # to compute this set.
+
+            # Each argument must have at least one new element to not repeat
+            # work. We'll range over the number of new model values within our
+            # argument.
             for num_new in range(1, mfun.arity + 1):
                 new_args = combinations_with_replacement(last_new, r=num_new)
                 old_args = combinations_with_replacement(old_closure, r=mfun.arity - num_new)
+                # Determine every possible ordering of the concatenated
+                # new and old model values.
                 for new_arg, old_arg in product(new_args, old_args):
                     for args in permutations(new_arg + old_arg):
                         cached_args[mfun.arity].append(args)

vsp.py

@@ -3,40 +3,62 @@ Check to see if the model has the variable
 sharing property.
 """
 from itertools import chain, combinations, product
-from typing import Dict, Set, Tuple, List
+from typing import Dict, List, Optional, Set, Tuple
 from model import (
-    Model, ModelFunction, ModelValue, model_closure
+    Model, model_closure, ModelFunction, ModelValue
 )
 from logic import Implication, Operation
 
-def preseed(initial_set: Set[ModelValue], cache:List[Tuple[Set[ModelValue], Set[ModelValue]]]):
+def preseed(
+        initial_set: Set[ModelValue],
+        cache:List[Tuple[Set[ModelValue], Set[ModelValue]]]):
     """
-    Cache contains caches of model closure calls:
+    Given a cache of previous model_closure calls,
-    Ex:
+    use this to compute an initial model closure
-    {1, 2, 3} -> {....}
+    set based on the initial set.
 
+    Basic Idea:
+    Let {1, 2, 3} -> X be in the cache.
     If {1,2,3} is a subset of initial set,
-    then {....} is the subset of the output of model_closure.
+    then X is the subset of the output of model_closure.
 
-    We'll use the output to speed up the saturation procedure
+    This is used to speed up subsequent calls to model_closure
     """
     candidate_preseed: Tuple[Set[ModelValue], int] = (None, None)
 
     for i, o in cache:
         if i < initial_set:
             cost = len(initial_set - i)
+            # If i is a subset with less missing elements than
+            # the previous candidate, then it's the new candidate.
             if candidate_preseed[1] is None or cost < candidate_preseed[1]:
                 candidate_preseed = o, cost
 
     same_set = candidate_preseed[1] == 0
     return candidate_preseed[0], same_set
 
-def has_vsp(model: Model, interpretation: Dict[Operation, ModelFunction]) -> bool:
+class VSP_Result:
-    """
+    def __init__(
-    Tells you whether a model has the
+            self, has_vsp: bool, subalgebra1: Optional[Set[ModelValue]] = None,
-    variable sharing property.
+            subalgebra2: Optional[Set[ModelValue]] = None, x: Optional[ModelValue] = None,
-    """
+            y: Optional[ModelValue] = None):
+        self.has_vsp = has_vsp
+        self.subalgebra1 = subalgebra1
+        self.subalgebra2 = subalgebra2
+        self.x = x
+        self.y = y
 
+    def __str__(self):
+        if self.has_vsp:
+            return "Model has the variable sharing property."
+        else:
+            return "Model does not have the variable sharing property."
+
+def has_vsp(model: Model, interpretation: Dict[Operation, ModelFunction]) -> VSP_Result:
+    """
+    Checks whether a model has the variable
+    sharing property.
+    """
     impfunction = interpretation[Implication]
 
     # Compute I the set of tuples (x, y) where
@@ -109,6 +131,6 @@ def has_vsp(model: Model, interpretation: Dict[Operation, ModelFunction]) -> boo
                 break
 
         if falsified:
-            return True
+            return VSP_Result(True, carrier_set_left, carrier_set_right, x2, y2)
 
-    return False
+    return VSP_Result(False)