Source code for idp_engine.Run

# Copyright 2019 Ingmar Dasseville, Pierre Carbonnelle
# This file is part of Interactive_Consultant.
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <>.


Classes to execute the main block of an IDP program


import types
from z3 import Solver

from .Parse import IDP
from .Problem import Problem
from .Assignments import Status, Assignments
from .utils import NEWL

[docs]def model_check(theories, structures=None): """ output: "sat", "unsat" or "unknown" """ problem = Problem.make(theories, structures) z3_formula = problem.formula().translate() solver = Solver() solver.add(z3_formula) yield str(solver.check())
[docs]def model_expand(theories, structures=None, max=10, complete=False, extended=False, sort=False): """ output: a list of Assignments, ending with a string """ problem = Problem.make(theories, structures, extended=extended) if sort: ms = [str(m) for m in problem.expand(max=max, complete=complete)] ms = sorted(ms[:-1]) + [ms[-1]] out = "" for i, m in enumerate(ms[:-1]): out = out + (f"{NEWL}Model {i+1}{NEWL}==========\n{m}\n") yield out + f"{ms[-1]}" else: yield from problem.expand(max=max, complete=complete)
[docs]def model_propagate(theories, structures=None, sort=False): """ output: a list of Assignment """ problem = Problem.make(theories, structures) if sort: ms = [str(m) for m in problem._propagate(tag=Status.CONSEQUENCE)] ms = sorted(ms[:-1]) + [ms[-1]] out = "" for i, m in enumerate(ms[:-1]): out = out + (f"{NEWL}Model {i+1}{NEWL}==========\n{m}\n") yield out + f"{ms[-1]}" else: yield from problem._propagate(tag=Status.CONSEQUENCE)
[docs]def decision_table(theories, structures=None, goal_string="", timeout=20, max_rows=50, first_hit=True, verify=False): """returns a decision table for `goal_string`, given `theories` and `structures`. Args: goal_string (str, optional): the last column of the table. timeout (int, optional): maximum duration in seconds. Defaults to 20. max_rows (int, optional): maximum number of rows. Defaults to 50. first_hit (bool, optional): requested hit-policy. Defaults to True. verify (bool, optional): request verification of table completeness. Defaults to False Yields: str: a textual representation of each rule """ problem = Problem.make(theories, structures, extended=True) for model in problem.decision_table(goal_string, timeout, max_rows, first_hit, verify): row = f'{NEWL}∧ '.join(str(a) for a in model if a.sentence.code != goal_string) has_goal = model[-1].sentence.code == goal_string yield((f"{(f' {row}{NEWL}') if row else ''}" f"⇒ {str(model[-1]) if has_goal else '?'}")) yield("") yield "end of decision table"
def pretty_print(x=""): if type(x) is tuple and len(x)==2: # result of Problem.explain() facts, laws = x for f in facts: print(str(f)) for l in laws: print(l.annotations['reading']) elif isinstance(x, types.GeneratorType): for i, xi in enumerate(x): if isinstance(xi, Assignments): print(f"{NEWL}Model {i+1}{NEWL}==========") print(xi) else: print(xi) else: print(x)
[docs]def execute(self): """ Execute the IDP program """ main = str(self.procedures['main']) mybuiltins = {} mylocals = {**self.vocabularies, **self.theories, **self.structures} mylocals['model_check'] = model_check mylocals['model_expand'] = model_expand mylocals['model_propagate'] = model_propagate mylocals['decision_table'] = decision_table mylocals['pretty_print'] = pretty_print mylocals['Problem'] = Problem exec(main, mybuiltins, mylocals)
IDP.execute = execute Done = True