csp.py

#!/usr/bin/python
#
# Copyright (c) 2005-2014 - Gustavo Niemeyer <gustavo@niemeyer.net>
#
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from __future__ import absolute_import, division, print_function

from heuristics import variable_heuristic, value_heuristic
from constraint import Solver


class HeuristicRecursiveBacktrackingSolver(Solver):
    """
    Recursive problem solver with backtracking capabilities
    Examples:
    >>> result = [[('a', 1), ('b', 2)],
    ...           [('a', 1), ('b', 3)],
    ...           [('a', 2), ('b', 3)]]
    >>> problem = Problem(RecursiveBacktrackingSolver())
    >>> problem.addVariables(["a", "b"], [1, 2, 3])
    >>> problem.addConstraint(lambda a, b: b > a, ["a", "b"])
    >>> solution = problem.getSolution()
    >>> sorted(solution.items()) in result
    True
    >>> for solution in problem.getSolutions():
    ...     sorted(solution.items()) in result
    True
    True
    True
    >>> problem.getSolutionIter()
    Traceback (most recent call last):
       ...
    NotImplementedError: RecursiveBacktrackingSolver doesn't provide iteration
    """

    def __init__(self, value_heuristic_id=None, variable_heuristic_id=None, forwardcheck=True):
        """
        @param variable_heuristic: string identifier of the variable heuristic to use
                                   choose from {degree, mrv, random, deg+mrv, mrv+random}
                                   leave blank for no variable heuristic

        @param value_heuristic: string identifier of the value heruistic to use
                                choose from {random, lcv, least_used}
                                leave blank for no value heuristic

        @param forwardcheck: If false forward checking will not be requested
                             to constraints while looking for solutions
                             (default is true)
        @type  forwardcheck: bool
        """
        self._forwardcheck = forwardcheck

        self._variable_heuristic_id = variable_heuristic_id
        self._value_heuristic_id = value_heuristic_id

    def recursiveBacktracking(
        self, solutions, domains, vconstraints, assignments, single
    ):

        # assignments is a dictionary of {variable: value, ...}

        ##############################################################
        # Use different heuristics for selecting unassigned variable #
        ##############################################################
        lst = variable_heuristic(domains, vconstraints, self._variable_heuristic_id)

        for item in lst:
            if item[-1] not in assignments:
                # Found an unassigned variable. Let's go.
                break
        else:
            # No unassigned variables. We've got a solution.
            solutions.append(assignments.copy())
            return solutions

        variable = item[-1]
        assignments[variable] = None

        forwardcheck = self._forwardcheck
        if forwardcheck:
            pushdomains = [domains[x] for x in domains if x not in assignments]
        else:
            pushdomains = None

        ################################################
        # Change heuristics for order of domain values #
        ################################################
        newlst = value_heuristic(assignments, domains, domains[variable], self._value_heuristic_id)

        for value in newlst:
            assignments[variable] = value
            if pushdomains:
                for domain in pushdomains:
                    domain.pushState()
            for constraint, variables in vconstraints[variable]:
                if not constraint(variables, domains, assignments, pushdomains):
                    # Value is not good.
                    break
            else:
                # Value is good. Recurse and get next variable.
                self.recursiveBacktracking(
                    solutions, domains, vconstraints, assignments, single
                )
                if solutions and single:
                    return solutions
            if pushdomains:
                for domain in pushdomains:
                    domain.popState()
        del assignments[variable]
        return solutions

    def getSolution(self, domains, constraints, vconstraints):
        solutions = self.recursiveBacktracking([], domains, vconstraints, {}, True)
        return solutions and solutions[0] or None

    def getSolutions(self, domains, constraints, vconstraints):
        return self.recursiveBacktracking([], domains, vconstraints, {}, False)