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        "author": "Jeff Hui",
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        "description": "PyConstraints\n===\n\nA simple, constraints satisfaction problem solver. Used for the [YACS][] course\nscheduler project.\n\n[yacs]: http://github.com/jeffh/yacs\n\nUsage\n-----\n\nThe Problem is the primary interface:\n\n    >>> from pyconstraints import Problem\n\nAnd then specify your problem to solve with various constraints:\n\n    >>> p = Problem()\n    >>> p.add_variable('x', range(4)) # variable-name, domain\n    >>> p.add_variable('y', range(4))\n    # give constraint function and list of variables used\n    >>> p.add_constraint(lambda x, y: x != y, ['x', 'y'])\n    >>> p.add_constraint(lambda x: x % 2 == 0)\n\nThen get your solutions:\n\n    >>> p.get_solutions()\n    # => ({'y': 0, 'x': 2},\n    #     {'y': 1, 'x': 0},\n    #     {'y': 1, 'x': 2},\n    #     {'y': 2, 'x': 0},\n    #     {'y': 3, 'x': 0},\n    #     {'y': 3, 'x': 2})\n\nOr iteratively:\n\n    >>> p.iter_solutions().next()\n    # => {'y': 0, 'x': 2}\n\nAnd that's it!\n\nUsing Another Solver\n--------------------\n\nSimply pass the solver to the Problem constructor:\n\n    >>> from pyconstraints import BruteForceSolver, BacktrackingSolver\n    >>> p = Problem(BacktrackingSolver()) # BruteForceSolver is default\n\nBecause the BruteForceSolver uses itertools, there may be cases where it is\nfaster than the BacktrackingSolver.\n\n\nWriting Your Own Solver\n-----------------------\n\nFor convinence, there is a ``pyconstraints.SolverInterface`` Abstract-Base Class if you want to\nimplement all the features manually:\n\n    @abstractproperty\n    def solutions_seen(self):\n        \"Returns the number of solutions currently seen by the solver.\"\n\n    @abstractproperty\n    def solutions_at_points(self):\n        \"\"\"Returns a dictionary of {iteration_index: solution} of all known\n        solutions while iterating.\n        \"\"\"\n\n    @abstractmethod\n    def set_conditions(self, variables, constraints):\n        \"\"\"Called by the Problem class to assign the variables and constraints\n        for the problem.\n\n            variables = {variable-name: list-of-domain-values}\n            constraints = [(constraint_function,\n                            variable-names,\n                            default-variable-values)]\n        \"\"\"\n\n    @abstractmethod\n    def restore_point(self, starting_point=None):\n        \"Restores the iteration state to a given starting point.\"\n\n    @abstractmethod\n    def save_point(self):\n        \"\"\"Returns data to indicate a way to restore to the current iteration\n        point.\n        \"\"\"\n\n    @abstractmethod\n    def __iter__(self):\n        \"Yields solutions.\"\n\nBut for convinence, you can inherit from the ``pyconstraints.SolverBase`` class\nwhich provides a primitive implementation for all the interface methods except\nfor ``__iter__`` and ``set_conditions``.\n\n\nTodo\n-----\n\n- Speed up backtracking solver\n- Add more solvers?",
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