A simple constraints satisfaction solver

## Project description

PyConstraints

===

A simple, constraints satisfaction problem solver. Used for the [YACS][] course

scheduler project.

[yacs]: http://github.com/jeffh/yacs

Usage

-----

The Problem is the primary interface:

>>> from pyconstraints import Problem

And then specify your problem to solve with various constraints:

>>> p = Problem()

>>> p.add_variable('x', range(4)) # variable-name, domain

>>> p.add_variable('y', range(4))

# give constraint function and list of variables used

>>> p.add_constraint(lambda x, y: x != y, ['x', 'y'])

>>> p.add_constraint(lambda x: x % 2 == 0)

Then get your solutions:

>>> p.get_solutions()

# => ({'y': 0, 'x': 2},

# {'y': 1, 'x': 0},

# {'y': 1, 'x': 2},

# {'y': 2, 'x': 0},

# {'y': 3, 'x': 0},

# {'y': 3, 'x': 2})

Or iteratively:

>>> p.iter_solutions().next()

# => {'y': 0, 'x': 2}

And that's it!

Using Another Solver

--------------------

Simply pass the solver to the Problem constructor:

>>> from pyconstraints import BruteForceSolver, BacktrackingSolver

>>> p = Problem(BacktrackingSolver()) # BruteForceSolver is default

Because the BruteForceSolver uses itertools, there may be cases where it is

faster than the BacktrackingSolver.

Writing Your Own Solver

-----------------------

For convinence, there is a ``pyconstraints.SolverInterface`` Abstract-Base Class if you want to

implement all the features manually:

@abstractproperty

def solutions_seen(self):

"Returns the number of solutions currently seen by the solver."

@abstractproperty

def solutions_at_points(self):

"""Returns a dictionary of {iteration_index: solution} of all known

solutions while iterating.

"""

@abstractmethod

def set_conditions(self, variables, constraints):

"""Called by the Problem class to assign the variables and constraints

for the problem.

variables = {variable-name: list-of-domain-values}

constraints = [(constraint_function,

variable-names,

default-variable-values)]

"""

@abstractmethod

def restore_point(self, starting_point=None):

"Restores the iteration state to a given starting point."

@abstractmethod

def save_point(self):

"""Returns data to indicate a way to restore to the current iteration

point.

"""

@abstractmethod

def __iter__(self):

"Yields solutions."

But for convinence, you can inherit from the ``pyconstraints.SolverBase`` class

which provides a primitive implementation for all the interface methods except

for ``__iter__`` and ``set_conditions``.

Todo

-----

- Speed up backtracking solver

- Add more solvers?

===

A simple, constraints satisfaction problem solver. Used for the [YACS][] course

scheduler project.

[yacs]: http://github.com/jeffh/yacs

Usage

-----

The Problem is the primary interface:

>>> from pyconstraints import Problem

And then specify your problem to solve with various constraints:

>>> p = Problem()

>>> p.add_variable('x', range(4)) # variable-name, domain

>>> p.add_variable('y', range(4))

# give constraint function and list of variables used

>>> p.add_constraint(lambda x, y: x != y, ['x', 'y'])

>>> p.add_constraint(lambda x: x % 2 == 0)

Then get your solutions:

>>> p.get_solutions()

# => ({'y': 0, 'x': 2},

# {'y': 1, 'x': 0},

# {'y': 1, 'x': 2},

# {'y': 2, 'x': 0},

# {'y': 3, 'x': 0},

# {'y': 3, 'x': 2})

Or iteratively:

>>> p.iter_solutions().next()

# => {'y': 0, 'x': 2}

And that's it!

Using Another Solver

--------------------

Simply pass the solver to the Problem constructor:

>>> from pyconstraints import BruteForceSolver, BacktrackingSolver

>>> p = Problem(BacktrackingSolver()) # BruteForceSolver is default

Because the BruteForceSolver uses itertools, there may be cases where it is

faster than the BacktrackingSolver.

Writing Your Own Solver

-----------------------

For convinence, there is a ``pyconstraints.SolverInterface`` Abstract-Base Class if you want to

implement all the features manually:

@abstractproperty

def solutions_seen(self):

"Returns the number of solutions currently seen by the solver."

@abstractproperty

def solutions_at_points(self):

"""Returns a dictionary of {iteration_index: solution} of all known

solutions while iterating.

"""

@abstractmethod

def set_conditions(self, variables, constraints):

"""Called by the Problem class to assign the variables and constraints

for the problem.

variables = {variable-name: list-of-domain-values}

constraints = [(constraint_function,

variable-names,

default-variable-values)]

"""

@abstractmethod

def restore_point(self, starting_point=None):

"Restores the iteration state to a given starting point."

@abstractmethod

def save_point(self):

"""Returns data to indicate a way to restore to the current iteration

point.

"""

@abstractmethod

def __iter__(self):

"Yields solutions."

But for convinence, you can inherit from the ``pyconstraints.SolverBase`` class

which provides a primitive implementation for all the interface methods except

for ``__iter__`` and ``set_conditions``.

Todo

-----

- Speed up backtracking solver

- Add more solvers?

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