The Python constraint module offers solvers for Constraint Satisfaction Problems (CSPs) over finite domains in simple and pure Python. CSP is class of problems which may be represented in terms of variables (a, b, ...), domains (a in [1, 2, 3], ...), and constraints (a < b, ...).
This interactive Python session demonstrates the module basic operation:
>>> from constraint import *
>>> problem = Problem()
>>> problem.addVariable("a", [1,2,3])
>>> problem.addVariable("b", [4,5,6])
>>> problem.getSolutions()
[{'a': 3, 'b': 6}, {'a': 3, 'b': 5}, {'a': 3, 'b': 4},
{'a': 2, 'b': 6}, {'a': 2, 'b': 5}, {'a': 2, 'b': 4},
{'a': 1, 'b': 6}, {'a': 1, 'b': 5}, {'a': 1, 'b': 4}]
>>> problem.addConstraint(lambda a, b: a*2 == b,
("a", "b"))
>>> problem.getSolutions()
[{'a': 3, 'b': 6}, {'a': 2, 'b': 4}]
>>> problem = Problem()
>>> problem.addVariables(["a", "b"], [1, 2, 3])
>>> problem.addConstraint(AllDifferentConstraint())
>>> problem.getSolutions()
[{'a': 3, 'b': 2}, {'a': 3, 'b': 1}, {'a': 2, 'b': 3},
{'a': 2, 'b': 1}, {'a': 1, 'b': 2}, {'a': 1, 'b': 3}]
The following example solves the classical Eight Rooks problem:
>>> problem = Problem()
>>> numpieces = 8
>>> cols = range(numpieces)
>>> rows = range(numpieces)
>>> problem.addVariables(cols, rows)
>>> for col1 in cols:
... for col2 in cols:
... if col1 < col2:
... problem.addConstraint(lambda row1, row2: row1 != row2,
... (col1, col2))
>>> solutions = problem.getSolutions()
>>> solutions
>>> solutions
[{0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 1, 7: 0},
{0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 2, 6: 0, 7: 1},
{0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 1, 6: 2, 7: 0},
{0: 7, 1: 6, 2: 5, 3: 4, 4: 3, 5: 1, 6: 0, 7: 2},
...
{0: 7, 1: 5, 2: 3, 3: 6, 4: 2, 5: 1, 6: 4, 7: 0},
{0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 2, 6: 0, 7: 4},
{0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 2, 6: 4, 7: 0},
{0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 4, 6: 2, 7: 0},
{0: 7, 1: 5, 2: 3, 3: 6, 4: 1, 5: 4, 6: 0, 7: 2},
...]
This example solves a 4x4 magic square:
>>> problem = Problem()
>>> problem.addVariables(range(0, 16), range(1, 16 + 1))
>>> problem.addConstraint(AllDifferentConstraint(), range(0, 16))
>>> problem.addConstraint(ExactSumConstraint(34), [0, 5, 10, 15])
>>> problem.addConstraint(ExactSumConstraint(34), [3, 6, 9, 12])
>>> for row in range(4):
... problem.addConstraint(ExactSumConstraint(34),
[row * 4 + i for i in range(4)])
>>> for col in range(4):
... problem.addConstraint(ExactSumConstraint(34),
[col + 4 * i for i in range(4)])
>>> solutions = problem.getSolutions()
The following solvers are available:
- Backtracking solver
- Recursive backtracking solver
- Minimum conflicts solver
Predefined constraint types currently available:
FunctionConstraint
AllDifferentConstraint
AllEqualConstraint
ExactSumConstraint
MaxSumConstraint
MinSumConstraint
InSetConstraint
NotInSetConstraint
SomeInSetConstraint
SomeNotInSetConstraint
Documentation for the module is available at: http://labix.org/doc/constraint/
$ pip install python-constraint
This GitHub organization and repository is a global effort to help to maintain python-constraint which was written by Gustavo Niemeyer and originaly located at https://labix.org/python-constraint
- Create some unit tests - DONE
- Enable continuous integration - DONE
- Port to Python 3 (Python 2 being also supported) - DONE
- Respect Style Guide for Python Code (PEP8) - DONE
- Improve code coverage writting more unit tests - ToDo
- Move doc to Sphinx or MkDocs - https://readthedocs.org/ - ToDo
But it's probably better to open an issue.