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:
>>>fromconstraintimport*>>>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(lambdaa, 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)
>>>forcol1incols:
... forcol2incols:
... ifcol1<col2:
... problem.addConstraint(lambdarow1, 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])
>>>forrowinrange(4):
... problem.addConstraint(ExactSumConstraint(34),
[row*4+iforiinrange(4)])
>>>forcolinrange(4):
... problem.addConstraint(ExactSumConstraint(34),
[col+4*iforiinrange(4)])
>>>solutions=problem.getSolutions()The following solvers are available:
- Backtracking solver
- Recursive backtracking solver
- Minimum conflicts solver
Predefined constraint types currently available:
FunctionConstraintAllDifferentConstraintAllEqualConstraintExactSumConstraintMaxSumConstraintMinSumConstraintInSetConstraintNotInSetConstraintSomeInSetConstraintSomeNotInSetConstraint
Documentation for the module is available at: http://labix.org/doc/constraint/
$ pip install python-constraintThis 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.