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# python-constraint / python-constraint

Constraint Solving Problem resolver for Python

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## python-constraint

### Introduction

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, ...).

### Examples

#### Basics

This interactive Python session demonstrates the module basic operation:

```>>> from constraint import *
>>> problem = Problem()
>>> 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.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}]```

#### Rooks problem

The following example solves the classical Eight Rooks problem:

```>>> problem = Problem()
>>> numpieces = 8
>>> cols = range(numpieces)
>>> rows = range(numpieces)
>>> 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},
...]```

#### Magic squares

This example solves a 4x4 magic square:

```>>> problem = Problem()
>>> problem.addVariables(range(0, 16), range(1, 16 + 1))
>>> problem.addConstraint(ExactSumConstraint(34), [0, 5, 10, 15])
>>> problem.addConstraint(ExactSumConstraint(34), [3, 6, 9, 12])
>>> for row in range(4):
[row * 4 + i for i in range(4)])
>>> for col in range(4):
[col + 4 * i for i in range(4)])
>>> solutions = problem.getSolutions()```

### Features

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`

### API documentation

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

### Contact

But it's probably better to open an issue.

Constraint Solving Problem resolver for Python

1.4.0 Latest
Nov 5, 2018

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