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Python-constraint is a good package for solving CSP problems in Python. • Installing it. • Using it. • Examples in. – Magic Squares. – Map coloring.
PyVSC: SystemVerilog-Style Constraints and Coverage in Python
This paper describes PyVSC a library that provides these features. Keywords—functional verification
New Constrained Random and Metric-Driven Verification
As the HVLs syntax for constrained randomization and functional coverage is fixed is executed in Python
FICO Xpress Optimizer Python interface Reference Manual
3 Jun 2017 This chapter illustrates the modeling capabilities of the Xpress Python interface. It shows how to create variables constraints of ...
Constraint Satisfaction Problems in Python
14 Jul 2011 1 Introduction. 2 Constraints in Python. 3 Examples. 4 Questions. Michael Sioutis. Constraint Satisfaction Problems in Python ...
Constraint Satisfaction Problems in Python
14 Jul 2011 Each constraint Ci limits the values that variables can take. (e.g. V1 = V2). Michael Sioutis. Constraint Satisfaction Problems in Python. Page ...
arXiv:2009.00326v3 [cs.AI] 18 Dec 2021
18 Dec 2021 seen as a Python-embedded CP (Constraint Programming) modeling language. Currently PyCSP3 is focused on XCSP3-core [8]
Constraint-Based Reconstruction and Analyses of Metabolic Models
7 Jul 2022 Keywords: cancer metabolism
CSP in Python
Python. Overview. • Python_constraint is a simple package for solving CSP problems in Python. • Installing it easy_install python-?constraint.
Modeling Sudoku Puzzles with Python
Possible values are CP for constraint propagation lp for linear programming
Constraint Satisfaction Problems in Python
Constraint Programming In Python Possible? Constraint satisfaction problems are mathematical problems de ned as a set of objects whose state must satisfy a number of constraints or limitations We only need to specify the problem even better if we could do it in Python and make use of its powerful features
Searches related to python constraint PDF
the type of constraints nature of the equations involved permissible value of the decision variables deterministic nature of the variables number of objective functions Classi?cation (1) Optimization problems can be classi?ed based on the type ofconstraints Unconstrained optimization Constrained optimization Classi?cation (2)
Introduction
The first thing we have to understand while dealing with constraint programming is that the way of thinking is very different from our usual way of thinking when we sit down to write code. Constraint programming is an example of the declarative programming paradigm, as opposed to the usual imperativeparadigm that we use most of the time. A paradigm...
Installing The python-constraint Module
In this article we'll be working with a module called python-constraint(Note: there's a module called "constraint" for Python, that is not what we want), which aims to bring the constraint programming idea to Python. To install this module, open the terminal and run:
Basics of Using python-constraint
This is the generalized skeleton of programs written using this module (Note: we use import constraint and not import python-constraint) 1. import constraint 2. define a variable as our problem 3. add variables and their respective intervals to our problem 4. add built-in/custom constraints to our problem 5. fetch the solutions 6. go through the so...
Warm-Up Examples
Here's a type of problem constraint programming is fun to use on, called cryptarithmetic puzzles. In the following form of cryptarithmetic puzzles, each character represents a different digit (the leading characters can't be 0): Think about how you'd solve this using regular Python. In fact, I encourage you to look up the solution for this problem ...
Harder Examples
Now let's roll up our sleeves and get started. It shouldn't be too difficult if you've understood the previous examples. We'll first figure out how much of each chocolate we can have if we ONLY brought that type, so we can have the upper bound of our intervals. For example, for Chocolate A, based on weight we can bring at most 30 bars, based on val...
Conclusion and Drawbacks
As fun and different as constraint programming is, it certainly has its drawbacks. Everyproblem solved using constraint programming can be written in imperative style with an equal or (as in most cases) better runtime. It's only natural that the developer understands the problem better than he can describe it to python-constraint. A very important ...
List of Built-In Constraints
When using constraints that can take a list of multipliers as a parameter (Like ExactSum or MinSum) take care to explicitly say the order or variables if necessary, like we did in Example E
What is a constraint in Python?
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:
Do I need a Python-constraint module?
No, you describe what you need - you need the values from some column, from some table, where some conditions are met. In this article we'll be working with a module called python-constraint (Note: there's a module called "constraint" for Python, that is not what we want), which aims to bring the constraint programming idea to Python.
What is a constraint satisfaction problem?
Constraint satisfaction problems are mathematical problems de ned as a set of objects whose state must satisfy a number of constraints or limitations. We only need to specify the problem, even better if we could do it in Python and make use of its powerful features... We can!With the python-contraint1 module.
What is a constraint programming paradigm?
Constraint programming is an example of the declarative programming paradigm, as opposed to the usual imperative paradigm that we use most of the time. What is a programming paradigm? A paradigm means "an example" or "a pattern" of something. A programming paradigm is often described as a "way of thinking" or "way of programming".
CSP inPython
Overview•Python-constraintis a good package for solving CSP problems in Python•Installing it•Using it•Examples in -Magic Squares-Map coloring-Sudoku puzzles-HW?: Battleships
Installation•On your own computer-pip install python-constraint-sudopip install python-constraint•Install locally on gl-pip3 install -user python-constraints•Install locally on UMBC Jupyterhub server by executing this once in a notebook-!pip install -user python-constraints•Clone source from github-https://github.com/python-constraint
Simple Example>>> from constraint import *>>> p = Problem()>>> p.addVariable("a", [1,2,3])>>> p.addVariable("b", [4,5,6])>>> p.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}]>>> p.addConstraint(lambda x,y: 2*x == y, ('a', 'b'))>>> p.getSolutions()[{'a': 3, 'b': 6}, {'a': 2, 'b': 4}]
Simple Example>>> from constraint import *>>> p = Problem()>>> p.addVariable( "a" [1,2,3])>>> p.addVariable("b", [4,5,6])>>> p.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}]>>> p.addConstraint(
lambda x,y: 2*x==y, ('a','b'))>>> p.getSolutions()[{'a': 3, 'b': 6}, {'a': 2, 'b': 4}]variable namedomainconstraint functiontwo variables9 solutions (instantiations)
Magic Square•An NxNarray of integers where all rows, columns and diagonals sum to the same number•Given N (e.g., 3) and magic sum (e.g., 15), find cell values•What are the-Variables & their domains-Constraints
Magic Square•An NxNarray of integers whereall rows, columns and diagonals sum to the same number•Given N (e.g., 3) & magic sum (e.g., 15), find cell values•What are the-Variables& their domains-Constraints012345678[1,2,...9]All variables have different valuesv0+v1+v2 ==15, v0+v3+v6 == 15, ...
Magic Square•An NxNarray of integers where all rows, columns and diagonals sum to the same number•Given N (e.g., 3) and the magic sum (e.g., 15) find the cell values•What are the-Variables & their domains-Constraints
3x3 Magic Squarefrom constraint import *p = Problem()p.addVariables(range(9), range(1,10))p.addConstraint(
AllDifferentConstraint
(), range(9))p.addConstraint(ExactSumConstraint
(15), [0,4,8])p.addConstraint(ExactSumConstraint(15), [2,4,6])for row in range(3):p.addConstraint(ExactSumConstraint(15),[row*3+i for i in range(3)])for col in range(3):p.addConstraint(ExactSumConstraint(15),[col+3*i for i in range(3)])numbers as variables: 0..8built-in constraint functionsdomain of each is 1..10variables involved with constraint
3x3 Magic Squaresols = p.getSolutions()print solsfor s in sols:printfor row in range(3):for col in range(3):print s[row*3+col],print
3x3 Magic Square> python ms3.py[{0:6,1:7,2:2,...8:4}, {0:6,1:...}, ...]6 7 21 5 98 3 46 1 87 5 32 9 4... six more solutions ...
Predefined Constraints•Constraints on a set of variables:-AllDifferentConstraint()-AllEqualConstraint()-MaxSumConstraint()-ExactSumConstraint()-MinSumConstraint()•Examples:p.addConstraint(ExactSumConstraint(100),[11,...19])p.addConstraint(AllDifferentConstraint(),[11,...19])
NewConstraint Functions•Define newconstraintfunctions that take:-F: a function of N (N>0) arguments-V: a list of N variables•Can be defined & referenced by name or defined locally via lambda expressions-p.addConstraint(lambda x,y:x==2*y,[11,22])-defdblfn(x,y): return x == 2*yP.addConstraint(dblfn, [11,22])
Map Coloringdefcolor(map, colors=['red','green','blue']):(vars, adjoins) = parse_map(map)p = Problem()p.addVariables(vars, colors)for (v1, v2) in adjoins:p.addConstraint(lambda x,y: x!=y, [v1, v2])solution = p.getSolution()if solution:for v in vars:print "%s:%s " % (v, solution[v]),printelse:print 'No solution found :-('austrailia= "SA:WA NT Q NSW V; NT:WA Q; NSW: Q V; T:"
Map Coloringaustralia= 'SA:WA NT Q NSW V; NT:WA Q; NSW: Q V; T:'defparse_map(neighbors):adjoins = []regions = set()specs = [spec.split(':') for spec in neighbors.split(';')]for (A, Aneighbors) in specs:A = A.strip();regions.add(A)for B in Aneighbors.split():regions.add(B)adjoins.append([A,B])return (list(regions), adjoins)
Sudokudef sudoku(initValue):p = Problem()# Define a variable for each cell: 11,12,13...21,22,23...98,99for iin range(1, 10) :p.addVariables(range(i*10+1, i*10+10), range(1, 10))# Each row has different valuesfor iin range(1, 10) :p.addConstraint(AllDifferentConstraint(), range(i*10+1, i*10+10))# Each columhas different valuesfor iin range(1, 10) :p.addConstraint(AllDifferentConstraint(), range(10+i, 100+i, 10))# Each 3x3 box has different valuesp.addConstraint(AllDifferentConstraint(), [11,12,13,21,22,23,31,32,33])p.addConstraint(AllDifferentConstraint(), [41,42,43,51,52,53,61,62,63])p.addConstraint(AllDifferentConstraint(), [71,72,73,81,82,83,91,92,93])p.addConstraint(AllDifferentConstraint(), [14,15,16,24,25,26,34,35,36])p.addConstraint(AllDifferentConstraint(), [44,45,46,54,55,56,64,65,66])p.addConstraint(AllDifferentConstraint(), [74,75,76,84,85,86,94,95,96])p.addConstraint(AllDifferentConstraint(), [17,18,19,27,28,29,37,38,39])p.addConstraint(AllDifferentConstraint(), [47,48,49,57,58,59,67,68,69])p.addConstraint(AllDifferentConstraint(), [77,78,79,87,88,89,97,98,99])# add unary constraints for cells with initial non-zero valuesfor iin range(1, 10) :for j in range(1, 10):value = initValue[i-1][j-1]if value: p.addConstraint(lambda var, val=value: var == val, (i*10+j,))return p.getSolution()
Sudoku Inputeasy = [[0,9,0,7,0,0,8,6,0],[0,3,1,0,0,5,0,2,0],[8,0,6,0,0,0,0,0,0],[0,0,7,0,5,0,0,0,6],[0,0,0,3,0,7,0,0,0],[5,0,0,0,1,0,7,0,0],[0,0,0,0,0,0,1,0,9],[0,2,0,6,0,0,0,5,0],[0,5,4,0,0,8,0,7,0]]
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