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(('WA', 'NT'), const_different),
(('WA', 'SA'), const_different),
(('SA', 'NT'), const_different),
(('SA', 'Q'), const_different),
(('NT', 'Q'), const_different),
(('SA', 'NSW'), const_different),
(('Q', 'NSW'), const_different),
(('SA', 'V'), const_different),
(('NSW', 'V'), const_different),
]
my_problem = CspProblem(variables, domains, constraints)
print backtrack(my_problem)
print backtrack(my_problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE)
print backtrack(my_problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE)
print backtrack(my_problem, value_heuristic=LEAST_CONSTRAINING_VALUE)
print backtrack(my_problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE, value_heuristic=LEAST_CONSTRAINING_VALUE)
print backtrack(my_problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE, value_heuristic=LEAST_CONSTRAINING_VALUE)
print min_conflicts(my_problem)
# a constraint that requires one variable to be different than 1
def const_not_1(variables, values):
return values[0] != 1
constraints = [
(('A', 'B', 'C'), const_different),
(('A', 'C'), const_one_bigger_other),
(('A', 'C'), const_one_odd_one_even),
(('A',), const_not_1)
]
variables, domains, constraints = convert_to_binary(variables, domains, constraints)
problem = CspProblem(variables, domains, constraints)
result = backtrack(problem)
print result
# result, {'A':2, 'B': 3, 'C': 1})
constraints = [
(('WA', 'NT'), const_different),
(('WA', 'SA'), const_different),
(('SA', 'NT'), const_different),
(('SA', 'Q'), const_different),
(('NT', 'Q'), const_different),
(('SA', 'NSW'), const_different),
(('Q', 'NSW'), const_different),
(('SA', 'V'), const_different),
(('NSW', 'V'), const_different),
]
my_problem = CspProblem(variables, domains, constraints)
print backtrack(my_problem)
print backtrack(my_problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE)
print backtrack(my_problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE)
print backtrack(my_problem, value_heuristic=LEAST_CONSTRAINING_VALUE)
print backtrack(my_problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE, value_heuristic=LEAST_CONSTRAINING_VALUE)
print backtrack(my_problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE, value_heuristic=LEAST_CONSTRAINING_VALUE)
print min_conflicts(my_problem)
(('Amanda', 'Joanne'), constraint_func),
(('Amanda', 'Derek'), constraint_func),
(('Brian', 'Derek'), constraint_func),
(('Brian', 'Kelly'), constraint_func),
(('Joanne', 'Michelle'), constraint_func),
(('Joanne', 'Amanda'), constraint_func),
(('Joanne', 'Derek'), constraint_func),
(('Joanne', 'Kelly'), constraint_func),
(('Derek', 'Kelly'), constraint_func),
]
# Solve the problem
problem = CspProblem(names, colors, constraints)
# Print the solution
output = backtrack(problem)
print('\nColor mapping:\n')
for k, v in output.items():
print(k, '==>', v)
}
constraints = [
(('John', 'Anna', 'Tom'), constraint_unique),
(('Tom', 'Anna'), constraint_bigger),
(('John', 'Patricia'), constraint_odd_even),
]
problem = CspProblem(variables, domains, constraints)
print('\nSolutions:\n\nNormal:', backtrack(problem))
print('\nMost constrained variable:', backtrack(problem,
variable_heuristic=MOST_CONSTRAINED_VARIABLE))
print('\nHighest degree variable:', backtrack(problem,
variable_heuristic=HIGHEST_DEGREE_VARIABLE))
print('\nLeast constraining value:', backtrack(problem,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nMost constrained variable and least constraining value:',
backtrack(problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nHighest degree and least constraining value:',
backtrack(problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nMinimum conflicts:', min_conflicts(problem))
return constraints
def display_solution(sol):
for i in uppercase[:9]:
print " ".join([str(sol["%s%d" % (i, j)]) for j in xrange(1, 10)])
domains.update(parsepuzzle(sudoku))
# -- Hand made binary constraints --
constraints = mkconstraints()
start = time()
domains0 = deepcopy(domains)
my_problem = CspProblem(variables, domains0, constraints)
sol = backtrack(my_problem)
elapsed = time() - start
display_solution(sol)
print "Took %d seconds to finish using binary constraints" % elapsed # because of AC3 should be quick
# -- N-ary constraints made binary using hidden variables --
domains1 = deepcopy(domains)
start = time()
variables1, domains1, constraints = convert_to_binary(variables, domains1, mknaryconstraints())
my_problem = CspProblem(variables1, domains1, constraints)
sol = backtrack(my_problem)
elapsed = time() - start
display_solution(sol)
print "Took %d seconds to finish using binary constraints (hidden variables)" % elapsed
(('John', 'Anna', 'Tom'), constraint_unique),
(('Tom', 'Anna'), constraint_bigger),
(('John', 'Patricia'), constraint_odd_even),
]
problem = CspProblem(variables, domains, constraints)
print('\nSolutions:\n\nNormal:', backtrack(problem))
print('\nMost constrained variable:', backtrack(problem,
variable_heuristic=MOST_CONSTRAINED_VARIABLE))
print('\nHighest degree variable:', backtrack(problem,
variable_heuristic=HIGHEST_DEGREE_VARIABLE))
print('\nLeast constraining value:', backtrack(problem,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nMost constrained variable and least constraining value:',
backtrack(problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nHighest degree and least constraining value:',
backtrack(problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nMinimum conflicts:', min_conflicts(problem))
'Tom': [2, 4],
'Patricia': [2, 3, 4],
}
constraints = [
(('John', 'Anna', 'Tom'), constraint_unique),
(('Tom', 'Anna'), constraint_bigger),
(('John', 'Patricia'), constraint_odd_even),
]
problem = CspProblem(variables, domains, constraints)
print('\nSolutions:\n\nNormal:', backtrack(problem))
print('\nMost constrained variable:', backtrack(problem,
variable_heuristic=MOST_CONSTRAINED_VARIABLE))
print('\nHighest degree variable:', backtrack(problem,
variable_heuristic=HIGHEST_DEGREE_VARIABLE))
print('\nLeast constraining value:', backtrack(problem,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nMost constrained variable and least constraining value:',
backtrack(problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nHighest degree and least constraining value:',
backtrack(problem, variable_heuristic=HIGHEST_DEGREE_VARIABLE,
value_heuristic=LEAST_CONSTRAINING_VALUE))
print('\nMinimum conflicts:', min_conflicts(problem))