How to use the simpleai.search.LEAST_CONSTRAINING_VALUE function in simpleai

return len(values) == len(set(values))  # remove repeated values and count

constraints = [
    (('F', 'T', 'U', 'W', 'R', 'O'), const_different),
    (('O', 'R', 'C_10'), lambda vars_, values: values[0] + values[0] == values[1] + 10 * values[2]),
    (('C_10', 'W', 'U', 'C_100'), lambda vars_, values: values[0] + values[1] + values[1] == values[2] + 10 * values[3]),
    (('C_100', 'T', 'O', 'C_1000'), lambda vars_, values: values[0] + values[1] + values[1] == values[2] + 10 * values[3]),
    (('C_1000', 'F'), lambda vars_, values: values[0] == values[1])
]

original_constraints = deepcopy(constraints)
original_domains = deepcopy(domains)

start = time()
problem = CspProblem(variables, original_domains, original_constraints)
result = backtrack(problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE, value_heuristic=LEAST_CONSTRAINING_VALUE)
elapsed = time() - start
print result
print "Took %d seconds to finish using n-ary constraints" % elapsed


start = time()
variables, domains, constraints = convert_to_binary(variables, domains, constraints)
problem = CspProblem(variables, domains, constraints)
result = backtrack(problem, value_heuristic=LEAST_CONSTRAINING_VALUE)
elapsed = time() - start
print result
print "Took %d seconds to finish using binary constraints" % elapsed

(('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)

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))

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))

(('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)

original_constraints = deepcopy(constraints)
original_domains = deepcopy(domains)

start = time()
problem = CspProblem(variables, original_domains, original_constraints)
result = backtrack(problem, variable_heuristic=MOST_CONSTRAINED_VARIABLE, value_heuristic=LEAST_CONSTRAINING_VALUE)
elapsed = time() - start
print result
print "Took %d seconds to finish using n-ary constraints" % elapsed


start = time()
variables, domains, constraints = convert_to_binary(variables, domains, constraints)
problem = CspProblem(variables, domains, constraints)
result = backtrack(problem, value_heuristic=LEAST_CONSTRAINING_VALUE)
elapsed = time() - start
print result
print "Took %d seconds to finish using binary constraints" % elapsed