def random_2in4sat(num_variables, num_clauses, vartype=dimod.BINARY, satisfiable=True):
"""todo"""
if num_variables < 4:
raise ValueError("a 2in4 problem needs at least 4 variables")
if num_clauses > 16 * _nchoosek(num_variables, 4): # 16 different negation patterns
raise ValueError("too many clauses")
# also checks the vartype argument
csp = ConstraintSatisfactionProblem(vartype)
variables = list(range(num_variables))
constraints = set()
if satisfiable:
values = tuple(vartype.value)
planted_solution = {v: choice(values) for v in variables}
configurations = [(0, 0, 1, 1), (0, 1, 0, 1), (1, 0, 0, 1),
(0, 1, 1, 0), (1, 0, 1, 0), (1, 1, 0, 0)]
while len(constraints) < num_clauses:
# sort the variables because constraints are hashed on configurations/variables
# because 2-in-4 sat is symmetric, we would not get a hash conflict for different
# variable orders
constraint_variables = sorted(sample(variables, 4))
# pick (uniformly) a configuration and determine which variables we need to negate to
# match the chosen configuration
config = choice(configurations)
pos = tuple(v for idx, v in enumerate(constraint_variables) if config[idx] == (planted_solution[v] > 0))
neg = tuple(v for idx, v in enumerate(constraint_variables) if config[idx] != (planted_solution[v] > 0))
const = sat2in4(pos=pos, neg=neg, vartype=vartype)
assert const.check(planted_solution)
constraints.add(const)
else:
while len(constraints) < num_clauses:
# sort the variables because constraints are hashed on configurations/variables
# because 2-in-4 sat is symmetric, we would not get a hash conflict for different
# variable orders
constraint_variables = sorted(sample(variables, 4))
# randomly determine negations
pos = tuple(v for v in constraint_variables if random() > .5)
neg = tuple(v for v in constraint_variables if v not in pos)
const = sat2in4(pos=pos, neg=neg, vartype=vartype)
constraints.add(const)
for const in constraints:
csp.add_constraint(const)
# in case any variables didn't make it in
for v in variables:
csp.add_variable(v)
return csp
def random_2in4sat(num_variables,
num_clauses,
vartype=dimod.BINARY,
satisfiable=True):
"""Random two-in-four (2-in-4) constraint satisfaction problem.
Args:
num_variables (integer): Number of variables (at least four).
num_clauses (integer): Number of constraints that together constitute the
constraint satisfaction problem.
vartype (Vartype, optional, default='BINARY'): Variable type. Accepted
input values:
* Vartype.SPIN, 'SPIN', {-1, 1}
* Vartype.BINARY, 'BINARY', {0, 1}
satisfiable (bool, optional, default=True): True if the CSP can be satisfied.
Returns:
CSP (:obj:`.ConstraintSatisfactionProblem`): CSP that is satisfied when its variables
are assigned values that satisfy a two-in-four satisfiability problem.
Examples:
This example creates a CSP with 6 variables and two random constraints and checks
whether a particular assignment of variables satisifies it.
>>> import dwavebinarycsp
>>> import dwavebinarycsp.factories as sat
>>> csp = sat.random_2in4sat(6, 2)
>>> csp.constraints # doctest: +SKIP
[Constraint.from_configurations(frozenset({(1, 0, 1, 0), (1, 0, 0, 1), (1, 1, 1, 1), (0, 1, 1, 0), (0, 0, 0, 0),
(0, 1, 0, 1)}), (2, 4, 0, 1), Vartype.BINARY, name='2-in-4'),
Constraint.from_configurations(frozenset({(1, 0, 1, 1), (1, 1, 0, 1), (1, 1, 1, 0), (0, 0, 0, 1),
(0, 1, 0, 0), (0, 0, 1, 0)}), (1, 2, 4, 5), Vartype.BINARY, name='2-in-4')]
>>> csp.check({0: 1, 1: 0, 2: 1, 3: 1, 4: 0, 5: 0}) # doctest: +SKIP
True
"""
if num_variables < 4:
raise ValueError("a 2in4 problem needs at least 4 variables")
if num_clauses > 16 * _nchoosek(num_variables,
4): # 16 different negation patterns
raise ValueError("too many clauses")
# also checks the vartype argument
csp = ConstraintSatisfactionProblem(vartype)
variables = list(range(num_variables))
constraints = set()
if satisfiable:
values = tuple(vartype.value)
planted_solution = {v: choice(values) for v in variables}
configurations = [(0, 0, 1, 1), (0, 1, 0, 1), (1, 0, 0, 1),
(0, 1, 1, 0), (1, 0, 1, 0), (1, 1, 0, 0)]
while len(constraints) < num_clauses:
# sort the variables because constraints are hashed on configurations/variables
# because 2-in-4 sat is symmetric, we would not get a hash conflict for different
# variable orders
constraint_variables = sorted(sample(variables, 4))
# pick (uniformly) a configuration and determine which variables we need to negate to
# match the chosen configuration
config = choice(configurations)
pos = tuple(v for idx, v in enumerate(constraint_variables)
if config[idx] == (planted_solution[v] > 0))
neg = tuple(v for idx, v in enumerate(constraint_variables)
if config[idx] != (planted_solution[v] > 0))
const = sat2in4(pos=pos, neg=neg, vartype=vartype)
assert const.check(planted_solution)
constraints.add(const)
else:
while len(constraints) < num_clauses:
# sort the variables because constraints are hashed on configurations/variables
# because 2-in-4 sat is symmetric, we would not get a hash conflict for different
# variable orders
constraint_variables = sorted(sample(variables, 4))
# randomly determine negations
pos = tuple(v for v in constraint_variables if random() > .5)
neg = tuple(v for v in constraint_variables if v not in pos)
const = sat2in4(pos=pos, neg=neg, vartype=vartype)
constraints.add(const)
for const in constraints:
csp.add_constraint(const)
# in case any variables didn't make it in
for v in variables:
csp.add_variable(v)
return csp