def test_qm_Ge_constraint(self):
i = dimod.Integer('i', upper_bound=7)
j = dimod.Integer('j', upper_bound=9)
x = dimod.Binary('x')
cqm = CQM()
cqm.add_constraint(i + j + x >= 5)
bqm, inverter = dimod.cqm_to_bqm(cqm, lagrange_multiplier=1)
for bin_sample in dimod.ExactSolver().sample(bqm).lowest().samples():
int_sample = inverter(bin_sample)
self.assertGreaterEqual(int_sample['i'] + int_sample['j'] + int_sample['x'], 5)
def test_qm_objective_only(self):
i = dimod.Integer('i', upper_bound=7)
j = dimod.Integer('j', upper_bound=9)
x = dimod.Binary('x')
qm = i*j + 5*j*x + 8*i + 3*x + 5
cqm = CQM.from_qm(qm)
bqm, inverter = dimod.cqm_to_bqm(cqm)
sampleset = dimod.ExactSolver().sample(bqm)
for bin_sample, energy in sampleset.data(['sample', 'energy']):
int_sample = inverter(bin_sample)
self.assertEqual(qm.energy(int_sample), energy)
def test_serializable(self):
i = dimod.Integer('i', upper_bound=7)
j = dimod.Integer('j', upper_bound=9)
x = dimod.Binary('x')
cqm = CQM()
cqm.add_constraint(i + j + x >= 5)
bqm, inverter = dimod.cqm_to_bqm(cqm, lagrange_multiplier=1)
newinverter = dimod.constrained.CQMToBQMInverter.from_dict(
json.loads(json.dumps(inverter.to_dict())))
for bin_sample in dimod.ExactSolver().sample(bqm).lowest().samples():
int_sample = newinverter(bin_sample)
self.assertGreaterEqual(int_sample['i'] + int_sample['j'] + int_sample['x'], 5)
def test_sample_CONSTRAINED_MIXED(self):
# Using various Vartypes, including the `add_discrete` method
cqm = dimod.ConstrainedQuadraticModel()
x = {(u, v): dimod.Binary((u, v))
for u, v in product(['u1', 'u2'], range(3))}
cqm.add_discrete([('u1', v) for v in range(3)], label='u1')
cqm.add_discrete([('u2', v) for v in range(3)], label='u2')
y, z = dimod.Spin('y'), dimod.Integer('z',
lower_bound=1,
upper_bound=3)
obj1 = x[('u1', 0)] * y - x[('u2', 1)] * y
obj2 = x[('u1', 0)] * z + 2 * x[('u1', 2)] * x[('u2', 2)]
cqm.set_objective(obj1 + obj2)
cqm.add_constraint(z == 2)
response = dimod.ExactCQMSolver().sample_cqm(cqm)
# every possible combination should be present, respecting the discrete constraints
self.assertEqual(len(response), 54)
self.assertEqual(response.record.sample.shape,
(54, len(cqm.variables)))
# only 18 samples should be feasible
feasible_responses = response.filter(lambda d: d.is_feasible)
self.assertEqual(len(feasible_responses), 18)
dimod.testing.assert_sampleset_energies_cqm(response, cqm)
def test_qm(self):
x = dimod.Binary('x')
s = dimod.Spin('s')
i = dimod.Integer('i')
self.assertTrue((x + s + i).is_almost_equal(1.001 * (x + s + i),
places=2))
self.assertFalse((x + s + i).is_almost_equal(1.1 * (x + s + i),
places=2))
def test_integer(self):
i = dimod.Integer('i', lower_bound=-1000)
j, k = dimod.Integers('jk')
cqm = CQM()
label_le = cqm.add_constraint(i + j*k <= 5)
label_ge = cqm.add_constraint(i + j >= 1000)
sample = {'i': 105, 'j': 4, 'k': 5}
self.assertEqual(cqm.violations(sample), {label_le: 120.0, label_ge: 891.0})
sample = {'j': -1, 'i': 1004, 'k': 1000}
self.assertEqual(cqm.violations(sample, clip=False), {label_ge: -3.0, label_le: -1.0})
def test_sample_CONSTRAINED_INTEGER(self):
# using Integer variables, with inequality constraint:
cqm = dimod.ConstrainedQuadraticModel()
x, y, z = dimod.Integer(
'x', lower_bound=1, upper_bound=3), dimod.Integer(
'y', lower_bound=4,
upper_bound=5), dimod.Integer('z',
lower_bound=-2,
upper_bound=1)
cqm.set_objective(x * y + 2 * y * z)
cqm.add_constraint(x * z >= 1)
response = dimod.ExactCQMSolver().sample_cqm(cqm)
# every possible combination should be present
self.assertEqual(len(response), 24)
self.assertEqual(response.record.sample.shape,
(24, len(cqm.variables)))
# only six samples should be feasible
feasible_responses = response.filter(lambda d: d.is_feasible)
self.assertEqual(len(feasible_responses), 6)
dimod.testing.assert_sampleset_energies_cqm(response, cqm)
