def test_quad_to_linear(self):
J = {(0, 1): -1, (0, 1, 2): 1, (0, 1, 3): 1}
h = {}
off = .5
poly = J.copy()
poly.update({(v, ): bias for v, bias in h.items()})
poly[()] = off
bqm = make_quadratic(J,
10.0,
bqm=dimod.BinaryQuadraticModel.from_ising(
h, {}, off))
variables = set(h).union(*J)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_several_terms(self):
poly = {(0, 1, 2): -1, (1, 2, 3): 1, (0, 2, 3): .5,
(0,): .4,
(): .5}
bqm = make_quadratic(poly, 5.0,
bqm=dimod.BinaryQuadraticModel.empty(dimod.SPIN))
variables = set().union(*poly)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_several_terms(self):
poly = {(0, 1, 2): -1, (1, 2, 3): 1, (0, 2, 3): .5,
(0,): .4,
(): .5}
cqm = make_quadratic_cqm(poly, dimod.SPIN, cqm=dimod.ConstrainedQuadraticModel())
variables = set().union(*poly)
aux_variables = tuple(set(cqm.variables) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
if cqm.check_feasible(aux_sample):
reduced_energies.append(cqm.objective.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_quad_to_linear(self):
J = {(0, 1): -1, (0, 1, 2): 1, (0, 1, 3): 1}
h = {}
off = .5
poly = J.copy()
poly.update({(v,): bias for v, bias in h.items()})
poly[()] = off
cqm0 = dimod.ConstrainedQuadraticModel()
cqm0.set_objective(dimod.BinaryQuadraticModel.from_ising(h, {}, off))
cqm = make_quadratic_cqm(J, dimod.SPIN, cqm=cqm0)
variables = set(h).union(*J)
aux_variables = tuple(set(cqm.variables) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
if cqm.check_feasible(aux_sample):
reduced_energies.append(cqm.objective.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_poly_energies(self):
linear = {0: 1.0, 1: 1.0}
j = {(0, 1, 2): 0.5}
poly = dimod.BinaryPolynomial.from_hising(linear, j)
samples = [[1, 1, -1], [1, -1, 1], [1, 1, 1], [-1, 1, -1]]
en = poly_energies(samples, poly)
self.assertListEqual(list(en), [1.5, -0.5, 2.5, 0.5])
en = poly_energy(samples[0], poly)
self.assertAlmostEqual(en, 1.5)
with self.assertRaises(ValueError):
poly_energy(samples, poly)
poly = {('a',): 1.0, ('b',): 1.0, ('a', 'b', 'c'): 0.5}
samples = [{'a': 1, 'b': 1, 'c': -1},
{'a': 1, 'b': -1, 'c': 1},
{'a': 1, 'b': 1, 'c': 1},
{'a': -1, 'b': 1, 'c': -1}]
en = poly_energies(samples, poly)
self.assertListEqual(list(en), [1.5, -0.5, 2.5, 0.5])
en = poly_energy(samples[0], poly)
self.assertAlmostEqual(en, 1.5)
with self.assertRaises(ValueError):
poly_energy(samples, poly)
def test_no_higher_order(self):
poly = {(0, 1): -1, (1, 2): 1}
bqm = make_quadratic(poly, 1.0, dimod.SPIN)
variables = set().union(*poly)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
for config in itertools.product((-1, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, poly)
reduced_energies = []
for aux_config in itertools.product((-1, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_binary_polynomial(self):
HUBO = {(0, 1, 2): .5, (0, 1): 1.3, (2, 4, 1): -1, (3, 2): -1}
bqm = make_quadratic(HUBO, 1000.0, dimod.BINARY)
variables = set().union(*HUBO)
aux_variables = tuple(set(bqm.linear) - variables)
variables = tuple(variables)
self.assertTrue(aux_variables)
for config in itertools.product((0, 1), repeat=len(variables)):
sample = dict(zip(variables, config))
energy = poly_energy(sample, HUBO)
reduced_energies = []
for aux_config in itertools.product((0, 1),
repeat=len(aux_variables)):
aux_sample = dict(zip(aux_variables, aux_config))
aux_sample.update(sample)
reduced_energies.append(bqm.energy(aux_sample))
self.assertAlmostEqual(energy, min(reduced_energies))
def test_poly_energies(self):
from dimod.higherorder import create_poly
linear = {0: 1.0, 1: 1.0}
j = {(0, 1, 2): 0.5}
poly = create_poly(linear, j)
samples = [[1, 1, -1], [1, -1, 1], [1, 1, 1], [-1, 1, -1]]
en = poly_energies(samples, poly)
self.assertListEqual(list(en), [1.5, -0.5, 2.5, 0.5])
en = poly_energy(samples[0], poly)
self.assertAlmostEqual(en, 1.5)
with self.assertRaises(ValueError):
poly_energy(samples, poly)
poly = {('a', ): 1.0, ('b', ): 1.0, ('a', 'b', 'c'): 0.5}
samples = [{
'a': 1,
'b': 1,
'c': -1
}, {
'a': 1,
'b': -1,
'c': 1
}, {
'a': 1,
'b': 1,
'c': 1
}, {
'a': -1,
'b': 1,
'c': -1
}]
en = poly_energies(samples, poly)
self.assertListEqual(list(en), [1.5, -0.5, 2.5, 0.5])
en = poly_energy(samples[0], poly)
self.assertAlmostEqual(en, 1.5)
with self.assertRaises(ValueError):
poly_energy(samples, poly)
