def test_one_edge(self):
MN = dnx.markov_network(
{'ab': {
(0, 0): 1.2,
(1, 0): .4,
(0, 1): 1.3,
(1, 1): -4
}})
def test_one_node(self):
potentials = {'a': {(0,): 1.2, (1,): .4}}
bqm = dnx.markov_network_bqm(dnx.markov_network(potentials))
for edge, potential in potentials.items():
for config, energy in potential.items():
sample = dict(zip(edge, config))
self.assertAlmostEqual(bqm.energy(sample), energy)
def test_typical(self):
potentials = {'a': {(0,): 1.5, (1,): -.5},
'ab': {(0, 0): 1.2, (1, 0): .4,
(0, 1): 1.3, (1, 1): -4},
'bc': {(0, 0): 1.7, (1, 0): .4,
(0, 1): -1, (1, 1): -4},
'd': {(0,): -.5, (1,): 1.6}}
bqm = dnx.markov_network_bqm(dnx.markov_network(potentials))
samples = dimod.ExactSolver().sample(bqm)
for sample, energy in samples.data(['sample', 'energy']):
en = 0
for interaction, potential in potentials.items():
config = tuple(sample[v] for v in interaction)
en += potential[config]
self.assertAlmostEqual(en, energy)
def test_typical(self):
potentials = {
'a': {
(0, ): 1.5,
(1, ): -.5
},
'ab': {
(0, 0): 1.2,
(1, 0): .4,
(0, 1): 1.3,
(1, 1): -4
},
'bc': {
(0, 0): 1.7,
(1, 0): .4,
(0, 1): -1,
(1, 1): -4
},
'd': {
(0, ): -.5,
(1, ): 1.6
}
}
MN = dnx.markov_network(potentials)
bqm = markov_network_bqm(MN)
samples = sample_markov_network(MN,
dimod.ExactSolver(),
fixed_variables={'c': 0})
for sample in samples:
self.assertEqual(sample['c'], 0)
en = 0
for interaction, potential in potentials.items():
config = tuple(sample[v] for v in interaction)
en += potential[config]
self.assertAlmostEqual(en, bqm.energy(sample))
def test_typical_return_sampleset(self):
potentials = {
'a': {
(0, ): 1.5,
(1, ): -.5
},
'ab': {
(0, 0): 1.2,
(1, 0): .4,
(0, 1): 1.3,
(1, 1): -4
},
'bc': {
(0, 0): 1.7,
(1, 0): .4,
(0, 1): -1,
(1, 1): -4
},
'd': {
(0, ): -.5,
(1, ): 1.6
}
}
MN = dnx.markov_network(potentials)
samples = sample_markov_network(MN,
dimod.ExactSolver(),
fixed_variables={'c': 0},
return_sampleset=True)
for sample, energy in samples.data(['sample', 'energy']):
self.assertEqual(sample['c'], 0)
en = 0
for interaction, potential in potentials.items():
config = tuple(sample[v] for v in interaction)
en += potential[config]
self.assertAlmostEqual(en, energy)
def test_one_node(self):
MN = dnx.markov_network({'a': {(0, ): 1.2, (1, ): .4}})
def test_empty(self):
MN = dnx.markov_network({})
