def test_energy_minimization(self):
sample0 = {0: -1, 1: -1, 2: +1, 3: +1}
sample1 = {0: +1, 1: -1, 2: +1, 3: -1}
samples = [sample0, sample1]
embedding = {'a': {0, 1}, 'b': {2}, 'c': {3}}
linear = {'a': -1, 'b': 0, 'c': 0}
quadratic = {}
chain_break_method = dimod.embedding.MinimizeEnergy(linear=linear, quadratic=quadratic)
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=chain_break_method))
source0, source1 = source_samples
# no broken chains
self.assertEqual(source0, {'a': -1, 'b': +1, 'c': +1})
# in this case 'a' being spin-up minimizes the energy
self.assertEqual(source1, {'a': +1, 'b': +1, 'c': -1})
linear = {'a': 1, 'b': 0, 'c': 0}
quadratic = {('a', 'b'): -5}
chain_break_method = dimod.embedding.MinimizeEnergy(linear=linear, quadratic=quadratic)
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=chain_break_method))
source0, source1 = source_samples
# no broken chains
self.assertEqual(source0, {'a': -1, 'b': +1, 'c': +1})
# in this case 'a' being spin-up minimizes the energy due to the quadratic bias
self.assertEqual(source1, {'a': +1, 'b': +1, 'c': -1})
# now we need two broken chains
sample = {0: +1, 1: -1, 2: +1, 3: -1, 4: +1}
samples = [sample]
embedding = {'a': {0, 1}, 'b': {2, 3}, 'c': {4}}
quadratic = {('a', 'b'): -1, ('b', 'c'): 1}
chain_break_method = dimod.embedding.MinimizeEnergy(quadratic=quadratic)
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=chain_break_method))
source, = source_samples
self.assertEqual(source, {'b': -1, 'c': +1, 'a': -1})
def test_discard(self):
sample0 = {0: -1, 1: -1, 2: +1}
sample1 = {0: +1, 1: -1, 2: +1}
samples = [sample0, sample1]
embedding = {'a': {0, 1, 2}}
# specify that majority vote should be used
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=dimod.embedding.discard))
# no samples should be returned because they are all broken
self.assertEqual(len(source_samples), 0)
# now set up an embedding that works for one sample and not the other
embedding = {'a': {0, 1}, 'b': {2}}
# specify that discard should be used
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=dimod.embedding.discard))
# only the first sample should be returned
self.assertEqual(len(source_samples), 1)
self.assertEqual(source_samples, [{'a': -1, 'b': +1}])
def test_majority_vote(self):
"""should return the most common value in the chain"""
sample0 = {0: -1, 1: -1, 2: +1}
sample1 = {0: +1, 1: -1, 2: +1}
samples = [sample0, sample1]
embedding = {'a': {0, 1, 2}}
# specify that majority vote should be used
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=dimod.embedding.majority_vote))
self.assertEqual(source_samples, [{'a': -1}, {'a': +1}])
def test_majority_vote_with_response(self):
sample0 = {0: -1, 1: -1, 2: +1}
sample1 = {0: +1, 1: -1, 2: +1}
samples = [sample0, sample1]
# now set up an embedding that works for one sample and not the other
embedding = {'a': {0, 1, 2}}
# load the samples into a dimod response
response = dimod.Response.from_dicts(samples, {'energy': [0 for __ in samples]})
# specify that majority vote should be used
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=dimod.embedding.majority_vote))
self.assertEqual(len(source_samples), 2)
self.assertEqual(source_samples, [{'a': -1}, {'a': +1}])
def test_discard_with_response(self):
sample0 = {0: -1, 1: -1, 2: +1}
sample1 = {0: +1, 1: -1, 2: +1}
samples = [sample0, sample1]
# now set up an embedding that works for one sample and not the other
embedding = {'a': {0, 1}, 'b': {2}}
# load the samples into a dimod response
response = dimod.Response.from_dicts(samples, {'energy': [0 for __ in samples]})
# specify that discard should be used
source_samples = list(dimod.iter_unembed(samples, embedding, chain_break_method=dimod.embedding.discard))
# only the first sample should be returned
self.assertEqual(len(source_samples), 1)
self.assertEqual(source_samples, [{'a': -1, 'b': +1}])
