def test_sampling(self):
# for all practical purposes the distribution of energies here should
# guarantee the last sample always wins
winner = {'a': 1, 'b': 1}
skewed = dimod.SampleSet.from_samples([{
'a': 0,
'b': 0
}, {
'a': 0,
'b': 1
}, {
'a': 1,
'b': 0
}, winner],
energy=[100, 100, 100, -100],
vartype='BINARY')
state = hybrid.State(samples=skewed)
# cold sampling
res = EnergyWeightedResampler(beta=1, seed=1234).run(state).result()
samples = res.samples.aggregate()
self.assertEqual(len(samples), 1)
self.assertDictEqual(dict(list(samples.samples())[0]), winner)
# hot sampling
res = EnergyWeightedResampler(beta=0, seed=1234).run(state).result()
samples = res.samples.aggregate()
self.assertGreater(len(samples), 1)
def test_beta_progressor(self):
beta_schedule = [1, 2, 3]
prog = ProgressBetaAlongSchedule(beta_schedule=beta_schedule)
betas = []
while True:
try:
betas.append(prog.run(hybrid.State()).result().beta)
except:
break
self.assertEqual(betas, beta_schedule)
def test_beta_use(self):
ss = dimod.SampleSet.from_samples([{'a': 0}], energy=[0], vartype='SPIN')
state = hybrid.State(samples=ss)
# beta not given at all
with self.assertRaises(ValueError):
res = EnergyWeightedResampler().run(state).result()
# beta given on construction
res = EnergyWeightedResampler(delta_beta=0).run(state).result()
self.assertEqual(res.samples.info['delta_beta'], 0)
# beta given on runtime, to run method
res = EnergyWeightedResampler().run(state, delta_beta=1).result()
self.assertEqual(res.samples.info['delta_beta'], 1)
# beta given in state
state.delta_beta = 2
res = EnergyWeightedResampler().run(state).result()
self.assertEqual(res.samples.info['delta_beta'], 2)