def testMetropolisHastingsStep(self):
if not tf.executing_eagerly():
return
accepted, is_accepted, _ = fun_mcmc.metropolis_hastings_step(
current_state=0., proposed_state=1., energy_change=-np.inf)
self.assertAllEqual(1., accepted)
self.assertAllEqual(True, is_accepted)
accepted, is_accepted, _ = fun_mcmc.metropolis_hastings_step(
current_state=0., proposed_state=1., energy_change=np.inf)
self.assertAllEqual(0., accepted)
self.assertAllEqual(False, is_accepted)
accepted, is_accepted, _ = fun_mcmc.metropolis_hastings_step(
current_state=0., proposed_state=1., energy_change=np.nan)
self.assertAllEqual(0., accepted)
self.assertAllEqual(False, is_accepted)
accepted, is_accepted, _ = fun_mcmc.metropolis_hastings_step(
current_state=None, proposed_state=1., energy_change=np.nan)
self.assertAllEqual(1., accepted)
self.assertAllEqual(False, is_accepted)
accepted, _, _ = fun_mcmc.metropolis_hastings_step(
current_state=tf.zeros(1000),
proposed_state=tf.ones(1000),
energy_change=-tf.math.log(0.5 * tf.ones(1000)),
seed=tfp_test_util.test_seed())
self.assertAllClose(0.5,
tf.reduce_mean(input_tensor=accepted),
rtol=0.1)
def testMetropolisHastingsStepStructure(self):
struct_type = collections.namedtuple('Struct', 'a, b')
current = struct_type([1, 2], (3, [4, [0, 0]]))
proposed = struct_type([5, 6], (7, [8, [0, 0]]))
accepted, is_accepted, _ = fun_mcmc.metropolis_hastings_step(
current_state=current,
proposed_state=proposed,
energy_change=-np.inf)
self.assertAllEqual(True, is_accepted)
self.assertAllEqual(tf.nest.flatten(proposed),
tf.nest.flatten(accepted))