def test_antithetic_normal_is_symmetrical(self):
shape = [512]
antithetic_normal_samples = dynamics.random_antithetic_normal(shape)
with self.session() as session:
[samples,
sym_samples] = session.run(tf.split(antithetic_normal_samples, 2))
self.assertAllEqual(samples, -sym_samples)
def test_antithetic_normal_lowers_variance(self):
shape = [512]
num_trials = 128
key_ph = tf.placeholder(shape=(), dtype=tf.int32)
normal_samples = dynamics.random_normal(shape, key=key_ph)
antithetic_normal_samples = dynamics.random_antithetic_normal(
shape, key=key_ph)
mean_estimator = tf.reduce_mean(normal_samples)
antithetic_mean_estimator = tf.reduce_mean(antithetic_normal_samples)
mean_estimates = []
antithetic_mean_estimates = []
with self.session() as session:
for i in range(num_trials):
mean_estimates.append(
session.run(mean_estimator, feed_dict={key_ph: i}))
antithetic_mean_estimates.append(
session.run(antithetic_mean_estimator, feed_dict={key_ph: i}))
self.assertLessEqual(
np.std(antithetic_mean_estimates), np.std(mean_estimates))