def test_mc_estimator_converges_with_multiple_fixed_estimates_nd(self):
num_dims = 8
mean = np.arange(1.27, 1.349, 0.01)
stddev = 2.0
batch_size = int(1e5)
dummy_key_placeholder = tf.placeholder(shape=(), dtype=tf.int32)
mean_est = tf.constant(mean)
mean_sq_est = tf.ones([num_dims]) * (stddev**2)
tol = 5e-3
conf_level = 0.99
max_num_steps = (util.half_clt_conf_interval(conf_level, 1, stddev) /
(np.min(mean) * tol))**2 + 1
with self.test_session() as session:
(mean_est_eval, rel_half_conf_interval,
converged) = monte_carlo_manager.mc_estimator(
mean_est, mean_sq_est, batch_size, dummy_key_placeholder, {},
tol, conf_level, max_num_steps, session)
self.assertAllAlmostEqual(mean_est_eval, mean * np.ones([num_dims]))
self.assertTrue(converged)
self.assertAllGreaterEqual(
rel_half_conf_interval * mean,
util.half_clt_conf_interval(conf_level, max_num_steps * batch_size,
stddev))
def test_mc_estimator_converges_with_fixed_estimates(self):
mean = 1.3
stddev = 2.0
batch_size = int(1e5)
dummy_key_placeholder = tf.placeholder(shape=(), dtype=tf.int32)
mean_est = tf.constant(mean)
mean_sq_est = tf.constant(stddev**2)
tol = 5e-3
conf_level = 0.99
max_num_steps = (util.half_clt_conf_interval(conf_level, 1, stddev) /
(mean * tol))**2 + 1
with self.test_session() as session:
(mean_est_eval, rel_half_conf_interval,
converged) = monte_carlo_manager.mc_estimator(
mean_est, mean_sq_est, batch_size, dummy_key_placeholder, {},
tol, conf_level, max_num_steps, session)
self.assertAlmostEqual(mean_est_eval, mean)
self.assertTrue(converged)
self.assertGreaterEqual(
rel_half_conf_interval,
util.half_clt_conf_interval(conf_level, max_num_steps * batch_size,
stddev) / mean)
def test_half_clt_conf_interval_unsupported_type(self):
confidence_level = 0.95
num_samples = 400
# Integer stddev not supported.
stddev = 2
with self.assertRaises(TypeError):
util.half_clt_conf_interval(confidence_level, num_samples, stddev)
def test_half_clt_conf_interval_with_zero_stdev(self):
confidence_level = 0.95
num_samples = 400
# Test zero scaler.
stddev = 0.0
correct_value = stddev / np.sqrt(num_samples) * 1.959963984540
conf_interval_half_width = util.half_clt_conf_interval(
confidence_level, num_samples, stddev)
self.assertAlmostEqual(correct_value, conf_interval_half_width)
# Test array with zero elements.
stddev = np.array([2.0, 0.0])
correct_value = stddev / np.sqrt(num_samples) * 1.959963984540
conf_interval_half_width = util.half_clt_conf_interval(
confidence_level, num_samples, stddev)
self.assertAllAlmostEqual(correct_value, conf_interval_half_width)
def test_half_clt_conf_interval_is_correct(self):
confidence_level = 0.95
num_samples = 400
# Test scaler float value.
stddev = 2.0
correct_value = stddev / np.sqrt(num_samples) * 1.959963984540
conf_interval_half_width = util.half_clt_conf_interval(
confidence_level, num_samples, stddev)
self.assertAlmostEqual(correct_value, conf_interval_half_width)
# Test array float values.
stddev = np.array([2.0, 1.0])
correct_value = stddev / np.sqrt(num_samples) * 1.959963984540
conf_interval_half_width = util.half_clt_conf_interval(
confidence_level, num_samples, stddev)
self.assertAllAlmostEqual(correct_value, conf_interval_half_width)
def _run_mc_step():
"""Run one mini-batch step of the Monte-Carlo method."""
feed_dict.update({key_placeholder: num_steps})
batch_mean_est, batch_mean_sq_est = tf_session.run(
(mean_est, mean_sq_est), feed_dict=feed_dict)
new_mean_est_eval = util.running_mean_estimate(mean_est_eval,
batch_mean_est,
num_samples, batch_size)
new_mean_sq_est_eval = util.running_mean_estimate(
mean_sq_est_eval, batch_mean_sq_est, num_samples, batch_size)
std_est_eval = util.stddev_est(new_mean_est_eval, new_mean_sq_est_eval)
half_conf_interval = util.half_clt_conf_interval(
confidence, num_samples + batch_size, std_est_eval)
return new_mean_est_eval, new_mean_sq_est_eval, half_conf_interval
