def testRunningVarianceMaxPoints(self):
window_size = 100
rng = np.random.RandomState(_test_seed())
data = tf.convert_to_tensor(
np.concatenate(
[rng.randn(window_size), 1. + 2. * rng.randn(window_size * 10)],
axis=0))
def kernel(rvs, idx):
rvs, _ = fun_mcmc.running_variance_step(
rvs, data[idx], window_size=window_size)
return (rvs, idx + 1), (rvs.mean, rvs.variance)
_, (mean, var) = fun_mcmc.trace(
state=(fun_mcmc.running_variance_init([], data.dtype), 0),
fn=kernel,
num_steps=len(data),
)
# Up to window_size, we compute the running mean/variance exactly.
self.assertAllClose(np.mean(data[:window_size]), mean[window_size - 1])
self.assertAllClose(np.var(data[:window_size]), var[window_size - 1])
# After window_size, we're doing exponential moving average, and pick up the
# mean/variance after the change in the distribution. Since the moving
# average is computed only over ~window_size points, this test is rather
# noisy.
self.assertAllClose(1., mean[-1], atol=0.2)
self.assertAllClose(4., var[-1], atol=0.8)
def testRunningVariance(self, shape, aggregation):
rng = np.random.RandomState(_test_seed())
data = tf.convert_to_tensor(rng.randn(*shape))
true_aggregation = (0,) + (() if aggregation is None else tuple(
[a + 1 for a in util.flatten_tree(aggregation)]))
true_mean = np.mean(data, true_aggregation)
true_var = np.var(data, true_aggregation)
def kernel(rvs, idx):
rvs, _ = fun_mcmc.running_variance_step(rvs, data[idx], axis=aggregation)
return (rvs, idx + 1), ()
(rvs, _), _ = fun_mcmc.trace(
state=(fun_mcmc.running_variance_init(true_mean.shape,
data[0].dtype), 0),
fn=kernel,
num_steps=len(data),
trace_fn=lambda *args: ())
self.assertAllClose(true_mean, rvs.mean)
self.assertAllClose(true_var, rvs.variance)
