def test_mixture_dev(self):
mixture_weights = np.array([
[1.0/3, 1.0/3, 1.0/3],
[0.750, 0.250, 0.000]
])
component_means = np.array([
[1.0, 1.0, 1.0],
[-5, 0, 1.25]
])
component_devs = np.array([
[1.0, 1.0, 1.0],
[0.01, 2.0, 0.1]
])
# The first case should trivially have a standard deviation of 1.0 because
# all components are identical and have that standard deviation.
# The second case was computed by hand.
expected_devs = np.array([
1.0,
2.3848637277
])
weights_tf = array_ops.constant(mixture_weights)
means_tf = array_ops.constant(component_means)
sigmas_tf = array_ops.constant(component_devs)
mix_dev = distribution_util.mixture_stddev(weights_tf,
means_tf,
sigmas_tf)
with self.test_session() as sess:
actual_devs = sess.run(mix_dev)
self.assertAllClose(actual_devs, expected_devs)
def _stddev(self):
with ops.control_dependencies(self._assertions):
distribution_means = [d.mean() for d in self.components]
distribution_devs = [d.stddev() for d in self.components]
cat_probs = self._cat_probs(log_probs=False)
stacked_means = array_ops.stack(distribution_means, axis=-1)
stacked_devs = array_ops.stack(distribution_devs, axis=-1)
cat_probs = [self._expand_to_event_rank(c_p) for c_p in cat_probs]
broadcasted_cat_probs = (array_ops.stack(cat_probs, axis=-1) *
array_ops.ones_like(stacked_means))
batched_dev = distribution_utils.mixture_stddev(
array_ops.reshape(broadcasted_cat_probs, [-1, len(self.components)]),
array_ops.reshape(stacked_means, [-1, len(self.components)]),
array_ops.reshape(stacked_devs, [-1, len(self.components)]))
# I.e. re-shape to list(batch_shape) + list(event_shape).
return array_ops.reshape(batched_dev,
array_ops.shape(broadcasted_cat_probs)[:-1])
def _stddev(self):
with ops.control_dependencies(self._assertions):
distribution_means = [d.mean() for d in self.components]
distribution_devs = [d.stddev() for d in self.components]
cat_probs = self._cat_probs(log_probs=False)
stacked_means = array_ops.stack(distribution_means, axis=-1)
stacked_devs = array_ops.stack(distribution_devs, axis=-1)
cat_probs = [self._expand_to_event_rank(c_p) for c_p in cat_probs]
broadcasted_cat_probs = (array_ops.stack(cat_probs, axis=-1) *
array_ops.ones_like(stacked_means))
batched_dev = distribution_utils.mixture_stddev(
array_ops.reshape(broadcasted_cat_probs, [-1, len(self.components)]),
array_ops.reshape(stacked_means, [-1, len(self.components)]),
array_ops.reshape(stacked_devs, [-1, len(self.components)]))
# I.e. re-shape to list(batch_shape) + list(event_shape).
return array_ops.reshape(batched_dev,
array_ops.shape(broadcasted_cat_probs)[:-1])
def test_mixture_dev(self):
mixture_weights = np.array([[1.0 / 3, 1.0 / 3, 1.0 / 3],
[0.750, 0.250, 0.000]])
component_means = np.array([[1.0, 1.0, 1.0], [-5, 0, 1.25]])
component_devs = np.array([[1.0, 1.0, 1.0], [0.01, 2.0, 0.1]])
# The first case should trivially have a standard deviation of 1.0 because
# all components are identical and have that standard deviation.
# The second case was computed by hand.
expected_devs = np.array([1.0, 2.3848637277])
weights_tf = array_ops.constant(mixture_weights)
means_tf = array_ops.constant(component_means)
sigmas_tf = array_ops.constant(component_devs)
mix_dev = distribution_util.mixture_stddev(weights_tf, means_tf,
sigmas_tf)
with self.test_session() as sess:
actual_devs = sess.run(mix_dev)
self.assertAllClose(actual_devs, expected_devs)
