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 = tf.constant(mixture_weights)
means_tf = tf.constant(component_means)
sigmas_tf = tf.constant(component_devs)
mix_dev = distribution_util.mixture_stddev(weights_tf,
means_tf,
sigmas_tf)
self.assertAllClose(expected_devs, self.evaluate(mix_dev))
def test_reproduce_bug_169359285(self):
mixture_weight_vector = [
[3.46246948e-10, 1.91437426e-08, 0.999999881, 7.18619435e-12,
2.2613047e-09, 1.99994091e-10, 1.10241352e-07, 3.18231095e-08]]
mean_vector = [
[823.835449, -897.290955, 2554.36426, -2.47223473,
1082.69666, 504.688751, 1291.00989, 954.730896]]
stddev_vector = [
[0.000924979395, 0.000693350448, 0.00416708598, 5.63207436,
0.000353494543, 0.00704913, 0.0127898213, 0.00763763487]]
mixture_weight_vector = tf.convert_to_tensor(mixture_weight_vector)
mean_vector = tf.convert_to_tensor(mean_vector)
stddev_vector = tf.convert_to_tensor(stddev_vector)
result = self.evaluate(distribution_util.mixture_stddev(
mixture_weight_vector, mean_vector, stddev_vector))
self.assertAllClose(
np.array([0.70164], dtype=np.float32), result, rtol=1e-5)
def _stddev(self):
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 = tf.stack(distribution_means, axis=-1)
stacked_devs = tf.stack(distribution_devs, axis=-1)
cat_probs = [self._expand_to_event_rank(c_p) for c_p in cat_probs]
broadcasted_cat_probs = (tf.stack(cat_probs, axis=-1) *
tf.ones_like(stacked_means))
batched_dev = distribution_util.mixture_stddev(
tf.reshape(broadcasted_cat_probs, [-1, len(self.components)]),
tf.reshape(stacked_means, [-1, len(self.components)]),
tf.reshape(stacked_devs, [-1, len(self.components)]))
# I.e. re-shape to list(batch_shape) + list(event_shape).
return tf.reshape(batched_dev, tf.shape(broadcasted_cat_probs)[:-1])
def _stddev(self):
with tf.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 = tf.stack(distribution_means, axis=-1)
stacked_devs = tf.stack(distribution_devs, axis=-1)
cat_probs = [self._expand_to_event_rank(c_p) for c_p in cat_probs]
broadcasted_cat_probs = (
tf.stack(cat_probs, axis=-1) * tf.ones_like(stacked_means))
batched_dev = distribution_utils.mixture_stddev(
tf.reshape(broadcasted_cat_probs, [-1, len(self.components)]),
tf.reshape(stacked_means, [-1, len(self.components)]),
tf.reshape(stacked_devs, [-1, len(self.components)]))
# I.e. re-shape to list(batch_shape) + list(event_shape).
return tf.reshape(batched_dev, tf.shape(broadcasted_cat_probs)[:-1])
