def testVonMisesSampleMoments(self):
locs_v = np.array([-1., 0.3, 2.3])
concentrations_v = np.array([1., 2., 10.])
von_mises = tfd.VonMises(self.make_tensor(locs_v),
self.make_tensor(concentrations_v),
validate_args=True)
n = 10000
seed = test_util.test_seed()
samples = von_mises.sample(n, seed=seed)
expected_mean = von_mises.mean()
actual_mean = tf.atan2(tf.reduce_mean(tf.sin(samples), axis=0),
tf.reduce_mean(tf.cos(samples), axis=0))
expected_variance = von_mises.variance()
standardized_samples = samples - tf.expand_dims(von_mises.mean(), 0)
variance_samples = 1. - tf.cos(standardized_samples)
[
expected_mean_val, expected_variance_val, actual_mean_val,
variance_samples_
] = self.evaluate(
[expected_mean, expected_variance, actual_mean, variance_samples])
# TODO(axch, cgs): atan2(means) is not mean(atan2), but maybe there
# is a formulation of what this is testing that does use IID samples
# and is amenable to assertAllMeansClose?
self.assertAllClose(actual_mean_val, expected_mean_val, rtol=0.1)
self.assertAllMeansClose(variance_samples_,
expected_variance_val,
axis=0,
rtol=0.1)
def testVonMisesSampleMoments(self):
locs_v = np.array([-1., 0.3, 2.3])
concentrations_v = np.array([1.0, 2.0, 10.0])
von_mises = tfd.VonMises(self.make_tensor(locs_v),
self.make_tensor(concentrations_v),
validate_args=True)
n = 10000
seed = test_util.test_seed()
samples = von_mises.sample(n, seed=seed)
expected_mean = von_mises.mean()
actual_mean = tf.atan2(tf.reduce_mean(tf.sin(samples), axis=0),
tf.reduce_mean(tf.cos(samples), axis=0))
expected_variance = von_mises.variance()
standardized_samples = samples - tf.expand_dims(von_mises.mean(), 0)
actual_variance = 1. - tf.reduce_mean(tf.cos(standardized_samples),
axis=0)
[
expected_mean_val, expected_variance_val, actual_mean_val,
actual_variance_val
] = self.evaluate(
[expected_mean, expected_variance, actual_mean, actual_variance])
self.assertAllClose(expected_mean_val, actual_mean_val, rtol=0.1)
self.assertAllClose(expected_variance_val,
actual_variance_val,
rtol=0.1)
def transform_box(bboxes, transform):
"""Transform bboxes."""
center_xyz = bboxes[..., :3]
dimensions = bboxes[..., 3:6]
rot = bboxes[..., 6:]
center_xyz = transform_points_with_transform(center_xyz, transform)
rot += tf.atan2(transform[..., 1:2, 0:1], transform[..., 0:1, 0:1])
rot = wrap_angle_rad(rot)
bboxes = tf.concat([center_xyz, dimensions, rot], axis=-1)
return bboxes