def test_normal_onevox_sample():
""" sample() method returns correct shape of data and is consistent with input mean/var """
with tf.Session() as session:
means = np.array([3.0], dtype=np.float32)
variances = np.array([2.0], dtype=np.float32)
post = NormalPosterior(means, variances)
session.run(tf.global_variables_initializer())
sample = session.run(post.sample(100))
assert list(sample.shape) == [1, 1, 100]
# Check for silly values
assert np.all(sample < 100)
assert np.all(sample > -100)
def test_normal_multivox_sample():
""" test_normal_onevox_sample for more than one voxel """
with tf.Session() as session:
nvoxels_in = 34
means = np.random.normal(5.0, 3.0, [nvoxels_in])
variances = np.square(np.random.normal(2.5, 1.6, [nvoxels_in]))
post = NormalPosterior(means, variances)
session.run(tf.global_variables_initializer())
sample = session.run(post.sample(100))
assert sample.shape[0] == nvoxels_in
assert sample.shape[1] == 1
assert sample.shape[2] == 100
# Check for silly values
assert np.all(sample < 100)
assert np.all(sample > -100)