def test_knot_weights_jacobian_is_correct(self, num_knots, degree,
cyclical):
"""Tests that Jacobian is correct."""
positions_init = np.random.random_sample((10, 1))
scale = num_knots if cyclical else num_knots - degree
positions_init *= scale
# Wrap this in identity because some assert_* ops look at the constant
# tensor value and mark it as unfeedable.
positions = tf.identity(tf.convert_to_tensor(value=positions_init))
weights = bspline.knot_weights(positions=positions,
num_knots=num_knots,
degree=degree,
cyclical=cyclical,
sparse_mode=False)
sparse_weights = bspline.knot_weights(positions=positions,
num_knots=num_knots,
degree=degree,
cyclical=cyclical,
sparse_mode=True)[0]
with self.subTest(name="dense_mode"):
self.assert_jacobian_is_correct(positions, positions_init, weights)
with self.subTest(name="sparse_mode"):
self.assert_jacobian_is_correct(positions, positions_init,
sparse_weights)
def test_knot_weights_jacobian_is_correct(self, num_knots, degree,
cyclical):
"""Tests that Jacobian is correct."""
positions_init = np.random.random_sample((10, 1))
scale = num_knots if cyclical else num_knots - degree
positions_init *= scale
positions = tf.convert_to_tensor(value=positions_init)
weights = bspline.knot_weights(positions=positions,
num_knots=num_knots,
degree=degree,
cyclical=cyclical,
sparse_mode=False)
sparse_weights = bspline.knot_weights(positions=positions,
num_knots=num_knots,
degree=degree,
cyclical=cyclical,
sparse_mode=True)[0]
with self.subTest(name="dense_mode"):
self.assert_jacobian_is_correct(positions, positions_init, weights)
with self.subTest(name="sparse_mode"):
self.assert_jacobian_is_correct(positions, positions_init,
sparse_weights)
def test_full_degree_non_cyclical_knot_weights(self, positions):
"""Tests that noncyclical weights are correct when using max degree."""
cyclical_weights = bspline.knot_weights(
positions=positions, num_knots=3, degree=2, cyclical=True)
noncyclical_weights = bspline.knot_weights(
positions=positions, num_knots=3, degree=2, cyclical=False)
self.assertAllClose(cyclical_weights, noncyclical_weights)
def test_knot_weights_preset(self, position, weights, degree):
"""Tests that knot weights are correct when degree < num_knots - 1."""
self.assertAllClose(
bspline.knot_weights(position,
num_knots=3,
degree=degree,
cyclical=True), weights)
def sparse_mode_fn(positions):
return bspline.knot_weights(
positions=positions,
num_knots=num_knots,
degree=degree,
cyclical=cyclical,
sparse_mode=True)[0]
def test_interpolate_with_weights_preset(self, positions, knots):
"""Tests that interpolate_with_weights works correctly."""
degree = 1
cyclical = False
interp1 = bspline.interpolate(knots, positions, degree, cyclical)
weights = bspline.knot_weights(positions, 2, degree, cyclical)
interp2 = bspline.interpolate_with_weights(knots, weights)
self.assertAllClose(interp1, interp2)
def test_knot_weights_sparse_mode_preset(self, positions, gt_weights,
gt_shifts, degree, cyclical):
"""Tests that sparse mode returns correct results."""
weights, shifts = bspline.knot_weights(positions,
num_knots=3,
degree=degree,
cyclical=cyclical,
sparse_mode=True)
self.assertAllClose(weights, gt_weights)
self.assertAllClose(shifts, gt_shifts)
