def testTriL(self):
with self.test_session():
shift = np.array([-1, 0, 1], dtype=np.float32)
tril = np.array([[[1, 0, 0],
[2, -1, 0],
[3, 2, 1]],
[[2, 0, 0],
[3, -2, 0],
[4, 3, 2]]],
dtype=np.float32)
scale = linalg.LinearOperatorLowerTriangular(tril, is_non_singular=True)
affine = AffineLinearOperator(
shift=shift, scale=scale, validate_args=True)
x = np.array([[[1, 0, -1],
[2, 3, 4]],
[[4, 1, -7],
[6, 9, 8]]],
dtype=np.float32)
# If we made the bijector do x*A+b then this would be simplified to:
# y = np.matmul(x, tril) + shift.
y = np.squeeze(np.matmul(tril, np.expand_dims(x, -1)), -1) + shift
ildj = -np.sum(np.log(np.abs(np.diagonal(
tril, axis1=-2, axis2=-1))),
axis=-1)
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj, affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
def testTriL(self):
with self.cached_session():
shift = np.array([-1, 0, 1], dtype=np.float32)
tril = np.array([[[3, 0, 0], [2, -1, 0], [3, 2, 1]],
[[2, 0, 0], [3, -2, 0], [4, 3, 2]]],
dtype=np.float32)
scale = linalg.LinearOperatorLowerTriangular(tril,
is_non_singular=True)
affine = AffineLinearOperator(shift=shift,
scale=scale,
validate_args=True)
x = np.array([[[1, 0, -1], [2, 3, 4]], [[4, 1, -7], [6, 9, 8]]],
dtype=np.float32)
# If we made the bijector do x*A+b then this would be simplified to:
# y = np.matmul(x, tril) + shift.
y = np.squeeze(np.matmul(tril, np.expand_dims(x, -1)), -1) + shift
ildj = -np.sum(
np.log(np.abs(np.diagonal(tril, axis1=-2, axis2=-1))))
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(
ildj,
affine.inverse_log_det_jacobian(y, event_ndims=2).eval())
self.assertAllClose(
-affine.inverse_log_det_jacobian(y, event_ndims=2).eval(),
affine.forward_log_det_jacobian(x, event_ndims=2).eval())
def testIdentity(self):
with self.test_session():
affine = AffineLinearOperator(
validate_args=True)
x = np.array([[1, 0, -1], [2, 3, 4]], dtype=np.float32)
y = x
ildj = 0.
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj, affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
def testIdentity(self):
with self.test_session():
affine = AffineLinearOperator(validate_args=True)
x = np.array([[1, 0, -1], [2, 3, 4]], dtype=np.float32)
y = x
ildj = 0.
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj,
affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
def testDiag(self):
with self.test_session():
shift = np.array([-1, 0, 1], dtype=np.float32)
diag = np.array([[1, 2, 3],
[2, 5, 6]], dtype=np.float32)
scale = linalg.LinearOperatorDiag(diag, is_non_singular=True)
affine = AffineLinearOperator(
shift=shift, scale=scale, validate_args=True)
x = np.array([[1, 0, -1], [2, 3, 4]], dtype=np.float32)
y = diag * x + shift
ildj = -np.sum(np.log(np.abs(diag)), axis=-1)
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj, affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())
def testDiag(self):
with self.test_session():
shift = np.array([-1, 0, 1], dtype=np.float32)
diag = np.array([[1, 2, 3], [2, 5, 6]], dtype=np.float32)
scale = linalg.LinearOperatorDiag(diag, is_non_singular=True)
affine = AffineLinearOperator(shift=shift,
scale=scale,
validate_args=True)
x = np.array([[1, 0, -1], [2, 3, 4]], dtype=np.float32)
y = diag * x + shift
ildj = -np.sum(np.log(np.abs(diag)), axis=-1)
self.assertEqual(affine.name, "affine_linear_operator")
self.assertAllClose(y, affine.forward(x).eval())
self.assertAllClose(x, affine.inverse(y).eval())
self.assertAllClose(ildj,
affine.inverse_log_det_jacobian(y).eval())
self.assertAllClose(-affine.inverse_log_det_jacobian(y).eval(),
affine.forward_log_det_jacobian(x).eval())