def standard_kernel_tests(k, shapes=None, dtype=np.float64):
if shapes is None:
shapes = [((10, 2), (5, 2)),
((10, 1), (5, 1)),
((10,), (5,)),
((10,), ()),
((), (5,)),
((), ())]
# Check various shapes of arguments.
for shape1, shape2 in shapes:
x1 = B.randn(dtype, *shape1)
x2 = B.randn(dtype, *shape2)
# Check that the kernel computes consistently.
allclose(k(x1, x2), reversed(k)(x2, x1).T)
# Check `elwise`.
x2 = B.randn(dtype, *shape1)
allclose(k.elwise(x1, x2)[:, 0], B.diag(k(x1, x2)))
allclose(k.elwise(x1, x2), Kernel.elwise(k, x1, x2))
# The element-wise computation is more accurate, which is why we allow
# a discrepancy a bit larger than the square root of the machine
# epsilon.
allclose(k.elwise(x1)[:, 0], B.diag(k(x1)),
desc='', atol=1e-6, rtol=1e-6)
allclose(k.elwise(x1), Kernel.elwise(k, x1))
def kernel_generator(k):
# Check that the kernel computes.
x1 = np.random.randn(10, 2)
x2 = np.random.randn(5, 2)
yield assert_allclose, k(x1, x2), k(x2, x1).T
# Check `elwise`.
x1 = np.random.randn(10, 2)
x2 = np.random.randn(10, 2)
yield assert_allclose, k.elwise(x1, x2)[:, 0], B.diag(k(x1, x2))
yield assert_allclose, k.elwise(x1, x2), Kernel.elwise(k, x1, x2)
yield assert_allclose, k.elwise(x1)[:, 0], B.diag(k(x1))
yield assert_allclose, k.elwise(x1)[:, 0], B.diag(k(x1))
yield assert_allclose, k.elwise(x1), Kernel.elwise(k, x1)
def test_corner_cases():
with pytest.raises(RuntimeError):
Kernel()(1.)
def test_corner_cases():
yield raises, RuntimeError, lambda: Kernel()(1.)