def test_tensor_product():
k = TensorProductKernel(lambda x: B.sum(x ** 2, axis=1))
assert not k.stationary
with pytest.raises(RuntimeError):
k.length_scale
with pytest.raises(RuntimeError):
k.var
with pytest.raises(RuntimeError):
k.period
# Test equality.
assert k == k
assert k != TensorProductKernel(lambda x: x)
assert k != EQ()
# Standard tests:
standard_kernel_tests(k)
# Check computation of kernel.
k = TensorProductKernel(lambda x: x)
x1 = np.linspace(0, 1, 100)[:, None]
x2 = np.linspace(0, 1, 50)[:, None]
allclose(k(x1), x1 * x1.T)
allclose(k(x1, x2), x1 * x2.T)
k = TensorProductKernel(lambda x: x ** 2)
allclose(k(x1), x1 ** 2 * (x1 ** 2).T)
allclose(k(x1, x2), (x1 ** 2) * (x2 ** 2).T)
def test_tensor_product():
k = TensorProductKernel(lambda x: B.sum(x**2, axis=1))
# Verify that the kernel has the right properties.
assert not k.stationary
# Test equality.
assert k == k
assert k != TensorProductKernel(lambda x: x)
assert k != EQ()
# Standard tests:
standard_kernel_tests(k)
# Test computation of the kernel.
k = TensorProductKernel(lambda x: x)
x1 = np.linspace(0, 1, 100)[:, None]
x2 = np.linspace(0, 1, 50)[:, None]
approx(k(x1), x1 * x1.T)
approx(k(x1, x2), x1 * x2.T)
k = TensorProductKernel(lambda x: x**2)
approx(k(x1), x1**2 * (x1**2).T)
approx(k(x1, x2), (x1**2) * (x2**2).T)
