def test_case_fd_derivative():
x = np.linspace(0, 10, 50)[:, None]
y = np.sin(x)
model = Graph()
p = GP(.7 * EQ().stretch(1.), graph=model)
dp = (p.shift(-1e-3) - p.shift(1e-3)) / 2e-3
yield le, abs_err(np.cos(x) - dp.condition(p(x), y)(x).mean), 1e-4
def test_shifting():
model = Graph()
# Test construction:
p = GP(Linear(), TensorProductMean(lambda x: x ** 2), graph=model)
yield eq, str(p.shift(1)), 'GP(Linear() shift 1, <lambda> shift 1)'
# Test case:
p = GP(EQ(), graph=model)
p2 = p.shift(5)
n = 5
x = np.linspace(0, 10, n)[:, None]
y = p2(x).sample()
post = p.condition(p2(x), y)
yield assert_allclose, post(x - 5).mean, y
yield le, abs_err(B.diag(post(x - 5).var)), 1e-10
post = p2.condition(p(x), y)
yield assert_allclose, post(x + 5).mean, y
yield le, abs_err(B.diag(post(x + 5).var)), 1e-10
