def test_derivative():
# Test construction:
p = GP(EQ(), TensorProductMean(lambda x: x ** 2), graph=Graph())
yield eq, str(p.diff(1)), 'GP(d(1) EQ(), d(1) <lambda>)'
# Test case:
B.backend_to_tf()
s = B.Session()
model = Graph()
x = np.linspace(0, 1, 100)[:, None]
y = 2 * x
p = GP(EQ(), graph=model)
dp = p.diff()
# Test conditioning on function.
yield le, abs_err(s.run(dp.condition(p(x), y)(x).mean - 2)), 1e-3
# Test conditioning on derivative.
post = p.condition((B.cast(0., np.float64), B.cast(0., np.float64)),
(dp(x), y))
yield le, abs_err(s.run(post(x).mean - x ** 2)), 1e-3
s.close()
B.backend_to_np()
def test_derivative():
# Test construction:
p = GP(EQ(), TensorProductMean(lambda x: x ** 2), graph=Graph())
assert str(p.diff(1)) == 'GP(d(1) EQ(), d(1) <lambda>)'
# Test case:
model = Graph()
x = B.linspace(tf.float64, 0, 1, 100)[:, None]
y = 2 * x
p = GP(EQ(), graph=model)
dp = p.diff()
# Test conditioning on function.
assert abs_err(dp.condition(p(x), y)(x).mean, 2) <= 1e-3
# Test conditioning on derivative.
post = p.condition((B.cast(tf.float64, 0),
B.cast(tf.float64, 0)), (dp(x), y))
assert abs_err(post(x).mean, x ** 2) <= 1e-3
