def test_construction():
k = EQ()
x = np.random.randn(10, 1)
k(x)
k(x, x)
k(Observed(x))
k(Observed(x), Observed(x))
k(x, Observed(x))
k(Observed(x), x)
k.elwise(x)
k.elwise(x, x)
k.elwise(Observed(x))
k.elwise(Observed(x), Observed(x))
k.elwise(x, Observed(x))
k.elwise(Observed(x), x)
def test_elwise_cache_aliasing():
c = Cache()
k = EQ()
x1 = np.random.randn(10, 2)
x2 = np.random.randn(10, 2)
yield assert_allclose, k.elwise(x1, x2, c), Kernel.elwise(k, x1, x2, c)
def test_construction(x1, x2):
k = EQ()
k(x1)
k(x1, x2)
k.elwise(x1)
k.elwise(x1, x2)
# Test `MultiInput` construction.
approx(
k(MultiInput(x1, x2)),
B.concat2d([k(x1, x1), k(x1, x2)], [k(x2, x1), k(x2, x2)]),
)
approx(k(x1, MultiInput(x1, x2)), B.concat(k(x1, x1), k(x1, x2), axis=1))
approx(k(MultiInput(x1, x2), x2), B.concat(k(x1, x2), k(x2, x2), axis=0))
approx(
k.elwise(MultiInput(x1, x2)),
B.concat(k.elwise(x1, x1), k.elwise(x2, x2), axis=0),
)
with pytest.raises(ValueError):
k.elwise(MultiInput(x1), MultiInput(x1, x2))
with pytest.raises(ValueError):
k.elwise(x1, MultiInput(x1, x2))
with pytest.raises(ValueError):
k.elwise(MultiInput(x1, x2), x2)
