def test_delta_evaluations(x1, w1, x2, w2):
k = Delta()
n1 = num_elements(x1)
n2 = num_elements(x2)
# Check uniqueness checks.
approx(k(x1), B.eye(n1))
approx(k(x1, x2), B.zeros(n1, n2))
# Standard tests:
standard_kernel_tests(k)
# Test `Unique` inputs.
assert isinstance(k(Unique(x1), Unique(x1.copy())), Zero)
assert isinstance(k(Unique(x1), Unique(x1)), Diagonal)
assert isinstance(k(Unique(x1), x1), Zero)
assert isinstance(k(x1, Unique(x1)), Zero)
approx(k.elwise(Unique(x1), Unique(x1.copy())), B.zeros(n1, 1))
approx(k.elwise(Unique(x1), Unique(x1)), B.ones(n1, 1))
approx(k.elwise(Unique(x1), x1), B.zeros(n1, 1))
# Test `WeightedUnique` inputs.
assert isinstance(k(WeightedUnique(x1, w1), WeightedUnique(x1.copy(), w1)),
Zero)
assert isinstance(k(WeightedUnique(x1, w1), WeightedUnique(x1, w1)),
Diagonal)
assert isinstance(k(WeightedUnique(x1, w1), x1), Zero)
assert isinstance(k(x1, WeightedUnique(x1, w1)), Zero)
approx(k.elwise(WeightedUnique(x1, w1), WeightedUnique(x1.copy(), w1)),
B.zeros(n1, 1))
approx(k.elwise(WeightedUnique(x1, w1), WeightedUnique(x1, w1)),
B.ones(n1, 1))
approx(k.elwise(WeightedUnique(x1, w1), x1), B.zeros(n1, 1))
approx(k.elwise(x1, WeightedUnique(x1, w1)), B.zeros(n1, 1))
approx(k.elwise(x1, WeightedUnique(x1, w1)), B.zeros(n1, 1))
def test_delta_evaluations(x1, x2):
k = Delta()
n1 = B.shape(B.uprank(x1))[0]
n2 = B.shape(B.uprank(x2))[0]
# Check uniqueness checks.
allclose(k(x1), B.eye(n1))
allclose(k(x1, x2), B.zeros(n1, n2))
# Standard tests:
standard_kernel_tests(k)
# Test `Unique` inputs.
assert isinstance(k(Unique(x1), Unique(x1.copy())), Zero)
assert isinstance(k(Unique(x1), Unique(x1)), Diagonal)
assert isinstance(k(Unique(x1), x1), Zero)
assert isinstance(k(x1, Unique(x1)), Zero)
allclose(k.elwise(Unique(x1), Unique(x1.copy())), B.zeros(n1, 1))
allclose(k.elwise(Unique(x1), Unique(x1)), B.ones(n1, 1))
allclose(k.elwise(Unique(x1), x1), B.zeros(n1, 1))
allclose(k.elwise(x1, Unique(x1)), B.zeros(n1, 1))
def test_derivative_linear():
# Test derivative of kernel `Linear()`.
k = Linear()
x1 = B.randn(tf.float64, 10, 1)
x2 = B.randn(tf.float64, 5, 1)
# Test derivative with respect to first input.
allclose(k.diff(0, None)(x1, x2),
B.ones(tf.float64, 10, 5) * B.transpose(x2))
allclose(k.diff(0, None)(x1),
B.ones(tf.float64, 10, 10) * B.transpose(x1))
# Test derivative with respect to second input.
allclose(k.diff(None, 0)(x1, x2), B.ones(tf.float64, 10, 5) * x1)
allclose(k.diff(None, 0)(x1), B.ones(tf.float64, 10, 10) * x1)
# Test derivative with respect to both inputs.
ref = B.ones(tf.float64, 10, 5)
allclose(k.diff(0, 0)(x1, x2), ref)
allclose(k.diff(0)(x1, x2), ref)
ref = B.ones(tf.float64, 10, 10)
allclose(k.diff(0, 0)(x1), ref)
allclose(k.diff(0)(x1), ref)
