def test_array_dist_using_inner(fn):
[xarr, yarr], [x, y] = noise_elements(fn, 2)
weight_arr = _pos_array(fn)
w = NumpyFnArrayWeighting(weight_arr)
true_dist = np.linalg.norm(np.sqrt(weight_arr) * (xarr - yarr))
# Using 3 places (single precision default) since the result is always
# double even if the underlying computation was only single precision
assert almost_equal(w.dist(x, y), true_dist, places=3)
# Only possible for exponent=2
with pytest.raises(ValueError):
NumpyFnArrayWeighting(weight_arr, exponent=1, dist_using_inner=True)
# With free function
w_dist = npy_weighted_dist(weight_arr, use_inner=True)
assert almost_equal(w_dist(x, y), true_dist, places=3)
def test_array_dist(fn, exponent):
[xarr, yarr], [x, y] = noise_elements(fn, n=2)
weight_arr = _pos_array(fn)
weighting_arr = NumpyFnArrayWeighting(weight_arr, exponent=exponent)
if exponent == float('inf'):
true_dist = np.linalg.norm(weight_arr * (xarr - yarr),
ord=float('inf'))
else:
true_dist = np.linalg.norm(weight_arr**(1 / exponent) * (xarr - yarr),
ord=exponent)
assert almost_equal(weighting_arr.dist(x, y), true_dist)
# With free function
pdist_vec = npy_weighted_dist(weight_arr, exponent=exponent)
assert almost_equal(pdist_vec(x, y), true_dist)