def test_topk_argtopk3():
a = da.random.random((10, 20, 30), chunks=(4, 8, 8))
# As Array methods
assert_eq(a.topk(5, axis=1, split_every=2),
da.topk(a, 5, axis=1, split_every=2))
assert_eq(a.argtopk(5, axis=1, split_every=2),
da.argtopk(a, 5, axis=1, split_every=2))
def test_topk_argtopk3():
a = da.random.random((10, 20, 30), chunks=(4, 8, 8))
# As Array methods
assert_eq(a.topk(5, axis=1, split_every=2),
da.topk(a, 5, axis=1, split_every=2))
assert_eq(a.argtopk(5, axis=1, split_every=2),
da.argtopk(a, 5, axis=1, split_every=2))
def test_topk_argtopk2():
a = da.random.random((10, 20, 30), chunks=(4, 8, 8))
# Support for deprecated API for topk
with pytest.warns(UserWarning):
assert_eq(da.topk(a, 5), da.topk(5, a))
# As Array methods
assert_eq(a.topk(5, axis=1, split_every=2), da.topk(a, 5, axis=1, split_every=2))
assert_eq(a.argtopk(5, axis=1, split_every=2), da.argtopk(a, 5, axis=1, split_every=2))
def nlargestarg(a, n):
"""Return n largest values' indexes of the given array a.
Parameters
----------
a: {list, np.ndarray}
Data array.
n: int
The number of returned args.
Returns
-------
nlargestarg: list
The n largest args in array a.
"""
assert (validation._is_arraylike(a))
assert (n > 0)
if isinstance(a, (list, np.ndarray)):
argret = da.argtopk(da.from_array(a), n)
else:
argret = da.argtopk(a, n)
# ascent
return argret[argret.size - n:]
