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:]