def renyi_entropy(samps, alphas, Ks, **kwargs):
    # throw away the smallest one, which is dist to self
    knns, _ = knn_search(np.max(Ks) + 1, samps, samps, **kwargs)
    return renyi_entropy_knns(knns=knns[:, 1:], dim=samps.shape[1],
                              Ks=Ks, alphas=alphas)
Estimates Renyi entropy using a special case of our estimator above
dataset/ETHPy150Open dougalsutherland/py-sdm/sdm/jensen_divs.py/renyi_entropy