

def renyi_entropy(samps, alphas, Ks, **kwargs):
    '\n    Estimates Renyi entropy using a special case of our estimator above\n    (with alpha=alpha-1, beta=0).\n\n    samps: an (n_samps x dimensionality) matrix of samples\n    alphas: a sequence of alpha values to use\n    Ks: a sequence of K values to use\n    other kwargs: passed along to knn_search\n\n    Returns a matrix of entropy estimates: one row per alpha, col per K\n    '
    (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)

dataset/ETHPy150Open dougalsutherland/py-sdm/sdm/jensen_divs.py renyi_entropy/original