entropy_estimators.py 文件源码

def entropy(x, k=3, base=2):
    """ The classic K-L k-nearest neighbor continuous entropy estimator
        x should be a list of vectors, e.g. x = [[1.3], [3.7], [5.1], [2.4]]
        if x is a one-dimensional scalar and we have four samples
    """
    assert k <= len(x) - 1, "Set k smaller than num. samples - 1"
    d = len(x[0])
    N = len(x)
    intens = 1e-10  # small noise to break degeneracy, see doc.
    x = [list(p + intens * nr.rand(len(x[0]))) for p in x]
    tree = ss.cKDTree(x)
    nn = [tree.query(point, k + 1, p=float('inf'))[0][k] for point in x]
    const = digamma(N) - digamma(k) + d * log(2)
    return (const + d * np.mean(map(log, nn))) / log(base)