lnn.py 文件源码

def KDE_entropy(x):
    '''
        Estimate the entropy H(X) from samples {x_i}_{i=1}^N
        Using Kernel Density Estimator (KDE) and resubstitution

        Input: x: 2D list of size N*d_x

        Output: one number of H(X)
    '''

    N = len(x)
    d = len(x[0])
    local_est = np.zeros(N)
    for i in range(N):
        kernel = sst.gaussian_kde(x.transpose())
        local_est[i] = kernel.evaluate(x[i].transpose())
    return -np.mean(map(log,local_est))