compute_fv.py 文件源码

def fisher_vector(samples, means, covs, w):
    s0, s1, s2 =  likelihood_statistics(samples, means, covs, w)
    T = len(samples)
    covs = np.float32([np.diagonal(covs[k]) for k in range(0, covs.shape[0])])
        #pdb.set_trace()
    a = fisher_vector_weights(s0, s1, s2, means, covs, w, T)
    b = fisher_vector_means(s0, s1, s2, means, covs, w, T)
    c = fisher_vector_sigma(s0, s1, s2, means, covs, w, T)
    fv = np.concatenate([np.concatenate(a), np.concatenate(b), np.concatenate(c)])
    fv = normalize(fv)
    return fv