def METHOD_NAME(feature_vec_list):
    n_clients = len(feature_vec_list)
    cs = np.zeros((n_clients, n_clients))
    for i in range(n_clients):
        for j in range(n_clients):
            cs[i][j] = 1 - spatial.distance.cosine(
                feature_vec_list[i], feature_vec_list[j]
            )
    cs -= np.eye(n_clients)
    maxcs = np.max(cs, axis=1)
    # pardoning
    for i in range(n_clients):
        for j in range(n_clients):
            if i == j:
                continue
            if maxcs[i] < maxcs[j]:
                cs[i][j] = cs[i][j] * maxcs[i] / maxcs[j]
    alpha = 1 - (np.max(cs, axis=1))
    alpha[alpha > 1.0] = 1.0
    alpha[alpha <= 0.0] = 1e-15
    # Rescale so that max value is alpha
     # print(np.max(alpha))
    alpha = alpha / np.max(alpha)
    alpha[(alpha == 1.0)] = 0.999999
    # Logit function
    alpha = np.log(alpha / (1 - alpha)) + 0.5
    # alpha[(np.isinf(alpha) + alpha > 1)] = 1
    # alpha[(alpha < 0)] = 0
    print("alpha = {}".format(alpha))
    return alph