learning_function.py 文件源码

def _gaussian_fit(array):
    if array.shape[0] < 2:
        logging.error("Cannot fit a Gaussian with two distances.")
        return 0

    array_2 = -(np.array(sorted(array)).reshape(-1, 1))
    array = np.array(list(array_2) + list(array)).reshape(-1, 1)

    try:
        # new sklearn GMM
        gmm = mixture.GaussianMixture(n_components=3,
                                      covariance_type='diag')
        gmm.fit(array)
        # gmmmean = np.max(gmm.means_)
        # gmmsigma = gmm.covariances_[np.argmax(gmm.means_)]
    except AttributeError:
        # use old sklearn method
        gmm = mixture.GMM(n_components=3)
        gmm.fit(array)
        # gmmmean = np.max(gmm.means_)
        # gmmsigma = gmm.covars_[np.argmax(gmm.means_)]

    # Extract optimal threshold
    plt.hist(array, bins=50, normed=True)  # debug, print

    lin = np.linspace(0, 1, 10000)[:, np.newaxis]
    # plt.plot(lin, np.exp(gmm.score_samples(lin)[0]), 'r')
    pred = gmm.predict(lin)
    try:
        idx = np.min(np.where(pred == np.argmax(gmm.means_))[0])
    except ValueError:
        # print("Error", np.unique(pred))
        idx = 0

    plt.axvline(x=lin[idx], linestyle='--', color='r')
    # plt.gcf().savefig("threshold_naive{}.png".format(k))
    plt.close()
    threshold = lin[idx][0]  # threshold
    # np.save("threshold_naive", threshold)
    return threshold