6.4 GaussianMixture.py 文件源码

def test_GMM_cov_type(*data):
    '''
    test the performance with different cov_type
    :param data: data, target
    :return: None
    '''
    X,labels_true=data
    nums=range(1,50)

    cov_types=['spherical','tied','diag','full']
    markers="+o*s"
    fig=plt.figure()
    ax=fig.add_subplot(1,1,1)

    for i ,cov_type in enumerate(cov_types):
        ARIs=[]
        for num in nums:
            clst=mixture.GaussianMixture(n_components=num,covariance_type=cov_type)
            clst.fit(X)
            predicted_labels=clst.predict(X)
            ARIs.append(adjusted_rand_score(labels_true,predicted_labels))
        ax.plot(nums,ARIs,marker=markers[i],label="covariance_type:{0}".format(cov_type))

    ax.set_xlabel("n_components")
    ax.legend(loc="best")
    ax.set_ylabel("ARI")
    fig.suptitle("GMM")
    plt.show()