print(cov_mat_list) """from sklearn.cluster import KMeans obj = KMeans(n_clusters = 3, init = 'k-means++', max_iter = 100, n_init = 10, random_state = 0) y_Kmeans = obj.fit_predict(x) print(obj.cluster_centers_[:])""" GMM_obj = GMM(3, x, means, cov_mat_list, mixture_coeff) GMM_obj.fit(0.0002) print(GMM_obj.mean_vec) print(GMM_obj.cov_mat) print(GMM_obj.mixture_coeff) y_pred = GMM_obj.ClusterPredict(x) plt.scatter(GMM_obj.x_train[y_pred == 0, 0], GMM_obj.x_train[y_pred == 0, 1], s = 20, c = 'red', label = 'Cluster 1') plt.scatter(GMM_obj.x_train[y_pred == 1, 0], GMM_obj.x_train[y_pred == 1, 1], s = 20, c = 'green', label = 'Cluster 2') plt.scatter(GMM_obj.x_train[y_pred == 2, 0], GMM_obj.x_train[y_pred == 2, 1], s = 20, c = 'blue', label = 'Cluster 3') plt.scatter(GMM_obj.mean_vec[:, 0], GMM_obj.mean_vec[:, 1], s = 50, c = 'yellow', label = 'Centroids') plt.show() plt.scatter(GMM_obj.x_train[:, 0], GMM_obj.x_train[:, 1]) plt.show() from sklearn.mixture import GaussianMixture obj = GaussianMixture(3, tol = 0.0002, covariance_type = 'full').fit(x) print(obj.means_) print(obj.covariances_) print(obj.weights_)
