# import numpy as np
# from sklearn.cluster import MeanShift
# from sklearn.datasets.samples_generator import make_blobs
# import matplotlib.pyplot as plt
# from mpl_toolkits.mplot3d import Axes3D
# from matplotlib import style
# style.use("ggplot")
#
# centers = [[1,1,1],[5,5,5],[3,10,10]]
#
# X, _ = make_blobs(n_samples = 100, centers = centers, cluster_std = 0.5)
# print(X)
#
# ms = MeanShift()
# ms.fit(X)
# labels = ms.labels_
# cluster_centers = ms.cluster_centers_
#
# print(cluster_centers)
# n_clusters_ = len(np.unique(labels))
# print("Number of estimated clusters:", n_clusters_)
#
# colors = 10*['r','g','b','c','k','y','m']
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
#
# for i in range(len(X)):
#     ax.scatter(X[i][0], X[i][1], X[i][2], c=colors[labels[i]], marker='o')
#
# ax.scatter(cluster_centers[:,0],cluster_centers[:,1],cluster_centers[:,2],
#             marker="x",color='k', s=150, linewidths = 5, zorder=10)
#
# plt.show()

import numpy as np
a = np.array([0,0, 0])
ind = (- a).argsort()[:3]
print(a[ind])
print(not a.any())