MeanShift.py

import numpy as np
from sklearn.cluster import MeanShift
from sklearn.datasets 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=1)

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()