<pre><code>import matplotlib.pyplot as plt
from sklearn.datasets import make_swiss_roll
from mpl_toolkits.mplot3d import Axes3D
X, t = make_swiss_roll(n_samples=1000, noise=0.2, random_state=42)
from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=3) # Number of clusters == 3
kmeans = kmeans.fit(X) # Fitting the input data
labels = kmeans.predict(X) # Getting the cluster labels
centroids = kmeans.cluster_centers_ # Centroid values
# print("Centroids are:", centroids) # From sci-kit learn
fig = plt.figure(figsize=(10,10))
ax = fig.gca(projection='3d')
x = np.array(labels==0)
y = np.array(labels==1)
z = np.array(labels==2)
ax.scatter(centroids[:,0],centroids[:,1],centroids[:,2],c="black",s=150,label="Centers",alpha=1)
ax.scatter(X[x,0],X[x,1],X[x,2],c="blue",s=40,label="C1")
ax.scatter(X[y,0],X[y,1],X[y,2],c="yellow",s=40,label="C2")
ax.scatter(X[z,0],X[z,1],X[z,2],c="red",s=40,label="C3")
</code></pre>
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