<p>尝试此操作,现在群集为黑色X:</p>
<pre><code>from sklearn.datasets import make_swiss_roll
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=(20,10))
ax = fig.add_subplot(111, projection='3d')
x = np.array(labels==0)
y = np.array(labels==1)
z = np.array(labels==2)
ax.scatter(X[x][:, 0], X[x][:, 1], X[x][:, 2], color='red')
ax.scatter(X[y][:, 0], X[y][:, 1], X[y][:, 2], color='blue')
ax.scatter(X[z][:, 0], X[z][:, 1], X[z][:, 2], color='yellow')
ax.scatter(centroids[:, 0], centroids[:, 1], centroids[:, 2],
marker='x', s=169, linewidths=10,
color='black', zorder=50)
</code></pre>
<p><a href="https://i.stack.imgur.com/PQfzb.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/PQfzb.png" alt="enter image description here"/></a></p>