30 lines
1.2 KiB
Python
30 lines
1.2 KiB
Python
"""
|
|
This code is supported by the website: https://www.guanjihuan.com
|
|
The newest version of this code is on the web page: https://www.guanjihuan.com/archives/44839
|
|
"""
|
|
|
|
import os
|
|
os.environ["OMP_NUM_THREADS"] = "1" # KMeans is known to have a memory leak on Windows with MKL, when there are less chunks than available threads. You can avoid it by setting this environment variable
|
|
|
|
from sklearn.cluster import KMeans
|
|
from sklearn.datasets import make_blobs
|
|
import matplotlib.pyplot as plt
|
|
|
|
X, y = make_blobs(n_samples=300, centers=4, random_state=42) # 生成示例数据（四类）
|
|
print(X.shape)
|
|
print(y.shape)
|
|
plt.scatter(X[:, 0], X[:, 1]) # 显示数据
|
|
plt.show()
|
|
|
|
plt.scatter(X[:, 0], X[:, 1], c=y, cmap='viridis') # 通过颜色显示数据原有的标签
|
|
plt.show()
|
|
|
|
kmeans = KMeans(n_clusters=3, random_state=42) # 进行 KMeans 聚类（这里分为三类）
|
|
kmeans.fit(X)
|
|
labels = kmeans.labels_ # 获取聚类的标签
|
|
print(labels.shape)
|
|
|
|
plt.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis') # 绘制聚类结果
|
|
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red', marker='X') # 绘制聚类中心
|
|
plt.title('KMeans Result')
|
|
plt.show() |