1.随机生成样本数据， X = [randn(100,2)*0.75+ones(100,2); randn(100,2)*0.5-ones(100,2)]; (1)绘制数据; （提示:scatter函数或plot函数） (2)用C均值聚类将其分为两类，并绘制出结果。（提示：kmeans函数）

[代码](https://geek.csdn.net/educolumn/1572ef9b473b4e00f6b2bf6d428b7c27?spm=1055.2569.3001.10083)实现如下： ```python import numpy as np import matplotlib.pyplot as plt from sk[learn](https://geek.csdn.net/educolumn/00b6a1b1fad077eafad2b50b0230c4b0?spm=1055.2569.3001.10083).cluster import KMeans # 随机生成样本数据 X = np.vstack((np.random.randn(100, 2) * 0.75 + np.ones((100, 2)), np.random.randn(100, 2) * 0.5 - np.ones((100, 2)))) # 绘制数据 plt.scatter(X[:, 0], X[:, 1], s=30, color='b') plt.title('Ori[gin](https://geek.csdn.net/educolumn/1aef582107c22aa4d9f243890549bf39?spm=1055.2569.3001.10083)al Data') plt.show() # 用C均值[聚类](https://geek.csdn.net/educolumn/5addd310d23f5f482bc6ca11d02ba845?spm=1055.2569.3001.10083)将其分为两类 kmeans = KMeans(n_clusters=2, random_state=0).fit(X) labels = kmeans.labels_ # 绘制聚类结果 plt.scatter(X[:, 0], X[:, 1], c=labels, s=30, c[map](https://geek.csdn.net/educolumn/489f40071d2cdbfb1748ab6a5a24440e?spm=1055.2569.3001.10083)='viridis') plt.title('KMeans Clustering Results') plt.show() 运行结果如下图所示：