我无法绘制出期望颜色的聚类图。每一点都属于特定的聚类，因此每个聚类应该有特定的颜色，但如图所示，我无法得到期望的颜色。如何修改代码以获得预期的结果和美观的聚类图？
pca_ = PCA(n_components=3)X_Demo_fit_pca = pca_.fit_transform(Demo_df_Processed)kmeans_PCA = KMeans(n_clusters=4, init='k-means++', max_iter= 300, n_init= 10, random_state= 3)y_kmeans_PCA = kmeans_PCA.fit_predict(X_Demo_fit_pca)y_kmeans_PCADemo_df_Processed.head()fig = plt.figure()ax = fig.add_subplot(111, projection='3d')ax.scatter(X_Demo_fit_pca[:,0],X_Demo_fit_pca[:,1],X_Demo_fit_pca[:,2], c=y, edgecolor='k', s=40, alpha = 0.5)ax.set_title("First three PCA directions")ax.set_xlabel("Educational_Degree")ax.set_ylabel("Gross_Monthly_Salary")ax.set_zlabel("Claim_Rate")ax.dist = 10ax.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], kmeans.cluster_centers_[:,2], s = 100, c = 'r', label = 'Centroid')plt.autoscale(enable=True, axis='x', tight=True)plt.show()回答：
通过一些随机生成的数据和对代码进行一些修改，我想这就是你想要的（每个聚类中的数据点具有相同的颜色）：
import numpy as npimport pandas as pdfrom sklearn.decomposition import PCAfrom sklearn.cluster import KMeansimport matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D# 随机生成一些具有4个不同聚类的数据，请使用您自己的数据 Demo_df_Processed = pd.DataFrame(np.random.randint(-2100,-2000,size=(100, 4)), columns=list('ABCD'))Demo_df_Processed = Demo_df_Processed.append(pd.DataFrame(np.random.randint(-600,-500,size=(100, 4)), columns=list('ABCD')))Demo_df_Processed = Demo_df_Processed.append(pd.DataFrame(np.random.randint(500,600,size=(100, 4)), columns=list('ABCD')))Demo_df_Processed = Demo_df_Processed.append(pd.DataFrame(np.random.randint(2000,2100,size=(100, 4)), columns=list('ABCD')))pca_ = PCA(n_components=3)X_Demo_fit_pca = pca_.fit_transform(Demo_df_Processed)kmeans_PCA = KMeans(n_clusters=4, init='k-means++', max_iter= 300, n_init= 10, random_state= 3)y_kmeans_PCA = kmeans_PCA.fit_predict(X_Demo_fit_pca)y_kmeans_PCAfig = plt.figure(figsize=(20,10))ax = fig.add_subplot(111, projection='3d')ax.scatter(X_Demo_fit_pca[:,0],X_Demo_fit_pca[:,1],X_Demo_fit_pca[:,2], c=y_kmeans_PCA, cmap='viridis', edgecolor='k', s=40, alpha = 0.5)ax.set_title("First three PCA directions")ax.set_xlabel("Educational_Degree")ax.set_ylabel("Gross_Monthly_Salary")ax.set_zlabel("Claim_Rate")ax.dist = 10ax.scatter(kmeans_PCA.cluster_centers_[:,0], kmeans_PCA.cluster_centers_[:,1], kmeans_PCA.cluster_centers_[:,2], s = 300, c = 'r', marker='*', label = 'Centroid')plt.autoscale(enable=True, axis='x', tight=True) plt.show()
