# 使用PCA进行降维,以便更好地进行聚类分析pca = PCA(n_components=2) # 降至2维以便可视化 X_pca = pca.fit_transform(X_std) # 使用K-means进行聚类 k = 3 # 基于先前的分析决定将用户分为3个群体 kmeans = KMeans(n_clusters=k, random_state=42) y_kmeans = kmeans.fit_predict(X_pca) #...
[10, 2], [10, 4], [10, 0]]) >>> kmeans = KMeans(n_clusters=2, random_state=0, n_init="auto").fit(X) >>> kmeans.labels_ array([1, 1, 1, 0, 0, 0], dtype=int32) >>> kmeans.predict([[0, 0], [12, 3]]) array([1, 0], dtype=int32) >>> kmeans.cluster...
kmodel=KMeans(n_clusters=k,n_jobs=-1,random_state=888)kmodel.fit(pred_images)kpredictions=kmodel.predict(pred_images)print(kpredictions)# 预测的类别 #0:dog1:cat 将分类后的图像保存到不同文件夹下 代码语言:javascript 复制 foriin["cat","dog"]:os.mkdir(r"C:\Users\Administrator\DeepLearnin...
Kmeans(n_clusters=4 # 对于指定聚类的簇数,无默认值 ,init="random" # 表示从数据集中随机挑选K个样本点作为初始簇中心 ,n_init=10 # 用于指定该算法运行次数,每次运行时都会选择不同的初始促中心,目的是防止算法收敛于局部最优,默认10 ,max_iter=300 # 表示单次运行的迭代次数,默认300 ,tol=0.0001 # ...
clusters_swiss_roll = KMeans(n_clusters=100,random_state=1).fit_predict(X) fig2 = plt.figure() ax = fig2.add_subplot(111,projection='3d') ax.scatter(X[:,0],X[:,1],X[:,2],c = clusters_swiss_roll,cmap = 'Spectral')
3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 上图是数据的真实分布情况,接下来使用KMeans对数据进行聚类! # 将数据聚类成三类 from sklearn.cluster import KMeans n_clusters = 3 cluster = KMeans(n_clusters=n_clusters, random_state=1).fit(X) ...
static int[] InitClustering(int numTuples, int numClusters, int randomSeed) { Random random = new Random(randomSeed); int[] clustering = new int[numTuples]; for (int i = 0; i<numClusters; ++i)clustering[i] =i;for(inti=numClusters;i<clustering.Length; ++i)clustering[i] =random....
While these papers indicated: (1) the need for analyzing spectrum usage directly from measurement or indirectly from voice/data traffic volume; and (2) the need for prediction or classification models that are capable of understanding random spectrum usage, there is still a limitation of capturing...
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根据前一步的分析,我们决定使用 K=3 来进行聚类。接下来,我们将应用 K-means 算法对银行客户数据进行聚类,并将聚类结果可视化。 # 使用 K-means 聚类kmeans=KMeans(n_clusters=3,random_state=42)kmeans.fit(df_scaled)# 获取聚类标签labels=kmeans.labels_# 将聚类标签添加到原始数据框中df['Cluster']=la...