cluster=KMeans(n_clusters=3,random_state=123).fit(data) # 3.1 获取聚类结果 y_pred=cluster.labels_ # 3.2 获取质心 centers=cluster.cluster_centers_ # [[0.70726496 0.4508547 0.79704476 0.82478632], # [0.19611111 0.595 0.07830508 0.06083333], # [0.44125683 0.30737705 0.57571548 0.54918033]] # 3.3 ...
range_k_clusters = (2, 21) kmeans_result = [] for k in range(*range_k_clusters): # CLUSTERING kmeans = KMeans(n_clusters = k, n_jobs = -1, random_state = 123).fit(X) # REPLACE PIXELS WITH ITS CENTROID new_pixels = replaceWithCentroid(kmeans) # EVALUATE WCSS = kmeans.ine...
kmean= KMeans(n_clusters=4,random_state=420) result= kmean.fit(std_fac_score) labels= result.labels_#获取聚类标签 user_kmeans= data.join(pd.DataFrame(labels)) user_kmeans.rename(columns={0:'cluster'},inplace=True) user_kmeans.cluster.value_counts().plot(kind='pie') user_kmeans.cl...
clusters[centroid_i].append(x_i) returnclusters ### 计算质心 defcalculate_centroids(clusters, k, X): ''' 输入: clusters:上一步的聚类簇 k:质心个数,也是聚类个数 X:训练样本,NumPy数组 输出: centroids:更新后的质心矩阵 ''' # 特征数 n=X.shape[1] # 初始化质心矩阵,大小为质心个数×特征数...
kmeans=KMeans(n_clusters=-3,random_state=123)fit(iris dataScale)#构建并训练模型print("构建的K-Means模型为: \n",kmeans) 手撕模板实现K-means聚类算法 代码语言:javascript 复制 importnumpyasnp defkmeans(X,n_clusters,max_iter=100):n_samples,n_features=X.shape ...
## KMeans模型构建k_means = KMeans(init="k-means++", n_clusters=8,random_state=123)k_means.fit(X) ## 类别查看data['categories'] = k_means.labels_ ## 相关属性查看k_means.cluster_centers_k_means.verbose ## 机器帮助判断(等深分箱)result = k_means.cluster_centers_reuslt = pd.DataFr...
,random_state ) labels_ # 聚类结果标签 cluster_centers_ # 质心坐标 inertia_#总距平方和,受n_clusters影响 需要通过训练好的模型进行调用 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 注意如果量纲不统一,要进行标准化处理消除大量纲对结果的较大偏差。
cuML - RAPIDS Machine Learning Library. Contribute to rapidsai/cuml development by creating an account on GitHub.
samples,targets=datasets.make_blobs(n_samples=150,n_features=2,centers=3,random_state=1) plt.scatter(samples[:,0],samples[:,1],c=targets) 在这里插入图片描述 1、K值不合适 km=KMeans(n_clusters=2) km.fit(samples) KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=30...
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