idx = kmeans(X,k) performs k-means clustering to partition the observations of the n-by-p data matrix X into k clusters, and returns an n-by-1 vector (idx) containing cluster indices of each observation. Rows of X correspond to points and columns correspond to variables. By default,...
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from scipy.stats import pearsonr x : (N,) array_like y : (N,) array_like Returns: (Pearson’s correlation coefficient, p-value) 5.案例 6.2.4.2 斯皮尔曼相关系数(Rank IC) 1.作用: 反映变量之间相关关系密切程度的统计指标 2.公式计算案例(了解,不用记忆) 公式 n为等级个数,d为二列成对变...
(point))._1}// 获得更新的簇的向量和以及点数量valmergeContribs:((Vector,Long),(Vector,Long))=>(Vector,Long)=(p1,p2)=>{// y += a * x,向量相加BLAS.axpy(1.0,p2._1,p1._1)(p1._1,p1._2+p2._2)}val pointStats:Array[(Int,(Vector,Long))]=closest.aggregateByKey((Vectors....
stats(dist(norm.data), result$cluster) stats$avg.silwidth })) }) plot(K,rst,type='l',main='轮廓系数与K的关系', ylab='轮廓系数') 评估k,由于一般K不会太大,太大了也不易于理解,所以遍历K为2到8。由于kmeans具有一定随机性,并不是每次都收敛到全局最小,所以针对每一个k值,重复执行30次,取...
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+ cluster.stats(dist(nutrient1),kmeans(nutrient1,centers = k)$cluster)$avg.silwidth + }) > sw [1] 0.4755391 0.3022866 0.3736633 0.4061053 0.3962927 0.3534240 0.3284742 0.4001432 [9] 0.4039906 (4)使用线图绘制不同k值的平均轮廓值 > plot(nk,sw,type = "l",xlab="number of clusters",ylab =...
You can control the details of the minimization using name-value pair arguments available tokmeans; for example, you can specify the initial values of the cluster centroids and the maximum number of iterations for the algorithm. By default,kmeansuses thek-means++ algorithmto initialize cluster cen...