Example of usage: ‘aa_kde_em_clustering.m’ Main function: Idx = kde_em_clustering(files,Par) Input: -‘files’ is a cell containing the directories to data files. Each data file contains data from one measurement. The number of data files can be 2 or more. -‘Par’ is a structure containing the parameters for the clustering algorithm. There are 6 parameters: -Par.numcluster: Number of clusters, needs to be specified. -Par.normalize: Optional, 2(default) - normalize positive numbers to [0.5,1] and normalize negative numbers to [0,0.5], 1 - normalize all numbers to [0,1], 0 - do not normalize. Normalizing data before clustering can avoid one measurement dominating others. -Par.anchor: Optional, number of anchor points, default: 100. Increasing this number can improve accuracy of clustering result, but can increase running time. -Par.maxit: Optional, maximum iterations of EM algorithm, default: 400. Increasing this number can improve accuracy of clustering result, but can increase running time. -Par.Leps: Optional, termination criteria of EM algorithm, default: 1. Decreasing this number can improve accuracy of clustering result, but can increase running time. -Par.plot: Optional, 1(default) - plot clustering result. The algorithm plots the clustering results in 2D figures (PCA dimension reduction). Output: -‘Idx’ is a vector indicating the cluster label for each plant. 注意 需要先更新生成文件夹的名字，保持前后一致： new_folder = ['./clusterResultAverageDHACc10_step10_ljy/',int2str(window1)] mkdir(new_folder) fiout = ['./clusterResultAverageDHACc10_step10_ljy/',int2str(window1),'/',int2str(i), '.txt' ] Par.output = fiout; % outout file name