不错的GM_EM代码。用于聚类分析等方面。-　GM_EM- fit a Gaussian mixture model to N points located in n-dimensional

　space.



　Note: This function requires the Statistical Toolbox and, if you wish to

　plot (for k = 2), the function error_ellipse



　Elementary usage:

　GM_EM(X,k)-　fit a GMM to X, where X is N x n and k is the number of

　　　　　　　　clusters. Algorithm follows steps outlined in Bishop

　　　　　　　　(2009)　Pattern Recognition and Machine Learning , Chapter 9.



　Additional inputs:

　　bn_noise- allow for uniform background noise term ( T　or　F ,

　　default　T ). If　T , relevant classification uses the

　　(k+1)th cluster

　　reps- number of repetitions with different initial conditions

　　(default = 10). Note: only the best fit (in a likelihood sense) is

　　returned.

　　max_iters- maximum iteration number for EM algorithm (default = 100)

　　tol- tolerance value (default = 0.01)



　Outputs

　　idx- classification/labelling of data in X

　　mu- GM centres