crp_train | R Documentation |
Markov chain Monte Carlo methods for CRP Clustering
crp_train(data = data, alpha = 1, burn_in = 100, iteration = 1000, plot = TRUE)
data |
: a data.frame of data for clustering. Row is each data_i and column is dimensions of each data_i. |
alpha |
: a numeric of a CRP concentrate rate. |
burn_in |
: an abandoned iteration integer. |
iteration |
: an iteration integer. |
plot |
: a logical type of whether plot a result or not. |
result: a list has three elements. The "clusters" is cluster number and joined data number and cluster's mean and variance matrix. The "max" is the cluster number for data i join in. The "z" is the iteration history for each data i join in paticular cluster.
data <- array(0, dim=c(30, 2)) data[1, ] <- c(-2.43185475495409,2.03311203531621) data[2, ] <- c(-0.408783769317068,1.75003135626229) data[3, ] <- c(1.55675133967144,-1.56420523659905) data[4, ] <- c(0.657368060264562,-0.97383866031164) data[5, ] <- c(0.068212889245427,1.13418709295999) data[6, ] <- c(-1.73561815639666,1.81918235905252) data[7, ] <- c(1.03672457449158,-1.8569658734557) data[8, ] <- c(1.76100672727452,0.0761038984873157) data[9, ] <- c(1.65809552931208,-1.68283298969143) data[10, ] < c(-1.07764013453211,-0.32226716632212) data[11, ] <- c(-0.261606415434224,1.75394513146391) data[12, ] <- c(-0.70752394485502,-0.259888201772335) data[13, ] <- c(-1.38617236009739,-1.24305620615396) data[14, ] <- c(1.51663782772836,0.0161396983547837) data[15, ] <- c(-0.914042151747574,1.76495756094862) data[16, ] <- c(0.282796296711756,-0.0492279088948679) data[17, ] <- c(1.08831769386705,-0.954851525684512) data[18, ] <- c(-0.932745904717591,0.762387679372797) data[19, ] <- c(1.69617665862324,-1.11969182200371) data[20, ] <- c(-0.767903781929682,1.19342570049071) data[21, ] <- c(0.401780436172324,-0.457100405625718) data[22, ] <- c(0.98090840279728,-0.597487493647301) data[23, ] <- c(-1.29713416781756,0.765401326146141) data[24, ] <- c(-1.7620625656782,2.02686171889867) data[25, ] <- c(1.68478051448753,-0.806918914294913) data[26, ] <- c(0.466622923887724,0.197650126048092) data[27, ] <- c(1.4851646799543,-1.53289806708663) data[28, ] <- c(-2.12370907063395,1.6471140089684) data[29, ] <- c(-0.660332309091402,1.73989289688085) data[30, ] <- c(-0.957127602683051,-0.0156523076019355) data <- round(data, 3) result <- crp_train( data = data, alpha=1, burn_in=10, iteration=100, plot=TRUE )
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