R : Copyright 2005, The R Foundation for Statistical Computing
Version 2.1.1  (2005-06-20), ISBN 3-900051-07-0

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> ### * <HEADER>
> ###
> attach(NULL, name = "CheckExEnv")
> assign(".CheckExEnv", as.environment(2), pos = length(search())) # base
> ## add some hooks to label plot pages for base and grid graphics
> setHook("plot.new", ".newplot.hook")
> setHook("persp", ".newplot.hook")
> setHook("grid.newpage", ".gridplot.hook")
> 
> assign("cleanEx",
+        function(env = .GlobalEnv) {
+ 	   rm(list = ls(envir = env, all.names = TRUE), envir = env)
+            RNGkind("default", "default")
+ 	   set.seed(1)
+    	   options(warn = 1)
+ 	   delayedAssign("T", stop("T used instead of TRUE"),
+ 		  assign.env = .CheckExEnv)
+ 	   delayedAssign("F", stop("F used instead of FALSE"),
+ 		  assign.env = .CheckExEnv)
+ 	   sch <- search()
+ 	   newitems <- sch[! sch %in% .oldSearch]
+ 	   for(item in rev(newitems))
+                eval(substitute(detach(item), list(item=item)))
+ 	   missitems <- .oldSearch[! .oldSearch %in% sch]
+ 	   if(length(missitems))
+ 	       warning("items ", paste(missitems, collapse=", "),
+ 		       " have been removed from the search path")
+        },
+        env = .CheckExEnv)
> assign("..nameEx", "__{must remake R-ex/*.R}__", env = .CheckExEnv) # for now
> assign("ptime", proc.time(), env = .CheckExEnv)
> grDevices::postscript("cclust-Examples.ps")
> assign("par.postscript", graphics::par(no.readonly = TRUE), env = .CheckExEnv)
> options(contrasts = c(unordered = "contr.treatment", ordered = "contr.poly"))
> options(warn = 1)    
> library('cclust')
> 
> assign(".oldSearch", search(), env = .CheckExEnv)
> assign(".oldNS", loadedNamespaces(), env = .CheckExEnv)
> cleanEx(); ..nameEx <- "Rindexes"
> 
> ### * Rindexes
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: clustIndex
> ### Title: Cluster Indexes
> ### Aliases: clustIndex
> ### Keywords: cluster
> 
> ### ** Examples
> 
> # a 2-dimensional example
> x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2),
+          matrix(rnorm(100,mean=1,sd=0.3),ncol=2))
> cl<-cclust(x,2,20,verbose=TRUE,method="kmeans")
Iteration:   1    Changes:            51 
Iteration:   2    Changes:             2 
Iteration:   3    Changes:             0 
> resultindexes <- clustIndex(cl,x, index="all")
> resultindexes   
   calinski      cindex          db    hartigan   ratkowsky       scott 
302.6691152          NA   0.2192710   1.1276727   0.6147506 188.8758605 
    marriot        ball      trcovw      tracew    friedman       rubin 
227.7817782   7.4589482  49.9039448  15.2216923   5.6687377   6.6111565 
        ssi  likelihood     xuindex 
  0.9404286  15.8764532  -6.5077788 
> 
> 
> 
> cleanEx(); ..nameEx <- "cclust"
> 
> ### * cclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: cclust
> ### Title: Convex Clustering
> ### Aliases: cclust kmeans hardcl neuralgas print.cclust
> ### Keywords: cluster
> 
> ### ** Examples
> 
> ## a 2-dimensional example
> x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2),
+          matrix(rnorm(100,mean=1,sd=0.3),ncol=2))
> cl<-cclust(x,2,20,verbose=TRUE,method="kmeans")
Iteration:   1    Changes:            51 
Iteration:   2    Changes:             2 
Iteration:   3    Changes:             0 
> plot(x, col=cl$cluster)   
> 
> ## a 3-dimensional example 
> x<-rbind(matrix(rnorm(150,sd=0.3),ncol=3),
+          matrix(rnorm(150,mean=1,sd=0.3),ncol=3),
+          matrix(rnorm(150,mean=2,sd=0.3),ncol=3))
> cl<-cclust(x,6,20,verbose=TRUE,method="kmeans")
Iteration:   1    Changes:           100 
Iteration:   2    Changes:            10 
Iteration:   3    Changes:             7 
Iteration:   4    Changes:             9 
Iteration:   5    Changes:             3 
Iteration:   6    Changes:             0 
> plot(x, col=cl$cluster)
> 
> ## assign classes to some new data
> y<-rbind(matrix(rnorm(33,sd=0.3),ncol=3),
+          matrix(rnorm(33,mean=1,sd=0.3),ncol=3),
+          matrix(rnorm(3,mean=2,sd=0.3),ncol=3))
>          ycl<-predict(cl, y)
>          plot(y, col=ycl$cluster)
> 
> 
> 
> cleanEx(); ..nameEx <- "predict.cclust"
> 
> ### * predict.cclust
> 
> flush(stderr()); flush(stdout())
> 
> ### Name: predict.cclust
> ### Title: Assign clusters to new data
> ### Aliases: predict.cclust
> ### Keywords: cluster
> 
> ### ** Examples
> 
> # a 2-dimensional example
> x<-rbind(matrix(rnorm(100,sd=0.3),ncol=2),
+          matrix(rnorm(100,mean=1,sd=0.3),ncol=2))
> cl<-cclust(x,2,20,verbose=TRUE,method="kmeans")
Iteration:   1    Changes:            51 
Iteration:   2    Changes:             2 
Iteration:   3    Changes:             0 
> plot(x, col=cl$cluster)   
> 
> # a 3-dimensional example
> x<-rbind(matrix(rnorm(150,sd=0.3),ncol=3),
+          matrix(rnorm(150,mean=1,sd=0.3),ncol=3),
+          matrix(rnorm(150,mean=2,sd=0.3),ncol=3))
> cl<-cclust(x,6,20,verbose=TRUE,method="kmeans")
Iteration:   1    Changes:           100 
Iteration:   2    Changes:            10 
Iteration:   3    Changes:             7 
Iteration:   4    Changes:             9 
Iteration:   5    Changes:             3 
Iteration:   6    Changes:             0 
> plot(x, col=cl$cluster)
> 
> # assign classes to some new data
> y<-rbind(matrix(rnorm(33,sd=0.3),ncol=3),
+          matrix(rnorm(33,mean=1,sd=0.3),ncol=3),
+          matrix(rnorm(3,mean=2,sd=0.3),ncol=3))
> ycl<-predict(cl, y)
> plot(y, col=ycl$cluster)
> 
> 
> 
> ### * <FOOTER>
> ###
> cat("Time elapsed: ", proc.time() - get("ptime", env = .CheckExEnv),"\n")
Time elapsed:  0.14 0.01 0.15 0 0 
> grDevices::dev.off()
null device 
          1 
> ###
> ### Local variables: ***
> ### mode: outline-minor ***
> ### outline-regexp: "\\(> \\)?### [*]+" ***
> ### End: ***
> quit('no')