GenerateRCs <- function(data)
{
#    print(dim(data))
#    if (data==NULL) 
#    {
#        message <- tktoplevel()
#        label.widget <- tklabel(message, text="Must Import Data First")
#        button.widget <- tkbutton(message, text="OK", command=function() tkdestroy(message))
#        tkpack(label.widget, button.widget)
#        return()
#    } 
#    library(tkWidgets) 

    library(cluster)

    geneid = data[[2]]
    expts = data[[3]]
    data = data[[1]]
    datadim = dim(data)
    n=datadim[1]
    
    methods=c("SingleLink", "CompleteLink", "AverageLink", "PAM", "KMEANS")
    selection=pickItems(methods)
    nm = length(selection)
    params=GetRCParameter()
    nk=as.double(params[[1]])
      
    print(selection)
    print(params)

#   print(n)
#   print(len)
    pearsonmat = cor(t(data), use="pairwise")
#   print(dim(pearsonmat))
    pearsonmat = as.dist(1-abs(pearsonmat))

    
    #AVLINK
    avgo=FALSE
    for (i in 1:nm)
    {
            if (selection[i]=="AverageLink") avgo=TRUE
    }
    if (avgo==TRUE)
    {
        avlink = hclust(pearsonmat, method="average")
        avlink = cutree(avlink, k=nk)
    }

    print(nk);
    #SINGLINK
    singgo=FALSE
    for (i in 1:nm)
    {
            if (selection[i]=="SingleLink") singgo=TRUE
    }
    if (singgo==TRUE)
    {
        singlink = hclust(pearsonmat, method="single")
        singlink = cutree(singlink, k=nk)
    }
    #COMPLINK
    compgo=FALSE
    for (i in 1:nm)
    {
            if (selection[i]=="CompleteLink") compgo=TRUE
    }
    if (compgo==TRUE)
    {
        complink = hclust(pearsonmat, method="complete")
        complink = cutree(complink, k=nk)
    }
    #PAM
    pamgo=FALSE
    for (i in 1:nm)
    {
            if (selection[i]=="PAM") pamgo=TRUE
    }
    if (pamgo==TRUE)
    {
        pamres = pam(pearsonmat, k=nk)
        pamres = pamres$cluster
    }
    #KMEANS
    kmgo=FALSE
    for (i in 1:nm)
    {
            if (selection[i]=="KMEANS") kmgo=TRUE
    }
    if (kmgo==TRUE)
    {    
        #Get number of clusters
        
        kmeansres = kmeans(data, nk, 100)
        kmeansres = kmeansres$cluster
    }
    
#    print(singlink)
#    print(complink)
#    print(avlink)
#    print(pamres)
#    print(kmeansres)

    clusters = c()
    if (avgo==TRUE) 
    {
        clusters = c(clusters, avlink) 
#        print(avlink)
    }
    if (singgo==TRUE) 
    {
        clusters = c(clusters, singlink)
#        print(singlink)
    }
    if (compgo==TRUE) 
    {
        clusters = c(clusters, complink) 
#        print(complink)
    }
    if (pamgo==TRUE)   
    {
        clusters = c(clusters, pamres) 
#        print(pamres)
    }
    if (kmgo==TRUE) 
    {
        clusters = c(clusters, kmeansres) 
#        print(kmeansres)
    }
#    print(c("test:",nm,avgo,singgo,compgo,pamgo,kmgo,length(clusters)))
    clusters = matrix(clusters, n, nm)
    
    amat = Agreemat(t(clusters))
    print(dim(amat))
    rc = Robust(geneid,amat,nm)
    
#    print(rc)
    #WRITE TO TEXTFILE
#    print(length(rc))

    for (i in 1:length(rc))
    {   
#        print(rc[i])
        write(toString(rc[[i]]), file="RobustClusters.out", append=TRUE)
    }
    GenerateRCs = rc
}