R/kmeans.fd.R

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

#' @name kmeans.fd
#' @title K-Means Clustering for functional data
#' 
#' @description Perform k-means clustering on functional data.
#' 
#' @details The method searches the locations around which are grouped data (for a
#' predetermined number of groups).\cr
#' 
#' If \code{ncl=NULL}, randomizes the initial centers, \code{ncl=2} using
#' \code{kmeans.center.ini} function.\cr If \code{ncl} is an integer,
#' indicating the number of groups to classify,\cr are selected \code{ncl}
#' initial centers using \code{kmeans.center.ini} function.\cr If \code{ncl} is
#' a vector of integers, indicating the position of the initial centers with
#' \code{length(ncl)} equal to number of groups.\cr If \code{ncl} is a
#' \code{fdata} class objecct, \code{ncl} are the initial centers curves with
#' \code{nrow(ncl)} number of groups.\cr
#' 
#' @aliases kmeans.fd 
#' 
#' @param fdataobj \code{\link{fdata}} class object.
#' @param ncl See details section.
#' @param metric Metric function, by default \code{\link{metric.lp}}.
#' @param dfunc Type of depth measure, by default FM depth.
#' @param max.iter Maximum number of iterations for the detection of centers.
#' @param draw =TRUE, draw the curves in the color of the centers.
#' @param par.dfunc List of arguments to pass to the \code{dfunc} function .
# @param par.ini List of arguments to pass to the \code{kmeans.center.ini} function .
#' @param method Method for selecting initial centers. If
#' \code{method}=\emph{"Sample"} (by default) takes \code{n} times a random
#' selection by the \code{ncl} centers. The \code{ncl} curves with greater
#' distance are the initial centers. If \code{method}=\emph{"Exact"} calculated
#' all combinations (if < 1e+6) of \code{ncl} centers. The \code{ncl} curves with greater
#' distance are the initial centers (this method may be too slow).
#' @param cluster.size Minimum cluster size (by default is 5). If a cluster has fewer curves,
#' it is eliminated and the process is continued with a less cluster.
#' @param max.comb Maximum number of initial selection of
#' centers (only used when \code{method="exact"}).
#' @param par.metric List of arguments to pass to the \code{metric} function.
# @param group groups or classes
#' @param \dots Further arguments passed to or from other methods.
#' 
#' @return Return:
#' \itemize{
#'  \item \code{cluster}{ Indexes of groups assigned.}  
#' \item \code{centers}{ Curves centers.}
#  %\item{lcenters}{ Indexes of initial curves centers.}
#' }
#' 
#' @author Manuel Febrero-Bande, Manuel Oviedo de la Fuente
#' \email{manuel.oviedo@@udc.es}
#' 
#' @seealso See Also generic \link[stats]{kmeans} function.
#' 
#' @references Hartigan, J. A. and Wong, M. A. (1979). \emph{A K-means
#' clustering algorithm}. Applied Statistics 28, 100 \-108.
#' 
#' @keywords cluster
#' @examples
#' \dontrun{
#' library(fda.usc)
#' data(phoneme)
#' mlearn<-phoneme$learn[c(1:50,101:150,201:250),]
#' # Unsupervised classification
#' out.fd1=kmeans.fd(mlearn,ncl=3,draw=TRUE)
#' out.fd2=kmeans.fd(mlearn,ncl=3,draw=TRUE,method="exact")
#' # Different Depth function
#' ind=c(17,77,126)
#' out.fd3=kmeans.fd(mlearn,ncl=mlearn[ind,],draw=FALSE,
#' dfunc=func.trim.FM,par.dfunc=list(trim=0.1))
#' out.fd4=kmeans.fd(mlearn,ncl=mlearn[ind,],draw=FALSE,
#' dfunc=func.med.FM)
#' group=c(rep(1,50),rep(2,50),rep(3,50))
#' table(out.fd4$cluster,group)
#' }
#' 
#' @rdname kmeans.fd
#' @export
kmeans.fd=function(fdataobj,ncl=2,metric=metric.lp
                   ,dfunc=func.trim.FM,max.iter=100
                   ,par.metric=NULL,par.dfunc=list(trim=0.05)
                   ,method="sample", cluster.size=5,draw=TRUE,...) {
#if (is.data.frame(z)) z=data.matrix(z)
#else if (is.vector(z))     z <- data.matrix(t(z))
if (!is.fdata(fdataobj)) fdataobj=fdata(fdataobj)
 # nas1<-is.na.fdata(fdataobj)
nas1<-is.na(fdataobj)
 if (any(nas1))  stop("fdataobj contain ",sum(nas1)," curves with some NA value \n")
z<-fdataobj[["data"]]
tt<-fdataobj[["argvals"]]
rtt<-fdataobj[["rangeval"]]
nr=nrow(z)
nc=ncol(z)
if (is.vector(ncl)) {
  len.ncl=length(ncl)
  if (len.ncl==1) {
    par.ini<- list()
    par.ini$fdataobj=fdataobj
    par.ini$method=method
    par.ini$ncl=ncl
    par.ini$metric=metric
    par.ini$draw=draw
    par.ini$max.comb = 1e6
    par.ini$max.iter = max.iter
    if (!is.null(par.metric)) 
       par.ini$par.metric<-par.metric
    par.ini$... <- par.metric
    out1=do.call(kmeans.center.ini,par.ini)
    lxm<-out1$lcenters
    out1$d=rbind(out1$z.dist,out1$z.dist[lxm,])
    }  else {
     ngroups <- length(ncl)
     lxm <- ncl
     xm <- z[lxm,]
     out1 <- list()
     out1$fdataobj <- fdataobj
     out1$ncl <- len.ncl
     if (is.null(par.metric)) 
       par.metric <- list("p"=2,"w"=1)
     par.metric$fdata1 <- fdataobj
     mdist <- do.call(metric,par.metric)
     out1$z.dist <- mdist
     out1$d <- rbind(mdist,mdist[lxm,])
     out1$centers <- fdataobj[ncl,]
     out1$lcenters <- ncl
     class(out1) <- "kmeans.fd"
    }
  } else if (is.fdata(ncl)) {   # fdata centers
   lxm=NULL
   xm=ncl[["data"]]
   if (is.null(par.metric)
   ) par.metric=list("p"=2,"w"=1)
   par.metric$fdata1<-fdataobj
   #mdist=metric(fdataobj,...)
   mdist=do.call(metric,par.metric)
   par.metric2<-par.metric
   par.metric2$fdata2<-ncl
   mdist2=do.call(metric,par.metric2)
   out1 = list()
   out1$fdataobj<-fdataobj
   out1$centers = ncl
   out1$lcenters <- NULL
   ngroups=nrow(ncl)
   ncl = nrow(ncl)
   out1$d = rbind(mdist,t(mdist2))
   class(out1) = "kmeans.fd"
}
 ngroups=nrow(out1$centers[["data"]])
 a=0;aa<-i<-1
 same_centers=FALSE
if (is.null(colnames(out1$d))) 
  cnames<-colnames(out1$d)<-1:NCOL(out1$d)
else cnames<-colnames(out1$d)
while ((i<max.iter) && (!same_centers)) {
  iterar<-FALSE
 out3=kmeans.assig.groups(out1,draw=draw)
  names(out3$cluster) <- cnames
 
  tab <- table(out3$cluster)
  imin <- which.min(tab)[1]
  if (cluster.size > tab[imin] ) {
    warning(paste0(" One of the clusters only has ",tab[imin]
    , " curves and the minimum cluster size is ", cluster.size
    ,".\n The cluster is completed with the closest curves of the other clusters."))
    # names(sort(aa)[1:5])
    iclust <- out3$cluster == imin
    dist.aux <- out1$d[imin,]
    icambios <- as.numeric(names(sort( out1$d[imin,])[1:cluster.size]))
    #out1$d[imin,iclust]<- 0
    out1$d[imin,icambios]<- 0
    #out1$d[icambios,icambios]<- 0
    out1$z.dist<-out1$d
    out3$cluster[icambios] <- imin
    out2<-out3
     out1$cluster <- out3$cluster
    par.dfunc$fdataobj<-fdataobj[c(icambios)]
    out1$centers[imin]=do.call(dfunc,par.dfunc)
    iterar <-TRUE
    i=i+1
    
 }
#else{
#     # print("2. update")
     out2=kmeans.centers.update(out1, group=out3$cluster
                             , dfunc=dfunc, draw=draw
                             , par.dfunc=par.dfunc
                             #,cluster.size=cluster.size
                             ,...)
    if (!iterar){
    same_centers <- out2$centers$data == out3$centers$data
    out1$centers <- out2$centers
    i=i+1
    }
  #}
    #cat("iterations: ",i)
  }

out<-list("cluster"=out2$cluster,"centers"=out2$centers)
return(out)
}