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#### PAM : Partitioning Around Medoids
#### --- $Id: pam.q 8260 2023-08-11 20:10:20Z maechler $
pam <- function(x, k, diss = inherits(x, "dist"),
metric = c("euclidean", "manhattan"), ## FIXME: add "jaccard"
medoids = if(is.numeric(nstart)) "random",
nstart = if(variant == "faster") 1L else NA,
stand = FALSE, cluster.only = FALSE, do.swap = TRUE,
keep.diss = !diss && !cluster.only && n < 100,
keep.data = !diss && !cluster.only,
variant = c("original", "o_1", "o_2", "f_3", "f_4", "f_5", "faster"),
pamonce = FALSE, trace.lev = 0)
{
stopifnot(length(cluster.only) == 1, length(trace.lev) == 1)
nMax <- 65536 # 2^16 (as 1+ n(n-1)/2 must be < max_int = 2^31-1)
if((diss <- as.logical(diss))) {
## check type of input vector
if(anyNA(x)) stop("NA values in the dissimilarity matrix not allowed.")
if(keep.data) stop("Cannot keep data when 'x' is a dissimilarity!")
if(!inherits(x, "dissimilarity")) { # try to convert to
if(!is.null(dim(x))) {
x <- as.dist(x) # or give an error
} else {
## possibly convert input *vector*
if(!is.numeric(x) || is.na(n <- sizeDiss(x)))
stop("'x' is not and cannot be converted to class \"dissimilarity\"")
attr(x, "Size") <- n
}
class(x) <- dissiCl
if(is.null(attr(x,"Metric"))) attr(x, "Metric") <- "unspecified"
}
## adapt S dissimilarities to Fortran:
## convert upper matrix, read by rows, to lower matrix, read by rows.
n <- attr(x, "Size")
if(n > nMax)
stop(gettextf("have %d observations, but not more than %d are allowed",
n, nMax))
dv <- x[lower.to.upper.tri.inds(n)]
## prepare arguments for the Fortran call
dv <- c(0, dv) ## <- internally needed {FIXME! memory hog!}
storage.mode(dv) <- "double"
jp <- 1
mdata <- FALSE
ndyst <- 0
}
else {
## check input matrix and standardize, if necessary
x <- data.matrix(x)# dropping "automatic rownames" compatibly with daisy()
if(!(is.numeric(x) || is.logical(x))) stop("x is not a numeric dataframe or matrix.")
x2 <- x ; dimnames(x2) <- NULL
n <- nrow(x2)
if(n > nMax)
stop(gettextf("have %d observations, but not more than %d are allowed",
n, nMax))
if(stand) x2 <- scale(x2, scale = apply(x2, 2, meanabsdev))
## put info about metric, size and NAs in arguments for the Fortran call
metric <- match.arg(metric)
ndyst <- c("euclidean" = 1L, "manhattan" = 2L)[[metric]]
jp <- ncol(x2)
if((mdata <- any(inax <- is.na(x2)))) { # TRUE if x[] has any NAs
jtmd <- integer(jp)
jtmd[apply(inax, 2L, any)] <- -1L
## VALue for MISsing DATa
valmisdat <- 1.1* max(abs(range(x2, na.rm=TRUE)))
x2[inax] <- valmisdat
}
storage.mode(x2) <- "double"
}
if((k <- as.integer(k)) < 1 || k >= n)
stop("Number of clusters 'k' must be in {1,2, .., n-1}; hence n >= 2")
missVari <- missing(variant)
variant <- match.arg(variant) # incl. validity check
if(!missVari) {
if(!missing(pamonce))
stop("Set either 'variant' or 'pamonce', but not both")
pamonce <- -1L + ## 0 1 2 3 4 5 6
match(variant, c("original", "o_1", "o_2", "f_3", "f_4", "f_5", "faster"))
if(missing(medoids) && variant == "faster")
medoids <- "random"
} ## else if(!missing(pamonce)) Deprecated("use 'variant' instead")
if(randIni <- identical("random", medoids))
medoids <- sample.int(n, k)
else if(!is.null(medoids)) { # non-default: check provided medoids
## 'fixme': consider sort(medoids) {and rely on it in ../src/pam.c }
if(!is.integer(medoids))
medoids <- as.integer(medoids)
if(length(medoids) != k || any(medoids < 1L) || any(medoids > n) ||
any(duplicated(medoids)))
stop(gettextf(
"'medoids' must be NULL or vector of %d distinct indices in {1,2, .., n}, n=%d",
k, n))
## use observation numbers 'medoids' as starting medoids for 'swap' only
}
nisol <- integer(if(cluster.only) 1 else k)
if(do.swap) nisol[1] <- 1L
pamDo <- function(medoids) {
.Call(cl_Pam, k, n,
!diss, # == do_diss: compute d[i,j] them from x2[] and allocate in C
if(diss) dv else x2,
!cluster.only, ## == all_stats == "old" obj[1+ 0] == 0
medoids,
do.swap, trace.lev, keep.diss, pamonce,
## only needed if(!diss) [ <=> if(do_diss) ] :
if(mdata) rep(valmisdat, jp) else double(1), # valmd
if(mdata) jtmd else integer(jp), # jtmd
ndyst) # dist_kind
}
res <- pamDo(medoids)
## Error if have NA's in diss:
if(!diss && is.integer(res))
stop("No clustering performed, NAs in the computed dissimilarity matrix.")
if(randIni && nstart >= 2) {
it <- 0L
for(it in 2:nstart) {
r <- pamDo(medoids = sample.int(n, k))
if(r$obj[2] < res$obj[2]) {
if(trace.lev)
cat(sprintf("Found better objective, %g < %g (it=%d)\n",
r$obj[2], res$obj[2], it))
res <- r
}
}
} ## else just once
xLab <- if(diss) attr(x, "Labels") else dimnames(x)[[1]]
r.clu <- res$clu
if(length(xLab) > 0)
names(r.clu) <- xLab
if(cluster.only)
return(r.clu)
## Else, usually
medID <- res$med
if(any(medID <= 0))
stop("error from .C(cl_pam, *): invalid medID's")
sildim <- res$silinf[, 4]
if(diss) {
## add labels to Fortran output
r.med <- if(length(xLab) > 0) {
sildim <- xLab[sildim]
xLab[medID]
} else medID
}
else {
if(keep.diss) {
## adapt Fortran output to S:
## convert lower matrix, read by rows, to upper matrix, read by rows.
disv <- res$dys[-1]
disv[disv == -1] <- NA
disv <- disv[upper.to.lower.tri.inds(n)]
class(disv) <- dissiCl
attr(disv, "Size") <- nrow(x)
attr(disv, "Metric") <- metric
attr(disv, "Labels") <- dimnames(x)[[1]]
}
## add labels to Fortran output
r.med <- x[medID, , drop=FALSE]
if(length(xLab) > 0)
sildim <- xLab[sildim]
}
## add names & dimnames to Fortran output
r.obj <- structure(res$obj, .Names = c("build", "swap"))
r.isol <- factor(res$isol, levels = 0:2, labels = c("no", "L", "L*"))
names(r.isol) <- 1:k
r.clusinf <- res$clusinf
dimnames(r.clusinf) <- list(NULL, c("size", "max_diss", "av_diss",
"diameter", "separation"))
## construct S object
r <-
list(medoids = r.med, id.med = medID, clustering = r.clu,
objective = r.obj, isolation = r.isol,
clusinfo = r.clusinf,
silinfo = if(k != 1) {
silinf <- res$silinf[, -4, drop=FALSE]
dimnames(silinf) <-
list(sildim, c("cluster", "neighbor", "sil_width"))
list(widths = silinf,
clus.avg.widths = res$avsil[1:k],
avg.width = res$ttsil)
},
diss = if(keep.diss) { if(diss) x else disv },
call = match.call())
if(keep.data) { ## have !diss
if(mdata) x2[x2 == valmisdat] <- NA
r$data <- structure(x2, dimnames = dimnames(x))
}
class(r) <- c("pam", "partition")
r
}
### From Schubert, Dec 2020 --- but MM decides to rather implement pam(*, variant = "faster")
if(FALSE) ## FasterPAM : Faster Partitioning Around Medoids
fasterpam <- function(x, k, diss = inherits(x, "dist"),
metric = c("euclidean", "manhattan"), ## FIXME: add "jaccard"
medoids = NULL,
stand = FALSE, cluster.only = FALSE, # do.swap = TRUE, ## (not here)
keep.diss = !diss && !cluster.only && n < 100,
keep.data = !diss && !cluster.only,
## pamonce = FALSE, ## (not here)
trace.lev = 0)
{
if((diss <- as.logical(diss))) {
n <- attr(x, "Size")
} else {
n <- nrow(x)
}
if (is.null(medoids)) {
medoids = sample.int(n, k)
}
pam(x = x, k = k, diss = diss, metric = metric, medoids = medoids,
stand = stand, cluster.only = cluster.only, do.swap = TRUE,
keep.diss = keep.diss, keep.data = keep.data, pamonce = 6, trace.lev = trace.lev)
}
## non-exported:
.print.pam <- function(x, ...) {
cat("Medoids:\n"); print(cbind(ID = x$id.med, x$medoids), ...)
cat("Clustering vector:\n"); print(x$clustering, ...)
cat("Objective function:\n"); print(x$objective, ...)
}
print.pam <- function(x, ...)
{
.print.pam(x, ...)
cat("\nAvailable components:\n")
print(names(x), ...)
invisible(x)
}
summary.pam <- function(object, ...)
{
class(object) <- "summary.pam"
object
}
print.summary.pam <- function(x, ...)
{
.print.pam(x, ...)
cat("\nNumerical information per cluster:\n"); print(x$clusinfo, ...)
cat("\nIsolated clusters:\n L-clusters: ")
print(names(x$isolation[x$isolation == "L"]), quote = FALSE, ...)
cat(" L*-clusters: ")
print(names(x$isolation[x$isolation == "L*"]), quote = FALSE, ...)
if(length(x$silinfo) != 0) {
cat("\nSilhouette plot information:\n")
print(x$silinfo[[1]], ...)
cat("Average silhouette width per cluster:\n")
print(x$silinfo[[2]], ...)
cat("Average silhouette width of total data set:\n")
print(x$silinfo[[3]], ...)
}
if(!is.null(x$diss)) { ## Dissimilarities:
cat("\n"); print(summary(x$diss, ...))
}
cat("\nAvailable components:\n"); print(names(x), ...)
invisible(x)
}
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