Nothing
kGmedian <- function (X, ncenters=2, gamma=1, alpha=0.75, nstart = 10, nstartkmeans=10, iter.max=20)
{
###
X <- as.matrix(X)
p <- ncol(X)
n <- nrow(X)
### Initialization
if (is.matrix(ncenters)==FALSE) {
k <- ncenters
centers = kmeans(X, ncenters,nstart=nstartkmeans,iter.max=iter.max,algorithm="MacQueen")$centers
}
else {
k <- nrow(ncenters)
centers <- ncenters
}
Z <- stoKmed_rcpp(X, X, centers, gamma=gamma, alpha = alpha)
best <- sum(Z$wss)
if (nstart >= 2) {
for (i in 2:nstart) {
# ind.cent = sample(c(k:(m+k)),k)
# centers = x[ind.cent,]
ind.init = sample(c(1:n),k)
centers = X[ind.init, ]
x0 = X
ZZ <- stoKmed_rcpp(x0, X, centers, gamma=gamma, alpha = alpha)
if ((z <- sum(ZZ$wss)) < best) {
Z = ZZ
best = z
}
}
}
centers = matrix(Z$centers, k)
dimnames(centers) = list(1L:k, dimnames(X)[[2L]])
cluster = Z$cl
#if (!is.null(rn <- rownames(x)))
# names(cluster) <- rn
out <- list(cluster = cluster, centers = centers, withinsrs = Z$wss,
size = Z$nc)
# class(out) <- "kmeans"*/
return(out)
}
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