R/kmeans.start.R

In mda: Mixture and Flexible Discriminant Analysis

kmeans.start <-
function (x, g, subclasses) 
{
    cnames <- levels(g <- factor(g))
    J <- length(cnames)
    g <- as.numeric(g)
    weights <- as.list(cnames)
    names(weights) <- cnames
    subclasses <- rep(subclasses, length = length(cnames))
    R <- sum(subclasses)
    cl <- rep(seq(J), subclasses)
    cx <- x[seq(R), , drop = FALSE]
    for (j in seq(J)) {
        nc <- subclasses[j]
        which <- cl == j
        xx <- x[g == j, , drop = FALSE]
        if ((nc <= 1) || (nrow(xx) <= nc)) {
            cx[which, ] <- apply(xx, 2, mean)
            wmj <- matrix(1, sum(g == j), 1)
        }
        else {
###            start <- xx[sample(1:nrow(xx), size = nc), ]
          start=nc
          TT <- kmeans(xx, start)
            cx[which, ] <- TT$centers
            wmj <- diag(nc)[TT$cluster, ]
        }
        dimnames(wmj) <-
            list(NULL, paste("s", seq(dim(wmj)[2]), sep = ""))
        weights[[j]] <- wmj
    }
    list(x = cx, cl = factor(cl, labels = cnames), weights = weights)
}