Question

出于教育目的，我想保存kmeans-clustering-algorithm的每个迭代步骤。据我所知，有两种方法可以做到这一点：

逐步执行kmeans功能
建立自己的功能

我的问题是我首先想了解kmeans函数来执行自己的功能-但是我无法逐步执行kmeans函数。

最后，我想获得一个像这样的东西：

但是，当我尝试逐步执行kmeans中的R函数时。我从两个switch函数中得到一个错误。有人可以向我解释如何替换这里的开关功能吗？

我使用Hartigan-Wong算法。我想获取kmeans(iris[,3:4],3,nstart=15)

的每个迭代

错误来了 -如果我尝试执行

`nmeth <- switch(match.arg(algorithm), `Hartigan-Wong` = 1L, 
        Lloyd = 2L, Forgy = 2L, MacQueen = 3L)`

我得到Error in match.arg(algorithm) : 'arg' should be one of

如果我尝试执行do_one(nmeth)，我会得到Error in do_one(nmeth) : object 'C_kmns' not found

kmeans函数的代码：

function (x, centers, iter.max = 10L, nstart = 1L, algorithm = c("Hartigan-Wong", 
    "Lloyd", "Forgy", "MacQueen"), trace = FALSE) 
{
    .Mimax <- .Machine$integer.max
    do_one <- function(nmeth) {
        switch(nmeth, {
            isteps.Qtran <- as.integer(min(.Mimax, 50 * m))
            iTran <- c(isteps.Qtran, integer(max(0, k - 1)))
            Z <- .Fortran(C_kmns, x, m, p, centers = centers, 
                as.integer(k), c1 = integer(m), c2 = integer(m), 
                nc = integer(k), double(k), double(k), ncp = integer(k), 
                D = double(m), iTran = iTran, live = integer(k), 
                iter = iter.max, wss = double(k), ifault = as.integer(trace))
            switch(Z$ifault, stop("empty cluster: try a better set of initial centers", 
                call. = FALSE), Z$iter <- max(Z$iter, iter.max + 
                1L), stop("number of cluster centres must lie between 1 and nrow(x)", 
                call. = FALSE), warning(gettextf("Quick-TRANSfer stage steps exceeded maximum (= %d)", 
                isteps.Qtran), call. = FALSE))
        }, {
            Z <- .C(C_kmeans_Lloyd, x, m, p, centers = centers, 
                k, c1 = integer(m), iter = iter.max, nc = integer(k), 
                wss = double(k))
        }, {
            Z <- .C(C_kmeans_MacQueen, x, m, p, centers = as.double(centers), 
                k, c1 = integer(m), iter = iter.max, nc = integer(k), 
                wss = double(k))
        })
        if (m23 <- any(nmeth == c(2L, 3L))) {
            if (any(Z$nc == 0)) 
                warning("empty cluster: try a better set of initial centers", 
                  call. = FALSE)
        }
        if (Z$iter > iter.max) {
            warning(sprintf(ngettext(iter.max, "did not converge in %d iteration", 
                "did not converge in %d iterations"), iter.max), 
                call. = FALSE, domain = NA)
            if (m23) 
                Z$ifault <- 2L
        }
        if (nmeth %in% c(2L, 3L)) {
            if (any(Z$nc == 0)) 
                warning("empty cluster: try a better set of initial centers", 
                  call. = FALSE)
        }
        Z
    }
    x <- as.matrix(x)
    m <- as.integer(nrow(x))
    if (is.na(m)) 
        stop("invalid nrow(x)")
    p <- as.integer(ncol(x))
    if (is.na(p)) 
        stop("invalid ncol(x)")
    if (missing(centers)) 
        stop("'centers' must be a number or a matrix")
    nmeth <- switch(match.arg(algorithm), `Hartigan-Wong` = 1L, 
        Lloyd = 2L, Forgy = 2L, MacQueen = 3L)
    storage.mode(x) <- "double"
    if (length(centers) == 1L) {
        k <- centers
        if (nstart == 1L) 
            centers <- x[sample.int(m, k), , drop = FALSE]
        if (nstart >= 2L || any(duplicated(centers))) {
            cn <- unique(x)
            mm <- nrow(cn)
            if (mm < k) 
                stop("more cluster centers than distinct data points.")
            centers <- cn[sample.int(mm, k), , drop = FALSE]
        }
    }
    else {
        centers <- as.matrix(centers)
        if (any(duplicated(centers))) 
            stop("initial centers are not distinct")
        cn <- NULL
        k <- nrow(centers)
        if (m < k) 
            stop("more cluster centers than data points")
    }
    k <- as.integer(k)
    if (is.na(k)) 
        stop(gettextf("invalid value of %s", "'k'"), domain = NA)
    if (k == 1L) 
        nmeth <- 3L
    iter.max <- as.integer(iter.max)
    if (is.na(iter.max) || iter.max < 1L) 
        stop("'iter.max' must be positive")
    if (ncol(x) != ncol(centers)) 
        stop("must have same number of columns in 'x' and 'centers'")
    storage.mode(centers) <- "double"
    Z <- do_one(nmeth)
    best <- sum(Z$wss)
    if (nstart >= 2L && !is.null(cn)) 
        for (i in 2:nstart) {
            centers <- cn[sample.int(mm, k), , drop = FALSE]
            ZZ <- do_one(nmeth)
            if ((z <- sum(ZZ$wss)) < best) {
                Z <- ZZ
                best <- z
            }
        }
    centers <- matrix(Z$centers, k)
    dimnames(centers) <- list(1L:k, dimnames(x)[[2L]])
    cluster <- Z$c1
    if (!is.null(rn <- rownames(x))) 
        names(cluster) <- rn
    totss <- sum(scale(x, scale = FALSE)^2)
    structure(list(cluster = cluster, centers = centers, totss = totss, 
        withinss = Z$wss, tot.withinss = best, betweenss = totss - 
            best, size = Z$nc, iter = Z$iter, ifault = Z$ifault), 
        class = "kmeans")
}

kmeans：保存每个迭代步骤

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