Question

我将光谱聚类应用于具有4200行和2列的数据集。

spec <- specClust(df1, centers=7, nn = 7, method = "symmetric")

我有以下错误。

n .Call("R_igraph_arpack", func, extra, options, env, sym, PACKAGE = "igraph") : 
  At arpack.c:944 : ARPACK error, Maximum number of iterations reached
In addition: Warning message:
In .Call("R_igraph_arpack", func, extra, options, env, sym, PACKAGE = "igraph") :
  At arpack.c:776 :ARPACK solver failed to converge (1001 iterations, 0/7 eigenvectors converged)

如何增加arpack的迭代次数，因为这不起作用：

spec <- specClust(df1, centers=7, nn = 7, method = "symmetric",iter.max=301000)

Answer 1

进入specClust，...未向arpack电话传递任何内容。

我认为最简单的方法是复制specClust代码添加maxiter=10000并在脚本中提供该函数。

specCLust2 <- function (data, centers = NULL, nn = 7, method = "symmetric", 
    gmax = NULL, max.iter = 10000, ...) 
{
    call = match.call()
    if (is.data.frame(data)) 
        data = as.matrix(data)
    da = apply(data, 1, paste, collapse = "#")
    indUnique = which(!duplicated(da))
    indAll = match(da, da[indUnique])
    data2 = data
    data = data[indUnique, ]
    n <- nrow(data)
    data = scale(data, FALSE, TRUE)
    if (is.null(gmax)) {
        if (!is.null(centers)) 
            gmax = centers - 1L
        else gmax = 1L
    }
    test = TRUE
    while (test) {
        DC = mydist(data, nn)
        sif <- rbind(1:n, as.vector(DC[[2]]))
        g <- graph(sif, directed = FALSE)
        g <- decompose(g, min.vertices = 4)
        if (length(g) > 1) {
            if (length(g) >= gmax) 
                nn = nn + 2
            else test = FALSE
        }
        else test = FALSE
    }
    W <- DC[[1]]
    n <- nrow(data)
    wi <- W[, nn]
    SC <- matrix(1, nrow(W), nn)
    SC[] <- wi[DC[[2]]] * wi
    W = W^2/SC
    alpha = 1/(2 * (nn + 1))
    qua = abs(qnorm(alpha))
    W = W * qua
    W = dnorm(W, sd = 1)
    DC[[1]] = W
    L = Laplacian(DC, nn, method)
    f <- function(x, extra) as.vector(extra %*% x)
    if (is.null(centers)) 
        kmax = 25
    else kmax = max(centers)
###
#add the maxiter parameter to the arpack call, below
###    
    U <- arpack(f, extra = L, options = list(n = n, which = "SM", 
        nev = kmax, ncv = 2 * kmax, mode = 1, maxiter=max.iter), sym = TRUE)
    ind <- order(U[[1]])
    U[[2]] = U[[2]][indAll, ind]
    U[[1]] = U[[1]][ind]
    if (is.null(centers)) {
        tmp = which.max(diff(U[[1]])) + 1
        centers = which.min(AUC(U[[1]][1:tmp]))
    }
    if (method == "symmetric") {
        rs = sqrt(rowSums(U[[2]]^2))
        U[[2]] = U[[2]]/rs
    }
    result = kmeans(U[[2]], centers = centers, nstart = 20, ...)
    archeType = getClosest(U[[2]][indAll, ], result$centers)
    result$eigenvalue = U[[1]]
    result$eigenvector = U[[2]]
    result$data = data2
    result$indAll = indAll
    result$indUnique = indUnique
    result$L = L
    result$archetype = archeType
    result$call = call
    class(result) = c("specClust", "kmeans")
    result
}

kknn中的specClust（） - arpack迭代限制增加

1 个答案: