specClust() dans kknn - arpack augmentation de la limite d'itération

Question

J'applique la classification spectrale à un ensemble de données de 4200 lignes et 2 colonnes.

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

J'ai l'erreur ci-dessous.

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) 

Comment augmenter les itérations de arpack parce que ce travail ne marche pas:

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

Answer 1

Creuser dans le specClust, le ... ne passe rien à l'appel arpack.

La chose la plus simple à faire, je pense est de copier le code specClust ajouter maxiter=10000 et source de la fonction dans votre script.

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 
}