Unfortunatelly, i have to "fill" all the cells, with numbers..., so I need a
better machine, or i have to split the data for smaller
parts, but that way is much slower, but i see i dont have other alternative
way.
But thanx for your help, because i work with big networks too (10000
vertex), and with this package i sholuld speed up my work.. I have to
simulate "little world" networks -> Does anybody knows any package what
colud make a network like that type?
2007/1/8, Martin Maechler <[EMAIL PROTECTED]>:
>
> >>>>> "UweL" == Uwe Ligges <[EMAIL PROTECTED]>
> >>>>> on Sun, 07 Jan 2007 09:42:08 +0100 writes:
>
> UweL> Zoltan Kmetty wrote:
> >> Hi!
> >>
> >> I had some memory problem with R - hope somebody could
> >> tell me a solution.
> >>
> >> I work with very large datasets, but R cannot allocate
> >> enough memoty to handle these datasets.
> >>
> >> I want work a matrix with row= 100 000 000 and column=10
> >>
> >> A know this is 1 milliard cases, but i thought R could
> >> handle it (other commercial software like spss could do),
> >> but R wrote out everytime: not enough memory..
> >>
> >> any good idea?
>
> UweL> Buy a machine that has at least 8Gb (better 16Gb) of
> UweL> RAM and proceed ...
>
> Well, I doubt that Zoltan wants to *fill* his matrix with all
> non-zeros. If he does, Uwe and Roger are right.
>
> Otherwise, working with a *sparse* matrix, using the 'Matrix'
> (my recommendation, but I am biased) or 'SparseM' package, might
> well be feasible:
>
> install.packages("Matrix") # if needed; only once for your R
>
> library(Matrix) # each time you need it
>
>
> TsparseMatrix <- function(nrow, ncol, i,j,x)
> {
> ## Purpose: User friendly construction of sparse "Matrix" from triple
> ##
> ----------------------------------------------------------------------
> ## Arguments: (i,j,x): 2 integer and 1 numeric vector of the same
> length:
> ##
> ## The matrix M will have
> ## M[i[k], j[k]] == x[k] , for k = 1,2,..., length(i)
> ## and M[ i', j' ] == 0 for `` all other pairs (i',j')
> ##
> ----------------------------------------------------------------------
> ## Author: Martin Maechler, Date: 8 Jan 2007, 18:46
> nnz <- length(i)
> stopifnot(length(j) == nnz, length(x) == nnz,
> is.numeric(x), is.numeric(i), is.numeric(j))
> dim <- c(as.integer(nrow), as.integer(ncol))
> ## The conformability of (i,j) with 'dim' will be checked automatically
> ## by an internal "validObject()" that is part of new(.):
> new("dgTMatrix", x = x, Dim = dim,
> ## our "Tsparse" Matrices use 0-based indices :
> i = as.integer(i - 1:1),
> j = as.integer(j - 1:1))
> }
>
> For example :
>
> > TsparseMatrix(10,20, c(1,3:8), c(2,9,6:10), 7 * (1:7))
> 10 x 20 sparse Matrix of class "dgTMatrix"
>
> [1,] . 7 . . . . . . . . . . . . . . . . . .
> [2,] . . . . . . . . . . . . . . . . . . . .
> [3,] . . . . . . . . 14 . . . . . . . . . . .
> [4,] . . . . . 21 . . . . . . . . . . . . . .
> [5,] . . . . . . 28 . . . . . . . . . . . . .
> [6,] . . . . . . . 35 . . . . . . . . . . . .
> [7,] . . . . . . . . 42 . . . . . . . . . . .
> [8,] . . . . . . . . . 49 . . . . . . . . . .
> [9,] . . . . . . . . . . . . . . . . . . . .
> [10,] . . . . . . . . . . . . . . . . . . . .
>
> But
>
> nr <- 1e8
> nc <- 10
> set.seed(1)
> i <- sample(nr, 10000)
> j <- sample(nc, 10000)
> x <- round(rnorm(10000), 2)
>
> M <- TsparseMatrix(nr, nc, i=i, j=j, x=x)
>
> works,
> e.g. you can
>
> x <- 1:10
> system.time(y <- M %*% x) # needs around 4 sec on one of our better
> machines
> y <- as.vector(y)
>
> ## but you can become even more efficient, translating from the
> ## so-called "triplet" to the (recommended) "Csparse"
> ## representation:
> M. <- as(M, "CsparseMatrix")
>
> object.size(M) / object.size(M.)
> ## 1.328921; i.e. we saved 33%
>
> ## and
>
> system.time(y. <- M. %*% x) # much faster (1 sec)
>
> identical(as.vector(y.), y)
>
>
> --- --- ---
>
> I hope this is useful to you.
>
> Martin Maechler,
> ETH Zurich
>
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