>>>>> "SS" == Surendar Swaminathan <surendar.swaminat...@gmail.com>
>>>>> on Fri, 15 May 2009 15:55:23 -0700 writes:
>> Hello All,
>>
SS> Please help me with this problem.I have been having this problem for
over a
SS> month now and I could not find any information.I later realised that
error
SS> is with MATRIX package.
[...]
SS> This is my graph object.
SS> The file 'Bonacich Power.RData' (1.5 MB) is available for download at
SS> <
SS>
http://dropbox.unl.edu/uploads/20090522/7a0d0313f21fd6a8/Bonacich%20Power.RData
[...]
and you have asked almost the idetnical question 4 weeks ago,
and got a first answer, but then not a *real* answer.
I'm sorry for that, since I had started answering you,
and even more as I *did* solve the problem back then.. (April 22).
more below ...
SS> computer Configuration
SS> *WIndows XP service Pack 3 .0 GB RAM*
SS> I am using SPARSE matrix to solve the problem
SS> This is the code I use to obtain bonpower using Sparse Matrix &
SS> alternatively the code is in the following website
SS> http://igraph.wikidot.com/r-recipes#toc6
I'm really appaled that "R-recipes" on such a website give
such an unprofessional advice
{the following is so grabled because of your (implicit)
insistence on using HTML-ified e-mail .. }
SS> *bonpow.sparse <- function(graph, nodes=V(graph), loops=FALSE,
SS> exponent=1, rescale=TRUE, tol=1e-07) {*
SS> * ## remove loops if requested
SS> *
SS> * ## sparse adjacency matrix
SS> d <- get.adjacency(graph, sparse=TRUE)*
SS> * ## sparse identity matrix
SS> id <- spMatrix(vcount(graph), vcount(graph),
SS> i=1:vcount(graph), j=1:vcount(graph),
SS> x=rep(1, vcount(graph)))
SS> id <- as(id, "dgCMatrix")*
SS> * ## solve it
>>>> SS> ev <- solve(id - exponent * d, tol=tol) %*% degree(graph, mode="all")*
SS> * if (rescale) {
SS> ev <- ev/sum(ev)
SS> } else {
SS> ev <- ev * sqrt(vcount(graph)/sum((ev)^2))
SS> }*
SS> * ev[as.numeric(nodes) + 1]
SS> }*
SS> **
SS> I realised that the error is in Matrix Package
SS> *Error: cannot allocate vector of size 3.3 Gb
SS> In addition: Warning messages:
SS> 1: In solve(id - exponent * d, tol = tol) :
SS> Reached total allocation of 1535Mb: see help(memory.size)
SS> 2: In solve(id - exponent * d, tol = tol) :
SS> Reached total allocation of 1535Mb: see help(memory.size)
SS> 3: In solve(id - exponent * d, tol = tol) :
SS> Reached total allocation of 1535Mb: see help(memory.size)
SS> 4: In solve(id - exponent * d, tol = tol) :
SS> Reached total allocation of 1535Mb: see help(memory.size)*
SS> **
SS> sessionInfo()
SS> R version 2.9.0 (2009-04-17)
[ ............. ] {irrelevant here}
SS> Please help Matrix Experts
Well, I've marked the one line above
>>>> ev <- solve(id - exponent * d, tol=tol) %*% degree(graph, mode="all")*
<begin abusive language; excuse in advance ..>
which really contains stupid advice.
Yes, this is strongly put, but I think people who tell you to
solve
A x = b (A [n x n] matrix)
in R by x <- solve(A) %*% b
should not give advice on programming at all.
<end abusive language>
One of the proper advices would use
x <- solve(A, b)
and indeed that's even more crucial in the case where A is
sparse matrix: solve(A) is never sparse for sparse A (*),
but solve(A, b) can happen with a fast (and memory-efficient)
algorithm, and that's what happens also when you use the Matrix package.
I append a version of your function that is also slightly
improved in other places.
(*) and that's why your memory blows up when A is large and sparse !
SS> Thanks in advance
SS> Nathan
SS> [[alternative HTML version deleted]]
(still.... It would *really* help if you used a more sensible e-mail
configuration }
Regards,
Martin Maechler
-----------
Here's the corrected bonpow.sparse() function , actually with
extra code for diagnostic output, etc.
You should probably simplify it again :
## I got help from IGRAPH community to use sparse Matrix
##
## http://igraph.wikidot.com/r-recipes#toc6
##
### "Enhanced" by Martin Maechler:
bonpow.sparse <- function(graph, nodes = V(graph), loops = FALSE,
exponent = 1, adj.type = "both",
trace = TRUE,
rescale=FALSE, tol=1e-07)
{
stopifnot(require("igraph"),
require("Matrix"))
if(trace) {
c.width <- 30
C1 <- function(s) cat(sprintf("%-*s .. ", c.width, s))
C2 <- function() cat("[Ok]\n")
} else { C1 <- C2 <- function(...) {} }
## remove loops if requested
if (!loops) {
C1("simplify()ing graph")
graph <- simplify(graph, remove.multiple=FALSE, remove.loops=TRUE)
C2()
}
## sparse adjacency matrix
C1("d <- get.adjacency(., sparse)")
d <- get.adjacency(graph, type = adj.type, sparse=TRUE); C2()
if(trace >= 2)
cat("class(d): ", class(d),"\n")
if(!is.directed(graph)) {
## MM: unfortunately "igraph" does not return a *symmetric*
## --- sparse matrix directly, saving space and time,
## so we at least do it now :
C1("d <- as(d, \"symmetricMatrix\")")
d <- as(d, "symmetricMatrix"); C2()
}
## sparse identity matrix
vg <- vcount(graph)
if(FALSE) { ## "non-sense" :
C1("spMatrix(.) for Diagonal(vg)")
id <- spMatrix(vg, vg, i=1:vg, j=1:vg, x = rep(1, vg)); C2()
C1(" --> as(., \"dgCMatrix\")")
id <- as(id, "dgCMatrix"); C2()
}
else
id <- Diagonal(vg)
C1("M <- (id - exponent * d)")
M <- id - exponent * d ; C2()
C1("b <- degree(graph,.)")
b <- degree(graph, mode="out") ; C2()
if(trace >= 2) {
cat(" M : class ", class(M),"; dim: ", dim(M),"\n")
cat(" b : class ", class(b),"; length: ", length(b),"\n")
}
## solve it
## MM: This is "the horror" -- ("it's the economy, stupid !!")
## particularly as solve(M) is *never* sparse !!!
## He should use solve(M, b) !!
if(FALSE) {
C1("solve(M) %*% b")
ev <- solve(M, tol=tol) %*% b; C2()
} else {
C1("solve(M, b)")
ev <- solve(M, b) ; C2()
}
if (rescale) {
ev <- ev/sum(ev)
} else {
ev <- ev * sqrt(vg/sum((ev)^2))
}
ev[as.numeric(nodes) + 1]
}
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