[R] svm of e1071 package

[Saeed Abu Nimeh]

I think the problem is that you have R configured as 32-bits. If that
is the case, then you will only have access to 4 gigs of RAM (see
http://www.brianmadden.com/blogs/brianmadden/archive/2004/02/19/the-4gb-windows-memory-limit-what-does-it-really-mean.aspx).
Try booting up an ubuntu instance in the cloud and then install R
using the 64-bit configuration. I am interested to know if this solves
the problem. Let me know.
Thanks,
Saeed

On Tue, Apr 6, 2010 at 5:07 AM, Shyamasree Saha [shs] <shs at aber.ac.uk> wrote:
> Hello List,
>
> I am having a great trouble using svm function in e1071 package. I have 4gb of data that i want to use to train svm. I am using Amazon cloud, my Amazon Machine Image(AMI) has 34.2 GB of memory. my R process was killed several times when i tried to use 4GB of data for svm. Now I am using a subset of that data and it is only 1.4 GB.  i remove all unnecessary objects before calling svm(). I have monitored the memory consumption and found that before i call svm() my AMI has 25GB of free memory. after calling svm(), this free memory starts going down and at the end i have only 1.7 gb of memory and R gives me error that it can not create vector of size 3.4 gb. Its true that if i do not have enough memory then how R will create the vector. But my question is how svm function is eating up that 25gb of memory?? do i have anything to do to solve this problem or its a problem in e1071 package ? by "problem in e1071 package", i mean does svm() in e1071 normally consume that high amount !
>  of memory? if svm() really consume this much memory then i have to think of some other way to train svm. if 34gb ram is not enough for 1.4 gb of data then i am in trouble. Amazon has maximum 68.4gb ram.
>
> Please help. Thanks in advance.
>
> Regards
> Shyama
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>