similar to: Plans for tighter integration of reference classes in R.

Hi, I am trying to allow certain nodes to override variables in inherited classes. Here''s an example: node ''my.server.com'' inherits webserver {} node ''other.server.com'' inherits webserver { $var = "newvalue" } node webserver { $var = "value" include snmp, dns, classx } In this example classx ends up using

Is it correct that there is no way of avoiding deep copying of data structures? Or asked from a different perspective, is it true that there are no pointers? :) (not that I am a fan of pointer, they just let me decide when to do deep copy on my own when the memory manager doesnt do it for me :) ) I was considering writing code in R that would need the internal representation of complex graph

Could of requests: 1) Is there any example or writeup on the difficulties of extending reference classes across packages? Just so I can fully understand the issues. 2) In what sorts of situations does the performance of reference classes cause problems? Sure, it's an order of magnitude slower than constructing a simple environment, but those timings are in microseconds, so one would need a

I'm using refClass for a complex multi-directional tree structure with possibly 100,000s of nodes. The refClass design is very impressive and I'd love to use it, but I've found that the size of refClass instances are very large and creation time is slow. For example, below is a RefClass and normal S4 class. The RefClass requires about 4KB per instance vs 500B for the S4 class --

I am using environments to avoid making copies (by keeping references). But it seems like there is a hidden copy going on somewhere - for example in the code fragment below, I am creating a reference to "y" (of size 500MB) and storing the reference in object "data". But when I save "data" and then restore it in another R session, gc() claims it is using twice the

I am using environments to avoid making copies (by keeping references). But it seems like there is a hidden copy going on somewhere - for example in the code fragment below, I am creating a reference to "y" (of size 500MB) and storing the reference in object "data". But when I save "data" and then restore it in another R session, gc() claims it is using twice the

> 1) Is there any example or writeup on the difficulties of extending > reference classes across packages? Just so I can fully understand the > issues. Here's a simple example: library(scales) library(methods) MyRange <- setRefClass("MyRange", contains = "DiscreteRange") a_range <- MyRange() a_range$train(1:10) # Error in a_range$train(1:10) : could not

When a reference class method is accessed with .self$x, it has different behavior from .self[['x']]. The former copies the function to the object's environment (with some attributes attached), and the latter just return NULL (unless it has already been accessed once with .self$x). Is this how it's supposed to work? Here's an example that illustrates:

Good afternoon, earlier today I wrote a mail to the user mailing list about current status of the current development. As pointed out by Mr. Rücker, it seems to be good to also write to this list with a bit more technical details. Please note that this mail represents current master branch ("2.5.x"). We do recommend to use current stable branch ("2.4.x") for production.

Hi Gabe, On 09/29/2015 02:51 PM, Gabriel Becker wrote: > Herve, > > The problem then would be that for A a refClass whose fields take up N > bytes (in the sense that you mean), if we do > > B <- A > > A and B would look like the BOTH take up N bytes, for a total of 2N, > whereas AFAIK R would only be using ~ N + 2*56 bytes, right? Yes, but that's still a *much*

On Sun, Apr 06, 2003 at 04:40:42PM -0500, David W. Tamkin wrote: > If processing time is not a big factor -- say, I could put up with four > to six times the duration of compressing at -8 -- what command-line > settings could one use to get even more compression? > > I have a case where the FLACs encoded at -8 are about 653.3 MB, but the > set comes with artwork whose jpegs

Miroslav Lichvar wrote: > Ok, you need 0.04% improvement, that should not be a problem. Perhaps a little more than that, since the sizes I listed were after stripping out the padding and all metadata blocks except SEEKTABLE and STREAMINFO. > Try flac --lax -e -p -l 32 -r 10 --no-padding > and if it is not enough, increase -r up to 16. Thank you. I'll do that. What, though,

On Sat, Apr 12, 2003 at 05:49:06PM -0500, David W. Tamkin wrote: > Early this past week, Miroslav Lichvar suggested for me: > > >Ok, you need 0.04% improvement, that should not be a problem. Try > >flac --lax -e -p -l 32 -r 10 --no-padding > > Thank you again, Miroslav. I tried that, and it took almost two full > days (surprisingly, Windows ME stayed up that long

I was just thinking about the problem of host groups and such trying to set up our puppet infrastructure properly and came to the realization that using MCollective better in puppet dashboard would allow for more cloud like scaling of infrastructure services. Here is the concept: Right now in puppet dashboard groups can have nodes, facts (parameters) and Classes. What I am suggesting would

If processing time is not a big factor -- say, I could put up with four to six times the duration of compressing at -8 -- what command-line settings could one use to get even more compression? I have a case where the FLACs encoded at -8 are about 653.3 MB, but the set comes with artwork whose jpegs are 50.5 MB (I tried zipping the jpegs, realizing it would do very little, and the zip file

Early this past week, Miroslav Lichvar suggested for me: > Ok, you need 0.04% improvement, that should not be a problem. Try > flac --lax -e -p -l 32 -r 10 --no-padding Thank you again, Miroslav. I tried that, and it took almost two full days (surprisingly, Windows ME stayed up that long without crashing) to re-encode the entire set on my 266-MHz machine. After all, in the help file

This is not intended for v1.14. In fact, we may decide that the second patch is too gross, although the first one still seems like a useful improvement in isolation. I will also point out that all our filters are in-tree, and set the user-controlled field .version to the current release string. We could replace the second patch with a simpler one that just checks ._api_version as an int (as

Hi all, I've constructed the following function to test whether or not an object was created from a reference class: isRefClassObject <- function(x) isS4(x) && is.environment(attr(x,'.xData')) && exists('.refClassDef',attr(x,'.xData')) but I'm unsure if it's a complete test or if there's a better way to test. Regardless, It would be

I was wondering: are the downstream dependencies of a package rebuilt when a package is updated on CRAN? (I'm referring to the binary packages, of course.) The reason I ask is because there are cases where this can cause problems. Suppose that when pkgB is built, it calls pkgA::makeClosure(), which returns a closure that refers to a function in pkgA. Suppose this code is in pkgA 1.0:

Hi, Currently object.size() is not very useful on environments as it always returns 56 bytes, no matter how big the environment is: env1 <- new.env() object.size(env1) # 56 bytes env2 <- new.env(hash=TRUE, size=75000000L) object.size(env2) # 56 bytes env3 <- list2env(list(a=runif(25000000), L=LETTERS)) object.size(env3) # 56 bytes This makes it pretty useless on