search for: setweights

Hi I'm trying to implement something similar to the following R snippet using C. I seem to have hit the wall on accessing class slots using C. library(fPortfolio) lppData <- 100 * LPP2005.RET[, 1:6] ewSpec <- portfolioSpec() nAssets <- ncol(lppData) setWeights(ewSpec) <- rep(1/nAssets, times = nAssets) ewPortfolio <- feasiblePortfolio( data = lppData, spec = ewSpec, constraints = "LongOnly") ewSpec is an object of type Portfolio Spec which has the following slots: model slot type = "MV" a string...

I thought I would post an example of using R.oo with inheritance and pass by reference for future searches. With PerMore I am inheriting Person. Then I am changing an object data memeber of an object of PerMore class using pass by reference ('mimiced' by R.oo) in an object of AgeMultiplier. I change a data member in the child class and the parent class for demonstration purposes. This is

Hi, I found two setResume()s in XendDomainInfo.py. One or the other is not necessary, is it? # grep -n -A 2 setResume ./tools/python/xen/xend/XendDomainInfo.py 1394: def setResume(self, isresume): 1395- self._resume = isresume 1396- -- 1409: def setResume(self, state): 1410- self._resume = state 1411- Signed-off-by: Masaki Kanno <kanno.masaki@jp.fujitsu.com> Best

Hello, I was wondering what would be the easiest way to append weights to a dataset in RWeka. Ideally, I'd like to have something like: m <- LogitBoost(Species ~ ., data=iris, weights = myweights) But that, as far as I understand, it is not implemented and I'd need to use a workaround. I know that when programming Weka in Java it is possible to assign weights to instances using

Hello, How do I inherit two classes using the R.oo package. Below is kind of a silly example, but I am trying to create class PerDog from classes Dog and Person. Error at bottom. I've tried a few other ways of using extend(), but nothing seems to get me what I want. Example: setConstructorS3("Person", function(age=NA) { this = extend(Object(), "Person", .age=age )

On Dec 3, 2010, at 11:21 AM, Adam Preuss wrote: > I am a student at the University of Alberta under the > supervision of José Nelson Amaral, and I have been working on > implementing path profiling into LLVM. I have completed my project > and would like to submit it. > > We are looking for a reviewer for the path profiling implementation. We > have sent previous requests

Thank you for your suggestions on the patch. If the patch is committed, I would be willing to maintain it, though I am not sure what is all involved or how I am made aware of changes that need to be made. The technical report https://www.cs.ualberta.ca/system/files/tech_report/2010/PreussPathProfLLVM.pdf contains my benchmarks relating to profiling overhead in LLVM. Over the next week I will

I am a student at the University of Alberta under the supervision of José Nelson Amaral, and I have been working on implementing path profiling into LLVM. I have completed my project and would like to submit it. We are looking for a reviewer for the path profiling implementation. We have sent previous requests to the llvmdev list but have so far been unsuccessful. Please see the attached

...... .. ..$ y : num [1:20] 528 470 480 514 531 ... .. ..$ REML : int 4 .. ..and 26 methods, of which 14 are possibly relevant: .. .. allInfo, copy#envRefClass, initialize, initialize#lmResp, initializePtr, initializePtr#lmResp, objective, ptr, ptr#lmResp, .. .. setOffset, setResp, setWeights, updateMu, wrss ..@ Gp : int [1:2] 0 4 ..@ call : language lmer(formula = RT ~ Condicion1 * Condicion2 + (1 | Subject), data = dat) ..@ frame :'data.frame': 20 obs. of 4 variables: .. ..$ RT : num [1:20] 528 470 480 514 531 ... .. ..$ Condicion1: Factor w/ 2 levels...

Javier, Creo que aquí aplica la ley de Linus que dice: "Dado un número suficientemente elevado de ojos, todos los errores se convierten en obvios". La persona que revisa y encuentra un error no necesariamente tiene que ser la misma que la que lo escribe. Una motivación muy importante al compartir un código es la de recibir los beneficios del control de calidad por parte de tus pares.

...: num [1:20] 528 470 480 514 531 ... > .. ..$ REML : int 4 > .. ..and 26 methods, of which 14 are possibly relevant: > .. .. allInfo, copy#envRefClass, initialize, initialize#lmResp, initializePtr, initializePtr#lmResp, objective, ptr, ptr#lmResp, > .. .. setOffset, setResp, setWeights, updateMu, wrss > ..@ Gp : int [1:2] 0 4 > ..@ call : language lmer(formula = RT ~ Condicion1 * Condicion2 + (1 | Subject), data = dat) > ..@ frame :'data.frame': 20 obs. of 4 variables: > .. ..$ RT : num [1:20] 528 470 480 514 531 ... > .. ..$ Condic...

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