similar to: "Divergence or Singularity"

I am using logistic regression model (lrm) of package Design. Can some one please tell me how to calculate the average Area Under Curve (AUC) for n-fold cross-validation The help for lrm function says to do cross validation like this f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE) val <- validate.lrm(f, method="cross", B=5) Now I dont know what to do with variable "val" to

Hi, I am using logistic regression model named lrm(Design) Rite now I was using Area Under Curve (AUC) for testing my model. But, now I have to calculate precision/recall of the model on test cases. For lrm, precision and recal would be simply defined with the help of 2 terms below: True Positive (TP) - Number of test cases where class 1 is given probability >= 0.5. False Negative (FP) -

Hi, I am trying to cross-validate a logistic regression model. I am using logistic regression model (lrm) of package Design. f <- lrm( cy ~ x1 + x2, x=TRUE, y=TRUE) val <- validate.lrm(f, method="cross", B=5) My class cy has values 0 and 1. "val" variable will give me indicators like slope and AUC. But, I also need the vector of predicted values of class variable

Hi, I have to build a logistic regression model on a data set that I have. I have three input variables (x1, x2, x3) and one output variable (y). The syntax of lrm function looks like this lrm(formula, data, subset, na.action=na.delete, method="lrm.fit", model=FALSE, x=FALSE, y=FALSE, linear.predictors=TRUE, se.fit=FALSE, penalty=0, penalty.matrix, tol=1e-7,

TL;DR This RFC is a joint effort by Intel and Saarland University to bring the divergence analysis of the Region Vectorizer [1,2,3,4,5] (dubbed the vectorization analysis of RV) to LLVM. The implementation is available on github for feedback [0]. The existing divergence analysis infrastructure in LLVM has conceptual limitations (structured control, SCEV based). The new analysis resolves bugs

Hi R helpers, I'm fitting large number of single factor logistic regression models as a way to immediatly discard factor which are insignificant. Everything works fine expect that for some factors I get error message "Singular information matrix in lrm.fit" which breaks whole execution loop... how to make LRM not to throw this error and simply skip factors with singularity

I am posting this here because the other posters in AppDB say the game works fine. For me, the video is slow and jerky, not playable at all. System: Dell XPS630 q9550 dual core-2, 8gm RAM, 2x NVIDIA 9800GT, lots of disk space (meets all system requirements) kUbuntu-10.04, all current updates, nvidia 195.36.24 driver. Have tried kde, gnome, and lxde desktops (compositing disabled), same problem in

Have you tried setting linux capabilities, like NET_BIND_SERVICE,CHOWN,SYS_CHROOT,SETGID? Have you checked the permissions of paths? I had to relocate the run dir with things like these && mkdir /var/dovecot \ && mkdir /var/lib/dovecot \ && (umask 027 ; mkdir /var/dovecot/login) \ && (umask 022 ; mkdir /var/dovecot/empty) \ && (umask

Hello, It seems to me that our current DivergenceAnalysis does not save which BasicBlocks may suffer from divergent control. Am I correct? I want to modify our DivergenceAnalysis to add a "bool isControlDivergent(BasicBlock*) const" method and save in the divergence propagator the basicblock that are divergent. I am not sure that is entirely correct, if you have input on that please

Hello, Yes? Where is allActive defined, I couldn't find it. Basically, a BB is control divergent if it's execution depends on a branch that itself depends on a divergent ssa value. On Fri, Jul 21, 2017 at 4:13 PM, Zaks, Ayal <ayal.zaks at intel.com> wrote: > What would be the definition of “isControlDivergent(BasicBlock*)”; the > complementary of “allActive(BasicBlock*)” –

Hi there, I'm trying to find the KL divergence measure between a prior and it's posterior distributions, and I'm using the KLdiv method in the flexmix package. plese see the example below: require(flexmix) x=seq(-4,4,length=100) d1=dnorm(x,0,1) d2=dunif(x,-3,3) y=cbind(d1,d2) kl=KLdiv(y) but let say, x1=seq(-5,5,length=100) d3=dunif(x1,-3,3) y1=cbind(d1,d3) kl1=KLdiv(y1) Notice

Hi all I'm facing an issue while running dovecot inside a singularity (https://sylabs.io/singularity/) container dovecot version is 2.3.4.1 (configuration below) running on debian buster, inside a container made with singularity version 3.4.2 unfortunately, when I try to start dovecot, it gives: Singularity test.sif:~> cat /var/log/mail.log Dec 30 17:23:38 testnode dovecot: master:

Hi Owen and Vinod, Thanks for sharing the paper! I like the idea a lot. Regarding the paper itself, Vinod, are the consensual branches (e.g., cbranch.ifnone) you mentioned in the paper publicly available in PTX ISA? Owen, could you explain more on the approach of using branch-if-none instructions in your mind? I believe you have lots of great insights, but I don't see how cbranch.ifnone

Hi all, I have some wind data (U and V components) and I would like to compute Vorticity and Divergence of these fields. Is there any R function that can easily do that? Thanks in advance for any help Igor Oliveira CSAG, Dept. Environmental & Geographical Science, University of Cape Town, Private Bag X3, Rondebosch 7701. Tel.: +27 (0)21 650 5774 South Africa Fax: +27 (0)21

Hi, I just came across the following issue regarding mixed effects models: In a longitudinal study individuals (variable ind) are observed for some response variable. One explanatory variable, f, entering the model as fixed effect, is a (2-level) factor. The expression of that factor is constant for each individual across time (say, the sex of the individual). ind enters the model as grouping

Hello guys, you'll will be happy to know that the current codebase now includes the divergence from randomness weighting schemes which are known to outperform a lot of known weighting schemes such as BM25. Thanks to the amazing mentorship of Olly Betts and Dan Colish, our search results will now be better than before and Xapian will be more preferred in the research community than it was

Hello guys, you'll will be happy to know that the current codebase now includes the divergence from randomness weighting schemes which are known to outperform a lot of known weighting schemes such as BM25. Thanks to the amazing mentorship of Olly Betts and Dan Colish, our search results will now be better than before and Xapian will be more preferred in the research community than it was

I'm using R 2.10.1 with the latest version of all packages (updated today). I'm confused as to why I'm getting a hard singularity in a simple set of experimental data: > blots ID Lot Age Conc 1 1 A 3 4.44 2 2 A 3 4.56 3 3 B 41 4.03 4 4 B 41 4.57 5 5 C 229 4.49 6 6 C 229 4.66 7 7 D 238 3.88 8 8 D 238 3.93 9 9 E 349 4.43 10 10 E 349

*Hi, I am considering a language extension to Clang for optimizing GPU programs. This extension will allow the compiler to use different optimization strategies for divergent and non-divergent branches (to be explained below). We have observed significant performance gain by leveraging this proposed extension, so I want to discuss it here to see how the community likes/dislikes the idea. I will

Hi, I am hoping some one can help with this. I am using nlme to fit a random coefficients model. It ran for hours before returning Error: Singularity in backsolve at level 0, block 1 The model is > plavix.nlme<-nlme(PLX_NRX~loglike(PLX_NRX,PD4_42D,GAT_34D,VIS_42D,MSL_42D,SPE_ROL,XM2_DUM,THX_DUM,b0,b1,b2,b3,b4,b5,b6,b7,alpha), + data=data, + fixed=list(b0 +