similar to: Error-correcting output code with multiclass problems

Dear all, I'm using the samr-package to identify significantly differentially expressed genes in microarray data. So far, I had no problems, but when I used a large multiclass data set with 327 samples, I obtained the following error/warning message: Warning message: Inf In factorial(length(y)) : value out of range in 'gammafn' Since y is the label vector and has length 327, the

Hi list, I'm toying with the idea of writing a m680x0 backend for LLVM, and the address modes of this chip are bewildering, to say the least. Here's a rough list off wikipedia for reference: * Register direct o data register, e.g. "D0" o address register, e.g. "A6" * Register indirect o Simple address, e.g. (A0) o

On Tue, Feb 10, 2009 at 8:27 AM, Villmow, Micah <Micah.Villmow at amd.com> wrote: > Bill, > Sorry if I wasn't clear enough. I wasn't referring to multiclass's that > define other classes, but with using patterns inside of a multiclass to > reduce redundant code. > For example: > multiclass IntSubtract<SDNode node> > { > def _i8 : Pat<(sub

Bill, Sorry if I wasn't clear enough. I wasn't referring to multiclass's that define other classes, but with using patterns inside of a multiclass to reduce redundant code. For example: multiclass IntSubtract<SDNode node> { def _i8 : Pat<(sub GPRI8:$src0, GPRI8:$src1), (ADD_i8 GPRI8:$src0, (NEGATE_i8 GPRI8:$src1))>; def _i32 : Pat<(sub

On 08/22/2017 03:59 AM, Alex Bradbury via llvm-dev wrote: > On 21 August 2017 at 13:23, Martin J. O'Riordan via llvm-dev > <llvm-dev at lists.llvm.org> wrote: >> But there is a downside. >> >> For each of the above I also have variations that are a result of different >> processor and ISA versions, and because of this I have to use >> ‘multiclass/defm’

I have been reading the “RFC/bikeshedding: Separation of instruction and pattern definitions in LLVM backends” topic with considerable interest. This is an approach I have been considering for taming our own large instruction set, and it looks like it structures our descriptions better than the conventional approach we have used so far. However, I have another form of TableGen taming that I

Hello all, I'm working with the svm (libsvm) implementation from library(e1071). Currently I'm trying to extend recursive feature elimination (R-SMV) to work with multiclass classification. My problem is that if I run svm for a 3 class problem I get a 2-D vector back from model$coefs, can someone explain me what this values are? I understand them in the 2-class problem where this is a

In TableGen, can multiclasses inherit from one another? I notice that there's a lot of redundancy in the X86 .td files that could go away with multiclass inheritance. -Dave

On Mar 23, 2009, at 5:14 PM, David Greene wrote: > In TableGen, can multiclasses inherit from one another? I notice > that there's > a lot of redundancy in the X86 .td files that could go away with > multiclass > inheritance. Nope, not currently. That would be a nice feature though! -Chris

On Mon, Feb 9, 2009 at 5:17 PM, Villmow, Micah <Micah.Villmow at amd.com> wrote: > Is there a way to define a multi-class pattern in tablegen? > Yes. See "multiclass" and "defm" in, say, X86Instr64bit.td, et al. -bw

Hello all, this is the first time I'm using R and e1071 package and SVM multiclass (and I'm not a statistician)! I'm very confused, then. The goal is: I have a sentence with sunny; it will be classified as "yes" sentence; I have a sentence with cloud, it will be classified as "maybe"; I have a sentence with rainy il will be classified as "no". The

Hi, I have been searching everywhere for an implementation of those algorithms, but I have only observed them in Matlab and on the literature. I noticed a package called 'ada' in CRAN but it is not for multi class. I would be happy with just Adaboost.m2, Smoteboost over adaboost.m2 or any other combination that could account for imbalanced multiclass classification problems. Thanks!

Hi, I'd like to disallow ARM's register-register preload instruction for NaCl (and keep the register-immediate), and instead of my localmod being to delete the 'rs' def from multiclass APreLoad I'd like to do the cleaner thing and have rs require IsNotNaCl. This unfortunately doesn't seem to work because the subsequent defm have their own Requires. The way preloads are

Is there a way to define a multi-class pattern in tablegen? Thanks, Micah Villmow Systems Engineer Advanced Technology & Performance Advanced Micro Devices Inc. S1-609 One AMD Place Sunnyvale, CA. 94085 P: 408-749-3966 -------------- next part -------------- An HTML attachment was scrubbed... URL:

Dear useRs: Release 2.0.0 of the randomSurvivalForest package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 2.0.0 Release 2.0.0 represents a major upgrade in the functionality and stability of the original 1.0.0 release. Key changes are as follows: o Two new splitting rules, 'logrankscore' and

Dear useRs: Release 2.0.0 of the randomSurvivalForest package is now available. --------------------------------------------------------------------------------- CHANGES TO RELEASE 2.0.0 Release 2.0.0 represents a major upgrade in the functionality and stability of the original 1.0.0 release. Key changes are as follows: o Two new splitting rules, 'logrankscore' and

Dear Sir, I am a R beginning user. I would like to apply the bagging algorithm to my data in order to classify a certain disease by Bagging Support Vector Machine Ensemble. My problem is that even if I am reading the book, looking at the examples in internet and available in R, learning a lot of theoretical things on about SVM esemble or Bagging method, I can't apply Bagged SVM esemble or

Dear WizaRds, I am trying to compute the (adjusted) Rand Index in order to comprehend the variable selection heuristic (VS-KM) according to Brusco/ Cradit 2001 (Psychometrika 66 No.2 p.249-270, 2001). Unfortunately, I am unable to correctly use cl_ensemble and cl_agreement (package: clue). Here is what I am trying to do: library(clue) ## Let p1..p4 be four partitions of the kind

Dear useRs, Version 0.8-1 of the `party' package will appear on CRAN and its mirrors in due course. This version implements two new tools: o `mob', an object-oriented implementation of a recently suggested algorithm for model-based recursive partitioning (Zeileis, Hothorn, Hornik, 2005) has been added. It works out of the box for partitioning (generalized) linear

Dear useRs, Release 1.0.0 of the new R package 'randomSurvivalForest' is now available on CRAN and its mirrors. The package implements Ishwaran and Kogalur's Random Survival Forests algorithm for right censored survival data. The algorithm is closely patterned after Breiman's random forests, but suitably modified for the survival setting. Some key features are: o An ensemble