search for: tunings

In some cases we do want to make the decision based on the target. For Hexagon, we don't support GDB anymore, only LLDB, so we always want LLDB tuning. The clang driver should have a way to specify that. -- Qualcomm Innovation Center, Inc. The Qualcomm Innovation Center, Inc. is a member of Code Aurora Forum, a Linux Foundation Collaborative Project -----Original Message----- From:

Is there a method to list the components of an object, instead of looking at the help for that method? Let me be more clear with an example data(iris) ## tune `svm' for classification with RBF-kernel (default in svm), ## using one split for training/validation set obj <- tune(svm, Species~., data = iris, ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),

Comments on the patch raise the following questions, probably better discussed here. First: Should LLVM default to "no tuning" rather than a target-specific default? There are two natural follow-up questions: What would "no tuning" actually mean? Where would the target-specific defaulting occur? I originally came down against the "no tuning" option, in favor of the

Hi, I have two model classes; Tune and Title and they are joined in a many to many relationship through a Variation class. Here''s some stub code of the models: class Tune < ActiveRecord::Base has_many :variations, :dependent => :destroy has_many :titles, :through => :variations ... class Title < ActiveRecord::Base has_many :variations, :dependent => :destroy

Hello, I would like to submit two papers that use LLVM to the Related Publications section. Both papers focus on code isolation applied to perform piecewise compiler optimizations. The code isolation process is performed by CERE, an open source tool based on LLVM. The second paper is an extended version of the first one. 1) Piecewise Holistic Autotuning of Compiler and Runtime Parameters

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Hi, I run the following tuning function for svm. It's very strange that every time i run this function, the best.parameters give different values. [A] >svm.tune <- tune(svm, train.x, train.y, validation.x=train.x, validation.y=train.y, ranges = list(gamma = 2^(-1:2), cost = 2^(-3:2))) # where train.x and train.y are matrix

Dear R people, I want to use the tune.nnet function of e1071 package to tune nnet . I am unable to understand the parameters of tune.nnet from the e1071 pdf document. I have performed nnet on a traindata and want to test it for class prediction with a testdata. I want to know the values of size,decay,range etc. parameters for which the prediction of testdata is best. Can anyone please tell me

Hello all, I am using the e1071 SVM with the tune options for classification, which work pretty well, given the examples of using tune.svm function for classification. But I have not found any example to tune the SVM novelty detection (one-classification) parameters (gamma, cost, nu), for example this are some of the options I have tried with no success: obj<-tune(svm, x,y, type

Hi, I am trying to use tune.svm in e1071 package. the command i use is tobj <- tune.svm(labels, data= data, cost = 10^(1:2)) Should the last column of the 'data' contain the labels as well? I want to use the linear kernel. But it gives me the error "Error in model.frame.default(formula, data) : 'data' must be a data.frame, not a matrix or an array" Do you know why

This is basically a reboot of the previous thread titled About the "debugger target" except that "target" was really too strong a term for what I had intended to use this feature for. "Debugger tuning" is more like it. You don't need to have read the previous thread, I'll recap here. Fundamentally, Clang/LLVM uses DWARF as the specification for the

I don’t think there was a driver patch so far, was there? -- adrian > On May 6, 2015, at 1:19 PM, Eric Christopher <echristo at gmail.com> wrote: > > Does the patch do all of this? > > -eric > > On Wed, May 6, 2015 at 1:18 PM Robinson, Paul <Paul_Robinson at playstation.sony.com <mailto:Paul_Robinson at playstation.sony.com>> wrote: > I just skimmed

CentOS Errata and Bugfix Advisory 2019:0496 Upstream details at : https://access.redhat.com/errata/RHBA-2019:0496 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: ce7752b7fd1c7b911135eaab8c195179989fe0058d0575f168ec13e9018c6239 tuned-2.10.0-6.el7_6.3.noarch.rpm

CentOS Errata and Bugfix Advisory 2019:1901 Upstream details at : https://access.redhat.com/errata/RHBA-2019:1901 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: 6d0b8790381395501b1333d4a1e13849b10f10737de6d2a78f1763605e0cdd3b tuned-2.10.0-6.el7_6.4.noarch.rpm

CentOS Errata and Bugfix Advisory 2019:2605 Upstream details at : https://access.redhat.com/errata/RHBA-2019:2605 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: 35dbeac8128a78150bf3d0546116edbad238e4e09cb8dafd070405a513562a15 tuned-2.11.0-5.el7_7.1.noarch.rpm

CentOS Errata and Bugfix Advisory 2020:5008 Upstream details at : https://access.redhat.com/errata/RHBA-2020:5008 The following updated files have been uploaded and are currently syncing to the mirrors: ( sha256sum Filename ) x86_64: 314863a27a19049fe9480f37f1d931ad115a3966777cfe9685ce45f0eaf763a2 tuned-2.11.0-10.el7.noarch.rpm

Dear All , 1- I'm trying to access the values of fitted(model) after model<- tune.svm( ) but seemingly it is not poosible. How can I access to values of fitted ? However ,it is possible only after model<- svm( ) 2- How can I access to the other values such as the number of Support Vectors , gamma, cost , nu , epsilon , after model<- tune.svm( ) ? these are not possible? I

> A few more things that vote for debugger tuning: > > - LLDB doesn't like to have DWARF that has a class A that inherits from > class B, but only a forward declaration of class B is provided. Hmm do we emit that kind of thing today? In a naïve test, I'm seeing the full description of class B. > - LLDB wants the .apple_XXX accelerator tables, GDB wants >

Dear Mihail, I've added these two publications to the publications page. Please review it and let me know if I need to make any changes. In particular, if you have URLs to use for the papers, having those would be greatly appreciated. Regards, John Criswell On 11/28/17 12:05 PM, Mihail Popov via llvm-dev wrote: > > Hello, > > I would like to submit two papers that use LLVM

Another example would be .debug_pubnames and .debug_pubtypes sections. Currently these default to omitted for Darwin and PS4, but included everywhere else. My initial patch for "tuning" changes the PS4 platform criterion to the SCE debugger predicate; quite likely the "not Darwin" criterion ought to be "not LLDB" or in other words "on for GDB only."