similar to: [Bug 61879] New: Python binding of gbm's gbm_create_device fail to create_device

Hi, I have a geforce 4mx 440 agp 8x, and I'm trying to use the GBM library, (as jbarnes in: http://virtuousgeek.org/blog/index.php/jbarnes/2011/10/ and David Hermann in KMSCON: https://github.com/dvdhrm/kmscon), without success. when I try to create a gbm_device, I get: (below the code.) nouveau_drm_screen_create: unknown chipset nv18 dri_init_screen_helper: failed to create pipe_screen

Full_Name: Adolfo Jiménez Pérez Version: 1.8.0 (2003-10-08) OS: Window'98 2ed Submission from: (NULL) (81.203.134.156) I've a crash when reading a specific file with the command: > read.table("file.dat") This file can be readed with the 1.1.1 version using the same command and R reads it correctly. The content file is this: ---->file beginning<---------

The Wine development release 1.7.30 is now available. What's new in this release (see below for details): - More support for fonts in DirectWrite. - Improved ATL thunk support. - A few more C runtime functions. - Regedit import/export fixes. - Various bug fixes. The source is available from the following locations: http://prdownloads.sourceforge.net/wine/wine-1.7.30.tar.bz2

The Wine development release 1.7.29 is now available. What's new in this release (see below for details): - Support for shaping and BiDi mirroring in DirectWrite. - Some page fault handling fixes. - A few more C runtime functions. - Various bug fixes. The source is available from the following locations: http://prdownloads.sourceforge.net/wine/wine-1.7.29.tar.bz2

Hello, I have a classification tree. I want to obtain a ROC curve for this test. What is the easiest way to obtain one? -Anjali --------------------------------- [[alternative HTML version deleted]]

This is a way to create virtio based devices from user space. This is the background for this patch: We have some images works fine under qemu, we'd like to also run the same image on Google Cloud. Currently Google Cloud doesn't support virtio-vga. I had a patch to create a virtio-vga from kernel directly: https://www.spinics.net/lists/dri-devel/msg248573.html Then I got feedback from

This is a way to create virtio based devices from user space. This is the background for this patch: We have some images works fine under qemu, we'd like to also run the same image on Google Cloud. Currently Google Cloud doesn't support virtio-vga. I had a patch to create a virtio-vga from kernel directly: https://www.spinics.net/lists/dri-devel/msg248573.html Then I got feedback from

On Fri, Nov 28, 2014 at 12:32:43AM -0500, Ilia Mirkin wrote: > On Thu, Nov 27, 2014 at 11:39 AM, Thierry Reding > <thierry.reding at gmail.com> wrote: > > Tegra K1 and later use a GPU that can be driven by the Nouveau driver. > > But the GPU is a pure render node and has no display engine, hence the > > scanout needs to happen on the Tegra display hardware. The GPU

On Wed, Apr 29, 2020 at 08:59:18PM -0700, lepton wrote: > On Wed, Apr 29, 2020 at 4:58 AM Gerd Hoffmann <kraxel at redhat.com> wrote: > > > > > 3) Need to be verbose on how the vring processing work in the commit log of > > > patch 1 > > > > Ecven better a file documenting the interface somewhere in > > Documentation/ > I put a uvirtio-vga.c

The Wine development release 1.7.14 is now available. What's new in this release (see below for details): - More Task Scheduler support. - Improvements for AVI encoding support. - More VisualBasic interfaces in MSXML. - Support for deflate content encoding in Wininet. - Some fixes for monochrome printers. - Various bug fixes. The source is available from the following locations:

I am trying to start a large number of SMP domains (> 50). However, I am unable to create more than 7 domains. When I try creating the 8th domain, I get this error: Using config file "myconf7". VIRTUAL MEMORY ARRANGEMENT: Loaded kernel: 0xc0100000->0xc0344c24 Init. ramdisk: 0xc0345000->0xc0345000 Phys-Mach map: 0xc0345000->0xc0347800 Page tables:

Dear gbm users, When running predict.gbm() on a "large" dataset (150,000 rows, 300 columns, 500 trees), I notice that the memory used by R grows beyond reasonable limits. My 14GB of RAM are often not sufficient. I am interpreting this as a memory leak since there should be no reason to expand memory needs once the data are loaded and passed to predict.gbm() ? Running R version 2.9.2 on

Dear all, the help file for predict.gbm states that "The predictions from gbm do not include the offset term. The user may add the value of the offset to the predicted value if desired." I am just not sure how exactly, especially for a Poisson model, where I believe the offset is multiplicative ? For example: library(MASS) fit1 <- glm(Claims ~ District + Group + Age +

Hello LIST, I'd like to use tune from e1071 to do a grid search for hyperparameter values in gbm. However, I can not get this to work. I note that there is no wrapper for gbm but that it is possible to use non-wrapped functions (like lm) without problem. Here's a snippet of code to illustrate. > data(mtcars) obj <- >

Dear List, I´m working on a classification problem. My response has 60 levels. I`m very interested in boosted trees like AdaBoost or gradient boosting machine as implemented in the package "gbm". Unfortunately gbm is only applicable for 2-class problems. Is anybody out there who can help me? Is there a way to use gbm() for multi-class problems? Maybe there is a way to transform my

Hello. I'm currently stuck with the same "what does interaction.depth really mean" stuff. Did you find out what the right answer is? Best regards, Boris Yangel. [[alternative HTML version deleted]]

I am using the gbm package for generalized boosted regression models, and would like to be able to extract the coefficients produced for storage in a database. I am already using R to automatically generate formulas that I can export to a database and store. For example, I have been using Dr. Harrell's lrm package to perform logistic regression, e.g.: output <-

HI, Dear Greg and R community, I have one question about the output of gbm package. the output of Boosting should be f(x), from it , how to calculate the probability for each observations in data set? SInce it is stochastic, how can guarantee that each observation in training data are selected at least once? IF SOME obs are not selected, how to calculate the training error? Thanks? --

I'm attempting to fit boosted regression trees to a censored response using IPCW weighting. I've implemented this through two libraries, mboost and gbm, which I believe should yield models that would perform comparably. This, however, is not the case - mboost performs much better. This seems odd. This issue is meaningful since the output of this regression needs to be implemented in a

On page 409 of "Applied Predictive Modeling" by Max Kuhn, it states that the gbm function can accomodate only two class problems when referring to the distribution parameter. >From gbm help re: the distribution parameter: Currently available options are "gaussian" (squared error), "laplace" (absolute loss), "tdist" (t-distribution