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Dear R users, There is a error message when I run the following code. It is used to load microarray data and use the top 1000 genes for training svm to classify test set . > library(e1071) Loading required package: class > f=read.table("F:\\lab\\ microarray analysis\\VEH LPS\\exprs.txt",

Hello! I've posted to the list several times about a strange issue I'm having with dovecot LDA. When dovecot-lda is called in the context of a pipe script that is executed as the "vmail" user via the Dovecot Antispam plugin, dovecot-lda exits prematurely with status code 134. The dovecot deliver manual at http://wiki2.dovecot.org/LDA#logging states very clearly, "If

Parts are re-used even on NVA3, others from GF100 on Signed-off-by: Roy Spliet <nouveau at spliet.org> --- drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h | 17 +++ drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgf100.c | 92 +++++++++----- drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgk104.c | 140 +--------------------- drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgt215.c | 61 ++++++++++ 4

Hi, Say I have two packages, test1 and test2, that both define the generic method train (identical definition), and each has a specific train method for a different S4 object (foo and bar, resp.) I want to be able to call train(foo, x, y) and train(bar, x, y), which doesn't work since test2 masks test1, as seen below. The two solutions I can think of are to a) prefix train,

Hi, I'm a R newbie and want to accelerate the following pre-filtering step of a data set with more than 115,000 rows : #----------------- # Function to filter out constant data columns filter.const<-function(X, vectors=c('column', 'row'), tol=0){ realdata=c() filteredX<-matrix() if( vectors[1] == 'row' ){ for( row in (1:nrow(X)) ){ if(

Signed-off-by: Roy Spliet <nouveau at spliet.org> Tested-by: Ilia Mirkin <imirkin at alum.mit.edu> --- drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h | 1 + drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgt215.c | 128 +++++++++++++++++----- 2 files changed, 99 insertions(+), 30 deletions(-) diff --git a/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h

I use knncat to make a predictive model and get misclass rate > knncat.m<-knncat(training.new,k=c(10,20),classcol=5) > knncat.m Training set misclass rate: 36.88% then I try to calculate prediction accuracy by the following: > pr.knncat.train <- predict (knncat.m,training.new,training.new,train.classcol=5,newdata.classcol=5) > tb.knncat.train <-table (pr.knncat.train,

Dear all, I have a sample with 920 observations. I want to create a loop which takes 300 of these observations for the prediction and the rest to estimate the model. My idea was to create something like this: cs.training.dat <- read.table... cs.training.dat_sub1 <- subset(cs.training.dat, Income>10) cs.training.dat_sub2 <- subset(cs.training.dat_sub1, Dept.Ratio<=1)

cv.fold<-function(i, size=3, rang=0.3){ cat('Fold ', i, '\n') out.fold.c <-((i-1)*c.each.part +1):(i*c.each.part) out.fold.n <-((i-1)*n.each.part +1):(i*n.each.part) train.cv <- n.cc[-out.fold.c, c(2:2401, 2417)] train.nv <- n.nn[-out.fold.n, c(2:2401, 2417)] train.v<-rbind(train.cv, train.nv) #training data for feature

All, For my understanding, I wanted to see if I can get glmnet predictions using both the predict function and also by multiplying coefficients by the variable matrix. This is not worked out. Could anyone suggest where I am going wrong? I understand that I may not have the mean/intercept correct, but the scaling is also off, which suggests a bigger mistake. Thanks for your help. Juliet Hannah

Hello, Anyone else experiencing problems with the latest spandsp (pre3) and last libtiff beta? I'm getting 8 bytes long file, with the TIFF header only during such connection: -- Accepting call from 'XXXXXXX' to 'YYYYYY' on channel 0/2, span 1 -- Executing SetVar("Zap/2-1", "FAXFILE=/tmp/foch.tif") in new stack -- Executing

Dear all, I have a dataset train <- cbind(c(0,2,2,1,0), c(8,9,4,0,2), 6:10, c(-1, 1, 1, -1, 1)) test <- cbind(1:5, c(0,1,5,1,3), c(1,1,2,0,3) ,c(1, 1, -1, 1, 1)) I want to find which rows of train and test it different in its last column (column 4). The solution must be something like train [,1] [,2] [,3] [,4] [1,] 0 8 6 -1 [3,] 2 4 8 1 [4,] 1 0 9 -1

Hi. I am trying to construct a svmLinear model using the "caret" package (see code below). Using the same data, without changing any setting, sometimes it constructs the model successfully, and sometimes I get an index out of bounds error. Is this unexpected behaviour? I would appreciate any insights this issue. Thanks. ~Kendric > train.y [1] S S S S R R R R R R R R R R R R R R R

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TL;DR; We can improve compiler optimizations driven by heuristics by replacing those heuristics with machine-learned policies (ML models). Policies are trained offline and ship as part of the compiler. Determinism is maintained because models are fixed when the compiler is operating in production. Fine-tuning or regressions may be handled by incorporating the interesting cases in the ML training set, retraining the compiler, and redeploying it. For a fir...

Dear R list, I came across dgCMatrix. I believe this class is associated with sparse matrix. I see there are 8 attributes to train$data, I am confused why are there so many, some are vectors, what do they do? Here's the R code: library(xgboost) data(agaricus.train, package='xgboost') data(agaricus.test, package='xgboost') train <- agaricus.train test <- agaricus.test

Hi, It is most probably just my R-ignorance, but I have following problem with using predict(). I train the model using 164 cases and then I try to use it on the data set with 35 cases, but I am getting 164 predictions ? R-code below illustrates in more detail what I am doing. Truly yours, R train = read.csv("train.csv", header = TRUE, row.names = "mol",

Signed-off-by: Roy Spliet <nouveau at spliet.org> --- drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgf100.c | 59 +++++++++++++++++------ 1 file changed, 43 insertions(+), 16 deletions(-) diff --git a/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgf100.c b/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgf100.c index 62359c2..a469719 100644 --- a/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgf100.c +++

Hello all, I have generated a principal components regression model using the pcr() function from the PLS package (R version 2.12.0). I am getting a "non-conformable arguments" error when I try to use the predict() function on new data, but only when I try to read in the new data from a separate file. More specifically, when my data looks like this #########training data

No, no, these will not implement Fermi reclocking. This set of patches contains some of the preparatory work that I deem stable enough to move upstream. Notable changes - Training pattern upload routines from GK104+ now shared with GT215+ - Timing calculation for Fermi - GDDR5 MR calculation from VBIOS timing table v1.0. Also useful for that pesky GT 240. - A routine to translate a VBIOS init