search for: train

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", sep="\t",header=FALSE,col.names=c("Gene","VEH","VEH","VEH","VEH","VEH&...

...ilbox_is_trash(Junk): 0 imap: antispam: mail copy: from trash: 0, to trash: 0 imap: antispam: mailbox_is_spam(INBOX): 0 imap: antispam: mailbox_is_spam(Junk): 1 imap: antispam: mailbox_is_unsure(INBOX): 0 imap: antispam: mail copy: src spam: 0, dst spam: 1, src unsure: 0 imap: antispam: running mailtrain backend program /bin/bash imap: antispam: running mailtrain backend program /bin/bash imap: antispam: running mailtrain backend program parameter 1 /usr/local/bin/sa-learn-pipe.sh imap: antispam: running mailtrain backend program parameter 2 --spam By contrast, if I "su" to the "v...

...*, struct nvkm_fb *, enum nvkm_ram_type, u64 size, u32 tags, @@ -24,6 +25,22 @@ int gf100_ram_ctor(const struct nvkm_ram_func *, struct nvkm_fb *, int gf100_ram_get(struct nvkm_ram *, u64, u32, u32, u32, struct nvkm_mem **); void gf100_ram_put(struct nvkm_ram *, struct nvkm_mem **); +/* Training */ +struct gt215_ram_train { + u16 mask; + struct nvbios_M0209S remap; + struct nvbios_M0209S type00; + struct nvbios_M0209S type01; + struct nvbios_M0209S type04; + struct nvbios_M0209S type06; + struct nvbios_M0209S type07; + struct nvbios_M0209S type08; + struct nvbios_M0209S type09; +}; +int...

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, t...

...#39; ){ for( col in (1:ncol(X)) ){ if( length(which(X[,col]!=median(X[,col])))>tol ){ realdata[length(realdata)+1]=col } } filteredX=X[,realdata] } return(list(x=filteredX, ix=realdata)) } #----------------- # Filter out all all-constant columns in my training data set # # Read training data set with class information in the first column training <- read.csv('training_data.txt') dim(training) # => 49 rows and 525 columns # Prepare column names by stripping the underline and the number at the end colnames(training) <- sub('_\\d+...

...rtions(+), 30 deletions(-) diff --git a/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h b/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h index ce8a98e..ef9edc5 100644 --- a/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h +++ b/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ram.h @@ -32,6 +32,7 @@ struct gt215_ram_train { struct nvbios_M0209S type00; struct nvbios_M0209S type01; struct nvbios_M0209S type04; + struct nvbios_M0209S type05; struct nvbios_M0209S type06; struct nvbios_M0209S type07; struct nvbios_M0209S type08; diff --git a/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgt215.c b/drivers/gpu/drm...

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.t...

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) cs.training.dat_sub3 <- subset(cs.training.dat_sub2, Credit.Limit.Ratio<=1) for (i in 1:500){ y.2 <- cs.training.dat_...

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 selection # grow tree fit.dimer <- rpart(as.factor(out) ~ ., method="class", data=train.v) at<-grep(&quo...

...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 library(ElemStatLearn) library(glmnet) data(prostate) # training data data.train <- prostate[prostate$train,] y <- data.train$lpsa # isolate predictors data.train <- as.matrix(data.train[,-c(9,10)]) # test data data.test <- prostate[!prostate$train,] data.test <- as.matrix(data.test[,-c(9,10)]) # scale test data by using means and sd from...

...' to 'YYYYYY' on channel 0/2, span 1 -- Executing SetVar("Zap/2-1", "FAXFILE=/tmp/foch.tif") in new stack -- Executing RxFAX("Zap/2-1", "/tmp/foch.tif") in new stack DCS with final frame tag In state 9 Coarse carrier frequency 1700.18 (66) Training error 7.382022 Training succeeded (constellation mismatch 14.349960) Start rx document Start rx page - compression 2 Coarse carrier frequency 1700.21 (66) Training error 36.399169 Training failed (constellation mismatch 53.596561) Coarse carrier frequency 1651.45 (8) Training error 601.890016 Tr...

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,]...

...ng 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 R R R R R Levels: R S > train.x m1 m2 1 0.1756 0.6502 2 0.1110 -0.2217 3 0.0837 -0.1809 4 -0.3703 -0.2476 5 8.3825 2.8814 6 5.6400 12.9922 7 7.5537 7.4809 8 3.5005 5.7844 9 16.8541 16.6326 10 9.1851 8.7814 11 1.440...

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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 f...

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

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", comment.char="&qu...

...etions(-) 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 +++ b/drivers/gpu/drm/nouveau/nvkm/subdev/fb/ramgf100.c @@ -556,22 +556,49 @@ gf100_ram_train_init_0(struct nvkm_ram *ram, struct gt215_ram_train *train) struct nvkm_device *device = subdev->device; int i, j; - if ((train->mask & 0x03c3) != 0x03c3) { - nvkm_warn(subdev, "missing link training data\n"); - return -EINVAL; - } - - for (i = 0; i < 0x30; i++) { -...

...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 #1################# var1 var2 var3 response train 1 2 type1 33 TRUE 2 23 type2 44 TRUE ..... ....... 18 11...

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 script to a set of memx writes. Used by both "that GT 240" and Fer...