search for: xtest

Dear R-help, what are the limits on xtest? > NOT_A.rf <- randomForest (log10(Y[!A] ) ~ . , data = notA_desc , proximity=T ,xtest = A_desc) *** caught segfault *** address 0x9cdd000, cause 'memory not mapped' Segmentation fault I don't think that the matrix are large: notA_desc is 651 obs of 27 variables A_desc is...

...or another function in R ? Thanks Arnaud ###################################################################### ## function 1 x1 <- 1:100 y1 <- 5.468 * x + 3 # + rnorm(100,0, 10) dfxy <- cbind(x1,y1) # Objective function optfunc <- function(x, dfxy){ a <- x[1] b <- x[2] xtest <- dfxy[,1] yobs <- dfxy[,2] ysim <- a*xtest + b sum((ysim - yobs)^2) } out<- optim(par=c(0.2,5), fn=function(x){optfunc(x, dfxy)}, method = "Nelder-Mead", hessian = F) ## function 2 x2 <- seq(0.01, 0.1, length=100) y2 <- exp(30*x2) dfxy2 <- cbind(x2,y2) #...

This works: class X def X.initialize( stuff ) end end X.initialize("bla") However stubbing it doesn,t: require ''test/unit'' require ''stubba'' class X def X.initialize( stuff ) end end class XTest < Test::Unit::TestCase def test_ X.stubs(:initialize).with("bla") X.initialize("bla") end end Ruby lets me know that: Loaded suite /tmp/foo Started E(eval):1: warning: removing `initialize'' may cause serious problem Finished...

...nks in advance for any help! - Jim CT <- read.table("CT.txt",header=TRUE,sep="\t") CV <- read.table("CV.txt",header=TRUE,sep="\t") # Both CT & CV have the syntaxis X1, X2,...,X97,Y where all variables are numeric x <- CT[,-98] y <- CT[,98] xtest <- CV[,-98] ytest <- CV[,98] library(randomForest) model <- randomForest(x ,y , xtest, ytest,ntree=500,mtry=32,nodesize=5,nPerm=2) model #Call: # randomForest(x = x, y = y, xtest = xtest, ytest = ytest, ntree = 500, mtry = 32, nodesize = 5, # nPerm = 2) # Type of rand...

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

...I changed the classwt. I hoped that for example classwt=c(0.45,0.1,0.45) would result in fewer cases classified as class 2. Did I understand something wrong? Christian x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]), y=as.factor(traingroups), xtest=as.data.frame(mfilters[cvtest,]), ytest=as.factor(testgroups)) > x1rf$test$confusion 1 2 3 class.error 1 9954 30 19 0.00489853 2 139 1854 0 0.06974410 3 420 0 84 0.83333333 x1rf <- randomForest(x=as.data.frame(mfilters[cvtrain,]),...

Sir, This query is related to randomForest regression using R. I have a dataset called qsar.arff which I use as my training set and then I run the following function - rf=randomForest(x=train,y=trainy,xtest=train,ytest=trainy,ntree=500) where train is a matrix of predictors without the column to be predicted(the target column), trainy is the target column.I feed the same data for xtest and ytest too as shown. On verifying I found, rf$mse[500] and rf$test$mse[500] are different(the r-squares...

Yeah. I agree that "Chain operand is needed if the intrinsic is reading / writing memory.”, Just don’t know where and how to set it up. like intrinsic “int_x86_xtest: “ def int_x86_xtest : GCCBuiltin<"__builtin_ia32_xtest">, Intrinsic<[llvm_i32_ty], [], []>; “ "def X86xtest: SDNode<"X86ISD::XTEST", SDTypeProfile<1, 0, [SDTCisVT<0, i32>]>, [SDNPHasChain, SDNPSideEffect]>;...

...omForest) > load("datasets.RData") # import traindat and testdat > nlevels(traindat$predictor1) [1] 20 > nlevels(testdat$predictor1) [1] 19 > nrow(traindat) [1] 9838 > nrow(testdat) [1] 3841 > set.seed(10) > rf_orig <- randomForest(x=traindat[,-1], y=traindat[,1], xtest=testdat[,-1], ytest=testdat[,1],ntree=100) > data.frame(rf_orig$test$err.rate)[100,1] # Error on test-dataset [1] 0.3082531 # assign the levels of the training dataset th the test dataset for predictor 1 > levels(testdat$predictor1) <- levels(traindat$predictor1) > nlevels(train...

...t. I do not wish to explain why I must do this (if you really must know you can ask), its very complex. I have searched the internet, I have read documentation, etc, etc. I have found nothing on this topic. Again, I must send an event to a window without the SYNTHETIC flag being set. I can not use XTest because it does not send an event to a window it creates fake input that will become an event, but does not directly create an event. I have a hard time believing that this is truly impossible. I hope that it simply isn't documented. Thank you for your time, James Steven Supancic III - -----...

...phics grDevices utils datasets methods base other attached packages: [1] kohonen_2.0.5 class_7.3-1 loaded via a namespace (and not attached): [1] tools_2.10.1 > data(wines) > set.seed(7) > training <- sample(nrow(wines), 120) > Xtraining <- scale(wines[training, ]) > Xtest <- scale(wines[-training, ], + center = attr(Xtraining, "scaled:center"), + scale = attr(Xtraining, "scaled:scale")) > som.wines <- som(Xtraining, grid = somgrid(5, 5, "hexagonal")) > som.prediction <- predict(som.wines, newdata = Xtest, + trainX = Xtrai...

...rror in trmesh #get statistics to generate a similar dataset to test against > length(xcoords) [1] 26257 > length(ycoords) [1] 26257 > mean(xcoords) [1] 670462.4 > mean(ycoords) [1] 5005382 > sd(xcoords) [1] 149.3114 > sd(ycoords) [1] 181.5950 #generate the test data > xtest<-rnorm(26257,670462.4,149.3) > ytest<-rnorm(26257,5005382,181.60) # try ashape routine with success > alpha.shape<-ashape(xtest,ytest,15) > class(alpha.shape) [1] "ashape" Thanks for any insight into this! Murray ps I am able to compute the alpha shapes for this s...

Hi guys, In X86ISelLowering.cpp I saw” ... case Intrinsic::x86_rdrand_16: case Intrinsic::x86_rdrand_32: …. case Intrinsic::x86_avx512_gather_qpd_512: case Intrinsic::x86_avx512_gather_qps_512: .. “ those intrinsics are handled by “LowerINTRINSIC_W_CHAIN”. How the “INTRINSIC_W_CHAIN” opCode is set instead of “INTRINSIC_WO_CHAIN”? tks Kevin -------------- next part -------------- An

...es for test data, using the Kernel PLS method. Let's take the example in the R help: > data(NIR) > attach(NIR) > NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validation = "CV", method="kernelPLS") How can we get the predicted Y values ("Ypred") for Xtest in this case? As far as I checked, there is no parameter to specify the test data in "mvr" or "pls". I, therefore, thought about the "kernelpls" function as follows: > Kernelpls(Xtrain, Ytrain, ncomp = 21, Xtest) Is this the correct way of getting Ypred for...

...hod. Let's take the example in the R help: > > > data(NIR) > > attach(NIR) > > NIR.kernelpls <- mvr(Xtrain, Ytrain, 1:6, validation = "CV", > method="kernelPLS") > > > > How can we get the predicted Y values ("Ypred") for Xtest in > this case? > As far as I checked, there is no parameter to specify the test data in > "mvr" or "pls". I, therefore, thought about the "kernelpls" > function as > follows: > > > > > Kernelpls(Xtrain, Ytrain, ncomp = 21, Xtest) &g...

..., s*=s*-(tgTs/tgTtg)t*g t*h=s* th=s th2=tTt betah=(tTyh)/th2 update yh+1=yh-betahth buid up prediction y*h+1=y*h+betaht*h end of cycle ----------------------------------- R-code ##xe and ye are the explanatories and responses matrices, xtest and ytestsampls the variables for 1 sample x2<-scale(xe,scale=FALSE) y2<-scale(ye,scale=FALSE) lv<-1 xtest<-as.matrix(x2[1,]) t<-matrix(0,nrow(ye),1) c<-xe%*%t(xe) yh<-y2 ytestsampls<-0 ctest<-xe%*%xtest for (h in 1:lv) { s<-c%*%yh s<-scale(s,scale=FALSE) stes...

As per the schedule, I am pleased to announce the first release candidate of the standalone Xwayland 23.2.0 release (Xwayland 23.2.0 rc1). Some notable changes since Xwayland 23.1 include: - Optional support for emulated input by using libEI for XTEST, - Support for wp-tearing-control-v1, - Xwayland rootful is now resizable with libdecor. Testing of this release candidate would be greatly appreciated. Please report any issues at https://gitlab.freedesktop.org/xorg/xserver/-/issues The second release candidate is scheduled in two weeks f...

Dear all, I am trying to train a randomForest using all my control data (12,000 cases, ~ 20 explanatory variables, 2 classes). Because of memory constraints, I have split my data into 7 subsets and trained a randomForest for each, hoping that using combine() afterwards would solve the memory issue. Unfortunately, combine() still runs out of memory. Is there anything else I can do? (I am not using

...s 14 devices trace:wintab32:X11DRV_LoadTabletInfo Device 0: [id 2|name Virtual core pointer|type |num_classes 2|use 0] trace:wintab32:X11DRV_LoadTabletInfo Device 1: [id 3|name Virtual core keyboard|type |num_classes 1|use 1] trace:wintab32:X11DRV_LoadTabletInfo Device 2: [id 4|name Virtual core XTEST pointer|type |num_classes 2|use 4] trace:wintab32:X11DRV_LoadTabletInfo Is XExtension: Device, Keyboard, or Pointer warn:wintab32:X11DRV_LoadTabletInfo Skipping device 2 [name Virtual core XTEST pointer|type ]; not apparently a tablet cursor type device. If this is wrong, please report it to wine-...

Dear R-help, I was bitten by the behavior of all() when given logical(0): It is TRUE! (And any(logical(0)) is FALSE.) Wouldn't it be better to return logical(0) in both cases? The problem surfaced because some un-named individual called randomForest(x, y, xtest, ytest,...), and gave y as a two-level factor, but ytest as just numeric vector. I thought I check for that in my code by testing for if (!all(levels(y) == levels(ytest))) stop(...) but levels() on a non-factor returns NULL, and the comparison ended up being logical(0). Since all(logical(0)) is...