Displaying 20 results from an estimated 100 matches similar to: "stringdot"
2010 Sep 24
0
kernlab:ksvm:eps-svr: bug?
Hi,
A. In a nutshell:
The training error, obtained as "error (ret)", from the return value
of a ksvm () call for a eps-svr model is (likely) being computed
wrongly. "nu-svr" and "eps-bsvr" suffer from this as well.
I am attaching three files: (1) ksvm.R from the the kernlab package,
un-edited, (2) ksvm_eps-svr.txt: (for easier reading) containing only
eps-svr
2009 Dec 25
2
Help with SVM package Kernlab
Hi useR's,
I am resending this request since I got no response for my last post and I
am new to the list so pardon me if I am violating the protocol.
I am trying to use the "Kernlab" package for training and prediction using
SVM's. I am getting the following error when I am trying to use the predict
function:
> predictSvm = predict(modelforSVM, testSeq);
Error in
2012 May 05
2
Pasting with Quotes
Hello useRs!
So, I have a random question. I'm trying to build a character string, then
evaluate it. I think an example would be the easiest way to explain:
kern.vec = c("rbfdot","polydot")
for( j in 1:length( kern.vec ) )
{
formula = paste("ksvm( ind ~ . ,
data=d.temp[,c(ind_col,dep_cols)], kernel =",kern.vec[j],", prob.model=T
2009 Oct 04
3
error installing/compiling kernlab
Hi everybody,
I''m using R on a 64-bit Ubuntu 9.04 (Jaunty). I prefer to install R
packages from source, even if they are available in Synaptic. The
problem is that I can''t install/compile kernlab. Everything works fine
until it gets to the lazy loading part:
** preparing package for lazy loading
Creating a new generic function for "terms" in "kernlab"
2009 Apr 28
1
kernlab - custom kernel
hi,
I am using R's "kernlab" package, exactly i am doing classification using
ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some
error.
# Following R code works (with promotergene dataset):
library("kernlab")
s <- function(x, y) {
sum((x*y)^1.25)
}
class(s) <- "kernel"
data("promotergene")
gene <- ksvm(Class ~ .,
2011 Dec 08
0
SVM performance using laplace kernel is too slow
I've created an SVM in R using the kernlab package, however it's running incredibly slow (20,000 predictions takes ~45 seconds on win64 R distribution). CPU is running at 25% and RAM utilization is a mere 17% ... it's not a hardware bottleneck. Similar calculations using data mining algorithms in SQL Server analysis services run about 40x faster.
Through trial and error, we
2008 Jul 29
0
stringdot ?
Dear all,
I am using kernlab package in R, and I have amino acid sequences with different lenghts as input for a SVM and I need to go through this sequences using windows (sliding or fixed) of size X.
Does anyone has any suggestions about which function I should use?
I thought I could use stringdot, but I am not sure whether it will do what I need.., I have defined my stringdot as:
mystringdot
2007 Aug 08
0
ksvm-kernel
HI
I am new to R.
I have one problem in the predict function of the kernlab.
I want to use ksvm and predict with kernelmatrix (S4 method for signature 'kernelMatrix')
#executing the following sentences
library(kernlab)
# identity kernel
k <- function(x,y) {
n<-length(x)
cont<-0
for(i in 1:n){
if(x[i]==y[i]){
cont<-cont+1
}
}
cont
}
class(k) <-
2009 Oct 23
1
Data format for KSVM
Hi,
I have a process using svm from the e1071 library. it works.
I want to try using the KSVM library instead. The same data used wiht
e1071 gives me an error with KSVM.
My data is a data.frame.
sample code:
svm_formula <- formula(y ~ a + B + C)
svm_model <- ksvm(formula, data=train_data, type="C-svc",
kernel="rbfdot", C=1)
I get the following error:
2012 Jul 31
1
kernlab kpca predict
Hi!
The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the
2012 Aug 19
1
kernlab | ksvm error
Dear list,
I am using the ksvm function from kernlab as follows:
(1) learning
> svm.pol4 <- ksvm(class.labs ~ ., data = train.data, prob.model = T, scale
= T, kernel = "polydot")
(2) prediction
> svm.pol.prd4 <- predict(svm.pol4, train.data, type = "probabilities")[,2]
But unfortunately, when calling the prediction, once in every 10s of times
(using the exact
2012 Nov 15
1
Can't see what i did wrong..
with
pred.pca<-predict(splits[[i]]$pca,trainingData at samples)[,1:nPCs]
dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2)));
results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc",
C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T)
and a degree of 5 i get an error of 0 reported by the ksvm
2012 Feb 13
2
kernlab - error message: array(0, c(n, p)) : 'dim' specifies too large an array
Hi,
For another trainingset I get this error message, which again is rather cryptic to me:
Setting default kernel parameters
Error in array(0, c(n, p)) : 'dim' specifies too large an array
RMate stopped at line 0 of selection
Calls: rvm ... .local -> backsolve -> as.matrix -> chol -> diag -> array
thanks for any suggestions!
2009 Sep 06
2
Regarding SVM using R
Hi Abbas,
Before I try to give you answers, I just want to mention that you
should send R related reqests to the R-help list, and not me
personally because (i) there's a greater likelihood that it will get
answered in a timely manner, and (ii) people who might have a similar
problem down the road might benefit from any answer via searching the
list archives ... anyway:
On Sep 5, 2009, at
2011 Jan 24
5
Train error:: subscript out of bonds
Hi,
I am trying to construct a svmpoly model using the "caret" package (please
see code below). Using the same data, without changing any setting, I am
just changing the seed value. Sometimes it constructs the model
successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out
of bounds?.
For example when I set seed to 357 following code produced result only for 8
2012 Dec 10
0
Time Series Prediction using Gaussian Process
*Hello All,*
I tried figuring out the problem, I was trying to use laplacedot to predict
the long term, which however would not do a good job.
Then, I tried to do a point by point prediction and building the model
again, everytime. It shows me better results. I tried writing my kernel
function (matern covariance function), and attached is the result of that.
Red lines show the fit and blue lines
2013 Nov 15
1
Inconsistent results between caret+kernlab versions
I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +
2011 Oct 06
0
linear classifiers with sparse matrices
I've been trying to get some linear classifiers (LiblineaR, kernlab,
e1071) to work with a sparse matrix of feature data. In the case of
LiblineaR and kernlab, it seems I have to coerce my data into a dense
matrix in order to train a model. I've done a number of searches,
read through the manuals and vignettes, but I can't seem to see how to
use either of these packages with sparse
2014 Sep 30
1
String Kernel for R 3.1.1
Dear all ,
May you inform me how to use the function of gapweightkernel() from stringkernel Library . where the library is not for R 3.1.1 . I am using R 3.1.1 .
sincerely
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2007 Sep 12
0
one-class SVM in kernlab
Hello,
I'm trying to using ksvm() in the kernlab package to fit a one-class
SVC, but I get a strage result on the cross-validation error estimate.
For example, consider this code:
data(spam)
classifier <- ksvm(type~.,data=spam[which(spam[,'type']=='spam'),],
type="one-svc",kernel="rbfdot",kpar=list(sigma=0.1),nu=0.05,cross=10)
what I get is:
>