similar to: New Package: largeVis

Hello everyone, When looking for information about clustering of spatial data in R I was directed towards DBSCAN. I've read some docs about it and theb new questions have arisen. DBSCAN requires some parameters, one of them is "distance". As my data are three dimensional, longitude, latitude and temperature, which "distance" should I use? which dimension is related to

Hello list. My apologies if this topic has been discussed before on the list but I was unable to find it. I'm working on a way to cluster PCAP files according to the events recorded within them. I've decided to use Bro-IDS for feature extraction. I am looking at dbscan within the FPC library to accomplish my goal. Is it possible to feed a data frame to dbscan with more than two columns

----- Original Message ----- > From: "Dirk Eddelbuettel" <edd at debian.org> > To: r-devel at r-project.org > Cc: "Erik Bernhardsson" <erik at malfunction.org>, "Dirk Eddelbuettel" <edd at debian.org> > Sent: Tuesday, January 13, 2015 7:30:47 AM > Subject: [Rd] Request for help with UBSAN and total absense of CRAN response > >

Dirk, The vagrant setup I use to test my packages with UBSAN also seems to replicate the error reported by CRAN (together with some other warnings). I have attached the files (I hope they get through the filters). I suppose you know what to do with them. Jan Dirk Eddelbuettel <edd at debian.org> schreef: > CRAN has a package of mine in upload limbo because it failed UBSAN.

CRAN has a package of mine in upload limbo because it failed UBSAN. I am not entirely ignorant on the topic of sanitizers and SAN / ASAN / UBSAN; we created not one but two Docker containers with ASAN and USBAN: https://registry.hub.docker.com/u/rocker/r-devel-san/ https://registry.hub.docker.com/u/rocker/r-devel-ubsan-clang/ as well as predecessors to them in earlier Docker repos. Yet I

Hi All , For an academic project I am trying to do the following Step 1 ) Draw and cluster a N ( lets say 3 ) column dataset by dbscan algorithm using R-project’s fpc package ( let say they are "training clusters" ) , Using dbscan as number of clusters are not know before hand Step 2 ) Once that is done i want to spread some new data points in the

Hi, I'm trying to implement the DBSCAN algorithm to get O(N*LogN) complexity and I'd need a spatial tree of some sort (kd,r,bd..), or a function that computes radius-based search on spatial data, i.e. given a radius eps finds ALL the points which fall in the corresponding hypersphere centered on the current examined point. Is there a package with this features? So far I found RANN

I saw that R language has a cluster package which has in built PAM, CLARA and Kmeans (and many more) Clustering Algorithms. But, I couldnot find DBSCAN, ROCK, BIRCH algorithms (which I feel are standard ones). Aren't these implemented as well? Bhanu Kalyan K B.Tech Final Year, CSE Tel: +91-9885238228 Alternate E-Mail: reach4kalyan@gmail.com --------------------------------- We

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 <- >

Hi all, I thought I'd just point out, to those not having yet seen this, that today there was a classification challenge posted for astronomy. The web-site is http://www.hep.anl.gov/SNchallenge/ [I have nothing to do with this project so don't ask me any details!] Basically the idea behind is that future surveys of the sky will turn up a large number of supernovae (SNe). The

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: >

A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()

A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()

Hi there, I'm trying to do a regression experiment on a multidimensional dataset where both x and y in the model are multidimensional vectors. I'm using R version 2.9.2, updated packages, on a Linux box. I've tried gausspr(), ksvm() and rvm(), and the models are computed fine, but I'm always getting the same error message when I try to use predict(): "Error in

I'd like to announce the availability of the "tsne" package. It provides the T distributed Stocastic Neighbor Embedding algorithm by van der Maaten and Hinton: 1. http://ict.ewi.tudelft.nl/~lvandermaaten/t-SNE.html 2. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. [

I'd like to announce the availability of the "tsne" package. It provides the T distributed Stocastic Neighbor Embedding algorithm by van der Maaten and Hinton: 1. http://ict.ewi.tudelft.nl/~lvandermaaten/t-SNE.html 2. L.J.P. van der Maaten and G.E. Hinton. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9(Nov):2579-2605, 2008. [

Hola! Este mail llegó a r-help hoy, yo lo mandó también a esta lista. Es interesante con competiciones de predicción/clasificación! ¿Alguien que quiere cooperar? Kjetil ---------- Forwarded message ---------- From: Kjetil Halvorsen <kjetilbrinchmannhalvorsen@gmail.com> Date: Fri, Jan 29, 2010 at 12:19 Subject: Fwd: [R] Classification of supernovae - a challenge To: Kjetil Halvorsen

Hola Rubén, Totalmente de acuerdo con los consejos de Carlos. Ten en cuenta que aunque R disponga de varios (bastantes) GB de memoria, algunos de los objetos que va creando durante el proceso pueden consumirla por completo. Es decir, que aunque tu ordenador tenga 64GB de RAM, es posible que no queden 10 GB libres para almacenar un objeto (porque ya hay demasiados objetos grandes en la

So I thought I'd try to use the documentation on llvm backends to try to create a DLX backend. I think I've got most of the stuff for the .td files done but I've got some problems. * Do I need to represent the PC in my XXXRegisterInfo.td file; the branch instruction effects it but you can directly access it ... I'm thinking not. * In my Instruction subclasses (in

--- src/gallium/drivers/nv50/nv50_program.c | 162 +++++++++++++++++++++++++------ src/gallium/drivers/nv50/nv50_program.h | 1 + 2 files changed, 132 insertions(+), 31 deletions(-) diff --git a/src/gallium/drivers/nv50/nv50_program.c b/src/gallium/drivers/nv50/nv50_program.c index 5594560..16bf2f1 100644 --- a/src/gallium/drivers/nv50/nv50_program.c +++