similar to: "tsne" package for t-SNE dimensionality reduction

--- src/gallium/drivers/nv50/nv50_program.c | 91 +++++++++++++++++++++---------- 1 files changed, 61 insertions(+), 30 deletions(-) diff --git a/src/gallium/drivers/nv50/nv50_program.c b/src/gallium/drivers/nv50/nv50_program.c index e7beb26..381e396 100644 --- a/src/gallium/drivers/nv50/nv50_program.c +++ b/src/gallium/drivers/nv50/nv50_program.c @@ -790,6 +790,9 @@ emit_precossin(struct

--- src/gallium/drivers/nv50/nv50_program.c | 118 +++++++++++++++++++++---------- 1 files changed, 80 insertions(+), 38 deletions(-) diff --git a/src/gallium/drivers/nv50/nv50_program.c b/src/gallium/drivers/nv50/nv50_program.c index 16bf2f1..75c5cea 100644 --- a/src/gallium/drivers/nv50/nv50_program.c +++ b/src/gallium/drivers/nv50/nv50_program.c @@ -810,7 +810,11 @@ emit_precossin(struct

CRAN (and crantastic) updates this week New packages ------------ * apcluster (1.0.1) Ulrich Bodenhofer http://crantastic.org/packages/apcluster The apcluster package implements Frey's and Dueck's Affinity Propagation clustering in R. The algorithms are analogous to the Matlab code published by Frey and Dueck. * BioPhysConnectoR (1.6-1) Franziska Hoffgaard

Hi, there: Can anyone here kindly point some good reference or links on this topic? Esp. some solutions from BioConductor or R, when dealing with microarray-like, "fat" data? thanks, -- Weiwei Shi, Ph.D "Did you always know?" "No, I did not. But I believed..." ---Matrix III [[alternative HTML version deleted]]

I anticipate lacking of prior experience with dimensionality reduction problems. Some scientists concerned with drug discovery performed several steered Molecular Dynamics simulations of the alanine-dipeptide molecule dragged by a radial force from an equilibrium conformation to another different equilibrium conformation. They sampled at regular intervals 7 dihedral angles, 5 bending angles, and

Dear R-users, I am looking for a way to assign to slices of arrays where dimensionality of the array is not a-priory known. Specifically, I would like to be able to generalize the following example of dimensionality 2 to an arbitrary diminsionality: In this example we create an array x, a smaller array y and then assign y to a slice of x. > dimnmx <- list(c('a','b'),

Dear all, not really a R question but: If I want to check for the classification accuracy of a LDA with previous PCA for dimensionality reduction by means of the LOOCV method: Is it ok to do the PCA on the WHOLE dataset ONCE and then run the LDA with the CV option set to TRUE (runs LOOCV) -- OR-- do I need - to compute for each 'test-bag' (the n-1 observations) a PCA

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

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

Yo creo que el problema principal está en betapart.core(), que crea y mantiene simultáneamente en memoria varios objetos de alrededor de 1 Gb que luego combina en una lista... Probablemente es entre las líneas el código de betaper.core donde hay que ir eliminando objetos. Saludos, Marcelino El 22/12/2015 a las 18:26, Carlos J. Gil Bellosta escribió: > Hola, ¿qué tal? > > Yo no

Antes de nada, me gustaría daros las gracias por toda vuestra ayuda. He estado probando todo lo que me habéis dicho a la vez, y no hay manera, sigo teniendo el problema con el espacio. En cuanto al tamaño de la base de datos, es más grande de lo que puse, me equivoqué y puse el tamaño de una base anterior con la que estuve trabajando, la actual tiene 36866 filas x 6500 columnas. He seguido todas

Hola a tod en s, Soy nuevo en R-help-es, por lo que perdonadme si debería haber mandado este email a una sección en particular. Quería consultaros un error que me aparece en R al hacer una matriz de disimilitud a partir de una tabla con 6000 columnas x 11000 filas. El mensaje que da R es "cannot allocate vector of size 10Gb", y me resulta extraño ya que tengo 64 Gb de RAM disponible.

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 +++

Here's our testcase: #include <stdio.h> struct flags { unsigned frog: 1; unsigned foo : 1; unsigned bar : 1; unsigned bat : 1; unsigned baz : 1; unsigned bam : 1; }; int main() { struct flags flags; flags.bar = 1; flags.foo = 1; if (flags.foo == 1) { printf("Pass\n"); return 0; } else {

On 12/21/2016 4:45 PM, Phil Tomson via llvm-dev wrote: > Here's our testcase: > > #include <stdio.h> > > struct flags { > unsigned frog: 1; > unsigned foo : 1; > unsigned bar : 1; > unsigned bat : 1; > unsigned baz : 1; > unsigned bam : 1; > }; > > int main() { > struct flags flags; > flags.bar = 1; >

Just saw this go by my twitter stream: http://staff.science.uva.nl/~delaat/sne-2009-2010/p02/report.pdf via @legeza -- bda cyberpunk is dead. long live cyberpunk.

Dear R users, I?m please to announce the available on CRAN of new package largeVis.(*) largeVis offers three major features: - A fast implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of

Dear R users, I?m please to announce the available on CRAN of new package largeVis.(*) largeVis offers three major features: - A fast implementation of the LargeVis algorithm. LargeVis is for visualizing high-dimensional datasets, similar to (and of similar quality to) t-SNE. But, LargeVis runs in O(n) time, which makes it feasible to use on datasets with millions of rows and thousands of