search for: sparsiti

Hi, I have sparse (tridiagonal) matrices, and I use the Matrix package for handling them. For certain computations, I need to either set the first row to zero, or double the last row. I find that neither operation preserves sparsity, eg > m <- Diagonal(5) > m 5 x 5 diagonal matrix of class "ddiMatrix" [,1] [,2] [,3] [,4] [,5] [1,] 1 . . . . [2,] . 1

Buenas noches grupo. Saludos cordiales. He seguido en la búsqueda de una forma que me permita realizar la comparación de dos documentos pertenecientes a los años 2005 y 2013, y que pueda representar finalmente con wordcloud y con una table en la que las columnas sean los años de cada informe "2005" y "2013", y las filas sean las palabras con la frecuencia de cada una de ellas

...ith varying the element size to 256, and > on the other size, interference graphs could be reduced in space and > time a lot by varying the element size down to 64 > I'm happy to give it a default value of 128. Interesting. Out of curiosity, do you happen to know roughly the sizes and sparsities where using 256-bit elements was faster? Looking at the implementation a little more, I see ElementList is a list of pointers to elements: + typedef std::list<SparseBitmapElement<ElementSize> *> ElementList; The extra pointer indirection is unfortunate; It'd be nice to have...

> On Oct 21, 2017, at 7:50 AM, Martin Maechler <maechler at stat.math.ethz.ch> wrote: > >>>>>> C W <tmrsg11 at gmail.com> >>>>>> on Fri, 20 Oct 2017 15:51:16 -0400 writes: > >> Thank you for your responses. I guess I don't feel >> alone. I don't find the documentation go into any detail. > >> I also find

So I was transferring a 2GB virtual machine disk image image over a slow wireless link. Of course I used --sparse, to keep the image small on the destination end as well as on the source end. Much to my surprise, I noticed that the transfer took a long time even when it got past the first 0.5GB of actually-populated file. A little sleuthing with strace revealed that the source rsync was dutifully

Hola, La referencia (gracias por proporcionarla) que has incluido es bastante clara y se puede seguir. ¿Has podido sobre tus dos discursos utilizar la misma lógica? La forma de salir de dudas, para empezar, es que adjuntaras el código que estás empleando por ver si hay algún error evidente. Aunque la forma adecuada para que te podamos ayudar es con un ejemplo reproducible: código + datos.

On 9/4/07, Dan Gohman <djg at cray.com> wrote: > On Fri, Aug 31, 2007 at 08:10:33PM -0400, Daniel Berlin wrote: > > Suggestions, criticisms, etc, are welcome. > > I haven't studied the implementation, but I have a few comments on > the interface, and some style comments, below. > > > Index: include/llvm/ADT/SparseBitmap.h > >

>>>>> C W <tmrsg11 at gmail.com> >>>>> on Fri, 20 Oct 2017 15:51:16 -0400 writes: > Thank you for your responses. I guess I don't feel > alone. I don't find the documentation go into any detail. > I also find it surprising that, >> object.size(train$data) > 1730904 bytes >>

Dear LLVMers, I have been working on Douglas's range analysis, and today, after toiling with it for two years, we have a very mature and robust implementation, which is publicly available at http://code.google.com/p/range-analysis/. We can, at this point, perform range analysis on very large benchmarks in a few seconds. To give you an idea, we take less than 10 seconds to globally analyze

On 3/29/12 3:59 PM, Victor Campos wrote: > Dear LLVMers, > > I have been working on Douglas's range analysis, and today, after > toiling with it for two years, we have a very mature and robust > implementation, which is publicly available at > http://code.google.com/p/range-analysis/. We can, at this point, > perform range analysis on very large benchmarks in a few

What is available to help design experiments with non-standard requirements? I have a recurring need to solve these kinds of problems, with deadlines of next Wednesday for two sample cases. The first of the two is "mission impossible", while the second is merely difficult. The following outlines briefly the two problems and the approach I'm currently considering. I'd

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Hi, I was wondering if there was an R package or routines for the Dantzig Selector (Candes & Tao, 2007). I know Emmanuel Candes has Matlab routines to do this but I was wondering if someone had ported those to R. Thanks, T ---Reference--- @article{candes2007dantzig, title={{The Dantzig selector: statistical estimation when p is much larger than n}}, author={Candes, E. and Tao, T.},

I wish to estimate sparse causal networks from simulated time series data. Although there's some discussion about this problem in the literature (at least a few authors have used lasso and l(1,2) regularization to enforce sparsity in multivariate autoregressive models, e.g., http://user.cs.tu-berlin.de/~nkraemer/papers/grplasso_causality.pdf), I can't find any R packages with these

Hello people, I am in need for testing Marginal Homogeneity for sparse (more then 2X2) matrixes. After searching, what I found by now is that for more then 2 by 2 matrixes, one turns to "stuart maxwell tests" that are available in two packages: irr - see: stuart.maxwell.mh coin - see: mh_test But I couldn't find in the documentation how valid the results are for sparse matrixes,

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)

We have put a new package sparsenet on CRAN. Sparsenet fits regularization paths for sparse model selection via coordinate descent, using a penalized least-squares framework and a non-convex penalty. The package is based on our JASA paper Rahul Mazumder, Jerome Friedman and Trevor Hastie: SparseNet : Coordinate Descent with Non-Convex Penalties. (JASA 2011)

Hi, Does anyone know what is maximal term length in Document Term Matrix? <<DocumentTermMatrix (documents: 255, terms: 858)>> Non-/sparse entries: 8081/210709 Sparsity : 96% Maximal term length: 12 Weighting : term frequency (tf) Thanks for any help! Elahe

I installed suse 9.3 recently after successfully running 9.1 for some time and being able to serve up files to windows computers on my home network. Since the new install, I can no longer edit and save word files shared by samba on the linux machine from a windows machine, while other file types are OK (html, jpg, txt). I can create a new word file, but they can't be subsequently

SoftImpute is a new package for matrix completion - i.e. for imputing missing values in matrices. SoftImpute was written by myself and Rahul Mazumder. softImpute uses uses squared-error loss with nuclear norm regularization - one can think of it as the "lasso" for matrix approximation - to find a low-rank approximation to the observed entries in the matrix. This low-rank approximation