search for: sparsity

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 . . . [3,] . . 1 . . [4,] . . . 1 . [5,] . . . . 1 > m[1,] <- 0 > m 5 x...

...lt;-TermDocumentMatrix(info.cor.cl) result<-list(name = informes, tdm= info.tdm) } >tdm<-lapply(informes, TDM, path = pathname) Resultado: > tdm [[1]] [[1]]$name [1] "2013" [[1]]$tdm <<TermDocumentMatrix (terms: 1540, documents: 1)>> Non-/sparse entries: 1540/0 Sparsity : 0% Maximal term length: 18 Weighting : term frequency (tf) [[2]] [[2]]$name [1] "2005" [[2]]$tdm <<TermDocumentMatrix (terms: 1849, documents: 1)>> Non-/sparse entries: 1849/0 Sparsity : 0% Maximal term length: 19 Weighting : term frequ...

On Tue, Sep 04, 2007 at 10:35:10AM -0400, Daniel Berlin wrote: > On 9/4/07, Dan Gohman <djg at cray.com> wrote: > > On Fri, Aug 31, 2007 at 08:10:33PM -0400, Daniel Berlin wrote: > > > + template <int ElementSize> > > > + class SparseBitmap { > > > > Do you expect clients will often want custom ElementSize values? Otherwise, > > it seems

...hmetic with them can be much faster because it is based on > sophisticated sparse matrix linear algebra, notably the > sparse Cholesky decomposition for solve() etc. > > Of course the efficency only applies if most of the > matrix entries _are_ 0. > You can measure the "sparsity" or rather the "density", of a > matrix by > > nnzero(A) / length(A) > > where length(A) == nrow(A) * ncol(A) as for regular matrices > (but it does *not* integer overflow) > and nnzero(.) is a simple utility from Matrix > which -- very efficiently for sp...

...e disk-bound on the source system (as they should be). (And, of course, --sparse would automatically promote the compression level to "really-crappy" if it was at "none" before.) Well, okay, they shouldn't even be disk bound; the source system should be able to discover the sparsity of the file without making 1.5GB-worth of read calls. Does POSIX (or do specific OSes) offer a call that provides a map of allocated regions in the file? Source rsync: 2.5.6 Destination rsync: 2.5.5 Diligence: I searched for 'sparse' in the faqomatic, the bug database, the current issues p...

...informes, TDM, path = pathname) > > > > Resultado: > > > >> tdm > > [[1]] > > [[1]]$name > > [1] "2013" > > [[1]]$tdm > > <<TermDocumentMatrix (terms: 1540, documents: 1)>> > > Non-/sparse entries: 1540/0 > > Sparsity : 0% > > Maximal term length: 18 > > Weighting : term frequency (tf) > > > > [[2]] > > [[2]]$name > > [1] "2005" > > [[2]]$tdm > > <<TermDocumentMatrix (terms: 1849, documents: 1)>> > > Non-/sparse entrie...

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 > >

...memory and 2. matrix arithmetic with them can be much faster because it is based on sophisticated sparse matrix linear algebra, notably the sparse Cholesky decomposition for solve() etc. Of course the efficency only applies if most of the matrix entries _are_ 0. You can measure the "sparsity" or rather the "density", of a matrix by nnzero(A) / length(A) where length(A) == nrow(A) * ncol(A) as for regular matrices (but it does *not* integer overflow) and nnzero(.) is a simple utility from Matrix which -- very efficiently for sparseMatrix objects -- gives the number...

...is/wiki/gallery that will give you an idea of what kind of information we can find. Our analysis comes together with a dynamic profiler that points the minimum and maximum values that each variable assumes during program execution too. And it uses a live range splitting strategy to obtain data-flow sparsity that is lightning fast. It is more than 100x faster than the original implementation of SSI in LLVM 2.7, for instance. There are a number of LLVMers, outside my university, that use our analysis. So, I would like to propose a summer of code that consists in (i) integrating our infra-structure in...

...give > you an idea of what kind of information we can find. Our analysis > comes together with a dynamic profiler that points the minimum and > maximum values that each variable assumes during program execution > too. And it uses a live range splitting strategy to obtain data-flow > sparsity that is lightning fast. It is more than 100x faster than the > original implementation of SSI in LLVM 2.7, for instance. There are a > number of LLVMers, outside my university, that use our analysis. What version of LLVM does your analysis use currently? It sounds like your analysis is fast...

...ady knows that at least 2 of the between-plot factors should be highly significant. MERELY DIFFICULT: 10 factors with 6 at 3 levels and 4 at 2 levels in either 12 or 24 plots split in 2 on one of the 3-level factors. This problem is easier, because we have more runs and can rely more on effect sparsity / tapering of effect sizes, following Burnham and Anderson (2002 ) Model Selection and Multi-Model Inference, 2nd ed.; (Springer) Any ideas, references, etc., would be greatly appreciated. Thanks, Spencer Graves

...z2006.com/program.asp> ASC / NZSA 2006 with less than two months to go has attracted substantial registrations to its outstanding scientific program featuring ; * 4 Plenary Sessions featuring leading international speakers: Professor David Donoho - Stanford University, Title: Sparsity in Estimation and Detection Professor Peter Hall - ANU, Title: PCA For FDA Professor Ray Chambers - University of Southampton Title: Models and Auxiliary Information in Survey Sampling Professor Xiao-Li Meng - Harvard University Title: (Data) Size Does Matter, But You Might Be In for a Sur...

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 capabilities. Has anyone in the R community experimented with such or put code out for this problem? Many thanks. John -- John M. Drake,...

...a jackknife to estimate the covariance matrix of those estimates. We test marginal homogeneity using a Wald statistic, or by adapting the efficient score statistic from the independent-samples case."*) This leaves me with the following questions: 1) How can one know how "badly" the sparsity hearts the assumptions of the stuart maxwell test? 2) Is there a simple alternative for this test? (and how to use it?) 3) Is there a not-so-simple alternative for this test? (and how to use it?) Many thanks, Tal ---------------------------------------------- My contact information: Tal G...

...k 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) http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf We use Zhang's MC+ penalty to impose sparsity in model selection. This penalty parametrizes a family ranging between L1 and L0 regularization. One nice feature of this family is that the single-coordinate optimization problems are convex, making it ideal for coordinate descent. The package fits the regularization surface for each parameter -...

...k 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) http://www.stanford.edu/~hastie/Papers/Sparsenet/jasa_MFH_final.pdf We use Zhang's MC+ penalty to impose sparsity in model selection. This penalty parametrizes a family ranging between L1 and L0 regularization. One nice feature of this family is that the single-coordinate optimization problems are convex, making it ideal for coordinate descent. The package fits the regularization surface for each parameter -...

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

...e svd imputation * softImpute has an option "type" which can be "svd" or "als" (alternating least squares), for specifying which of the two approaches above should be used. *included in the package is svd.als, an efficient rank-restricted svd algorithm that can exploit sparsity and other special structure, and accept warm starts. * a function biScale is provided, for centering and scaling both rows and columns of matrix to have means zero and variance 1. The centering and scaling constants are stored on the object. For sparse matrices with centering, the centered object...