search for: nnzero

...ive general question, I allow myself to answer in public such that the answer my help others as well. Let's use an example from package 'nadiv': require(nadiv) ?makeA # to learn about the function ## create a largish example Awarcol <- makeA(ggTutorial[1:2000, 1:3]) str(Awarcol) nnzero(Awarcol) object.size(Awarcol) object.size(as.matrix(Awarcol)) # ~ 14 times large image(Awarcol) # wait a bit .. ... but it's worth it! ------------- > Awarcol <- makeA(ggTutorial[1:2000, 1:3]) > str(Awarcol) Formal class 'dsCMatrix' [package "Matrix"] with 7 slots...

...sparse matrix, with the subset being still in sparse form. Easily achievable with mm[i,,drop=F] , right? Well, it doesn't work on the matrix I'm working on. This is a very large wikipedia Matrix (now I can play with it as I just got 16Gb of memory): > mm at Dim [1] 793251 1027355 > nnzero(mm) [1] 205746204 > class(mm) [1] "dgTMatrix" attr(,"package") [1] "Matrix" Imagine I want just the first row: > a = mm[1,,drop=F] > length(a) [1] 1027355 > nnzero(a) [1] 1291 >class(a) "numeric" However, _a_ is just a numeric (dense) vector...

....diag.dsC, .solve.dgC.chol, .solve.dgC.qr, .solve.dgC.lu, diagN2U, diagU2N, .diagU2N, .diag2tT, .diag2sT, .diag2mat, drop0, expand, expm, facmul, fac2sparse, fac2Sparse, forceSymmetric, T2graph, graph2T, anyDuplicatedT, uniqTsparse, isTriangular, isDiagonal, isLDL, is.null.DN, invPerm, lu, nearPD, nnzero, formatSpMatrix, formatSparseM, .formatSparseSimple, printSpMatrix, printSpMatrix2, qrR, rankMatrix, readHB, readMM, sparse.model.matrix, sparseVector, symmpart, skewpart, tril, triu, updown, pack, unpack, .updateCHMfactor, .validateCsparse, writeMM, cBind, rBind ERROR: lazy loading failed for pack...

...se 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 of nonzero entries of the matrix. All of these classes are form...

...rse 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 of nonzero entries of the matrix. &gt...

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$data) 1730904 bytes > object.size(as.matrix(train$data)) 6575016 bytes the dgCMatrix actually takes less memory, though it *looks* like the opposite. Cheers! On Fri, Oct 20, 2017 at 3:22 PM, David Winsemius

...dgC.qr, .solve.dgC.lu, diagN2U, diagU2N, > .diagU2N, .diag2tT, .diag2sT, .diag2mat, drop0, expand, expm, facmul, > fac2sparse, fac2Sparse, forceSymmetric, T2graph, graph2T, > anyDuplicatedT, uniqTsparse, isTriangular, isDiagonal, isLDL, > is.null.DN, invPerm, lu, nearPD, nnzero, formatSpMatrix, > formatSparseM, .formatSparseSimple, printSpMatrix, printSpMatrix2, > qrR, rankMatrix, readHB, readMM, sparse.model.matrix, sparseVector, > symmpart, skewpart, tril, triu, updown, pack, unpack, > .updateCHMfactor, .validateCsparse, writeMM, cBind, rBind...