Random Sparse Matrix

rsparsematrix {Matrix}

R Documentation

Random Sparse Matrix

Description

Generate a random sparse matrix efficiently. The default has rounded gaussian non-zero entries, and rand.x = NULL generates random pattern matrices, i.e. inheriting from nsparseMatrix.

Usage

rsparsematrix(nrow, ncol, density, nnz = round(density * maxE),
              symmetric = FALSE,
              rand.x = function(n) signif(rnorm(n), 2), ...)

Arguments

`nrow, ncol`	number of rows and columns, i.e., the matrix dimension (`dim`).
`density`	optional number in [0,1], the density is the proportion of non-zero entries among all matrix entries. If specified it determines the default for `nnz`, otherwise `nnz` needs to be specified.
`nnz`	number of non-zero entries, for a sparse matrix typically considerably smaller than `nrow*ncol`. Must be specified if `density` is not.
`symmetric`	logical indicating if result should be a matrix of class `symmetricMatrix`. Note that in the symmetric case, `nnz` denotes the number of non zero entries of the upper (or lower) part of the matrix, including the diagonal.
`rand.x`	`NULL` or the random number generator for the `x` slot, a `function` such that `rand.x(n)` generates a numeric vector of length `n`. Typical examples are `rand.x = rnorm`, or `rand.x = runif`; the default is nice for didactical purposes.
`...`	optionally further arguments passed to `sparseMatrix()`, notably `giveCsparse`.

Details

The algorithm first samples “encoded” (i,j)s without replacement, via one dimensional indices, if not symmetric sample.int(nrow*ncol, nnz), then—if rand.x is not NULL—gets x <- rand.x(nnz) and calls sparseMatrix(i=i, j=j, x=x, ..). When rand.x=NULL, sparseMatrix(i=i, j=j, ..) will return a pattern matrix (i.e., inheriting from nsparseMatrix).

Value

a sparseMatrix, say M of dimension (nrow, ncol), i.e., with dim(M) == c(nrow, ncol), if symmetric is not true, with nzM <- nnzero(M) fulfilling nzM <= nnz and typically, nzM == nnz.

Author(s)

Martin Maechler

Examples

set.seed(17)# to be reproducible
M <- rsparsematrix(8, 12, nnz = 30) # small example, not very sparse
M
M1 <- rsparsematrix(1000, 20,  nnz = 123,  rand.x = runif)
summary(M1)

## a random *symmetric* Matrix
(S9 <- rsparsematrix(9, 9, nnz = 10, symmetric=TRUE)) # dsCMatrix
nnzero(S9)# ~ 20: as 'nnz' only counts one "triangle"

## a random patter*n* aka boolean Matrix (no 'x' slot):
(n7 <- rsparsematrix(5, 12, nnz = 10, rand.x = NULL))

## a [T]riplet representation sparseMatrix:
T2 <- rsparsematrix(40, 12, nnz = 99, giveCsparse=FALSE)
head(T2)

[Package Matrix version 1.2-17 Index]