R/nnzero.R

Defines functions .nnz.dispatching .nnz.fallback .nnz

## METHODS FOR GENERIC: nnzero
## * used to retrieve number of nonzero elements,
##   i.e., number of elements excl. both structural and non-structural zeros
## * like MATLAB's nnz() but more sophisticated due to handling of NA
## ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

## na.counted:
## FALSE ... NA is treated as    zero and so excluded from count
##  TRUE ... NA is treated as nonzero and so included   in count
##    NA ... NA is indeterminate (could be zero or nonzero) hence count is NA

## For logical, integer, double, and complex vectors
.nnz <- function(x, na.counted = NA, nnzmax = length(x))
    .Call(R_nnz, x, na.counted, nnzmax)

## For any class with methods for 'is.na' and '!='
.nnz.fallback <- function(x, na.counted = NA)
    sum(if(is.na(na.counted))
            x != 0
        else if(na.counted)
            is.na(x) | x != 0
        else !is.na(x) & x != 0)

.nnz.dispatching <- function(x, na.counted = NA)
    switch(typeof(x), logical =, integer =, double =, complex = .nnz,
           .nnz.fallback)(x, na.counted)

setMethod("nnzero",    "ANY", .nnz.fallback)
setMethod("nnzero", "vector", .nnz.dispatching)
setMethod("nnzero",  "array", .nnz.dispatching)

rm(.nnz.dispatching)

setMethod("nnzero", "CHMfactor",
	  function(x, na.counted = NA)
              callGeneric(as(x, "CsparseMatrix"), na.counted))

setMethod("nnzero", "diagonalMatrix",
	  function(x, na.counted = NA)
              if(x@diag == "N") .nnz(x@x, na.counted) else x@Dim[1L])

setMethod("nnzero", "indMatrix",
          function(x, na.counted = NA) x@Dim[1L])

setMethod("nnzero", "sparseMatrix",
	  function(x, na.counted = NA) {
	      d <- x@Dim
              if(any(d == 0L))
                  return(0L)
              cld <- getClassDef(class(x))
              N <- if(extends(cld, "CsparseMatrix"))
                       x@p[d[2L]+1L]
                   else if(extends(cld, "RsparseMatrix"))
                       x@p[d[1L]+1L]
                   else length((x <- .Call(Tsparse_aggregate, x))@i)
              if(!extends(cld, "nsparseMatrix"))
                  N <- .nnz(x@x, na.counted, N)
              if(extends(cld, "generalMatrix"))
                  N
              else if(extends(cld, "symmetricMatrix"))
                  N + N - .nnz(diag(x), na.counted)
              else if(x@diag != "N")
                  N + d[1L]
              else N
          })

setMethod("nnzero", "denseMatrix",
	  function(x, na.counted = NA) {
              d <- x@Dim
              if(any(d == 0L))
                  return(0L)
              xx <- x@x
              cld <- getClassDef(class(x))
              if(extends(cld, "ndenseMatrix"))
                  na.counted <- TRUE
              if(extends(cld, "generalMatrix"))
                  return(.nnz(xx, na.counted))
              n <- d[1L]
              upper <- x@uplo == "U"
              if(extends(cld, "unpackedMatrix"))
                  xx <- xx[indTri(n, upper, diag = TRUE, packed = FALSE)]
              N <- .nnz(xx, na.counted)
              if(extends(cld, "symmetricMatrix"))
                  N + N - .nnz(diag(x), na.counted)
              else if(x@diag != "N")
                  N + n - .nnz(xx[indDiag(n, upper, packed = TRUE)], na.counted)
              else N
          })

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Matrix documentation built on Nov. 11, 2022, 9:06 a.m.