https://github.com/cran/Matrix
Tip revision: d2fe595da814057e6d64fbb8c6fb4452e6cf522e authored by Doug and Martin on 28 July 2009, 00:00:00 UTC
version 0.999375-30
version 0.999375-30
Tip revision: d2fe595
Tsparse.R
#### "TsparseMatrix" : Virtual class of sparse matrices in triplet-format
## more efficient than going via Csparse:
setAs("matrix", "TsparseMatrix",
function(from)
if(is.numeric(from)) mat2dgT(from)
else if(is.logical(from)) as(Matrix(from, sparse=TRUE), "TsparseMatrix")
else stop("not-yet-implemented coercion to \"TsparseMatrix\""))
setAs("numeric", "TsparseMatrix",
function(from) as(as.matrix(from), "TsparseMatrix"))
setAs("TsparseMatrix", "matrix",
function(from) .Call(dgTMatrix_to_matrix, as(from, "dgTMatrix")))
## in ../src/Tsparse.c : |-> cholmod_T -> cholmod_C -> chm_sparse_to_SEXP
## adjusted for triangular matrices not represented in cholmod
.T.2.C <- function(from) .Call(Tsparse_to_Csparse, from, ##
is(from, "triangularMatrix"))
setAs("TsparseMatrix", "CsparseMatrix", .T.2.C)
.T.2.n <- function(from) {
## No: coercing to n(sparse)Matrix gives the "full" pattern including 0's
## if(any(is0(from@x))) ## 0 or FALSE -- the following should have drop0Tsp(.)
## from <- as(drop0(from), "TsparseMatrix")
if(is(from, "triangularMatrix")) # i.e. ?tTMatrix
new("ntTMatrix", i = from@i, j = from@j,
uplo = from@uplo, diag = from@diag,
Dim = from@Dim, Dimnames = from@Dimnames)
else if(is(from, "symmetricMatrix")) # i.e. ?sTMatrix
new("nsTMatrix", i = from@i, j = from@j, uplo = from@uplo,
Dim = from@Dim, Dimnames = from@Dimnames)
else
new("ngTMatrix", i = from@i, j = from@j,
Dim = from@Dim, Dimnames = from@Dimnames)
}
setAs("TsparseMatrix", "nsparseMatrix", .T.2.n)
setAs("TsparseMatrix", "nMatrix", .T.2.n)
.T.2.l <- function(from) {
cld <- getClassDef(class(from))
xx <- if(extends(cld, "nMatrix"))
rep.int(TRUE, length(from@i)) else as.logical(from@x)
if(extends(cld, "triangularMatrix")) # i.e. ?tTMatrix
new("ltTMatrix", i = from@i, j = from@j, x = xx,
uplo = from@uplo, diag = from@diag,
Dim = from@Dim, Dimnames = from@Dimnames)
else if(extends(cld, "symmetricMatrix")) # i.e. ?sTMatrix
new("lsTMatrix", i = from@i, j = from@j, x = xx, uplo = from@uplo,
Dim = from@Dim, Dimnames = from@Dimnames)
else
new("lgTMatrix", i = from@i, j = from@j, x = xx,
Dim = from@Dim, Dimnames = from@Dimnames)
}
setAs("TsparseMatrix", "lsparseMatrix", .T.2.l)
setAs("TsparseMatrix", "lMatrix", .T.2.l)
## Special cases ("d", "l", "n") %o% ("g", "s", "t") :
## used e.g. in triu()
setAs("dgTMatrix", "dgCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, FALSE))
setAs("dsTMatrix", "dsCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, FALSE))
setAs("dtTMatrix", "dtCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, TRUE))
setAs("lgTMatrix", "lgCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, FALSE))
setAs("lsTMatrix", "lsCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, FALSE))
setAs("ltTMatrix", "ltCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, TRUE))
setAs("ngTMatrix", "ngCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, FALSE))
setAs("nsTMatrix", "nsCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, FALSE))
setAs("ntTMatrix", "ntCMatrix",
function(from) .Call(Tsparse_to_Csparse, from, TRUE))
### "[" :
### -----
## Test for numeric/logical/character
## method-*internally* ; this is not strictly OO, but allows to use
## the following utility and hence much more compact code.
## Otherwise have to write methods for all possible combinations of
## (i , j) \in
## (numeric, logical, character, missing) x (numeric, log., char., miss.)
intI <- function(i, n, dn, give.dn = TRUE)
{
## Purpose: translate numeric | logical | character index
## into 0-based integer
## ----------------------------------------------------------------------
## Arguments: i: index vector (numeric | logical | character)
## n: array extent { == dim(.) [margin] }
## dn: character col/rownames or NULL { == dimnames(.)[[margin]] }
## ----------------------------------------------------------------------
## Author: Martin Maechler, Date: 23 Apr 2007
has.dn <- !is.null(dn)
DN <- has.dn && give.dn
if(is(i, "numeric")) {
storage.mode(i) <- "integer"
if(any(is.na(i)))
stop("'NA' indices are not (yet?) supported for sparse Matrices")
if(any(i < 0L)) {
if(any(i > 0L))
stop("you cannot mix negative and positive indices")
i0 <- (0:(n - 1L))[i]
} else {
if(length(i) && max(i, na.rm=TRUE) > n)
stop("index larger than maximal ",n)
if(any(z <- i == 0)) i <- i[!z]
i0 <- i - 1L # transform to 0-indexing
}
if(DN) dn <- dn[i]
}
else if (is(i, "logical")) {
i0 <- (0:(n - 1L))[i]
if(DN) dn <- dn[i]
} else { ## character
if(!has.dn)
stop("no 'dimnames[[.]]': cannot use character indexing")
i0 <- match(i, dn)
if(any(is.na(i0))) stop("invalid character indexing")
if(DN) dn <- dn[i0]
i0 <- i0 - 1L
}
if(!give.dn) i0 else list(i0 = i0, dn = dn)
}
.ind.prep <- function(xi, intIlist, iDup = duplicated(i0), anyDup = any(iDup))
{
## Purpose: do the ``common things'' for "*gTMatrix" indexing for 1 dim.
## and return match(.,.) + li = length of corresponding dimension
##
## xi = "x@i" ; intIlist = intI(i, dim(x)[margin], ....)
i0 <- intIlist$i0
stopifnot(is.numeric(i0))# cheap fast check (i0 may have length 0 !)
m <- match(xi, i0, nomatch=0)
if(anyDup) { # assuming anyDup <- any(iDup <- duplicated(i0))
## i0i: where in (non-duplicated) i0 are the duplicated ones
i0i <- match(i0[iDup], i0)
i.x <- which(iDup) - 1L
jm <- lapply(i0i, function(.) which(. == m))
}
c(list(m = m, li = length(i0),
i0 = i0, anyDup = anyDup, dn = intIlist$dn),
## actually, iDup is rarely needed in calling code
if(anyDup) list(iDup = iDup, i0i = i0i, i.x = i.x,
jm = unlist(jm), i.xtra = rep.int(i.x, sapply(jm, length))))
}
.ind.prep2 <- function(i, margin, di, dn)
{ ## Purpose: do the ``common things'' for "*gTMatrix" sub-assignment
## for 1 dimension, 'margin' ,
## and return match(.,.) + li = length of corresponding dimension
##
## i is "index"; margin in {1,2};
## di = dim(x) { used when i is not character }
## difference to .ind.prep(): use 1-indices; no match(xi,..), no dn at end
intI(i, n = di[margin], dn = dn[[margin]], give.dn = FALSE)
}
## Select rows
setMethod("[", signature(x = "TsparseMatrix", i = "index", j = "missing",
drop = "logical"),
function (x, i, j, ..., drop) { ## select rows
na <- nargs()
Matrix.msg("Tsp[i,m,l] : nargs()=", na, .M.level = 2)
if(na == 3)
## x[i, drop=TRUE/FALSE] *vector* indexing !
return(.M.vectorSub(x,i))
clx <- getClassDef(class(x))
has.x <- !extends(clx, "nsparseMatrix")
x.sym <- extends(clx, "symmetricMatrix")
x.tri <- !x.sym && extends(clx, "triangularMatrix")
gDo <- (x.sym || (x.tri && x@diag == "U"))
if(gDo)
x <- as(x, paste(.M.kind(x, clx), "gTMatrix", sep=''))
d <- x@Dim
n <- d[1]
ip <- .ind.prep(x@i, intI(i, n = n, dimnames(x)[[1]]))
Di1 <- ip$li
drop.it <- drop && (Di1 == 1L || d[2] == 1L)
if(x.tri) { # triangular
if(Di1 == n && identical(ip$i0, 0:(n-1))) # *full* result
return(if (drop.it) x[1,1] else x)
## else: result will *not* be triangular
if(!gDo)
x <- as(x, paste(.M.kind(x, clx), "gTMatrix", sep=''))
}
x@Dim[1] <- Di1
if(!is.null(ip$dn)) x@Dimnames[[1]] <- ip$dn
sel <- ip$m > 0L
x@i <- ip$m[sel] - 1L
if(ip$anyDup) { ## duplicated rows selected: extend sel
sel <- c(which(sel), ip$jm)
x@i <- c(x@i, ip$i.xtra)
}
x@j <- x@j[sel]
if (has.x) x@x <- x@x[sel]
if (drop.it) drop(as(x,"matrix")) else x
})
## Select columns
setMethod("[", signature(x = "TsparseMatrix", i = "missing", j = "index",
drop = "logical"),
function (x, i, j, ..., drop) { ## select columns
Matrix.msg("Tsp[m,i,l] : nargs()=", nargs(), .M.level = 2)
clx <- getClassDef(class(x))
has.x <- !extends(clx, "nsparseMatrix")
x.sym <- extends(clx, "symmetricMatrix")
x.tri <- !x.sym && extends(clx, "triangularMatrix")
gDo <- (x.sym || (x.tri && x@diag == "U"))
if(gDo)
x <- as(x, paste(.M.kind(x, clx), "gTMatrix", sep=''))
d <- x@Dim
n <- d[2]
ip <- .ind.prep(x@j, intI(j, n = n, dimnames(x)[[2]]))
Di2 <- ip$li
drop.it <- drop && (d[1] == 1L || Di2 == 1L)
if(x.tri) { # triangular
if(Di2 == n && identical(ip$i0, 0:(n-1))) # *full* result
return(if (drop.it) x[1,1] else x)
## else: result will *not* be triangular
if(!gDo)
x <- as(x, paste(.M.kind(x, clx), "gTMatrix", sep=''))
}
x@Dim[2] <- Di2
if(!is.null(ip$dn)) x@Dimnames[[2]] <- ip$dn
sel <- ip$m > 0L
x@j <- ip$m[sel] - 1L
if(ip$anyDup) { ## duplicated columns selected: extend sel
sel <- c(which(sel), ip$jm)
x@j <- c(x@j, ip$i.xtra)
}
x@i <- x@i[sel]
if (has.x) x@x <- x@x[sel]
if (drop.it) drop(as(x,"matrix")) else x
})
## [.data.frame has : drop = if (missing(i)) TRUE else length(cols) == 1)
setMethod("[", signature(x = "TsparseMatrix",
i = "index", j = "index", drop = "logical"),
function (x, i, j, ..., drop)
{
Matrix.msg("Tsp[i,i,l] : nargs()=", nargs(), .M.level = 2)
## (i,j, drop) all specified
di <- dim(x)
dn <- dimnames(x)
clx <- getClassDef(class(x))
has.x <- !extends(clx, "nsparseMatrix")
isSym <- extends(clx, "symmetricMatrix")
x.tri <- !isSym && extends(clx, "triangularMatrix")
if(isSym) {
isSym <- (length(i) == length(j) && mode(i) == mode(j) &&
isTRUE(all(i == j)))# work for i,j NA
## result will *still* be symmetric --> keep symmetry!
gDo <- !isSym ## result no longer symmetric -> to "generalMatrix"
} else if(x.tri) {
ii <- intI(i, n = di[1], dn= dn[[1]])
ij <- intI(j, n = di[2], dn= dn[[2]])
gDo <- { (x@diag == "U") ||
## maybe result is no longer triangular
!(identical(ii$i0, ij$i0) && !is.unsorted(ii$i0))
}
}
else gDo <- FALSE
if(gDo) # go via "generalMatrix":
x <- as(x, paste(.M.kind(x, clx), "gTMatrix", sep=''))
if(isSym) { ## has only stored "half" of the indices,
## OTOH, i === j, so only need one intI() call
ip1 <- intI(i, n=di[1], dn[[1]]) # -> (i0, dn_1)
anyDup <- any(iDup <- duplicated(ip1$i0))
ip1 <- .ind.prep(x@i, ip1, iDup=iDup, anyDup=anyDup)
ip2 <- {
if(anyDup) list(m = match(x@j, ip1$i0, nomatch=0),
li = ip1$li)
else .ind.prep(x@j, ip1, iDup=iDup, anyDup=anyDup)
}
if(!is.null(dn[[2]])) # fix result colnames
ip2$dn <- dn[[2]][ip1$i0 + 1L]
} else {
if(!x.tri) {
ii <- intI(i, n = di[1], dn= dn[[1]])
ij <- intI(j, n = di[2], dn= dn[[2]])
}
ip1 <- .ind.prep(x@i, ii)
ip2 <- .ind.prep(x@j, ij)
}
nd <- c(ip1$li, ip2$li)
x@Dim <- nd
x@Dimnames <- list(ip1$dn, ip2$dn)
if(isSym) {
sel <- ip1$m & ip2$m
ii <- ip1$m[sel] - 1L
jj <- ip2$m[sel] - 1L
if(anyDup) { ## careful algorithm --- TODO: in C
sel <- which(sel)
## keep non-duplicated and "increment" for duplicated ones
ij <- pmin(ii, jj)
jj <- pmax(ii, jj) ; ii <- ij
## length(ii) == length(jj) == length(sel) _and_ ii <= jj
ix <- ip1$i.x
for(k in seq_along(ix)) {
## "recursively" add 1 row+column corresp. iDup[k]
i0 <- ip1$i0i[k] -1L # the (0-ind)column we want to repl
i.x <- ix[k] # < i0
e1 <- ii == i0
e2 <- jj == i0
j1 <- jj[e1] # >= ii[e1] == i0
j2 <- ii[e2] # <= jj[e2] == i0 < i.x
## now the "diagonal special":
if(any(e1 & e2)) {
## (e1 & e2)[m] = TRUE <==> ii[m] == jj[m] == i0
isD <- e1[e2] # logical of same length as j2
stopifnot(sum(isD) == 1)
j2[isD] <- i.x # instead of i0
}
l1x <- j1 < i.x # & j12 <- j1[l1x]
j11 <- j1[!l1x] # those >= i.x
s1 <- sel[e1]
ii <- c(ii, j2, j1[l1x], rep.int(i.x, length(j11)))
jj <- c(jj, rep.int(ix[k], length(j2)+sum(l1x)), j11)
sel <- c(sel, sel[e2], s1[l1x], s1[!l1x])
stopifnot(ii <= jj, length(sel) == length(ii))
}
if(x@uplo == "U") { ## i <= j : upper triangle
x@i <- ii
x@j <- jj
} else { ## i >= j : lower left triangle
x@i <- jj
x@j <- ii
}
}
else { ## not any Dup
if(x@uplo == "U") { ## i <= j : upper triangle
x@i <- pmin(ii, jj)
x@j <- pmax(ii, jj)
} else { ## i >= j : lower left triangle
x@i <- pmax(ii, jj)
x@j <- pmin(ii, jj)
}
}
}
else if(!ip1$anyDup && !ip2$anyDup) {
## "normal case": no duplicated indices (and not symmetric)
sel <- ip1$m & ip2$m
x@i <- ip1$m[sel] - 1L
x@j <- ip2$m[sel] - 1L
}
else { ## not Sym && (ip1$anyDup || ip2$anyDup) :
## duplicated rows or columns -- currently the cheap solution,
## Basically implement X[i,j] as X[i,] [,j] :
## FIXME: we are recomputing ip2 here
## - i - ------------------------------
sel <- ip1$m > 0L
x@i <- ip1$m[sel] - 1L
if(ip1$anyDup) { ## duplicated rows selected: extend sel
sel <- c(which(sel), ip1$jm)
x@i <- c(x@i, ip1$i.xtra)
}
x@j <- x@j[sel]
if (has.x) x@x <- x@x[sel]
## - j - ------------------------------
## ip2 <- .ind.prep(x@j, intI(j, n = di[2], dn = dn[[2]]))
## FIXME can we do better: current x@j is original x@j[sel]
ip2 <- .ind.prep(x@j, ij)
sel <- ip2$m > 0L
x@j <- ip2$m[sel] - 1L
if(ip2$anyDup) { ## duplicated columns selected: extend sel
sel <- c(which(sel), ip2$jm)
x@j <- c(x@j, ip2$i.xtra)
}
x@i <- x@i[sel]
}
if (has.x)
x@x <- x@x[sel]
if (drop && any(nd == 1)) drop(as(x, "matrix")) else x
})
## FIXME: Learn from .TM... below or rather .M.sub.i.2col(.) in ./Matrix.R
## ------ the following should be much more efficient than the
## subset.ij() based ./Matrix.R code :
if(FALSE)
## A[ ij ] where ij is (i,j) 2-column matrix :
setMethod("[", signature(x = "TsparseMatrix",
i = "matrix", j = "missing"),# drop="ANY"
function (x, i, j, ..., drop)
{
di <- dim(x)
dn <- dimnames(x)
## TODO check i (= 2-column matrix of indices) ---
## as in .M.sub.i.2col() in ./Matrix.R
j <- i[,2]
i <- i[,1]
if(is(x, "symmetricMatrix")) {
isSym <- isTRUE(all(i == j))# work for i,j NA
if(!isSym)
x <- as(x, paste(.M.kind(x), "gTMatrix", sep=''))
} else isSym <- FALSE
if(isSym) {
offD <- x@i != x@j
ip1 <- .ind.prep(c(x@i,x@j[offD]), intI(i, n= di[1], dn=dn[[1]]))
ip2 <- .ind.prep(c(x@j,x@i[offD]), intI(j, n= di[2], dn=dn[[2]]))
} else {
ip1 <- .ind.prep(x@i, intI(i, n = di[1], dn = dn[[1]]))
ip2 <- .ind.prep(x@j, intI(j, n = di[2], dn = dn[[2]]))
}
stop("FIXME: NOT YET FINISHED IMPLEMENTATION")
## The M[i_vec, j_vec] had -- we need "its diagonal" :
sel <- ip1$m & ip2$m
if(isSym) { # only those corresponding to upper/lower triangle
sel <- sel &
(if(x@uplo == "U") ip1$m <= ip2$m else ip2$m <= ip1$m)
}
x@i <- ip1$m[sel] - 1L
x@j <- ip2$m[sel] - 1L
if (!is(x, "nsparseMatrix"))
x@x <- c(x@x, if(isSym) x@x[offD])[sel]
if (drop && any(nd == 1)) drop(as(x,"matrix")) else x
})
###========= Sub-Assignment aka *Replace*Methods =========================
### FIXME: make this `very fast' for the very very common case of
### ----- M[i,j] <- v with i,j = length-1-numeric; v= length-1 number
### *and* M[i,j] == 0 previously
##
## --------- ----- FIXME(2): keep in sync with replCmat() in ./Csparse.R
## FIXME(3): It's terribly slow when used e.g. from diag(M[,-1]) <- value
## ----- which has "workhorse" M[,-1] <- <dsparseVector>
##
## workhorse for "[<-" :
replTmat <- function (x, i, j, ..., value)
{
## NOTE: need '...', i.e., exact signature such that setMethod()
## does not use .local() such that nargs() will work correctly:
di <- dim(x)
dn <- dimnames(x)
iMi <- missing(i)
jMi <- missing(j)
## "FIXME": could pass this (and much ? more) when this function would not *be* a
## method but be *called* from methods
spV <- is(value, "sparseVector")
## own version of all0() that works both for sparseVector and atomic vectors:
.all0 <- function(v) if(spV) length(v@i) == 0 else all0(v)
na <- nargs()
if(na == 3) { ## i = vector (but *not* 2-col) indexing" M[i] <- v
Matrix.msg(sprintf(
"diagnosing replTmat(x,i,j,v): nargs()= 3; missing(i,j)= (%d,%d)",
iMi,jMi))
if(iMi) stop("internal bug: missing 'i' in replTmat(): please report")
if(is.character(i))
stop("[ <character> ] indexing not allowed: forgot a \",\" ?")
if(is.matrix(i))
stop("internal bug: matrix 'i' in replTmat(): please report")
## Now: have M[i] <- v with vector logical or "integer" i :
## Tmatrix maybe non-unique, have an entry split into a sum of several ones:
if(!is(x,"generalMatrix")) {
cl <- class(x)
x <- as(x, paste(.M.kind(x), "gTMatrix", sep=''))
Matrix.msg("'sub-optimal sparse 'x[i] <- v' assignment: Coercing class ",
cl," to ",class(x))
}
nr <- di[1]
x.i <- .Call(m_encodeInd2, x@i, x@j, di=di)
if(any(duplicated(x.i))) { ## == if(is_duplicatedT(x, di = di))
x <- uniqTsparse(x)
x.i <- .Call(m_encodeInd2, x@i, x@j, di=di)
}
if(is.logical(i)) { # full-size logical indexing
n <- prod(di)
if(n) {
if(length(i) < n) i <- rep(i, length.out = n)
i <- (0:(n-1))[i] # -> 0-based index vector as well {maybe LARGE!}
} else i <- integer(0)
} else i <- as.integer(i) - 1L ## 0-based indices
clx <- class(x)
clDx <- getClassDef(clx) # extends(), is() etc all use the class definition
has.x <- "x" %in% slotNames(clDx) # === slotNames(x)
## now have 0-based indices x.i (entries) and i (new entries)
## the simplest case:
if(.all0(value)) { ## just drop the non-zero entries
sel <- is.na(match(x.i, i))
if(any(!sel)) { ## non-zero there
x@i <- x@i[sel]
x@j <- x@j[sel]
if(has.x)
x@x <- x@x[sel]
}
return(x)
}
m <- length(i)
if(length(value) != m) { ## use recycling rules
if(m %% length(value) != 0)
warning("number of items to replace is not a multiple of replacement length")
value <- rep(value, length.out = m)
}
## matching existing non-zeros and new entries
isE <- !is.na(mi <- match(i, x.i)) ## use which(isE) , mi[isE]
## 1) Change the matching non-zero entries
if(has.x)
x@x[mi[isE]] <- value[isE]
## 2) add the new non-zero entries
i <- i[!isE]
x@i <- c(x@i, i %% nr)
x@j <- c(x@j, i %/% nr)
if(has.x)
x@x <- c(x@x, value[!isE])
return(x)
}
## nargs() == 4 :
## FIXME: use 'abIndex' or a better algorithm, e.g. if(iMi)
i1 <- if(iMi) 0:(di[1] - 1L) else .ind.prep2(i, 1, di, dn)
i2 <- if(jMi) 0:(di[2] - 1L) else .ind.prep2(j, 2, di, dn)
dind <- c(length(i1), length(i2)) # dimension of replacement region
lenRepl <- prod(dind)
lenV <- length(value)
if(lenV == 0) {
if(lenRepl != 0)
stop("nothing to replace with")
else return(x)
}
## else: lenV := length(value) is > 0
if(lenRepl %% lenV != 0)
stop("number of items to replace is not a multiple of replacement length")
if(!iMi && any(dup <- duplicated(i1, fromLast = TRUE))) {
notDup <- - which(dup)
i1 <- i1[notDup]
lenV <- length(value <- rep(value, length.out = lenRepl)[notDup])
dind[1] <- length(i1)
lenRepl <- dind[1] * dind[2]
}
if(!jMi && any(dup <- duplicated(i2, fromLast = TRUE))) {
keep <- !dup
i2 <- i2[keep]
lenV <- length(value <- rep(value, length.out = lenRepl)[keep])
dind[2] <- length(i2)
lenRepl <- dind[1] * dind[2]
}
clx <- class(x)
clDx <- getClassDef(clx) # extends() , is() etc all use the class definition
stopifnot(extends(clDx, "TsparseMatrix"))
## Tmatrix maybe non-unique, have an entry split into a sum of several ones:
if(is_duplicatedT(x, di = di))
x <- uniqTsparse(x)
toGeneral <- r.sym <- FALSE
if(extends(clDx, "symmetricMatrix")) {
mkArray <- if(spV) # TODO: room for improvement
function(v, dim) spV2M(v, dim[1],dim[2]) else array
r.sym <- (dind[1] == dind[2]) && all(i1 == i2) &&
(lenRepl == 1 || isSymmetric(value <- mkArray(value, dim=dind)))
if(r.sym) { ## result is *still* symmetric --> keep symmetry!
xU <- x@uplo == "U"
# later, we will consider only those indices above / below diagonal:
}
else toGeneral <- TRUE
}
else if(extends(clDx, "triangularMatrix")) {
xU <- x@uplo == "U"
r.tri <- ((any(dind == 1) || dind[1] == dind[2]) &&
if(xU) max(i1) <= min(i2) else max(i2) <= min(i1))
if(r.tri) { ## result is *still* triangular
if(any(i1 == i2)) # diagonal will be changed
x <- diagU2N(x) # keeps class (!)
}
else toGeneral <- TRUE
}
if(toGeneral) { # go to "generalMatrix" and continue
if((.w <- isTRUE(getOption("Matrix.warn"))) || isTRUE(getOption("Matrix.verbose")))
(if(.w) warning else message)(
"M[i,j] <- v : coercing symmetric M[] into non-symmetric")
x <- as(x, paste(.M.kind(x), "gTMatrix", sep=''))
clDx <- getClassDef(clx <- class(x))
}
## TODO (efficiency): replace 'sel' by 'which(sel)'
get.ind.sel <- function(ii,ij)
(match(x@i, ii, nomatch = 0) & match(x@j, ij, nomatch = 0))
## sel[k] := TRUE iff k-th non-zero entry (typically x@x[k]) is to be replaced
sel <- get.ind.sel(i1,i2)
has.x <- "x" %in% slotNames(clDx) # === slotNames(x)
## the simplest case: for all Tsparse, even for i or j missing
if(.all0(value)) { ## just drop the non-zero entries
if(any(sel)) { ## non-zero there
x@i <- x@i[!sel]
x@j <- x@j[!sel]
if(has.x)
x@x <- x@x[!sel]
}
return(x)
}
## else -- some( value != 0 ) --
if(lenV > lenRepl)
stop("too many replacement values")
## now have lenV <= lenRepl
## another simple, typical case:
if(lenRepl == 1) {
if(spV && has.x) value <- as(value, "vector")
if(any(sel)) { ## non-zero there
if(has.x)
x@x[sel] <- value
} else { ## new non-zero
x@i <- c(x@i, i1)
x@j <- c(x@j, i2)
if(has.x)
x@x <- c(x@x, value)
}
return(x)
}
## if(r.sym) # value already adjusted, see above
## lenRepl <- length(value) # shorter (since only "triangle")
if(!r.sym && lenV < lenRepl)
value <- rep(value, length.out = lenRepl)
## now: length(value) == lenRepl
## value[1:lenRepl]: which are structural 0 now, which not?
## v0 <- is0(value)
## - replaced by using isN0(as.vector(.)) on a typical small subset value[.]
## --> more efficient for sparse 'value' & large 'lenRepl' :
## FIXME [= FIXME(3) above]:
## ----- The use of seq_len(lenRepl) below is *still* inefficient
## and the vN0 <- isN0(as.vector(value[iI0])) is even more ...
if(any(sel)) {
## the 0-based indices of non-zero entries -- WRT to submatrix
non0 <- cbind(match(x@i[sel], i1),
match(x@j[sel], i2)) - 1L
iN0 <- 1L + .Call(m_encodeInd, non0, di = dind)
## 1a) replace those that are already non-zero with non-0 values
vN0 <- isN0(as.vector(value[iN0]))
if(any(vN0) && has.x)
x@x[sel][vN0] <- as.vector(value[iN0[vN0]])
## 1b) replace non-zeros with 0 --> drop entries
if(any(!vN0)) {
ii <- which(sel)[!vN0]
if(has.x)
x@x <- x@x[-ii]
x@i <- x@i[-ii]
x@j <- x@j[-ii]
}
iI0 <- if(length(iN0) < lenRepl)
seq_len(lenRepl)[-iN0] # == complementInd(non0, dind)
} else iI0 <- seq_len(lenRepl)
if(length(iI0)) {
if(r.sym) {
## should only set new entries above / below diagonal
iSel <- indTri(dind[1], upper=xU, diag=TRUE)
## select also the corresponding triangle of values
iI0 <- intersect(iI0, iSel)
}
if(any(vN0 <- isN0(as.vector(value[iI0])))) {
## 2) add those that were structural 0 (where value != 0)
ij0 <- decodeInd(iI0[vN0] - 1L, nr = dind[1])
x@i <- c(x@i, i1[ij0[,1] + 1L])
x@j <- c(x@j, i2[ij0[,2] + 1L])
if(has.x)
x@x <- c(x@x, as.vector(value[iI0[vN0]]))
}
}
x
} ## end{replTmat}
## A[ ij ] <- value, where ij is (i,j) 2-column matrix :
## ---------------- ./Matrix.R has a general cheap method
## This one should become as fast as possible -- is also used from Csparse.R --
.TM.repl.i.2col <- function (x, i, j, ..., value)
{
nA <- nargs()
if(nA != 3)
stop(gettextf("nargs() = %d should never happen; please report.", nA))
## else: nA == 3 i.e., M [ cbind(ii,jj) ] <- value or M [ Lmat ] <- value
if(is.logical(i)) {
Matrix.msg(".TM.repl.i.2col(): drop 'matrix' case ...")
## c(i) : drop "matrix" to logical vector
x[c(i)] <- value
return(x)
} else if(!is.numeric(i) || ncol(i) != 2)
stop("such indexing must be by logical or 2-column numeric matrix")
if(!is.integer(i)) storage.mode(i) <- "integer"
if(any(i < 0))
stop("negative values are not allowed in a matrix subscript")
if(any(is.na(i)))
stop("NAs are not allowed in subscripted assignments")
if(any(i0 <- (i == 0))) # remove them
i <- i[ - which(i0, arr.ind = TRUE)[,"row"], ]
if(length(attributes(i)) > 1) # more than just 'dim'; simplify: will use identical
attributes(i) <- list(dim = dim(i))
## now have integer i >= 1
m <- nrow(i)
if(m == 0)
return(x)
if(length(value) == 0)
stop("nothing to replace with")
## mod.x <- .type.kind[.M.kind(x)]
if(length(value) != m) { ## use recycling rules
if(m %% length(value) != 0)
warning("number of items to replace is not a multiple of replacement length")
value <- rep(value, length = m)
}
clx <- class(x)
clDx <- getClassDef(clx) # extends() , is() etc all use the class definition
stopifnot(extends(clDx, "TsparseMatrix"))
di <- dim(x)
nr <- di[1]
nc <- di[2]
i1 <- i[,1]
i2 <- i[,2]
if(any(i1 > nr)) stop("row indices must be <= nrow(.) which is ", nr)
if(any(i2 > nc)) stop("column indices must be <= ncol(.) which is ", nc)
## Tmatrix maybe non-unique, have an entry split into a sum of several ones:
if(is_duplicatedT(x, di = di))
x <- uniqTsparse(x)
toGeneral <- FALSE
if(r.sym <- extends(clDx, "symmetricMatrix")) {
## Tests to see if the assignments are symmetric as well
r.sym <- all(i1 == i2)
if(!r.sym) { # do have *some* Lower or Upper entries
iL <- i1 > i2
iU <- i1 < i2
r.sym <- sum(iL) == sum(iU) # same number
if(r.sym) {
iLord <- order(i1[iL], i2[iL])
iUord <- order(i2[iU], i1[iU]) # row <-> col. !
r.sym <- {
identical(i[iL, , drop=FALSE][iLord,],
i[iU, 2:1, drop=FALSE][iUord,]) &&
all(value[iL][iLord] ==
value[iU][iUord])
}
}
}
if(r.sym) { ## result is *still* symmetric --> keep symmetry!
## now consider only those indices above / below diagonal:
useI <- if(x@uplo == "U") i1 <= i2 else i2 <= i1
i <- i[useI, , drop=FALSE]
value <- value[useI]
}
else toGeneral <- TRUE
}
else if(extends(clDx, "triangularMatrix")) {
r.tri <- all(if(x@uplo == "U") i1 <= i2 else i2 <= i1)
if(r.tri) { ## result is *still* triangular
if(any(i1 == i2)) # diagonal will be changed
x <- diagU2N(x) # keeps class (!)
}
else toGeneral <- TRUE
}
if(toGeneral) { # go to "generalMatrix" and continue
if((.w <- isTRUE(getOption("Matrix.warn"))) || isTRUE(getOption("Matrix.verbose")))
(if(.w) warning else message)(
"M[ij] <- v : coercing symmetric M[] into non-symmetric")
x <- as(x, paste(.M.kind(x), "gTMatrix", sep=''))
clDx <- getClassDef(clx <- class(x))
}
ii.v <- .Call(m_encodeInd, i - 1L, di)# 0-indexing
if(any(d <- duplicated(rev(ii.v)))) { # reverse: "last" duplicated FALSE
warning("duplicate ij-entries in 'Matrix[ ij ] <- value'; using last")
nd <- !rev(d)
## i <- i [nd, , drop=FALSE]
ii.v <- ii.v [nd]
value <- value[nd]
}
ii.x <- .Call(m_encodeInd2, x@i, x@j, di)
m1 <- match(ii.v, ii.x)
i.repl <- !is.na(m1) # those that need to be *replaced*
if(isN <- extends(clDx, "nMatrix")) { ## no 'x' slot
isN <- all(value %in% c(FALSE, TRUE)) # will result remain "nMatrix" ?
if(!isN)
x <- as(x, paste(if(extends(clDx, "lMatrix")) "l" else "d",
.sparse.prefixes[.M.shape(x)], "TMatrix", sep=''))
}
has.x <- !isN ## isN <===> "remains pattern matrix" <===> has no 'x' slot
if(any(i.repl)) { ## some to replace at matching (@i, @j)
if(has.x)
x@x[m1[i.repl]] <- value[i.repl]
else { # nMatrix ; eliminate entries that are set to FALSE; keep others
if(any(isF <- !value[i.repl])) {
ii <- m1[i.repl][isF]
x@i <- x@i[ -ii]
x@j <- x@j[ -ii]
}
}
}
if(!all(i.repl)) { ## some new entries
i.j <- decodeInd(ii.v[!i.repl], nr)
x@i <- c(x@i, i.j[,1])
x@j <- c(x@j, i.j[,2])
if(has.x)
x@x <- c(x@x, value[!i.repl])
}
x
} ## end{.TM.repl.i.2col}
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "index", j = "missing",
value = "replValue"),
replTmat)
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "missing", j = "index",
value = "replValue"),
replTmat)
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "index", j = "index",
value = "replValue"),
replTmat)
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "matrix", j = "missing",
value = "replValue"),
.TM.repl.i.2col)
### When the RHS 'value' is a sparseVector, now can use replTmat as well
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "missing", j = "index",
value = "sparseVector"),
replTmat)
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "index", j = "missing",
value = "sparseVector"),
replTmat)
setReplaceMethod("[", signature(x = "TsparseMatrix", i = "index", j = "index",
value = "sparseVector"),
replTmat)
setMethod("solve", signature(a = "TsparseMatrix", b = "ANY"),
function(a, b, ...) solve(as(a, "CsparseMatrix"), b))
setMethod("solve", signature(a = "TsparseMatrix", b = "missing"),
function(a, b, ...) solve(as(a, "CsparseMatrix")))
## Want tril(), triu(), band() --- just as "indexing" ---
## return a "close" class:
setMethod("tril", "TsparseMatrix",
function(x, k = 0, ...)
as(tril(.T.2.C(x), k = k, ...), "TsparseMatrix"))
setMethod("triu", "TsparseMatrix",
function(x, k = 0, ...)
as(triu(.T.2.C(x), k = k, ...), "TsparseMatrix"))
setMethod("band", "TsparseMatrix",
function(x, k1, k2, ...)
as(band(.T.2.C(x), k1 = k1, k2 = k2, ...), "TsparseMatrix"))
## For the "general" T ones (triangular & symmetric have special methods):
setMethod("t", signature(x = "TsparseMatrix"),
function(x) {
cld <- getClassDef(class(x))
r <- new(cld)
r@i <- x@j
r@j <- x@i
if(any("x" == slotNames(cld)))
r@x <- x@x
r@Dim <- x@Dim[2:1]
r@Dimnames <- x@Dimnames[2:1]
r
})
setMethod("writeMM", "TsparseMatrix",
function(obj, file, ...)
.Call(Csparse_MatrixMarket, as(obj, "CsparseMatrix"),
as.character(file)))
