https://github.com/cran/Matrix
Tip revision: 0c8ec650caab17c6bded1f0ef2e8969e8690adea authored by Doug and Martin on 30 January 2009, 00:00:00 UTC
version 0.999375-21
version 0.999375-21
Tip revision: 0c8ec65
sparseMatrix.R
### Define Methods that can be inherited for all subclasses
### Idea: Coercion between *VIRTUAL* classes -- as() chooses "closest" classes
### ---- should also work e.g. for dense-triangular --> sparse-triangular !
##-> see als ./dMatrix.R, ./ddenseMatrix.R and ./lMatrix.R
setAs("ANY", "sparseMatrix", function(from) as(from, "CsparseMatrix"))
setAs("sparseMatrix", "generalMatrix", as_gSparse)
setAs("sparseMatrix", "symmetricMatrix", as_sSparse)
setAs("sparseMatrix", "triangularMatrix", as_tSparse)
spMatrix <- function(nrow, ncol,
i = integer(), j = integer(), x = numeric())
{
dim <- c(as.integer(nrow), as.integer(ncol))
## The conformability of (i,j,x) with itself and with 'dim'
## is checked automatically by internal "validObject()" inside new(.):
kind <- .M.kind(x)
new(paste(kind, "gTMatrix", sep=''), Dim = dim,
x = if(kind == "d") as.double(x) else x,
## our "Tsparse" Matrices use 0-based indices :
i = as.integer(i - 1L),
j = as.integer(j - 1L))
}
sparseMatrix <- function(i, j, p, x, dims, dimnames, index0 = FALSE)
{
## Purpose: user-level substitute for most new(<sparseMatrix>, ..) calls
## Author: Martin Maechler, Date: 6 Jan 2009, based on Doug Bates' idea
if((m.i <- missing(i)) + (m.j <- missing(j)) + (m.p <- missing(p)) != 1)
stop("exactly one of 'i', 'j', or 'p' must be missing from call")
if(!m.p) {
## we *could* let validObject() / .validateCsparse() check later ...
p <- as.integer(p)
if((lp <- length(p)) < 1 || p[1] != 0 || any((dp <- p[-1] - p[-lp]) < 0))
stop("'p' must be a non-decreasing vector (0, ...)")
}
isPat <- missing(x) ## <-> patter"n" Matrix
## "minimal dimensions" from (i,j,p) :
dims.min <- c(if(m.i) lp - 1L else max(i <- as.integer(i)),
if(m.j) lp - 1L else max(j <- as.integer(j)))
if(any(is.na(dims.min))) stop("NA's in (i,j) are not allowed")
if(missing(dims)) {
dims <- dims.min
} else { ## check dims
stopifnot(all(dims >= dims.min))
dims <- as.integer(dims)
}
kx <- if(isPat) "n" else .M.kind(x)
if(m.j) { ## -> Csparse
r <- new(paste(kx, "gCMatrix", sep=''))
r@Dim <- dims
r@p <- as.integer(p)
if(!isPat) r@x <- if(kx == "d" && !is.double(x)) as.double(x) else x
r@i <- as.integer(if(index0) i else i - 1L)
vv <- .validateCsparse(r, sort.if.needed=TRUE)## modify 'r' in-place !!!
if(is.character(vv)) stop(vv)
}
else if(m.i) { ## -> Rsparse
stop("(j,p) --> RsparseMatrix : not yet implemented")
}
else if(m.p) { ## -> Tsparse
r <- new(paste(kx, "gTMatrix", sep=''))
r@Dim <- dims
if(!isPat) r@x <- if(kx == "d" && !is.double(x)) as.double(x) else x
r@i <- as.integer(if(index0) i else i - 1L)
r@j <- as.integer(if(index0) j else j - 1L)
validObject(r)
r
}
if(!missing(dimnames)) {
## FIXME: should we check here, or validObject(r) or ??
r@Dimnames <- dimnames
}
r
}
sparseMatrix <- function(i = ep, j = ep, p, x, dims, dimnames, index1 = TRUE)
{
## Purpose: user-level substitute for most new(<sparseMatrix>, ..) calls
## Author: Douglas Bates, Date: 12 Jan 2009, based on Martin's version
if((m.i <- missing(i)) + (m.j <- missing(j)) + (m.p <- missing(p)) != 1)
stop("exactly one of 'i', 'j', or 'p' must be missing from call")
if(!m.p) {
p <- as.integer(p)
if((lp <- length(p)) < 1 || p[1] != 0 || any((dp <- p[-1] - p[-lp]) < 0))
stop("'p' must be a non-decreasing vector (0, ...)")
ep <- rep.int(seq_along(dp), dp)
}
## i and j are now both defined. Make them 1-based indices.
i1 <- as.logical(index1)[1]
i <- as.integer(i + !(m.i || i1))
j <- as.integer(j + !(m.j || i1))
## "minimal dimensions" from (i,j,p); no warnings from empty i or j :
dims.min <- suppressWarnings(c(max(i), max(j)))
if(any(is.na(dims.min))) stop("NA's in (i,j) are not allowed")
if(missing(dims)) {
dims <- dims.min
} else { ## check dims
stopifnot(all(dims >= dims.min))
dims <- as.integer(dims)
}
isPat <- missing(x) ## <-> patter"n" Matrix
kx <- if(isPat) "n" else .M.kind(x)
r <- new(paste(kx, "gTMatrix", sep=''))
r@Dim <- dims
if(!isPat) {
if(kx == "d" && !is.double(x)) x <- as.double(x)
if(length(x) != (n <- length(i))) { ## recycle
if(length(x) != 1 && n %% length(x) != 0)
warning("length(i) is not a multiple of length(x)")
x <- rep(x, length.out = n)
}
r@x <- x
}
r@i <- i - 1L
r@j <- j - 1L
validObject(r)
as(r, "CsparseMatrix")
}
## "graph" coercions -- this needs the graph package which is currently
## ----- *not* required on purpose
## Note: 'undirected' graph <==> 'symmetric' matrix
## Add some utils that may no longer be needed in future versions of the 'graph' package
graph.has.weights <- function(g) "weight" %in% names(edgeDataDefaults(g))
graph.wgtMatrix <- function(g)
{
## Purpose: work around "graph" package's as(g, "matrix") bug
## ----------------------------------------------------------------------
## Arguments: g: an object inheriting from (S4) class "graph"
## ----------------------------------------------------------------------
## Author: Martin Maechler, based on Seth Falcon's code; Date: 12 May 2006
## MM: another buglet for the case of "no edges":
if(numEdges(g) == 0) {
p <- length(nd <- nodes(g))
return( matrix(0, p,p, dimnames = list(nd, nd)) )
}
## Usual case, when there are edges:
has.w <- "weight" %in% names(edgeDataDefaults(g))
if(has.w) {
w <- unlist(edgeData(g, attr = "weight"))
has.w <- any(w != 1)
} ## now 'has.w' is TRUE iff there are weights != 1
m <- as(g, "matrix")
## now is a 0/1 - matrix (instead of 0/wgts) with the 'graph' bug
if(has.w) { ## fix it if needed
tm <- t(m)
tm[tm != 0] <- w
t(tm)
}
else m
}
setAs("graphAM", "sparseMatrix",
function(from) {
symm <- edgemode(from) == "undirected" && isSymmetric(from@adjMat)
## This is only ok if there are no weights...
if(graph.has.weights(from)) {
as(graph.wgtMatrix(from),
if(symm) "dsTMatrix" else "dgTMatrix")
}
else { ## no weights: 0/1 matrix -> logical
as(as(from, "matrix"),
if(symm) "nsTMatrix" else "ngTMatrix")
}
})
setAs("graph", "CsparseMatrix",
function(from) as(as(from, "graphNEL"), "CsparseMatrix"))
setAs("graphNEL", "CsparseMatrix",
function(from) as(as(from, "TsparseMatrix"), "CsparseMatrix"))
setAs("graphNEL", "TsparseMatrix",
function(from) {
nd <- nodes(from)
dm <- rep.int(length(nd), 2)
symm <- edgemode(from) == "undirected"
if(graph.has.weights(from)) {
eWts <- edgeWeights(from)
lens <- unlist(lapply(eWts, length))
i <- rep.int(0:(dm[1]-1), lens) # column indices (0-based)
To <- unlist(lapply(eWts, names))
j <- as.integer(match(To,nd) - 1L) # row indices (0-based)
## symm <- symm && <weights must also be symmetric>: improbable
## if(symm) new("dsTMatrix", .....) else
new("dgTMatrix", i = i, j = j, x = unlist(eWts),
Dim = dm, Dimnames = list(nd, nd))
}
else { ## no weights: 0/1 matrix -> logical
edges <- lapply(from@edgeL[nd], "[[", "edges")
lens <- unlist(lapply(edges, length))
## nnz <- sum(unlist(lens)) # number of non-zeros
i <- rep.int(0:(dm[1]-1), lens) # column indices (0-based)
j <- as.integer(unlist(edges) - 1) # row indices (0-based)
if(symm) { # symmetric: ensure upper triangle
tmp <- i
flip <- i > j
i[flip] <- j[flip]
j[flip] <- tmp[flip]
new("nsTMatrix", i = i, j = j, Dim = dm,
Dimnames = list(nd, nd), uplo = "U")
} else {
new("ngTMatrix", i = i, j = j, Dim = dm,
Dimnames = list(nd, nd))
}
}
})
setAs("sparseMatrix", "graph", function(from) as(from, "graphNEL"))
setAs("sparseMatrix", "graphNEL",
## since have specific method for Tsparse below, are Csparse or Rsparse,
## i.e. do not need to "uniquify" the T* matrix:
function(from) Tsp2grNEL(as(from, "TsparseMatrix"), need.uniq=FALSE))
Tsp2grNEL <- function(from, need.uniq = is_not_uniqT(from)) {
d <- dim(from)
if(d[1] != d[2])
stop("only square matrices can be used as incidence matrices for graphs")
n <- d[1]
if(n == 0) return(new("graphNEL"))
if(is.null(rn <- dimnames(from)[[1]]))
rn <- as.character(1:n)
if(need.uniq) ## Need to 'uniquify' the triplets!
from <- uniq(from)
if(isSymmetric(from)) { # either "symmetricMatrix" or otherwise
##-> undirected graph: every edge only once!
if(!is(from, "symmetricMatrix")) {
## a general matrix which happens to be symmetric
## ==> remove the double indices
from <- tril(from)
}
eMode <- "undirected"
} else {
eMode <- "directed"
}
## every edge is there only once, either upper or lower triangle
ft1 <- cbind(rn[from@i + 1L], rn[from@j + 1L])
## not yet: graph::ftM2graphNEL(.........)
ftM2graphNEL(ft1, W = from@x, V= rn, edgemode= eMode)
}
setAs("TsparseMatrix", "graphNEL", function(from) Tsp2grNEL(from))
### Subsetting -- basic things (drop = "missing") are done in ./Matrix.R
### FIXME : we defer to the "*gT" -- conveniently, but not efficient for gC !
## [dl]sparse -> [dl]gT -- treat both in one via superclass
## -- more useful when have "z" (complex) and even more
setMethod("[", signature(x = "sparseMatrix", i = "index", j = "missing",
drop = "logical"),
function (x, i,j, ..., drop) {
cld <- getClassDef(class(x))
##> why should this be needed; can still happen in <Tsparse>[..]:
##> if(!extends(cld, "generalMatrix")) x <- as(x, "generalMatrix")
## viaCl <- paste(.M.kind(x, cld), "gTMatrix", sep='')
x <- as(x, "TsparseMatrix")[i, , drop=drop]
##simpler than x <- callGeneric(x = as(x, "TsparseMatrix"), i=i, drop=drop)
## try_as(x, c(cl, sub("T","C", viaCl)))
if(is(x, "Matrix") && extends(cld, "CsparseMatrix"))
as(x, "CsparseMatrix") else x
})
setMethod("[", signature(x = "sparseMatrix", i = "missing", j = "index",
drop = "logical"),
function (x,i,j, ..., drop) {
cld <- getClassDef(class(x))
##> why should this be needed; can still happen in <Tsparse>[..]:
##> if(!extends(cld, "generalMatrix")) x <- as(x, "generalMatrix")
## viaCl <- paste(.M.kind(x, cld), "gTMatrix", sep='')
x <- as(x, "TsparseMatrix")[, j, drop=drop]
##simpler than x <- callGeneric(x = as(x, "TsparseMatrix"), j=j, drop=drop)
if(is(x, "Matrix") && extends(cld, "CsparseMatrix"))
as(x, "CsparseMatrix") else x
})
setMethod("[", signature(x = "sparseMatrix",
i = "index", j = "index", drop = "logical"),
function (x, i, j, ..., drop) {
cld <- getClassDef(class(x))
## be smart to keep symmetric indexing of <symm.Mat.> symmetric:
##> doSym <- (extends(cld, "symmetricMatrix") &&
##> length(i) == length(j) && all(i == j))
##> why should this be needed; can still happen in <Tsparse>[..]:
##> if(!doSym && !extends(cld, "generalMatrix"))
##> x <- as(x, "generalMatrix")
## viaCl <- paste(.M.kind(x, cld),
## if(doSym) "sTMatrix" else "gTMatrix", sep='')
x <- as(x, "TsparseMatrix")[i, j, drop=drop]
if(is(x, "Matrix") && extends(cld, "CsparseMatrix"))
as(x, "CsparseMatrix") else x
})
## setReplaceMethod("[", .........)
## -> ./Tsparse.R
## & ./Csparse.R & ./Rsparse.R {those go via Tsparse}
##
## Do not use as.vector() (see ./Matrix.R ) for sparse matrices :
setReplaceMethod("[", signature(x = "sparseMatrix", i = "missing", j = "ANY",
value = "sparseMatrix"),
function (x, i, j, ..., value)
callGeneric(x=x, , j=j, value = as(value, "sparseVector")))
setReplaceMethod("[", signature(x = "sparseMatrix", i = "ANY", j = "missing",
value = "sparseMatrix"),
function (x, i, j, ..., value)
callGeneric(x=x, i=i, , value = as(value, "sparseVector")))
setReplaceMethod("[", signature(x = "sparseMatrix", i = "ANY", j = "ANY",
value = "sparseMatrix"),
function (x, i, j, ..., value)
callGeneric(x=x, i=i, j=j, value = as(value, "sparseVector")))
## Group Methods
setMethod("Math",
signature(x = "sparseMatrix"),
function(x) callGeneric(as(x, "CsparseMatrix")))
## further group methods -> see ./Ops.R {"Summary": ./dMatrix.R }
### --- show() method ---
## FIXME(?) -- ``merge this'' (at least ``synchronize'') with
## - - - prMatrix() from ./Auxiliaries.R
## FIXME: prTriang() in ./Auxiliaries.R should also get align = "fancy"
##
printSpMatrix <- function(x, digits = getOption("digits"),
maxp = getOption("max.print"), zero.print = ".",
col.names, note.dropping.colnames = TRUE,
col.trailer = '', align = c("fancy", "right"))
{
cl <- getClassDef(class(x))
stopifnot(extends(cl, "sparseMatrix"))
validObject(x) # have seen seg.faults for invalid objects
d <- dim(x)
if(prod(d) > maxp) { # "Large" => will be "cut"
## only coerce to dense that part which won't be cut :
nr <- maxp %/% d[2]
m <- as(x[1:max(1, nr), ,drop=FALSE], "Matrix")
} else {
m <- as(x, "matrix")
}
dn <- dimnames(m) ## will be === dimnames(cx)
logi <- (extends(cl,"lsparseMatrix") || extends(cl,"nsparseMatrix") ||
extends(cl, "pMatrix"))
if(logi)
cx <- array("N", dim(m), dimnames=dn)
else { ## numeric (or --not yet implemented-- complex):
cx <- apply(m, 2, format)
if(is.null(dim(cx))) {# e.g. in 1 x 1 case
dim(cx) <- dim(m)
dimnames(cx) <- dn
}
}
if (missing(col.names))
col.names <- {
if(!is.null(cc <- getOption("sparse.colnames")))
cc
else if(is.null(dn[[2]]))
FALSE
else { # has column names == dn[[2]]
ncol(x) < 10
}
}
if(identical(col.names, FALSE))
cx <- emptyColnames(cx, msg.if.not.empty = note.dropping.colnames)
else if(is.character(col.names)) {
stopifnot(length(col.names) == 1)
cn <- col.names
switch(substr(cn, 1,3),
"abb" = {
iarg <- as.integer(sub("^[^0-9]*", '', cn))
colnames(cx) <- abbreviate(colnames(cx), minlength = iarg)
},
"sub" = {
iarg <- as.integer(sub("^[^0-9]*", '', cn))
colnames(cx) <- substr(colnames(cx), 1, iarg)
},
stop("invalid 'col.names' string: ", cn))
}
## else: nothing to do for col.names == TRUE
if(is.logical(zero.print))
zero.print <- if(zero.print) "0" else " "
if(logi) {
cx[!m] <- zero.print
cx[m] <- "|"
} else { # non logical
## show only "structural" zeros as 'zero.print', not all of them..
## -> cannot use 'm'
d <- dim(cx)
ne <- length(iN0 <- 1L + .Call(m_encodeInd, non0ind(x, cl), di = d))
if(0 < ne && ne < prod(d)) {
align <- match.arg(align)
if(align == "fancy" && !is.integer(m)) {
fi <- apply(m, 2, format.info) ## fi[3,] == 0 <==> not expo.
## now 'format' the zero.print by padding it with ' ' on the right:
## case 1: non-exponent: fi[2,] + as.logical(fi[2,] > 0)
## the column numbers of all 'zero' entries -- (*large*)
cols <- 1L + (0:(prod(d)-1L))[-iN0] %/% d[1]
pad <-
ifelse(fi[3,] == 0,
fi[2,] + as.logical(fi[2,] > 0),
## exponential:
fi[2,] + fi[3,] + 4)
## now be efficient ; sprintf() is relatively slow
## and pad is much smaller than 'cols'; instead of "simply"
## zero.print <- sprintf("%-*s", pad[cols] + 1, zero.print)
if(any(doP <- pad > 0)) {#
## only pad those that need padding - *before* expanding
z.p.pad <- rep.int(zero.print, length(pad))
z.p.pad[doP] <- sprintf("%-*s", pad[doP] + 1, zero.print)
zero.print <- z.p.pad[cols]
}
else
zero.print <- rep.int(zero.print, length(cols))
} ## else "right" : nothing to do
cx[-iN0] <- zero.print
} else if (ne == 0)# all zeroes
cx[] <- zero.print
}
if(col.trailer != '')
cx <- cbind(cx, col.trailer, deparse.level = 0)
## right = TRUE : cheap attempt to get better "." alignment
print(cx, quote = FALSE, right = TRUE, max = maxp)
invisible(x)
} ## printSpMatrix()
printSpMatrix2 <- function(x, digits = getOption("digits"),
maxp = getOption("max.print"), zero.print = ".",
col.names, note.dropping.colnames = TRUE,
suppRows = NULL, suppCols = NULL,
col.trailer = if(suppCols) "......" else "",
align = c("fancy", "right"))
{
d <- dim(x)
if((identical(suppRows,FALSE) && identical(suppCols, FALSE)) ||
(!isTRUE(suppRows) && !isTRUE(suppCols) && prod(d) <= maxp))
{
if(missing(col.trailer) && is.null(suppCols))
suppCols <- FALSE # for 'col.trailer'
printSpMatrix(x, digits=digits, maxp=maxp,
zero.print=zero.print, col.names=col.names,
note.dropping.colnames=note.dropping.colnames,
col.trailer=col.trailer, align=align)
} else { ## d[1] > maxp / d[2] >= nr : -- this needs [,] working:
validObject(x)
nR <- d[1] ## nrow
useW <- getOption("width") - (format.info(nR)[1] + 3+1)
## space for "[<last>,] "
## --> suppress rows and/or columns in printing ...
if(is.null(suppCols)) suppCols <- (d[2] * 2 > useW)
nc <- if(suppCols) (useW - (1 + nchar(col.trailer))) %/% 2 else d[2]
nr <- maxp %/% nc
if(is.null(suppRows)) suppRows <- (nr < nR)
sTxt <- c("in show(); maybe adjust 'options(max.print= *)'",
"\n ..............................\n")
if(suppRows) {
if(suppCols)
x <- x[ , 1:nc, drop = FALSE]
n2 <- ceiling(nr / 2)
printSpMatrix(x[seq_len(min(nR, max(1, n2))), , drop=FALSE],
digits=digits, maxp=maxp,
zero.print=zero.print, col.names=col.names,
note.dropping.colnames=note.dropping.colnames,
col.trailer = col.trailer, align=align)
cat("\n ..............................",
"\n ........suppressing rows ", sTxt, "\n", sep='')
## tail() automagically uses "[..,]" rownames:
printSpMatrix(tail(x, max(1, nr-n2)),
digits=digits, maxp=maxp,
zero.print=zero.print, col.names=col.names,
note.dropping.colnames=note.dropping.colnames,
col.trailer = col.trailer, align=align)
}
else if(suppCols) {
printSpMatrix(x[ , 1:nc , drop = FALSE],
digits=digits, maxp=maxp,
zero.print=zero.print, col.names=col.names,
note.dropping.colnames=note.dropping.colnames,
col.trailer = col.trailer, align=align)
cat("\n .....suppressing columns ", sTxt, sep='')
}
else stop("logic programming error in printSpMatrix2(), please report")
invisible(x)
}
} ## printSpMatrix2 ()
setMethod("print", signature(x = "sparseMatrix"), printSpMatrix)
setMethod("show", signature(object = "sparseMatrix"),
function(object) {
d <- dim(object)
cl <- class(object)
cat(sprintf('%d x %d sparse Matrix of class "%s"\n',
d[1], d[2], cl))
printSpMatrix2(object)
})
## For very large and very sparse matrices, the above show()
## is not really helpful; Use summary() as an alternative:
setMethod("summary", signature(object = "sparseMatrix"),
function(object, ...) {
d <- dim(object)
T <- as(object, "TsparseMatrix")
## return a data frame (int, int, {double|logical|...}) :
r <- if(is(object,"nsparseMatrix"))
data.frame(i = T@i + 1L, j = T@j + 1L)
else data.frame(i = T@i + 1L, j = T@j + 1L, x = T@x)
attr(r, "header") <-
sprintf('%d x %d sparse Matrix of class "%s", with %d entries',
d[1], d[2], class(object), length(T@i))
## use ole' S3 technology for such a simple case
class(r) <- c("sparseSummary", class(r))
r
})
print.sparseSummary <- function (x, ...) {
cat(attr(x, "header"),"\n")
print.data.frame(x, ...)
invisible(x)
}
setMethod("isSymmetric", signature(object = "sparseMatrix"),
function(object, tol = 100*.Machine$double.eps, ...) {
## pretest: is it square?
d <- dim(object)
if(d[1] != d[2]) return(FALSE)
## else slower test using t() --
## FIXME (for tol = 0): use cholmod_symmetry(A, 1, ...)
## for tol > 0 should modify cholmod_symmetry(..) to work with tol
## or slightly simpler, rename and export is_sym() in ../src/cs_utils.c
if (is(object, "dMatrix"))
## use gC; "T" (triplet) is *not* unique!
isTRUE(all.equal(.as.dgC.0.factors( object),
.as.dgC.0.factors(t(object)),
tol = tol, ...))
else if (is(object, "lMatrix"))
## test for exact equality; FIXME(?): identical() too strict?
identical(as( object, "lgCMatrix"),
as(t(object), "lgCMatrix"))
else if (is(object, "nMatrix"))
## test for exact equality; FIXME(?): identical() too strict?
identical(as( object, "ngCMatrix"),
as(t(object), "ngCMatrix"))
else stop("not yet implemented")
})
setMethod("isTriangular", signature(object = "CsparseMatrix"), isTriC)
setMethod("isTriangular", signature(object = "TsparseMatrix"), isTriT)
setMethod("isDiagonal", signature(object = "sparseMatrix"),
function(object) {
d <- dim(object)
if(d[1] != d[2]) return(FALSE)
## else
gT <- as(object, "TsparseMatrix")
all(gT@i == gT@j)
})
setMethod("determinant", signature(x = "sparseMatrix", logarithm = "missing"),
function(x, logarithm, ...)
determinant(x, logarithm = TRUE, ...))
setMethod("determinant", signature(x = "sparseMatrix", logarithm = "logical"),
function(x, logarithm = TRUE, ...)
determinant(as(x,"dsparseMatrix"), logarithm, ...))
setMethod("diag", signature(x = "sparseMatrix"),
function(x, nrow, ncol) diag(as(x, "CsparseMatrix")))
setMethod("dim<-", signature(x = "sparseMatrix", value = "ANY"),
function(x, value) {
if(!is.numeric(value) || length(value) != 2)
stop("dim(.) value must be numeric of length 2")
if(prod(dim(x)) != prod(value <- as.integer(value)))
stop("dimensions don't match the number of cells")
## be careful to keep things sparse
as(spV2M(as(x, "sparseVector"), nrow=value[1], ncol=value[2]),
class(x))
})
setMethod("norm", signature(x = "sparseMatrix", type = "character"),
function(x, type, ...) {
type <- toupper(substr(type[1], 1, 1))
switch(type, ## max(<empty>, 0) |--> 0
"O" = ,
"1" = max(colSums(abs(x)), 0), ## One-norm (L_1)
"I" = max(rowSums(abs(x)), 0), ## L_Infinity
"F" = sqrt(sum(x^2)), ## Frobenius
"M" = max(abs(x), 0), ## Maximum modulus of all
## otherwise:
stop("invalid 'type'"))
})
setMethod("rcond", signature(x = "sparseMatrix", norm = "character"),
function(x, norm, ...) {
d <- dim(x)
## FIXME: qr.R(qr(.)) warns about differing R (permutation!)
## really fix qr.R() *or* go via dense in any cases
rcond(if(d[1] == d[2]) {
warning("rcond(.) via sparse -> dense coercion")
as(x, "denseMatrix")
} else if(d[1] > d[2]) qr.R(qr(x)) else qr.R(qr(t(x))),
norm = norm, ...)
})
setMethod("cov2cor", signature(V = "sparseMatrix"),
function(V) {
## like stats::cov2cor() but making sure all matrices stay sparse
p <- (d <- dim(V))[1]
if (p != d[2])
stop("'V' is not a *square* matrix")
if(!is(V, "dMatrix"))
V <- as(V, "dMatrix")# actually "dsparseMatrix"
Is <- sqrt(1/diag(V))
if (any(!is.finite(Is))) ## original had 0 or NA
warning("diag(.) had 0 or NA entries; non-finite result is doubtful")
## TODO: if <diagonal> %*% <sparse> was implemented more efficiently
## we'd rather use that!
Is <- as(Diagonal(x = Is), "sparseMatrix")
r <- Is %*% V %*% Is
r[cbind(1L:p,1L:p)] <- 1 # exact in diagonal
as(r, "symmetricMatrix")
})
setMethod("is.na", signature(x = "sparseMatrix"),## NB: nsparse* have own method!
function(x) {
if(any((inax <- is.na(x@x)))) {
cld <- getClassDef(class(x))
if(extends(cld, "triangularMatrix") && x@diag == "U")
inax <- is.na((x <- .diagU2N(x, cld))@x)
r <- as(x, "lMatrix") # will be "lsparseMatrix" - *has* x slot
r@x <- inax
if(!extends(cld, "CsparseMatrix"))
r <- as(r, "CsparseMatrix")
as(.Call(Csparse_drop, r, 0), "nMatrix") # a 'pattern matrix
}
else is.na_nsp(x)
})
## all.equal(): similar to all.equal_Mat() in ./Matrix.R ;
## ----------- eventually defer to "sparseVector" methods:
setMethod("all.equal", c(target = "sparseMatrix", current = "sparseMatrix"),
function(target, current, check.attributes = TRUE, ...)
{
msg <- attr.all_Mat(target, current, check.attributes=check.attributes, ...)
if(is.list(msg)) return(msg[[1]])
## else
r <- all.equal(as(target, "sparseVector"), as(current, "sparseVector"),
check.attributes=check.attributes, ...)
if(is.null(msg) && (r.ok <- isTRUE(r))) TRUE else c(msg, if(!r.ok) r)
})
setMethod("all.equal", c(target = "sparseMatrix", current = "ANY"),
function(target, current, check.attributes = TRUE, ...)
{
msg <- attr.all_Mat(target, current, check.attributes=check.attributes, ...)
if(is.list(msg)) return(msg[[1]])
## else
r <- all.equal(as(target, "sparseVector"), current,
check.attributes=check.attributes, ...)
if(is.null(msg) && (r.ok <- isTRUE(r))) TRUE else c(msg, if(!r.ok) r)
})
setMethod("all.equal", c(target = "ANY", current = "sparseMatrix"),
function(target, current, check.attributes = TRUE, ...)
{
msg <- attr.all_Mat(target, current, check.attributes=check.attributes, ...)
if(is.list(msg)) return(msg[[1]])
## else
r <- all.equal(target, as(current, "sparseVector"),
check.attributes=check.attributes, ...)
if(is.null(msg) && (r.ok <- isTRUE(r))) TRUE else c(msg, if(!r.ok) r)
})
### Keep this namespace-hidden: Would need to return a classed object
## FIXME: still test this function for both methods, since currently
## ----- both dgCMatrix_cholsol and dgCMatrix_qrsol are only called from here!
lm.fit.sparse <- function(x, y, offset = NULL, method = c("qr", "cholesky"),
tol = 1e-7, singular.ok = TRUE, order = NULL,
transpose = FALSE) ## NB: meaning of 'transpose'
# is changed from original
### Fit a linear model, __ given __ a sparse model matrix 'x'
### using a sparse QR or a sparse Cholesky factorization
{
cld <- getClass(class(x))
stopifnot(extends(cld, "dsparseMatrix"))
## or if(!is(x, "dsparseMatrix")) x <- as(x, "dsparseMatrix")
yy <- as.numeric(y)
if (!is.null(offset)) {
stopifnot(length(offset) == length(y))
yy <- yy - as.numeric(offset)
}
method <- match.arg(method)
order <- {
if(is.null(order)) ## recommended default depends on method :
if(method == "qr") 3L else 1L
else as.integer(order) }
if(transpose) x <- t(x)
ans <- switch(method,
cholesky =
.Call(dgCMatrix_cholsol,# has AS_CHM_SP(x)
as(x, "CsparseMatrix"), yy),
qr =
.Call(dgCMatrix_qrsol, # has AS_CSP(): must be dgC or dtC:
if(cld@className %in% c("dtCMatrix", "dgCMatrix")) x
else as(x, "dgCMatrix"),
yy, order),
## otherwise:
stop("unknown method ", dQuote(method))
)
ans
}
fac2sparse <- function(from, to = c("d","i","l","n","z"), drop.unused.levels = TRUE)
{
## factor(-like) --> sparseMatrix {also works for integer, character}
fact <- if (drop.unused.levels) factor(from) else as.factor(from)
levs <- levels(fact)
n <- length(fact)
to <- match.arg(to)
## MM: using new() and then assigning slots has efficiency "advantage"
## of *not* validity checking
res <- new(paste(to, "gCMatrix", sep=''))
res@i <- as.integer(fact) - 1L # 0-based
res@p <- 0:n
res@Dim <- c(length(levs), n)
res@Dimnames <- list(levs, NULL)
if(to != "n")
res@x <- rep.int(switch(to,
"d" = 1., "i" = 1L, "l" = TRUE, "z" = 1+0i),
n)
res
}
## This version can deal with NA's -- but is less efficient (how much?) :
fac2sparse <- function(from, to = c("d","i","l","n","z"),
drop.unused.levels = TRUE)
{
## factor(-like) --> sparseMatrix {also works for integer, character}
fact <- if (drop.unused.levels) factor(from) else as.factor(from)
levs <- levels(fact)
n <- length(fact)
to <- match.arg(to)
i <- as.integer(fact) - 1L # 0-based indices
df <- data.frame(i = i, j = seq_len(n) - 1L)[!is.na(i),]
if(to != "n")
df$x <- rep.int(switch(to,
"d" = 1., "i" = 1L, "l" = TRUE, "z" = 1+0i),
nrow(df))
as(do.call("new", c(list(Class = paste(to, "gTMatrix", sep=''),
Dim = c(length(levs), n),
Dimnames = list(levs, names(fact))),
df)),
"CsparseMatrix")
}
setAs("factor", "sparseMatrix", function(from) fac2sparse(from, to = "d"))
## xtabs returning a sparse matrix. This is cut'n'paste
## of xtabs() in <Rsrc>/src/library/stats/R/xtabs.R ;
## with the new argument 'sparse'
xtabs <- function(formula = ~., data = parent.frame(), subset, sparse = FALSE,
na.action, exclude = c(NA, NaN), drop.unused.levels = FALSE)
{
if (missing(formula) && missing(data))
stop("must supply either 'formula' or 'data'")
if(!missing(formula)){
## We need to coerce the formula argument now, but model.frame
## will coerce the original version later.
formula <- as.formula(formula)
if (!inherits(formula, "formula"))
stop("'formula' missing or incorrect")
}
if (any(attr(terms(formula, data = data), "order") > 1))
stop("interactions are not allowed")
m <- match.call(expand.dots = FALSE)
if (is.matrix(eval(m$data, parent.frame())))
m$data <- as.data.frame(data)
m$... <- m$exclude <- m$drop.unused.levels <- m$sparse <- NULL
m[[1]] <- as.name("model.frame")
mf <- eval(m, parent.frame())
if(length(formula) == 2) {
by <- mf
y <- NULL
}
else {
i <- attr(attr(mf, "terms"), "response")
by <- mf[-i]
y <- mf[[i]]
}
by <- lapply(by, function(u) {
if(!is.factor(u)) u <- factor(u, exclude = exclude)
u[ , drop = drop.unused.levels]
})
if(!sparse) { ## identical to stats::xtabs
x <-
if(is.null(y))
do.call("table", by)
else if(NCOL(y) == 1)
tapply(y, by, sum)
else {
z <- lapply(as.data.frame(y), tapply, by, sum)
array(unlist(z),
dim = c(dim(z[[1]]), length(z)),
dimnames = c(dimnames(z[[1]]), list(names(z))))
}
x[is.na(x)] <- 0
class(x) <- c("xtabs", "table")
attr(x, "call") <- match.call()
x
} else { ## sparse
if (length(by) != 2)
stop("xtabs(*, sparse=TRUE) applies only to two-way tables")
rows <- by[[1]]
cols <- by[[2]]
rl <- levels(rows)
cl <- levels(cols)
if (is.null(y))
y <- rep.int(1, length(rows))
as(new("dgTMatrix",
i = as.integer(rows) - 1L,
j = as.integer(cols) - 1L,
x = as.double(y),
Dim = c(length(rl), length(cl)),
Dimnames = list(rl, cl)), "CsparseMatrix")
}
}
