| sparseVector-class {Matrix} | R Documentation |
Sparse Vector Classes: The virtual mother class
"sparseVector" has the five actual daughter classes
"dsparseVector", "isparseVector",
"lsparseVector", "nsparseVector", and
"zsparseVector", where we've mainly implemented methods for
the d*, l* and n* ones.
length:class "numeric" - the length
of the sparse vector. Note that "numeric" can be
considerably larger than the maximal "integer",
.Machine$integer.max, on purpose.
i:class "numeric" - the (1-based) indices of
the non-zero entries. Must not be NA and strictly
sorted increasingly.
Note that "integer" is “part of” "numeric",
and can (and often will) be used for non-huge sparseVectors.
x:(for all but "nsparseVector"):
the non-zero entries. This is of class "numeric" for class
"dsparseVector", "logical" for class
"lsparseVector", etc.
Note that "nsparseVector"s have no x slot.
Further, mainly for ease of method definitions, we've defined the
class union (see setClassUnion) of all sparse vector
classes which have an x slot, as class "xsparseVector".
signature(x = "sparseVector"): simply extracts
the length slot.
signature(object = "sparseVector"): The
show method for sparse vectors prints
“structural” zeroes as "." using the
non-exported prSpVector function which allows further
customization such as replacing "." by " " (blank).
Note that options(max.print) will influence how many
entries of large sparse vectors are printed at all.
signature(x = "sparseVector", mode = "character")
coerces sparse vectors to “regular”, i.e., atomic vectors.
This is the same as as(x, "vector").
..: see coerce below
signature(from = "sparseVector", to = "sparseMatrix"), and
signature(from = "sparseMatrix", to = "sparseVector"),
etc: coercions to and from sparse matrices (sparseMatrix) are
provided and work analogously as in standard R, i.e., a vector is
coerced to a 1-column matrix.
signature(x = "sparseVector", value = "integer")
coerces a sparse vector to a sparse Matrix, i.e., an object
inheriting from sparseMatrix, of the
appropriate dimension.
signature(x = "sparseVector"): as with R's
(package util) head, head(x,n) (for
n >= 1) is equivalent to x[1:n], but here can be much
more efficient, see the example.
signature(x = "sparseVector"): analogous to
head, see above.
signature(x = "sparseVector"): as
toeplitz(x), produce the n \times n
Toeplitz matrix from x, where n = length(x).
signature(x = "sparseVector") repeat x,
with the same argument list (x, times, length.out, each,
...) as the default method for rep().
signature(x = "nsparseVector") and
signature(x = "lsparseVector") return the
indices of the non-zero entries (which is trivial for sparse vectors).
signature(e1 = "sparseVector", e2 = "*"): define
arithmetic, compare and logic operations, (see
Ops).
signature(x = "sparseVector"): define
all the Summary methods.
signature(x = "atomicVector", i = ...): not only can you
subset (aka “index into”) sparseVectors x[i]
using sparseVectors i, but we also support efficient
subsetting of traditional vectors x by logical sparse
vectors (i.e., i of class "nsparseVector" or
"lsparseVector").
(x = "sparseVector"), and
(x = "nsparseVector"):
return logical or "nsparseVector" of the same
length as x, indicating if/where x is
NA (or NaN), finite or infinite, entirely
analogously to the corresponding base R functions.
c.sparseVector() is an S3 method for all
"sparseVector"s, but automatic dispatch only happens for the
first argument, so it is useful also as regular R function, see the
examples.
sparseVector() for friendly construction of sparse
vectors (apart from as(*, "sparseVector")).
getClass("sparseVector")
getClass("dsparseVector")
getClass("xsparseVector")# those with an 'x' slot
sx <- c(0,0,3, 3.2, 0,0,0,-3:1,0,0,2,0,0,5,0,0)
(ss <- as(sx, "sparseVector"))
ix <- as.integer(round(sx))
(is <- as(ix, "sparseVector")) ## an "isparseVector" (!)
(ns <- sparseVector(i= c(7, 3, 2), length = 10)) # "nsparseVector"
## rep() works too:
(ri <- rep(is, length.out= 25))
## Using `dim<-` as in base R :
r <- ss
dim(r) <- c(4,5) # becomes a sparse Matrix:
r
## or coercion (as as.matrix() in base R):
as(ss, "Matrix")
stopifnot(all(ss == print(as(ss, "CsparseMatrix"))))
## currently has "non-structural" FALSE -- printing as ":"
(lis <- is & FALSE)
(nn <- is[is == 0]) # all "structural" FALSE
## NA-case
sN <- sx; sN[4] <- NA
(svN <- as(sN, "sparseVector"))
v <- as(c(0,0,3, 3.2, rep(0,9),-3,0,-1, rep(0,20),5,0),
"sparseVector")
v <- rep(rep(v, 50), 5000)
set.seed(1); v[sample(v@i, 1e6)] <- 0
str(v)
system.time(for(i in 1:4) hv <- head(v, 1e6))
## user system elapsed
## 0.033 0.000 0.032
system.time(for(i in 1:4) h2 <- v[1:1e6])
## user system elapsed
## 1.317 0.000 1.319
stopifnot(identical(hv, h2),
identical(is | FALSE, is != 0),
validObject(svN), validObject(lis), as.logical(is.na(svN[4])),
identical(is^2 > 0, is & TRUE),
all(!lis), !any(lis), length(nn@i) == 0, !any(nn), all(!nn),
sum(lis) == 0, !prod(lis), range(lis) == c(0,0))
## create and use the t(.) method:
t(x20 <- sparseVector(c(9,3:1), i=c(1:2,4,7), length=20))
(T20 <- toeplitz(x20))
stopifnot(is(T20, "symmetricMatrix"), is(T20, "sparseMatrix"),
identical(unname(as.matrix(T20)),
toeplitz(as.vector(x20))))
## c() method for "sparseVector" - also available as regular function
(c1 <- c(x20, 0,0,0, -10*x20))
(c2 <- c(ns, is, FALSE))
(c3 <- c(ns, !ns, TRUE, NA, FALSE))
(c4 <- c(ns, rev(ns)))
## here, c() would produce a list {not dispatching to c.sparseVector()}
(c5 <- c.sparseVector(0,0, x20))
## checking (consistency)
.v <- as.vector
.s <- function(v) as(v, "sparseVector")
stopifnot(
all.equal(c1, .s(c(.v(x20), 0,0,0, -10*.v(x20))), tol=0),
all.equal(c2, .s(c(.v(ns), .v(is), FALSE)), tol=0),
all.equal(c3, .s(c(.v(ns), !.v(ns), TRUE, NA, FALSE)), tol=0),
all.equal(c4, .s(c(.v(ns), rev(.v(ns)))), tol=0),
all.equal(c5, .s(c(0,0, .v(x20))), tol=0)
)