sparseVector-class: Sparse Vector Classes

In Matrix: Sparse and Dense Matrix Classes and Methods

Sparse Vector Classes

Description

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.

Slots

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.

Methods

length

signature(x = "sparseVector"): simply extracts the length slot.

show

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.

as.vector

signature(x = "sparseVector", mode = "character") coerces sparse vectors to “regular”, i.e., atomic vectors. This is the same as as(x, "vector").

as

..: see coerce below

coerce

signature(from = "sparseVector", to = "sparseMatrix"), and

coerce

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.

dim<-

signature(x = "sparseVector", value = "integer") coerces a sparse vector to a sparse Matrix, i.e., an object inheriting from sparseMatrix, of the appropriate dimension.

head

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.

tail

signature(x = "sparseVector"): analogous to head, see above.

toeplitz

signature(x = "sparseVector"): as toeplitz(x), produce the n \times n Toeplitz matrix from x, where n = length(x).

rep

signature(x = "sparseVector") repeat x, with the same argument list (x, times, length.out, each, ...) as the default method for rep().

which

signature(x = "nsparseVector") and

which

signature(x = "lsparseVector") return the indices of the non-zero entries (which is trivial for sparse vectors).

Ops

signature(e1 = "sparseVector", e2 = "*"): define arithmetic, compare and logic operations, (see Ops).

Summary

signature(x = "sparseVector"): define all the Summary methods.

is.na, is.finite, is.infinite

(x = "sparseVector"), and

is.na, is.finite, is.infinite

(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.

zapsmall

signature(x = "sparseVectors"): typically used for numeric sparse vector: round() entries such that (relatively) very small entries become zero exactly.

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.

See Also

sparseVector() for friendly construction of sparse vectors (apart from as(*, "sparseVector")).

Examples

getClass("sparseVector")
getClass("dsparseVector")

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(exprs = {
    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,
              check.class = FALSE)
    all.equal(c5, .s(c(0,0, .v(x20))),                     tol = 0)
})

Matrix documentation built on Oct. 19, 2024, 1:08 a.m.