SparseArraySeed-class {DelayedArray} | R Documentation |
SparseArraySeed objects are used internally to support block processing of array-like objects.
## Constructor function: SparseArraySeed(dim, aind=NULL, nzdata=NULL, check=TRUE) ## Getters (in addition to dim() and length()): aind(x) nzdata(x) sparsity(x) ## Two low-level utilities: dense2sparse(x) sparse2dense(sas)
dim |
The dimensions (specified as an integer vector) of the SparseArraySeed object to create. |
aind |
A matrix containing the array indices of the nonzero data. This must be an integer matrix like one returned by
|
nzdata |
A vector of length |
check |
Should the object be validated upon construction? |
x |
A SparseArraySeed object for the An array-like object for |
sas |
A SparseArraySeed object. |
For SparseArraySeed()
: A SparseArraySeed instance.
For aind()
: The matrix containing the array indices of the
nonzero data.
For nzdata()
: The vector of nonzero data.
For sparsity()
: The number of zero-valued elements
in the implicit array divided by the total number of array
elements (a.k.a. the length of the array).
For dense2sparse()
: A SparseArraySeed instance.
For sparse2dense()
: An ordinary array.
The read_sparse_block
function.
block_processing for more information about block processing of an array-like object.
DelayedArray objects.
arrayInd
in the base package.
array objects in base R.
## --------------------------------------------------------------------- ## EXAMPLE 1 ## --------------------------------------------------------------------- aind1 <- rbind(c(2,4,3), c(2,1,3), c(5,4,3), c(5,3,3), c(5,4,1), c(5,1,1), c(5,4,2), c(5,4,1)) nzdata1 <- 11.11 * seq_len(nrow(aind1)) sas1 <- SparseArraySeed(5:3, aind1, nzdata1) dim(sas1) # the dimensions of the implicit array length(sas1) # the length of the implicit array aind(sas1) nzdata(sas1) sparsity(sas1) sparse2dense(sas1) as.array(sas1) # same as sparse2dense(sas1) ## Not run: as.matrix(sas1) # error! ## End(Not run) ## --------------------------------------------------------------------- ## EXAMPLE 2 ## --------------------------------------------------------------------- m2 <- matrix(c(5:-2, rep.int(c(0L, 99L), 11)), ncol=6) sas2 <- dense2sparse(m2) dim(sas2) length(sas2) aind(sas2) nzdata(sas2) sparsity(sas2) stopifnot(identical(sparse2dense(sas2), m2)) as.matrix(sas2) # same as sparse2dense(sas2) t(sas2) stopifnot(identical(as.matrix(t(sas2)), t(as.matrix(sas2)))) ## Go back and forth between SparseArraySeed and dgCMatrix objects: M2 <- as(sas2, "dgCMatrix") stopifnot(identical(M2, as(m2, "dgCMatrix"))) sas2b <- as(M2, "SparseArraySeed") ## 'sas2b' is the same as 'sas2' except that ## 'nzdata(sas2b)' is of type numeric instead of integer: all.equal(sas2b, sas2) typeof(nzdata(sas2b)) # numeric typeof(nzdata(sas2)) # integer ## --------------------------------------------------------------------- ## SEED CONTRACT ## --------------------------------------------------------------------- ## SparseArraySeed objects comply with the "seed contract". ## In particular they support extract_array(): extract_array(sas1, list(c(5, 3:2, 5), NULL, 3)) ## See '?extract_array' for more information about the "seed contract". ## This means that they can be wrapped in a DelayedArray object: A1 <- DelayedArray(sas1) A1 ## A big very sparse DelayedMatrix object: aind3 <- cbind(sample(25000, 600000, replace=TRUE), sample(195000, 600000, replace=TRUE)) nzdata3 <- runif(600000) sas3 <- SparseArraySeed(c(25000, 195000), aind3, nzdata3) sparsity(sas3) M3 <- DelayedArray(sas3) M3 colSums(M3[ , 1:20])