SparseArraySeed-class: SparseArraySeed objects

Description Usage Arguments Value See Also Examples

Description

SparseArraySeed objects are used internally to support block processing of array-like objects.

Usage

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
## 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)

Arguments

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 base::arrayInd, that is, with length(dim) columns and where each row is an n-uplet representing an array index.

nzdata

A vector of length nrow(aind) containing the nonzero data.

check

Should the object be validated upon construction?

x

A SparseArraySeed object for the aind, nzdata, and sparsity getters.

An array-like object for dense2sparse.

sas

A SparseArraySeed object.

Value

See Also

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
## ---------------------------------------------------------------------
## 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])

DelayedArray documentation built on Nov. 1, 2018, 2:27 a.m.