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### =========================================================================
### Operate natively on SparseArraySeed objects
### -------------------------------------------------------------------------
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Binding
###
### Similar to simple_abind() (see bind-arrays.R) but works on
### SparseArraySeed objects.
abind_SparseArraySeed_objects <- function(objects, along)
{
stopifnot(is.list(objects))
if (length(objects) == 0L)
return(NULL)
## Check dim compatibility.
dims <- get_dims_to_bind(objects, along)
if (is.character(dims))
stop(wmsg(dims))
if (length(objects) == 1L)
return(objects[[1L]])
## Compute 'ans_dim' and 'ans_dimnames'.
ans_dim <- combine_dims_along(dims, along)
ans_dimnames <- combine_dimnames_along(objects, dims, along)
## Combine the "nzindex" slots.
offsets <- cumsum(dims[along, -ncol(dims)])
nzindex_list <- lapply(seq_along(objects),
function(i) {
object <- objects[[i]]
nzindex <- object@nzindex
if (i >= 2L)
nzindex[ , along] = nzindex[ , along, drop=FALSE] +
offsets[[i - 1L]]
nzindex
}
)
ans_nzindex <- do.call(rbind, nzindex_list)
## Combine the "nzdata" slots.
ans_nzdata <- unlist(lapply(objects, slot, "nzdata"), use.names=FALSE)
SparseArraySeed(ans_dim, ans_nzindex, ans_nzdata, ans_dimnames, check=FALSE)
}
setMethod("rbind", "SparseArraySeed",
function(...)
{
abind_SparseArraySeed_objects(list(...), along=1L)
}
)
setMethod("cbind", "SparseArraySeed",
function(...)
{
abind_SparseArraySeed_objects(list(...), along=2L)
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### Various "unary isometric" array transformations
###
### A "unary isometric" array transformation is a transformation that returns
### an array-like object with the same dimensions as the input and where each
### element is the result of applying a function to the corresponding element
### in the input.
###
### Note that some "unary isometric" transformations preserve sparsity (e.g.
### is.na(), nchar(), round(), sqrt(), log1p(), etc...) and others don't
### (e.g. is.finite(), !, log(), etc..). We only implement the former.
###
### All the "unary isometric" array transformations implemented in this
### section return a SparseArraySeed object of the same dimensions as the
### input SparseArraySeed object.
###
.UNARY_ISO_OPS <- c("is.na", "is.infinite", "is.nan", "tolower", "toupper")
for (.Generic in .UNARY_ISO_OPS) {
setMethod(.Generic, "SparseArraySeed",
function(x)
{
GENERIC <- match.fun(.Generic)
new_nzdata <- GENERIC(x@nzdata)
BiocGenerics:::replaceSlots(x, nzdata=new_nzdata, check=FALSE)
}
)
}
setMethod("nchar", "SparseArraySeed",
function(x, type="chars", allowNA=FALSE, keepNA=NA)
{
new_nzdata <- nchar(x@nzdata, type=type, allowNA=allowNA, keepNA=keepNA)
BiocGenerics:::replaceSlots(x, nzdata=new_nzdata, check=FALSE)
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### anyNA()
###
setMethod("anyNA", "SparseArraySeed",
function(x, recursive=FALSE) anyNA(x@nzdata, recursive=recursive)
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### which()
###
.nzindex_order <- function(nzindex)
do.call(order, lapply(ncol(nzindex):1L, function(along) nzindex[ , along]))
setMethod("which", "SparseArraySeed",
function(x, arr.ind=FALSE, useNames=TRUE)
{
if (!identical(useNames, TRUE))
warning(wmsg("'useNames' is ignored when 'x' is ",
"a SparseArraySeed object or derivative"))
if (!isTRUEorFALSE(arr.ind))
stop(wmsg("'arr.ind' must be TRUE or FALSE"))
idx1 <- which(x@nzdata)
nzindex1 <- x@nzindex[idx1, , drop=FALSE]
oo <- .nzindex_order(nzindex1)
ans <- nzindex1[oo, , drop=FALSE]
if (arr.ind)
return(ans)
Mindex2Lindex(ans, dim=dim(x))
}
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### "Summary" group generic
###
### Members: max(), min(), range(), sum(), prod(), any(), all()
###
setMethod("Summary", "SparseArraySeed",
function(x, ..., na.rm=FALSE)
{
GENERIC <- match.fun(.Generic)
if (length(list(...)) != 0L)
stop(wmsg(.Generic, "() method for SparseArraySeed objects ",
"only accepts a single object"))
## Whether 'x' contains zeros or not doesn't make a difference for
## sum() and any().
if (.Generic %in% c("sum", "any"))
return(GENERIC(x@nzdata, na.rm=na.rm))
## Of course a typical SparseArraySeed object "contains" zeros
## (i.e. it would contain zeros if we converted it to a dense
## representation with sparse2dense()). However, this is not
## guaranteed so we need to make sure to properly handle the case
## where it doesn't (admittedly unusual and definitely an inefficient
## way to represent dense data!)
x_has_zeros <- length(x@nzdata) < length(x)
if (!x_has_zeros)
return(GENERIC(x@nzdata, na.rm=na.rm))
x_type <- typeof(x@nzdata)
if (.Generic == "all") {
## Mimic what 'all(sparse2dense(x))' would do.
if (x_type == "double")
warning("coercing argument of type 'double' to logical")
return(FALSE)
}
zero <- vector(x_type, length=1L)
GENERIC(zero, x@nzdata, na.rm=na.rm)
}
)
### We override the "range" method defined above via the "Summary" method
### because we want to support the 'finite' argument like S3 method
### base::range.default() does.
### S3/S4 combo for range.SparseArraySeed
range.SparseArraySeed <- function(..., na.rm=FALSE, finite=FALSE)
{
objects <- list(...)
if (length(objects) != 1L)
stop(wmsg("range() method for SparseArraySeed objects ",
"only accepts a single object"))
x <- objects[[1L]]
x_has_zeros <- length(x@nzdata) < length(x)
if (!x_has_zeros)
return(range(x@nzdata, na.rm=na.rm, finite=finite))
zero <- vector(typeof(x@nzdata), length=1L)
range(zero, x@nzdata, na.rm=na.rm, finite=finite)
}
### The signature of all the members of the S4 "Summary" group generic is
### 'x, ..., na.rm' (see getGeneric("range")) which means that the S4 methods
### cannot add arguments after 'na.rm'. So we add the 'finite' argument before.
setMethod("range", "SparseArraySeed",
function(x, ..., finite=FALSE, na.rm=FALSE)
range.SparseArraySeed(x, ..., na.rm=na.rm, finite=finite)
)
### - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
### mean()
###
.mean_SparseArraySeed <- function(x, na.rm=FALSE)
{
s <- sum(x@nzdata, na.rm=na.rm)
nval <- length(x)
if (na.rm)
nval <- nval - sum(is.na(x@nzdata))
s / nval
}
### S3/S4 combo for mean.SparseArraySeed
mean.SparseArraySeed <- function(x, na.rm=FALSE, ...)
.mean_SparseArraySeed(x, na.rm=na.rm, ...)
setMethod("mean", "SparseArraySeed", .mean_SparseArraySeed)
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