hashmap {bit64}

Hashing for 64bit integers

Description

This is an explicit implementation of hash functionality that underlies matching and other functions in R. Explicit means that you can create, store and use hash functionality directly. One advantage is that you can re-use hashmaps, which avoid re-building hashmaps again and again.

Usage

hashfun(x, ...)
## S3 method for class 'integer64'
hashfun(x, minfac=1.41, hashbits=NULL, ...)
hashmap(x, ...)
## S3 method for class 'integer64'
hashmap(x, nunique=NULL, minfac=1.41, hashbits=NULL, cache=NULL, ...)
hashpos(cache, ...)
## S3 method for class 'cache_integer64'
hashpos(cache, x, nomatch = NA_integer_, ...)
hashrev(cache, ...)
## S3 method for class 'cache_integer64'
hashrev(cache, x, nomatch = NA_integer_, ...)
hashfin(cache, ...)
## S3 method for class 'cache_integer64'
hashfin(cache, x, ...)
hashrin(cache, ...)
## S3 method for class 'cache_integer64'
hashrin(cache, x, ...)
hashdup(cache, ...)
## S3 method for class 'cache_integer64'
hashdup(cache, ...)
hashuni(cache, ...)
## S3 method for class 'cache_integer64'
hashuni(cache, keep.order=FALSE, ...)
hashmapuni(x, ...)
## S3 method for class 'integer64'
hashmapuni(x, nunique=NULL, minfac=1.5, hashbits=NULL, ...)
hashupo(cache, ...)
## S3 method for class 'cache_integer64'
hashupo(cache, keep.order=FALSE, ...)
hashmapupo(x, ...)
## S3 method for class 'integer64'
hashmapupo(x, nunique=NULL, minfac=1.5, hashbits=NULL, ...)
hashtab(cache, ...)
## S3 method for class 'cache_integer64'
hashtab(cache, ...)
hashmaptab(x, ...)
## S3 method for class 'integer64'
hashmaptab(x, nunique=NULL, minfac=1.5, hashbits=NULL, ...)

Arguments

x

an integer64 vector

hashmap

an object of class 'hashmap' i.e. here 'cache_integer64'

minfac

minimum factor by which the hasmap has more elements compared to the data x, ignored if hashbits is given directly

hashbits

length of hashmap is 2^hashbits

cache

an optional cache object into which to put the hashmap (by default a new cache is created)

nunique

giving correct number of unique elements can help reducing the size of the hashmap

nomatch

the value to be returned if an element is not found in the hashmap

keep.order

determines order of results and speed: FALSE (the default) is faster and returns in the (pseudo)random order of the hash function, TRUE returns in the order of first appearance in the original data, but this requires extra work

...

further arguments, passed from generics, ignored in methods

Details

function	see also	description
hashfun	digest	export of the hash function used in hashmap
hashmap	match	return hashmap
hashpos	match	return positions of x in hashmap
hashrev	match	return positions of hashmap in x
hashfin	%in%.integer64	return logical whether x is in hashmap
hashrin	%in%.integer64	return logical whether hashmap is in x
hashdup	duplicated	return logical whether hashdat is duplicated using hashmap
hashuni	unique	return unique values of hashmap
hashmapuni	unique	return unique values of x
hashupo	unique	return positions of unique values in hashdat
hashmapupo	unique	return positions of unique values in x
hashtab	table	tabulate values of hashdat using hashmap in keep.order=FALSE
hashmaptab	table	tabulate values of x building hasmap on the fly in keep.order=FALSE

Value

see details

Author(s)

Jens Oehlschlägel <[e-mail]>

See Also

match, runif64

Examples

x <- as.integer64(sample(c(NA, 0:9)))
y <- as.integer64(sample(c(NA, 1:9), 10, TRUE))
hashfun(y)
hx <- hashmap(x)
hy <- hashmap(y)
ls(hy)
hashpos(hy, x)
hashrev(hx, y)
hashfin(hy, x)
hashrin(hx, y)
hashdup(hy)
hashuni(hy)
hashuni(hy, keep.order=TRUE)
hashmapuni(y)
hashupo(hy)
hashupo(hy, keep.order=TRUE)
hashmapupo(y)
hashtab(hy)
hashmaptab(y)

stopifnot(identical(match(as.integer(x),as.integer(y)),hashpos(hy, x)))
stopifnot(identical(match(as.integer(x),as.integer(y)),hashrev(hx, y)))
stopifnot(identical(as.integer(x) %in% as.integer(y), hashfin(hy, x)))
stopifnot(identical(as.integer(x) %in% as.integer(y), hashrin(hx, y)))
stopifnot(identical(duplicated(as.integer(y)), hashdup(hy)))
stopifnot(identical(as.integer64(unique(as.integer(y))), hashuni(hy, keep.order=TRUE)))
stopifnot(identical(sort(hashuni(hy, keep.order=FALSE)), sort(hashuni(hy, keep.order=TRUE))))
stopifnot(identical(y[hashupo(hy, keep.order=FALSE)], hashuni(hy, keep.order=FALSE)))
stopifnot(identical(y[hashupo(hy, keep.order=TRUE)], hashuni(hy, keep.order=TRUE)))
stopifnot(identical(hashpos(hy, hashuni(hy, keep.order=TRUE)), hashupo(hy, keep.order=TRUE)))
stopifnot(identical(hashpos(hy, hashuni(hy, keep.order=FALSE)), hashupo(hy, keep.order=FALSE)))
stopifnot(identical(hashuni(hy, keep.order=FALSE), hashtab(hy)$values))
stopifnot(identical(as.vector(table(as.integer(y), useNA="ifany"))
, hashtab(hy)$counts[order.integer64(hashtab(hy)$values)]))
stopifnot(identical(hashuni(hy, keep.order=TRUE), hashmapuni(y)))
stopifnot(identical(hashupo(hy, keep.order=TRUE), hashmapupo(y)))
stopifnot(identical(hashtab(hy), hashmaptab(y)))

	## Not run: 
	message("explore speed given size of the hasmap in 2^hashbits and size of the data")
	message("more hashbits means more random access and less collisions")
	message("i.e. more data means less random access and more collisions")
	bits <- 24
	b <- seq(-1, 0, 0.1)
	tim <- matrix(NA, length(b), 2, dimnames=list(b, c("bits","bits+1")))
    for (i in 1:length(b)){
	  n <- as.integer(2^(bits+b[i]))
	  x <- as.integer64(sample(n))
	  tim[i,1] <- repeat.time(hashmap(x, hashbits=bits))[3]
	  tim[i,2] <- repeat.time(hashmap(x, hashbits=bits+1))[3]
	  print(tim)
      matplot(b, tim)
	}
	message("we conclude that n*sqrt(2) is enough to avoid collisions")
	
## End(Not run)