HI Jonathan,
Tested your code from Nabble:
Looks like your solution is the fastest, but:
N <- 1000
M <- 5
P <- 5000
set.seed(15)
A <- matrix(runif(N,1,1000),nrow=N,ncol=M)
set.seed(425)
B <- matrix(runif(M,1,1000),nrow=P,ncol=M)
library(matrixStats)
Marius.5.0 <- function(A,B) outer(rowMaxs(A),rowMins(B),'<') #Jonathan's code
system.time(z5.0 <- Marius.5.0(A,B))
# user system elapsed
# 0.280 0.040 0.321
Marius.4.0 <- function(A, B) apply(B, 1, function(x) colSums(x>=t(A))==ncol(A))
system.time(z4.0 <- Marius.4.0(A,B))
# user system elapsed
# 0.460 0.044 0.506
identical(z5.0,z4.0)
#[1] TRUE
# when I test it with the toy example:
A<-matrix(c(1:4,6,2),ncol=2,byrow=TRUE)
B <- matrix(1:10, ncol=2) # (5, 2) matrix
Marius.5.0(A,B)
# [,1] [,2] [,3] [,4] [,5]
#[1,] FALSE FALSE TRUE TRUE TRUE
#[2,] FALSE FALSE FALSE FALSE TRUE
#[3,] FALSE FALSE FALSE FALSE FALSE
Marius.4.0(A,B)
# [,1] [,2] [,3] [,4] [,5]
#[1,] TRUE TRUE TRUE TRUE TRUE
#[2,] FALSE FALSE TRUE TRUE TRUE
#[3,] FALSE FALSE FALSE FALSE FALSE
identical(Marius.5.0(A,B),Marius.4.0(A,B))
#[1] FALSE
A.K.
----- Original Message -----
From: Hofert Jan Marius <marius.hof...@math.ethz.ch>
To: arun <smartpink...@yahoo.com>
Cc: Thomas Stewart <tgs.public.m...@gmail.com>; "mailman, r-help"
<r-help@r-project.org>
Sent: Saturday, December 8, 2012 2:15 PM
Subject: RE: [R] How to efficiently compare each row in a matrix with each row
in another matrix?
The idea is good, but you don't need to create a list of the rows of A first,
apply does the job:
Marius.4.0 <- function(A, B)
apply(B, 1, function(x) colSums(x>=t(A))==ncol(A))
That was actually a bit faster than your version.
This is the fastest version so far. I compared it with C code called via .C: C
was 15% faster.
Cheers,
Marius
________________________________________
From: arun [smartpink...@yahoo.com]
Sent: Saturday, December 08, 2012 7:43 PM
To: Hofert Jan Marius
Cc: Thomas Stewart; mailman, r-help
Subject: Re: [R] How to efficiently compare each row in a matrix with each row
in another matrix?
Hi,
Just to add:
N <- 1000
M <- 5
P <- 5000
set.seed(15)
A <- matrix(runif(N,1,1000),nrow=N,ncol=M)
set.seed(425)
B <- matrix(runif(M,1,1000),nrow=P,ncol=M)
Marius.3.0<-function(A,B){do.call(cbind,lapply(split(B,row(B)),function(x)
colSums(x>=t(A))==ncol(A)))}
Marius.2.0 <- function(A, B){
nA <- nrow(A)
nB <- nrow(B)
C <- do.call(rbind, rep(list(B), nA)) >= matrix(rep(A, each=nB),
ncol=ncol(B))
matrix(rowSums(C) == ncol(A), nA, nB, byrow=TRUE)
}
system.time(z3.0<-Marius.3.0(A,B))
# user system elapsed
# 0.524 0.020 0.548
system.time(z2.0<-Marius.2.0(A,B))
# user system elapsed
# 0.968 0.216 1.189
system.time(z1<-perhaps(A,B))
# user system elapsed
# 1.264 0.204 1.473
attr(z3.0,"dim")<-dim(z2.0)
identical(z3.0,z2.0)
#[1] TRUE
identical(z1,z3.0)
#[1] TRUE
A.K.
----- Original Message -----
From: Marius Hofert <marius.hof...@math.ethz.ch>
To: R-help <r-help@r-project.org>
Cc:
Sent: Saturday, December 8, 2012 6:28 AM
Subject: [R] How to efficiently compare each row in a matrix with each row in
another matrix?
Dear expeRts,
I have two matrices A and B. They have the same number of columns but possibly
different number of rows. I would like to compare each row of A with each row
of B and check whether all entries in a row of A are less than or equal to all
entries in a row of B. Here is a minimal working example:
A <- rbind(matrix(1:4, ncol=2, byrow=TRUE), c(6, 2)) # (3, 2) matrix
B <- matrix(1:10, ncol=2) # (5, 2) matrix
( ind <- apply(B, 1, function(b) apply(A, 1, function(a) all(a <= b))) ) # (3,
5) = (nrow(A), nrow(B)) matrix
The question is: How can this be implemented more efficiently in R, that is, in
a faster way?
Thanks & cheers,
Marius
______________________________________________
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
