I have 2 matrices M1, M2. For each row in M1, I want to find the maximum value of the product of that row in M1 and each row in M2.
I have tried the following implementation which produces the result I want.
set.seed(1)
st_time = Sys.time()
M1 = matrix(runif(1000*10), nrow=1000, ncol=10)
M2 = matrix(runif(10000*10), nrow=10000, ncol=10)
score = apply(M1, 1, function(x){
w = M2 %*% diag(x)
row_max = apply(w, 1, max)
return(row_max)
})
required_output = t(score)
Sys.time() - st_time
This takes 16 seconds on my machine. Is there a faster implementation? Thanks!
Running in parallel gives an easier speed. On my machine, the serial version is 15 seconds, the parallel version is just under 4 seconds.
Load the package
# Comes with R
library(parallel)
# Make the cluster
# 8 cores, see detectCores()
cl = makeCluster(8)
Then we need to explicitly export M2
clusterExport(cl, "M2")
and run as normal
score_par = function() {
parApply(cl, M1, 1, function(x){
w = M2 %*% diag(x)
row_max = apply(w, 1, max)
return(row_max)
})
}
system.time(score_par())
Using a for
loop gives quite a speed up for me
set.seed(1)
M1 = matrix(runif(1000*10), nrow=1000, ncol=10)
M2 = matrix(runif(10000*10), nrow=10000, ncol=10)
st_time = Sys.time()
tm = t(M2)
out = matrix(0, nr=nrow(M1), nc=nrow(M2))
for(i in 1:nrow(M1)){
out[i, ] = matrixStats::colMaxs(M1[i, ]* tm)
}
Sys.time() - st_time
#Time difference of 1.835793 secs # was ~28secs with yours on my laptop
all.equal(required_output, out)
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