Hello everyone,
I'm trying to construct bins for each row in a matrix. I'm using apply() in
combination with hist() to do this. Performing this binning for a 10K-by-50
matrix takes about 5 seconds, but only 0.5 seconds for a 1K-by-500 matrix. This
suggests the bottleneck is accessing rows in apply() rather than the
calculations going on inside hist().
My initial idea is to process as many columns (as make sense for the intended
use) at once. However, I still have many many rows to process and I would
appreciate any feedback on how to speed this up.
Any thoughts?
Thanks,
Ariel
Here is the illustration:
# create data
m1 <- matrix(10*rnorm(50*10^4), ncol=50)
m2 <- matrix(10*rnorm(50*10^4), ncol=500)
# compute bins
bins <- seq(-100,100,1)
system.time({ out1 <- t(apply(m1,1, function(x) hist(x,breaks=bins,
plot=FALSE)$counts)) })
system.time({ out2 <- t(apply(m2,1, function(x) hist(x,breaks=bins,
plot=FALSE)$counts)) })
---
Ariel Ortiz-Bobea
Fellow
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