Zhandong Liu wrote:
I am switching from Matlab to R, but I found that R is 200 times slower than
matlab.
Since I am newbie to R, I must be missing some important programming tips.
Please help me out on this.
Here is the function:
## make the full pair-wise permutation of a vector
## input_fc=c(1,2,3);
## output_fc=(
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
);
grw_permute = function(input_fc){
fc_vector = input_fc
index = 1
k = length(fc_vector)
fc_matrix = matrix(0,2,k^2)
for(i in 1:k){
for(j in 1:k){
fc_matrix[index] = fc_vector[i]
fc_matrix[index+1] = fc_vector[j]
index = index+2
}
}
return(fc_matrix)
}
For an input vector of size 300. It took R 2.17 seconds to run.
But the same code in matlab only needs 0.01 seconds to run.
Am I missing sth in R.. Is there a away to optimize. ???
Thanks
This is pretty characteristic. With R, you really don't want nested
loops doing single-element accessing (if you have better things to do
with 2.16 seconds of our life). You will usually find that this sort of
problem is handled either using vectorized operations at a higher level,
or pushed into C code which is dynamically loaded. For the particular
problem, notice that the same result is obtained with
> system.time(rbind(rep(1:300,300),rep(1:300,each=300)))
user system elapsed
0.041 0.006 0.050
or even (OK, so it's transposed)
> system.time(expand.grid(1:300,1:300))
user system elapsed
0.027 0.011 0.040
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