Hi:
On Sun, Dec 26, 2010 at 4:18 AM, bbslover <dlu...@yeah.net> wrote:
>
> Dear all,
>
> My double for loop as follows, but it is little efficient, I hope all
> friends can give me a "vectorized" program to replace my code. thanks
>
> x: is a matrix 202*263, that is 202 samples, and 263 independent
> variables
>
> num.compd<-nrow(x); # number of compounds
> diss.all<-0
> for( i in 1:num.compd)
> for (j in 1:num.compd)
> if (i!=j) {
>
Isn't this just X'X?
> S1<-sum(x[i,]*x[j,])
>
Aren't each of S2 and S3 just diag(X'X)?
> S2<-sum(x[i,]^2)
>
S3<-sum(x[j,]^2)
> sim2<-S1/(S2+S3-S1)
> diss2<-1-sim2
> diss.all<-diss.all+diss2}
>
I tried
s1 <- crossprod(x)
s2 <- diag(s1)
s3 <-outer(s2, s2, '+') - s1
s1/s3
This yields a symmetric matrix with 1's along the diagonal and quantities
between 0 and 1 in the off-diagonal. Something like it could conceivably be
used as a similarity matrix. Is that what you're looking for with sim2?
I agree with Berend: it looks like a problem that could be easily solved
with some matrix algebra. R can do matrix algebra quite efficiently,
y'know...
(BTW, I tried this on a 1000 x 1000 input matrix:
system.time(myfunc(x))
user system elapsed
0.99 0.02 1.02
I expect it could be improved by an order of magnitude if one actually knew
what you were computing... )
HTH,
Dennis
it will cost a long time to finish this computation! i really need "rapid"
> code to replace my code.
>
> thanks
>
> kevin
>
>
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