Thanks everyone,
That's a 3000 fold speedup. Now if only I can get the same improvement on
the stMincuts iGraph algorithm.
On Fri, Apr 25, 2014 at 4:20 AM, Martin Maechler maech...@stat.math.ethz.ch
wrote:
Stefan Evert stefa...@collocations.de
on Fri, 25 Apr 2014 09:09:31 +0200
On 24 Apr 2014, at 23:56, Greg Snow 538...@gmail.com wrote:
library(Matrix)
adjM - Matrix(0,nrow=10,ncol=10)
locs - cbind( sample(1:10), sample(1:10) )
vals - rnorm(10)
adjM[ locs ] - vals
... and once you've got your data in this format, why not construct the sparse
matrix
Stefan Evert stefa...@collocations.de
on Fri, 25 Apr 2014 09:09:31 +0200 writes:
On 24 Apr 2014, at 23:56, Greg Snow 538...@gmail.com wrote:
library(Matrix)
adjM - Matrix(0,nrow=10,ncol=10)
locs - cbind( sample(1:10), sample(1:10) )
vals - rnorm(10)
I need to generate a sparse matrix. Currently I have the data held in two
regular matrices. One 'targets' holds the column subscripts while the other
'scores' holds the values. I have written a 'toy' sample below. Using this
approach takes about 90 seconds to populate a 3 x 3 element
Convert your 'targets' matrix into a 2 column matrix with the 1st
column representing the row and the 2nd the column where you want your
values, then change the values to a single vector and you can just use
the targets matrix as the subsetting in 1 step without (explicit)
looping, for example:
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