>>>>> Stefan Evert <[email protected]>
>>>>> on Fri, 25 Apr 2014 09:09:31 +0200 writes:
> On 24 Apr 2014, at 23:56, Greg Snow <[email protected]> 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 directly?
> adjM <- sparseMatrix(i = locs[,1], j = locs[,2], x = vals)
> I've found this to be very efficient and have used it with sparse
matrices containing up to around 100 million nonzero entries.
Yes, indeed, thank you Stefan!
Let me reiterate :
__ Unless you can use special constructors such as
__
__ Diagonal() # diagonal matrices
__ bdiag() # block diagonal matrices
__ bandSparse() # "banded diagonal" matrices
__ kronecker(a, b) # where a or b are sparse
__
__ (and maybe another one I forgot)
__
__ *the* way to efficiently construct large sparse matrices is
__ sparseMatrix()
__ or sometimes its alternative / precursor spMatrix().
__
__ Matrix(d, ...., sparse=TRUE)
__
__ is nice and fine only for relatively *small* matrices,
__ as it really works from a dense original 'd' (directly or via replication)
> Hope this helps,
I do hope, too.
I'm very happy for suggestions on how we as Matrix authors
could make this better known.
Recently, someone proposed to make the 'rsparseMatrix()'
utility function from help(sparseMatrix)
into an "official" Matrix package function. If I did that,
I could start using rsparseMatrix() in typical examples rather
than the current often use of Matrix() or
as(<traditional matrix>, "sparseMatrix")
both of which are perfect for the small examples that are
typical for help files.
Martin Maechler,
ETH Zurich
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