>>>>> Aaron Lun
>>>>> on Sun, 10 Feb 2019 15:22:17 +0000 writes:
> Dear list,
> The Matrix package exhibits some unexpected behaviour in its arithmetic
> methods for the edge case of a sparse matrix with a dimension of zero
> length. The example below is the most illustrative, where changing the
> contents of the vector causes the subtraction to fail for a sparse
> matrix with no columns:
>
>> library(Matrix)
>> x <- rsparsematrix(10, 0, density=0.1)
>>
>> x - rep(1, nrow(x)) # OK
>> x - rep(0, nrow(x)) # fails
> Error in .Ops.recycle.ind(e1, len = l2) :
> vector too long in Matrix - vector operation
This is indeed clearly a lapsus of us / mine as well as the
next examples: Will all be fixed "around" .Ops.recycle.ind()
> This is presumably because Matrix recognizes that subtraction of zero
> preserves sparsity and thus uses a different method in the second case.
> However, I would have expected subtraction of a zero vector to work if
> subtraction of a general vector is permissible. This is accompanied by
> a host of related errors for sparsity-preserving arithmetic:
>> x / 1 # OK
>> x / rep(1, nrow(x)) # fails
> Error in .Ops.recycle.ind(e1, len = l2) :
> vector too long in Matrix - vector operation
>>
>> x * 1 # OK
>> x * rep(1, nrow(x)) # fails
> Error in .Ops.recycle.ind(e1, len = l2) :
> vector too long in Matrix - vector operation
>
> A different error is raised for a sparse matrix with no rows:
>> y <- rsparsematrix(0, 10, density=0.1)
>>
>> y - numeric(1) # OK
>> y - numeric(0) # fails
> Error in y - numeric(0) : <Matrix> - numeric(0) is undefined
Thank you, that's another lapsus, I will fix before the next
release of Matrix.
> I would have expected to just get 'y' back, given that the same code
> works fine for other Matrix classes:
>> z <- as(y, "dgeMatrix")
>> z - numeric(0) # OK
sure.
> Correct behaviour of zero-dimension sparse matrices is practically
> important to me; I develop a number of packages that rely on Matrix
> classes, and in those packages, I do a lot of unit testing with zero-
> dimension inputs. This ensures that my functions return sensible
> results or fail gracefully in edge cases that might be encountered by
> users. The current behaviour of sparse Matrix arithmetic causes my unit
> tests to fail for no (obvious) good reason.
Interesting that you need 0-dim sparse matrices. I agree they
should work, too... and will fix
(but it seems they haven't been used much by others; else I
would have expected these cases to have been reported long ago).
Further note that in R,
maintainer("Matrix")
gives a nice address to send such findings.
(similarly for all other R packages !)
Thank you very much once more!
Martin
> Best,
> Aaron Lun
> Research Associate
> CRUK Cambridge Institute
> University of Cambridge
--
Martin <Maechler at ....>
Seminar für Statistik, ETH Zürich HG G 16 Rämistrasse 101
CH-8092 Zurich, SWITZERLAND
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