[SciPy-user] New sparse matrix functionality
nwagner at mecha.uni-stuttgart.de
Mon Feb 27 03:42:37 CST 2006
Ed Schofield wrote:
>I'd created a new SVN branch with some new functionality and changes to
>sparse matrices. This post describes what's new and what's different
>versus the main branch. If you have an existing SVN tree, you can pull
>in my branch with the command "svn switch
>One of the conclusions of Jonathan Guyer's comparison between
>scipy.sparse and PySparse in November
>and http://old.scipy.org/wikis/featurerequests/SparseSolvers) was that
>SciPy's support for efficient construction of sparse matrices is weak.
>My patch adds a new data type, lil_matrix, that stores non-zeros as a
>list of sorted Python lists. For the simple benchmark of creating a new
>10^3 x 10^5 matrix with 10^4 non-zero elements in random locations and
>converting to CSR and CSC matrices, the lil_matrix format is slightly
>more than twice as fast as dok_matrix. It's a row-wise format, so
>conversion to CSR is very fast, whereas conversion to CSC goes through
>CSR internally. Index lookups use binary searches, so they take log
>time. I think the implementation is already complete enough for most
>uses -- so please try it out and tell me how you go!
>Another of Jonathan's observations was that SciPy had no support for
>slicing matrices with A[i, :] like in PySparse. My patch adds support
>for slice notation and NumPy-style fancy indexing to dok_matrix and
>lil_matrix objects. With this it's possible to build sparse matrices
>quickly -- for example:
>>>>a = lil_matrix((3,5))
>>>>a[1,[2,3,4]] = range(3)
>>>>a[2,:] = 3 * a[1,:]
>A third new feature is a .sum() method, which takes a single axis
>argument like in NumPy.
>My branch also changes one aspect of the existing behaviour: the
>todense() method now returns a dense (NumPy) matrix, rather than a dense
>array. Converting to a dense array is now available under a toarray()
>method (or .A attribute) instead. The rationale behind this change is
>to emphasize that sparse matrices are closer to dense matrices than to
>dense arrays in their attributes (e.g. .T for transpose) and behaviour
>(e.g. * means inner product). I've also been careful to make
>multiplication between sparse matrices and dense row or column vectors
>(matrix objects with unit length or height) return matrices of the
>correct dimensions, rather than arrays. Several unit tests relied on
>the old behaviour, and I've changed these accordingly. Most of these
>test changes are just simplifications -- for example
> assert_array_equal((a*c).todense(), a.todense() * c)
> assert_array_equal((a*c).todense(), dot(a.todense(), c))
>-- but I'd appreciate some criticism and feedback on which behaviour
>These changes have highlighted a problem present in both the main trunk
>and my branch: that multiplying a dense matrix 'a' by a sparse matrix
>'b' is not possible using the syntax 'a*b'. I'll follow this up with a
>proposal to numpy-discussion on how we can solve this.
>SciPy-user mailing list
>SciPy-user at scipy.net
That sounds very interesting !
How do I install your branch ?
And can I use the current implementation of sparse once I have installed
your branch ?
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