core-matrix

intended for specific discussion around core.matrix (usage and development) For general data work check out #data-science
2016-01-21T02:57:54.000198Z

Vectorz has some interesting properties which I need and are far more important to me than raw performance on stuff like matrix multiplications / decompositions which is what the benchmark checks

2016-01-21T02:57:58.000199Z

stuff like:

2016-01-21T02:58:14.000200Z

- Being to be able to join vectors into concatenated "views" without copying

2016-01-21T02:58:21.000201Z

- Full n-dimensional array support

2016-01-21T02:58:30.000202Z

- Specialised matrix types (diagonal matrices etc.)

2016-01-21T02:58:36.000203Z

- Sparse matrices / vectors

2016-01-21T02:59:03.000204Z

- Views for subvectors and submatrices

2016-01-21T02:59:56.000207Z

- Specialised small primitive vectors, e.g. Vector3 that fit the same type heirarchy

2016-01-21T03:00:14.000208Z

None of the other matrix libraries offer all that, to my knowledge

2016-01-21T03:01:48.000209Z

There's also stuff like first class mathematical operator objects, immutable arrays etc. which may be useful for some people