On Fri, Jan 6, 2017 at 6:37 PM, <josef.p...@gmail.com> wrote: > > > > On Fri, Jan 6, 2017 at 8:28 PM, Ralf Gommers <ralf.gomm...@gmail.com> > wrote: > >> >> >> On Sat, Jan 7, 2017 at 2:21 PM, CJ Carey <perimosocord...@gmail.com> >> wrote: >> >>> >>> On Fri, Jan 6, 2017 at 6:19 PM, Ralf Gommers <ralf.gomm...@gmail.com> >>> wrote: >>> >>>> This sounds like a reasonable idea. Timeline could be something like: >>>> >>>> 1. Now: create new package, deprecate np.matrix in docs. >>>> 2. In say 1.5 years: start issuing visible deprecation warnings in numpy >>>> 3. After 2020: remove matrix from numpy. >>>> >>>> Ralf >>>> >>> >>> I think this sounds reasonable, and reminds me of the deliberate >>> deprecation process taken for scipy.weave. I guess we'll see how successful >>> it was when 0.19 is released. >>> >>> The major problem I have with removing numpy matrices is the effect on >>> scipy.sparse, which mostly-consistently mimics numpy.matrix semantics and >>> often produces numpy.matrix results when densifying. The two are coupled >>> tightly enough that if numpy matrices go away, all of the existing sparse >>> matrix classes will have to go at the same time. >>> >>> I don't think that would be the end of the world, >>> >> >> Not the end of the world literally, but the impact would be pretty major. >> I think we're stuck with scipy.sparse, and may at some point will add a new >> sparse *array* implementation next to it. For scipy we will have to add a >> dependency on the new npmatrix package or vendor it. >> > > That sounds to me like moving maintenance of numpy.matrix from numpy to > scipy, if scipy.sparse is one of the main users and still depends on it. >
What I was thinking was encouraging folks to use `arr.dot(...)` or `@` instead of `*` for matrix multiplication, keeping `*` for scalar multiplication. If those operations were defined for matrices, then at some point sparse could go to arrays and it would not be noticeable except for the treatment of 1-D arrays -- which admittedly might be a bit tricky. Chuck
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