Do we need Mat, Vec, TMmat, Diag, Sym and other matrix types?

[9il]

Hi All,

The Dlang multidimensional range type, ndslice, is a struct 
composed a an iterator, lengths and possibly strides. It does not 
own memory and does not know anything about its content. ndslice 
is a faster and extended version of numpy.ndarray.

After some work on commercial projects based on Lubeck[1] and 
ndslice I figure out what API and memory management is required 
to make Dlang super fast and  math friendly in the same time.

The concept is the following:
1. All memory is managed by a global BetterC thread safe ARC 
allocator. Optionally the allocator can be overloaded.
2. User can take an internal ndslice to use mir.ndslice API 
internally in functions.
2. auto matrixB = matrixA; // increase ARC
3. auto matrixB = matrixA.dup; // allocates new matrix
4. matrix[i] returns a Vec and increase ARC, matrix[i, j] returns 
a content of the cell.
5. Clever `=` expression based syntax. For example:

    // performs CBLAS call of GEMM and does zero memory allocations
    C = alpha * A * B + beta * C;

`Mat` and other types will support any numeric types, PODlike 
structs, plus special overload for `bool` based on `bitwise` [2].

I have a lot of work for next months, but looking for a good 
opportunity to make Mat happen.

For contributing or co-financing:
Ilya Yaroshenko at
gmail com

Best Regards,
Ilya

[1] https://github.com/kaleidicassociates/lubeck
[2] 
http://docs.algorithm.dlang.io/latest/mir_ndslice_topology.html#bitwise
[3] 
http://www.netlib.org/lapack/explore-html/d1/d54/group__double__blas__level3_gaeda3cbd99c8fb834a60a6412878226e1.html#gaeda3cbd99c8fb834a60a6412878226e1