Post
by Iyassou » Fri Aug 17, 2018 10:52 am
Essentially, hardcoding the determinant and inverse functions and using local variables as often as possible to avoid referencing globals often or at least twice in the method's body. For example, certain methods require property checking before doing their job and those property checks often access global variables that will be accessed later in the method, so bounding the global to a local helped improve performance.
The determinant and inverse functions were hardcoded. It's is possible to write much shorter determinant and inverse functions that use some form of recursivity to work with matrices of all sizes, but that approach is costly both computationally and in terms of memory usage. I started by writing out the formula for getting 3x3 and 4x4 matrices. Then I bound the matrix coefficients to local variables (a, b, c, d, ...) to prevent referencing self often with __getitem__ calls and wrote the exact algebraic computation directly into the return statement. It's ugly, but it is fast.
I'm not an active numpy user, so I can't say for sure. However, I know that what numpy calls "shape" I call "size" and that unlike jalawson, I haven't implemented numpy's matrix slicing functionality, so that area will require some adaptation. It shouldn't be too hard to do while making use of the "rows" and "cols" methods, which return regular lists i.e. use regular list slicing.
Edit: umatrix v1.1 supports numpy-like matrix slicing and dropped "size" in favour of "shape".
Last edited by
Iyassou on Mon Aug 20, 2018 6:45 am, edited 1 time in total.
