Machine Learning module for the Pandemonium Engine.
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PMLPP

This is an experimental Machine Learning module for the Pandemonium Engine.

Based on https://github.com/novak-99/MLPP .

Status

  • Reworked codestyle to match the engine's.
  • Reworked classes to work well with the engine.
  • Added custom MLPPVector and MLPPMatrix that store data in a single continuous array. Their downside is push back, and remove uses realloc().
  • Added binds for most of the classes.
  • Added unit tests.

Todos

Saves

Reimplement saving.

Bind remaining methods

Go through and bind all methods. Also add properties as needed.

Add initialization api to all classes that need it

The original library used contructors to initialize everything, but with the engine scripts can't rely on this, make sure all classes have initializations apis, and they bail out when they are in an uninitialized state.

Rework remaining apis.

Rework and bind the remaining apis, so they can be used from scripts.

Error handling

Make error macros usage consistent. Also a command line option should be available that disables them for math operations.

Crashes

There are still likely lots of crashes, find, and fix them.

Unit tests

  • Add more unit tests
  • Also use the engine's own unit test module. It still needs to be fininshed, would be a good idea doing it alongside this modules's tests.
  • They should only be built when you want them. Command line option: mlpp_tests=yes

Old classes

  • Remove all old classes once they are no longer needed.
  • Old classes should only be built when you want them. Command line option: mlpp_tests=yes or maybe something else?
  • Make old classes use old utilities.
  • Remove old apis from new classes that has been ported.

std::random

Replace remaining std::random usage with engine internals.

Tensor3D

Add a tensor3D class. Same as MLPPVector and MLPPMatrix, except it's 3d.

Tensor

Add a tensor class. Same as MLPPVector and MLPPMatrix, except it's n-d.

Make the api postfixes consistent

For example if we look at substraction():

substraction_v -> Non allocating MLPPVector substraction_nv -> Allocating MLPPVector substraction_m -> Non allocating MLPPMatrix substraction_nm -> Non allocating MLPPMatrix substraction_vt -> Non allocating vector based tensor substraction_nvt -> Allocating vector based tensor substraction_t3d -> Non allocating 3d tensor substraction_nt3d -> Allocating 3d tensor

More algos

Add more machine learning algorithms.