Added status to the readme.

README.md

@@ -4,3 +4,79 @@ 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.