Tweaks to the readme.

README.md

@@ -1,68 +1,6 @@
 # PMLPP
 
-This is an experimental Machine Learning module for the Pandemonium Engine. 
+A Machine Learning module for the Pandemonium Engine. Based on: https://github.com/novak-99/MLPP
-
-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.
-
-### Tensor
-
-Add a tensor class. Same as MLPPVector and MLPPMatrix, except it's n-d.
-
-### More algos
-
-Add more machine learning algorithms.
 
 
 ## Contents of the Library
@@ -235,7 +173,51 @@ Add more machine learning algorithms.
     4. Accuracy
     5. F1 score
 
+## 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`
+
+### std::random
+
+Replace remaining std::random usage with engine internals.
+
+### Tensor
+
+Add an N-dimensional tensor class.
+
+### More algos
+
+Add more machine learning algorithms.
 
 ## Citations
-Various different materials helped me along the way of creating ML++, and I would like to give credit to several of them here. [This](https://www.tutorialspoint.com/cplusplus-program-to-compute-determinant-of-a-matrix) article by TutorialsPoint was a big help when trying to implement the determinant of a matrix, and [this](https://www.geeksforgeeks.org/adjoint-inverse-matrix/) article by GeeksForGeeks was very helpful when trying to take the adjoint and inverse of a matrix.
 
+Originally created by Marc Melikyan: https://github.com/novak-99/MLPP