Update TODOs

README.md

@@ -148,13 +148,11 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
 # TODO
 
 - [ ] Make scaling of changes to constant a hyperparameter
-- [ ] Update hall of fame every iteration?
 - [ ] Calculate feature importances of future mutations, by looking at correlation between residual of model, and the features.
     - Store feature importances of future, and periodically update it.
 - [ ] Implement more parts of the original Eureqa algorithms: https://www.creativemachineslab.com/eureqa.html
 - [ ] Sympy printing
 - [ ] Consider adding mutation for constant<->variable
-- [ ] Consider adding mutation to pass an operator in through a new binary operator (e.g., exp(x3)->plus(exp(x3), ...))
 - [ ] Hierarchical model, so can re-use functional forms. Output of one equation goes into second equation?
 - [ ] Use NN to generate weights over all probability distribution conditional on error and existing equation, and train on some randomly-generated equations
 - [ ] Performance:
@@ -162,6 +160,11 @@ pd.DataFrame, Results dataframe, giving complexity, MSE, and equations
     - Current most expensive operations:
         - [ ] Calculating the loss function - there is duplicate calculations happening.
         - [x] Declaration of the weights array every iteration
+- [x] Make deletion op join deleted subtree to parent
+- [x] Update hall of fame every iteration?
+    - Seems to overfit early if we do this.
+- [x] Consider adding mutation to pass an operator in through a new binary operator (e.g., exp(x3)->plus(exp(x3), ...))
+    - (Added full insertion operator
 - [x] Add a node at the top of a tree
 - [x] Insert a node at the top of a subtree
 - [x] Record very best individual in each population, and return at end.