My code can be accessed at: git@github.com:jacarr4/randomized-optimization-final.git
There are several required python packages: numpy, pandas, scikit-learn, and mlrose_hiive. There may be others.

There are several scripts of interest.
To generate hyperparameters for the four RO problems:
* python -m find_hyperparameters --learner <learner> --problem <problem>
Learner can be one of:
* hill_climbing
* simulated_annealing
* genetic_alg
* mimic
Problem can be one of:
* onemax
* flipflop
* four_peaks

This script will display plots for the hyperparameter choices (which can be tweaked by editing the code - optimize_<alg>_hyperparams methods)

To run the four RO problems:
* python -m randomized_optimization --problem <problem>
Problem can be one of:
* onemax
* flipflop
* four_peaks

This script will display fitness vs iterations and fitness vs evaluations curves. 
You can edit the code in the solveWith<Method> methods to plug in hyperparameters generated by find_hyperparameters.py.

To run the neural network problem:
* python -m compute_neural_network --learner <learner> [--plot]
Learner can be one of:
* gradient_descent
* random_hill_climb
* simulated_annealing
* genetic_alg

This will run the neural network using the given learner to train it.
If run with --plot, the script will plot the learning and train time curves.
There is a loop in the run method that is helpful for training one hyperparameter at a time. 

That is all. Thank you!

Jacob Carr