This repository is for work submitted in Spring 2019 for the Machine Learning (7641) class offered by GATech via Udacity.
This project was built with Python 3.7.0.
The environment may be installed as such:
virtualenv .venv --python ~/local/path/to/3.7.0/bin/python
echo 'export PYTHONPATH="../:."' >> ./.venv/bin/activate
source ./.venv/bin/activate
python -m pip install --requirement requirements.txt
If running on a Mac, also run:
sed -i -e 's/: macosx/: TkAgg/g' ./.venv/lib/python3.7/site-packages/matplotlib/mpl-data/matplotlibrc
All pip prerequisites are listed in requirements.txt. They may be installed with pip using:
pip install -r requirements.txt
The environment may be activated with:
source ./.venv/bin/activate
It may be deactivated with: deactivate
├── algorithms
│ ├── genetic_algorithm.py
│ ├── mimic.py
│ ├── random_hill_climb.py
│ └── simulated_annealing.py
├── data
│ └── contraceptive.csv
├── neural_networks
│ ├── bar_chart.py
│ ├── neural_networks.py
│ └── nn_compare.py
├── plots
│ ├── complex_time
│ ├── fit_iter
│ │ └── *.png
│ ├── fit_time
│ │ └── *.png
│ └── neural_network
│ └── *.png
├── puzzles
│ ├── bar_chart.py
│ ├── k_coloring.py
│ ├── knapsack.py
│ ├── plotter.py
│ ├── puzzle_solver.py
│ ├── run_all.py
│ └── travelling_salesman.py
└── requirements.txt
Run run_all.py to run all the puzzle experiments.
Run neural_networks.py to run all neural network optimization experiments.
Run bar_chart.py to visualize.
All generated visualizations are saved under the plots directory.
A survey project exploring a number of randomized optimization algorithms on the contraceptives datasets.
- Yaniv Talmor