(Work in progress) NEAT (NeuroEvolution of Augmenting Topologies) is a method developed by Kenneth O. Stanley for evolving arbitrary neural networks. This project is a Python implementation of NEAT. You can find the original paper here.
- Python 3.6 - Tested for python 3.6
- Linux
[Temporary - Adds a command to your .bashrc script that configures the $PYTOHNPATH path variable for you]
$ git clone https://github.com/Samuelimza/NeuroEvolutionOfAugmentingTopologies.git
$ cd NeuroEvolutionOfAugmentingTopologies
$ echo "export PYTHONPATH="\${PYTHONPATH}:$(pwd)"" >> ~/.bashrc
$ source ~/.bashrc
Run tests -
$ python -m unittest discover -s tests
or simply $ ./runTests.sh
NEAT requires only a fitness_function(Neural_Networks) that evaluates neural networks and returns the fitness in a list as shown below:
import neat
def fitness_function(Neural_Networks):
fitness = []
for neural_network in Neural_Networks:
output = neural_network.activate(input_data)
# Calculate fitness
fitness.append(calculated_fitness)
return fitness
NEAT_instance = neat.Main.NEAT(fitness_function)
NEAT_instance.train()Osama Azmi - github
Open issues if you discover any bugs or for feedback and contributors are always welcome!
This project is licensed under the MIT License - see the LICENSE file for details