neat-gru-rust

Documentation

Crates.io doc

Examples

XOR

Right now this is the only working example. You can run it via: cargo run --example example

How to use

In Cargo.toml: [dependencies] neat-gru = "0.6.5" Create a struct that implements the Game trait ```rust use neatgru::game::Game; use neatgru::neuralnetwork::nn::NeuralNetwork; use neatgru::topology::topology::Topology; struct Player { pub net: NeuralNetwork, }

impl Player { pub fn new(net: NeuralNetwork) -> Player { Player { net, } } }

struct Simulation { players: Vec, }

impl Simulation { pub fn new() -> Simulation { Simulation { players: Vec::new(), } } }

impl Game for Simulation { // Loss function fn rungeneration(&mut self) -> Vec { let inputs = getinputs(); self.players.iter().map(|p| { let output = p.net.compute(inputs); let scores = compute_score(output, target); scores }).collect() }

// Reset networks
fn reset_players(&mut self, nets: Vec<NeuralNetwork<f64>>) {
    self.players.clear();
    self.players = nets
        .into_iter()
        .map(Player::new)
        .collect();
}

// Called at the end of training
fn post_training(&mut self, history: &[Topology<f64>]) {
    // Iter on best topologies and upload the best one
}

}

Async run_generation (has to be run inside an async runtime like Tokio) rust

[async_trait]

impl GameAsync for Simulation { // Loss function async fn rungeneration(&mut self) -> Vec { let inputs = getinputs().await; self.players.iter().map(|p| { let output = p.net.compute(inputs); let scores = compute_score(output, target); scores }).collect() } } ```

Launch a training ```rust fn run_sim() { let mut sim = Simulation::new();

let mut runner = Train::new(&mut sim);
runner
    .inputs(input_count)
    .outputs(output_count as i32)
    .iterations(nb_generations as i32)
    .max_layers((hidden_layers + 2) as i32)
    .max_per_layers(hidden_layers as i32)
    .max_species(max_species as i32)
    .max_individuals(max_individuals as i32)
    .delta_threshold(2.) // Delta parameter from NEAT paper
    .formula(0.8, 0.8, 0.3) // c1, c2 and c3 from NEAT paper
    .access_train_object(Box::new(|train| {
        let species_count = train.species_count();
        println!("Species count: {}", species_count);
    })) // Callback called after `reset_players` that gives you access to the train object during training
    .start(); // .start_async().await for async version

} ```