doxygen doc.conf
Add to your projects CMakeLists.txt:
add_subdirectory(NEAT-GRU NEAT)
target_link_libraries(trading NEAT_GRU)
mkdir Release
cd Release
cmake -DCMAKE_BUILD_TYPE=Release -D__MULTITHREADED__=1 .. .
sudo make install
-
Implement the abstract class
Game::Game:#include <neat/Game/Game.h> class Simulation: public Game { std::vector<double> do_run_generation() override {...} void do_reset_players(NN * nets, size_t count) override {...}; void do_post_training(Topology_ptr topology) override {...}; } -
Create a
Traininstance and give it a pointer to aSimulationinstance:auto * sim = new Simulation(); const int iterations = 1000; const int max_individuals = 300; // Individuals per generation const int inputs = 10; // Input neurons const int outputs = 5; // Output neurons Train train(sim, max_individuals, inputs, outputs); train.start(); // Runs the training, will output the resulting network to "topologies.json"
In Cargo.toml:
[dependencies]
neat-gru = "0.1.10"
Create a struct that implements the Game trait
use neat_gru::game::Game;
use neat_gru::neural_network::NeuralNetwork;
struct Player {
net: NeuralNetwork,
score: f64
}
impl Player {
pub fn new(net: NeuralNetwork) -> Player {
Player {
net: net,
score: 0f64
}
}
}
struct Simulation {
players: Vec<Player>
}
impl Simulation {
pub fn new() -> Simulation {
Simulation {
players: Vec::new()
}
}
}
impl Game for TradingSimulation {
// Loss function
fn run_generation(&mut self) -> Vec<f64> {
self.players.iter().map(... Your logic here ).collect()
}
// Reset networks
fn reset_players(&mut self, nets: &[NeuralNetwork]) {
self.players.clear();
self.players.reserve(nets.len());
self.players = nets
.into_iter()
.map(|net| Player::new(net.clone()))
.collect();
}
// Called at the end of training
fn post_training(&mut self, history: &[Topology]) {
// Iter on best topologies and upload the best one
}
}
Launch a training
let sim = Simulation::new();
let mut runner = Train::new();
runner
.simulation(sim)
.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)
.start();