added neat config file

Cargo.toml

@@ -14,3 +14,6 @@ cmaes = "0.2.1"
 ggez = "0.9.3"
 rand = "0.8.5"
 rand_distr = "0.4.3"
+serde = "1.0.198"
+serde_derive = "1.0.198"
+toml = "0.8.12"

neat_conf.toml

@@ -0,0 +1,18 @@
+population_size = 1000
+n_inputs = 4
+n_outputs = 1
+weights_mean = 0.0
+weights_stddev = 0.8
+perturbation_stddev = 0.2
+new_weight_probability = 0.1
+enable_probability = 0.25
+survival_threshold = 0.25
+connection_mutation_rate = 0.3
+node_mutation_rate = 0.03
+weight_mutation_rate = 0.8
+similarity_threshold = 15.0
+excess_weight = 1.0
+disjoint_weight = 1.0
+matching_weight = 3.0
+champion_copy_threshold = 4
+stagnation_threshold = 1500

neat_conf_2.toml

@@ -0,0 +1,18 @@
+population_size = 150
+n_inputs = 2
+n_outputs = 1
+weights_mean = 0.0
+weights_stddev = 0.8
+perturbation_stddev = 0.2
+new_weight_probability = 0.1
+enable_probability = 0.25
+survival_threshold = 0.25
+connection_mutation_rate = 0.3
+node_mutation_rate = 0.03
+weight_mutation_rate = 0.8
+similarity_threshold = 15.0
+excess_weight = 1.0
+disjoint_weight = 1.0
+matching_weight = 0.3
+champion_copy_threshold = 5
+stagnation_threshold = 1500

src/bin/main.rs

@@ -1,4 +1,6 @@
 use std::fs::File;
+use std::io::Read;
+use toml;
 use ggez::*;
 use clap::Parser;
 use neuroevolution::cli::*;
@@ -9,7 +11,6 @@ use neuroevolution::discrete_network::DiscreteNetwork;
 use neuroevolution::neuroevolution_algorithm::{NeuroevolutionAlgorithm, Algorithm};
 use neuroevolution::benchmarks::*;
 use neuroevolution::constants::*;
-use neuroevolution::gui::*;
 use neuroevolution::neat::*;
 
 fn main() {
@@ -51,26 +52,31 @@ fn main() {
             }
         }
         AlgorithmType::Neat => {
-            let config = Config {
-                population_size: 150,
-                n_inputs: 2,
-                n_outputs: 1,
-                weights_mean: 0.,
-                weights_stddev: 0.8,
-                perturbation_stddev: 0.2,
-                new_weight_probability: 0.1,
-                enable_probability: 0.25,
-                survival_threshold: 0.25,
-                connection_mutation_rate: 0.3,
-                node_mutation_rate: 0.03,
-                weight_mutation_rate: 0.8,
-                similarity_threshold: 15.0,
-                excess_weight: 1.,
-                disjoint_weight: 1.,
-                matching_weight: 0.3,
-                champion_copy_threshold: 5,
-                stagnation_threshold: 1500,
-            };
+            // let config = Config {
+            //     population_size: 150,
+            //     n_inputs: 2,
+            //     n_outputs: 1,
+            //     weights_mean: 0.,
+            //     weights_stddev: 0.8,
+            //     perturbation_stddev: 0.2,
+            //     new_weight_probability: 0.1,
+            //     enable_probability: 0.25,
+            //     survival_threshold: 0.25,
+            //     connection_mutation_rate: 0.3,
+            //     node_mutation_rate: 0.03,
+            //     weight_mutation_rate: 0.8,
+            //     similarity_threshold: 15.0,
+            //     excess_weight: 1.,
+            //     disjoint_weight: 1.,
+            //     matching_weight: 0.3,
+            //     champion_copy_threshold: 5,
+            //     stagnation_threshold: 1500,
+            // };
+
+            let mut neat_config_file = File::open("neat_conf.toml").unwrap();
+            let mut toml_config = String::new();
+            neat_config_file.read_to_string(&mut toml_config).unwrap();
+            let config: Config = toml::from_str(&toml_config).unwrap();
 
             let neat = Neat::new(config);
             alg = Algorithm::Neat(neat);
@@ -79,21 +85,29 @@ fn main() {
 
     match cli.gui {
         true => {
+            let mut conf_file = File::open("gui_conf.toml").unwrap();
+            let conf = conf::Conf::from_toml_file(&mut conf_file).unwrap();
+            let cb = ContextBuilder::new("Neuroevolution", "Samy Haffoudhi") .default_conf(conf);
+            let (ctx, event_loop) = cb.build().unwrap();
+
             match problem {
                 Benchmark::PoleBalancing => {
-                    panic!("Not implemented yet!");
+                    println!("Evolving algorithm...");
+                    alg.optimize(&problem, cli.iterations);
+                    println!("Fitness: {}", problem.evaluate(&alg));
+
+                    let pole_balancing_state = neuroevolution::pole_balancing::State::default();
+                    let state = neuroevolution::pole_balancing_gui::State::new(pole_balancing_state, alg);
+                    event::run(ctx, event_loop, state);
                 }
 
                 _ => {
-                    let state = State::new(alg, problem, N_ITERATIONS);
-                    let mut conf_file = File::open("gui_conf.toml").unwrap();
-                    let conf = conf::Conf::from_toml_file(&mut conf_file).unwrap();
-                    let cb = ContextBuilder::new("Neuroevolution", "Samy Haffoudhi") .default_conf(conf);
-                    let (ctx, event_loop) = cb.build().unwrap();
+                    let state = neuroevolution::gui::State::new(alg, problem, N_ITERATIONS);
                     event::run(ctx, event_loop, state);
                 }
             }
         }
+
         false => {
             alg.optimize(&problem, cli.iterations);
 

src/bin/pole_balancing_gui.rs

@@ -32,7 +32,7 @@ fn main() {
     let neat = Neat::new(config);
     let mut alg = Algorithm::Neat(neat);
     let problem = Benchmark::PoleBalancing;
-    println!("Optimizing algorithm...");
+    println!("Evolving algorithm...");
     alg.optimize(&problem, 20);
     println!("Fitness: {}", problem.evaluate(&alg));
 

src/neat.rs

@@ -1,5 +1,6 @@
 use rand::prelude::*;
 use rand_distr::Normal;
+use serde_derive::Deserialize;
 use crate::neural_network::*;
 use crate::neuroevolution_algorithm::*;
 use crate::benchmarks::Benchmark;
@@ -57,7 +58,7 @@ struct History {
     generation: u32,
 }
 
-#[derive(Debug)]
+#[derive(Debug, Deserialize)]
 pub struct Config {
     pub population_size: u32,
     pub n_inputs: u32,