Generic rewrite

- Genome is a trait that implements mutation, mating, distance measure etc.
        (opens up for more experimentation and extensibility)
- The default Genome implementor is NeuralNetwork which implements said functionality
        pretty much just as before. So NeuralNetwork is the new Genome.
        Some slight changes in how bias works had to be done to be able to mirror the way
        they do it in some paper that introduced or explained CTRNN iirc.
- Thus now we pass around Organisms not Genomes

- And..:
    - Update rand
    - Removed lots of unnecessary f64
    - Remove CtrnnNeuralNetwork (can just use Ctrnn for setting up the
        network)
    - Fix an error in species

.gitignore

@@ -12,14 +12,12 @@
 
 # Generated by Cargo
 /target/
-
-# Remove Cargo.lock from gitignore if creating an executable, leave it for libraries
-# More information here http://doc.crates.io/guide.html#cargotoml-vs-cargolock
 Cargo.lock
 
 # Vim
 *.swp
 *.swo
+rusty-tags.vi
 
 # Fmt
 **/*.rs.bk

Cargo.toml

@@ -8,13 +8,14 @@ license = "MIT"
 name = "rustneat"
 repository = "https://github.com/TLmaK0/rustneat"
 version = "0.3.0"
+edition = "2018"
 
 [dependencies]
 conv = "0.3.2"
 crossbeam = "0.2"
 lazy_static = "0.2.2"
 num_cpus = "1.0"
-rand = "0.4"
+rand = "0.6"
 rulinalg = "0.3.4"
 
 rusty_dashed = { version = "0.2.1", optional = true }

examples/function_approximation.rs

@@ -8,15 +8,13 @@ extern crate rusty_dashed;
 #[cfg(feature = "telemetry")]
 mod telemetry_helper;
 
-use rustneat::Environment;
-use rustneat::Organism;
-use rustneat::Population;
+use rustneat::{Environment, Organism, Population, NeuralNetwork};
 
 static mut BEST_FITNESS: f64 = 0.0;
 struct FunctionApproximation;
 
-impl Environment for FunctionApproximation {
-  fn test(&self, organism: &mut Organism) -> f64 {
+impl Environment<NeuralNetwork> for FunctionApproximation {
+  fn test(&self, organism: &mut NeuralNetwork) -> f64 {
       let mut output = vec![0f64];
       let mut distance = 0f64;
 
@@ -28,22 +26,22 @@ impl Environment for FunctionApproximation {
           outputs.push([x, (output[0] * 100f64) as i64]);
       }
 
+      let fitness = 100f64 / (1f64 + (distance / 1000.0));
       unsafe {
-            if organism.fitness > BEST_FITNESS {
-                BEST_FITNESS = organism.fitness;
+            if fitness > BEST_FITNESS {
+                BEST_FITNESS = fitness;
       #[cfg(feature = "telemetry")]
       telemetry!("approximation1", 1.0, format!("{:?}", outputs));
             }
       }
-
-      100f64 / (1f64 + (distance / 1000.0))
+      fitness
   }
 }
 
 fn main() {
     let mut population = Population::create_population(150);
     let mut environment = FunctionApproximation;
-    let mut champion: Option<Organism> = None;
+    let mut champion: Option<Organism<NeuralNetwork>> = None;
 
     #[cfg(feature = "telemetry")]
     telemetry_helper::enable_telemetry("?max_fitness=100&ioNeurons=1,2", true);

examples/simple_sample.rs

@@ -1,17 +1,15 @@
 extern crate rand;
 extern crate rustneat;
 
-use rustneat::Environment;
-use rustneat::Organism;
-use rustneat::Population;
+use rustneat::{Environment, Organism, Population, NeuralNetwork};
 
 #[cfg(feature = "telemetry")]
 mod telemetry_helper;
 
 struct XORClassification;
 
-impl Environment for XORClassification {
-    fn test(&self, organism: &mut Organism) -> f64 {
+impl Environment<NeuralNetwork> for XORClassification {
+    fn test(&self, organism: &mut NeuralNetwork) -> f64 {
         let mut output = vec![0f64];
         let mut distance: f64;
         organism.activate(vec![0f64, 0f64], &mut output);
@@ -23,7 +21,7 @@ impl Environment for XORClassification {
         organism.activate(vec![1f64, 1f64], &mut output);
         distance += (0f64 - output[0]).powi(2);
 
-        let fitness = 16f64 / (1f64 + distance);
+        let fitness = 16.0 / (1.0 + distance);
 
         fitness
     }
@@ -38,15 +36,24 @@ fn main() {
 
     let mut population = Population::create_population(150);
     let mut environment = XORClassification;
-    let mut champion: Option<Organism> = None;
+    let mut champion: Option<Organism<NeuralNetwork>> = None;
+    let mut i = 0;
     while champion.is_none() {
+        i += 1;
         population.evolve();
         population.evaluate_in(&mut environment);
+        let mut best_fitness = 0.0;
         for organism in &population.get_organisms() {
-            if organism.fitness > 15.5f64 {
+            if organism.fitness > best_fitness {
+                best_fitness = organism.fitness;
+            }
+            if organism.fitness > 15.5 {
                 champion = Some(organism.clone());
             }
         }
+        if i % 10 == 0 {
+            println!("Gen {}: {}", i, best_fitness);
+        }
     }
     println!("{:?}", champion.unwrap().genome);
 }

src/environment.rs

@@ -1,8 +1,8 @@
-use organism::Organism;
+use crate::Genome;
 
-/// A trait that is implemented by user to allow test of the Environment.
-pub trait Environment: Sync {
+/// A trait that is implemented by user to test the fitness of organisms.
+pub trait Environment<G: Genome>: Sync {
     /// This test will return the value required by this enviroment to test
     /// against
-    fn test(&self, organism: &mut Organism) -> f64;
+    fn test(&self, organism: &mut G) -> f64;
 }