NEAT 0.2.0

.github/workflows/ci-cd.yml

@@ -1,6 +1,9 @@
 name: CI-CD
 
-on: [push, pull_request]
+on:
+    push:
+        branches: [main]
+    pull_request: 
 
 jobs:
     test:

Cargo.toml

@@ -1,7 +1,7 @@
 [package]
 name = "neat"
 description = "Crate for working with NEAT in rust"
-version = "0.1.0"
+version = "0.2.0"
 edition = "2021"
 authors = ["Inflectrix"]
 repository = "https://github.com/inflectrix/neat"
@@ -11,16 +11,26 @@ keywords = ["genetic", "machine-learning", "ai", "algorithm", "evolution"]
 categories = ["algorithms", "science", "simulation"]
 license = "MIT"
 
+[package.metadata.docs.rs]
+features = ["serde"]
+rustdoc-args = ["--cfg", "docsrs"]
+
 # See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
 
 [features]
 default = ["max-index"]
 #crossover = ["genetic-rs/crossover"]
 rayon = ["genetic-rs/rayon", "dep:rayon"]
 max-index = []
+serde = ["dep:serde", "dep:serde-big-array"]
 
 
 [dependencies]
-genetic-rs = "0.2.1"
+genetic-rs = "0.3"
 rand = "0.8.5"
-rayon = { version = "1.8.1", optional = true }
+rayon = { version = "1.8.1", optional = true }
+serde = { version = "1.0.197", features = ["derive"], optional = true }
+serde-big-array = { version = "0.5.1", optional = true }
+
+[dev-dependencies]
+bincode = "1.3.3"

examples/basic.rs

@@ -15,7 +15,7 @@ impl RandomlyMutable for AgentDNA {
 impl Prunable for AgentDNA {}
 
 impl DivisionReproduction for AgentDNA {
-    fn spawn_child(&self, rng: &mut impl Rng) -> Self {
+    fn divide(&self, rng: &mut impl Rng) -> Self {
         let mut child = self.clone();
         child.mutate(self.network.mutation_rate, rng);
         child
@@ -112,7 +112,7 @@ fn main() {
         sim.next_generation();
     }
 
-    let fits: Vec<_> = sim.entities.iter().map(fitness).collect();
+    let fits: Vec<_> = sim.genomes.iter().map(fitness).collect();
 
     let maxfit = fits
         .iter()
@@ -130,7 +130,7 @@ fn main() {
         sim.next_generation();
     }
 
-    let fits: Vec<_> = sim.entities.iter().map(fitness).collect();
+    let fits: Vec<_> = sim.genomes.iter().map(fitness).collect();
 
     let maxfit = fits
         .iter()

src/lib.rs

@@ -2,11 +2,13 @@
 //! ### Feature Roadmap:
 //! - [x] base (single-core) crate
 //! - [x] rayon
+//! - [x] serde
 //! - [ ] crossover
 //!
 //! You can get started by looking at [genetic-rs docs](https://docs.rs/genetic-rs) and checking the examples for this crate.
 
 #![warn(missing_docs)]
+#![cfg_attr(docsrs, feature(doc_cfg))]
 
 /// A module containing the [`NeuralNetworkTopology`] struct. This is what you want to use in the DNA of your agent, as it is the thing that goes through nextgens and suppors mutation.
 pub mod topology;
@@ -18,3 +20,6 @@ pub mod runnable;
 pub use genetic_rs::prelude::*;
 pub use runnable::*;
 pub use topology::*;
+
+#[cfg(feature = "serde")]
+pub use nnt_serde::*;

src/runnable.rs

@@ -8,7 +8,7 @@ use rayon::prelude::*;
 #[cfg(feature = "rayon")]
 use std::sync::{Arc, RwLock};
 
-/// A runnable, stated Neural Network generated from a [NeuralNetworkToplogy]. Use [`NeuralNetwork::from`] to go from stateles to runnable.
+/// A runnable, stated Neural Network generated from a [NeuralNetworkTopology]. Use [`NeuralNetwork::from`] to go from stateles to runnable.
 /// Because this has state, you need to run [`NeuralNetwork::flush_state`] between [`NeuralNetwork::predict`] calls.
 #[derive(Debug)]
 #[cfg(not(feature = "rayon"))]
@@ -82,7 +82,7 @@ impl<const I: usize, const O: usize> NeuralNetwork<I, O> {
             n.state.value += self.process_neuron(l) * w;
         }
 
-        n.sigmoid();
+        n.activate();
 
         n.state.value
     }
@@ -112,7 +112,7 @@ impl<const I: usize, const O: usize> NeuralNetwork<I, O> {
 
         let mut nw = n.write().unwrap();
         nw.state.value += val;
-        nw.sigmoid();
+        nw.activate();
 
         nw.state.value
     }
@@ -240,6 +240,9 @@ pub struct Neuron {
 
     /// The current state of the neuron.
     pub state: NeuronState,
+
+    /// The neuron's activation function
+    pub activation: ActivationFn,
 }
 
 impl Neuron {
@@ -248,9 +251,9 @@ impl Neuron {
         self.state.value = self.bias;
     }
 
-    /// Applies the sigoid activation function to the state's current value.
-    pub fn sigmoid(&mut self) {
-        self.state.value = 1. / (1. + std::f32::consts::E.powf(-self.state.value))
+    /// Applies the activation function to the neuron
+    pub fn activate(&mut self) {
+        self.state.value = (self.activation.func)(self.state.value);
     }
 }
 
@@ -263,6 +266,7 @@ impl From<&NeuronTopology> for Neuron {
                 value: value.bias,
                 ..Default::default()
             },
+            activation: value.activation.clone(),
         }
     }
 }