write more tests

src/lib.rs

@@ -23,3 +23,68 @@ pub use topology::*;
 
 #[cfg(feature = "serde")]
 pub use nnt_serde::*;
+
+#[cfg(test)]
+mod tests {
+ use super::*;
+ use rand::prelude::*;
+
+ #[derive(RandomlyMutable, DivisionReproduction, Clone)]
+ struct AgentDNA {
+ network: NeuralNetworkTopology<2, 1>,
+ }
+
+ impl Prunable for AgentDNA {}
+
+ impl GenerateRandom for AgentDNA {
+ fn gen_random(rng: &mut impl Rng) -> Self {
+ Self {
+ network: NeuralNetworkTopology::new(0.01, 3, rng),
+ }
+ }
+ }
+
+ #[test]
+ fn basic_test() {
+ let fitness = |g: &AgentDNA| {
+ let network = NeuralNetwork::from(&g.network);
+ let mut fitness = 0.;
+ let mut rng = rand::thread_rng();
+
+ for _ in 0..100 {
+ let n = rng.gen::<f32>() * 10000.;
+ let base = rng.gen::<f32>() * 10.;
+ let expected = n.log(base);
+
+ let [answer] = network.predict([n, base]);
+ network.flush_state();
+
+ fitness += 5. / (answer - expected).abs();
+ }
+
+ fitness
+ };
+
+ #[cfg(not(feature = "rayon"))]
+ let mut rng = rand::thread_rng();
+
+ let mut sim = GeneticSim::new(
+ #[cfg(not(feature = "rayon"))]
+ Vec::gen_random(&mut rng, 100),
+ #[cfg(feature = "rayon")]
+ Vec::gen_random(100),
+ fitness,
+ division_pruning_nextgen,
+ );
+
+ for _ in 0..100 {
+ sim.next_generation();
+ }
+
+ let mut fits: Vec<_> = sim.genomes.iter().map(fitness).collect();
+
+ fits.sort_by(|a, b| a.partial_cmp(&b).unwrap());
+
+ dbg!(fits);
+ }
+}