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);
+    }
+}