extract activation impls into separate module (#51)

src/activation.rs

@@ -1,3 +1,7 @@
+pub mod fns;
+
+use fns::*;
+
 #[cfg(feature = "serde")]
 use serde::{Deserialize, Deserializer, Serialize, Serializer};
 
@@ -205,19 +209,4 @@ impl<'a> Deserialize<'a> for ActivationFn {
 
         Ok(f.unwrap().clone())
     }
-}
-
-/// The sigmoid activation function.
-pub fn sigmoid(n: f32) -> f32 {
-    1. / (1. + std::f32::consts::E.powf(-n))
-}
-
-/// The ReLU activation function.
-pub fn relu(n: f32) -> f32 {
-    n.max(0.)
-}
-
-/// Activation function that does nothing.
-pub fn linear_activation(n: f32) -> f32 {
-    n
-}
+}

src/activation/fns.rs

@@ -0,0 +1,14 @@
+/// The sigmoid activation function.
+pub fn sigmoid(n: f32) -> f32 {
+    1. / (1. + std::f32::consts::E.powf(-n))
+}
+
+/// The ReLU activation function.
+pub fn relu(n: f32) -> f32 {
+    n.max(0.)
+}
+
+/// Activation function that does nothing.
+pub fn linear_activation(n: f32) -> f32 {
+    n
+}

src/lib.rs

@@ -1,7 +1,7 @@
 pub mod activation;
 pub mod neuralnet;
 
-pub use activation::*;
+pub use activation::{*, fns::*};
 pub use neuralnet::*;
 
-pub use genetic_rs;
+pub use genetic_rs;

src/neuralnet.rs

@@ -10,7 +10,7 @@ use atomic_float::AtomicF32;
 use genetic_rs::prelude::*;
 use rand::Rng;
 
-use crate::{activation::*, activation_fn};
+use crate::{activation::{*, fns::*}, activation_fn};
 
 use rayon::prelude::*;