Use a normal distribution for mutations

Rather than StandardNormal, weights are generated with a Normal
distribution centered on 0 with a standard deviation of 0.75,
and mutations are performed by adding a number from that distribution to
the existing weight, rather than replacing the weight entirely.

Cargo.lock

@@ -142,6 +142,7 @@ dependencies = [
 name = "genetic"
 version = "0.1.0"
 dependencies = [
+ "lazy_static",
  "macroquad",
  "nalgebra",
  "rand",
@@ -203,6 +204,12 @@ version = "1.0.5"
 source = "registry+https://github.com/rust-lang/crates.io-index"
 checksum = "fad582f4b9e86b6caa621cabeb0963332d92eea04729ab12892c2533951e6440"
 
+[[package]]
+name = "lazy_static"
+version = "1.4.0"
+source = "registry+https://github.com/rust-lang/crates.io-index"
+checksum = "e2abad23fbc42b3700f2f279844dc832adb2b2eb069b2df918f455c4e18cc646"
+
 [[package]]
 name = "lewton"
 version = "0.9.4"

Cargo.toml

@@ -13,6 +13,7 @@ rand_distr = "0.4.3"
 serde = { version = "1.0.152", features = ["derive"] }
 serde_json = "1.0.91"
 tinyfiledialogs = "3.9.1"
+lazy_static = "1.4"
 
 [profile.dev]
 opt-level = 3

src/nn.rs

@@ -1,10 +1,14 @@
 use macroquad::{prelude::*, rand::gen_range};
 use nalgebra::*;
 use r::Rng;
-use rand_distr::StandardNormal;
+use rand_distr::Normal;
 use serde::{Deserialize, Serialize};
 extern crate rand as r;
 
+lazy_static::lazy_static! {
+  static ref CONNECTION_DISTRIBUTION: Normal<f32> = Normal::new(0.0, 0.75).unwrap();
+}
+
 #[derive(PartialEq, Debug, Clone, Copy, Serialize, Deserialize)]
 
 pub enum ActivationFunc {
@@ -39,8 +43,12 @@ impl NN {
                 .zip(config.iter().skip(1))
                 .map(|(&curr, &last)| {
                     // DMatrix::from_fn(last, curr + 1, |_, _| gen_range(-1., 1.))
-                    DMatrix::<f32>::from_distribution(last, curr + 1, &StandardNormal, &mut rng)
-                        * (2. / last as f32).sqrt()
+                    DMatrix::<f32>::from_distribution(
+                        last,
+                        curr + 1,
+                        &*CONNECTION_DISTRIBUTION,
+                        &mut rng,
+                    ) * (2. / last as f32).sqrt()
                 })
                 .collect(),
 
@@ -75,7 +83,9 @@ impl NN {
                 if gen_range(0., 1.) < self.mut_rate {
                     // *ele += gen_range(-1., 1.);
                     // *ele = gen_range(-1., 1.);
-                    *ele = r::thread_rng().sample::<f32, StandardNormal>(StandardNormal);
+                    *ele +=
+                        r::thread_rng().sample::<f32, Normal<f32>>(CONNECTION_DISTRIBUTION.clone());
+                    *ele = ele.min(10.0).max(-10.0);
                 }
             }
         }
@@ -85,12 +95,11 @@ impl NN {
         // println!("inputs: {:?}", inputs);
         let mut y = DMatrix::from_vec(inputs.len(), 1, inputs.to_vec());
         for i in 0..self.config.len() - 1 {
-            y = (&self.weights[i] * y.insert_row(self.config[i] - 1, 1.)).map(|x| {
-                match self.activ_func {
-                    ActivationFunc::ReLU => x.max(0.),
-                    ActivationFunc::Sigmoid => 1. / (1. + (-x).exp()),
-                    ActivationFunc::Tanh => x.tanh(),
-                }
+            let row = y.insert_row(self.config[i] - 1, 1.);
+            y = (&self.weights[i] * row).map(|x| match self.activ_func {
+                ActivationFunc::ReLU => x.max(0.),
+                ActivationFunc::Sigmoid => 1. / (1. + (-x).exp()),
+                ActivationFunc::Tanh => x.tanh(),
             });
         }
         y.column(0).data.into_slice().to_vec()