1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
use crate::racetrack::Racetrack;
use crate::rng::Rng;
use crate::vector::Vector;
use std::iter;
const N_MID_WEIGHTS: usize = 32;
#[derive(Clone, PartialEq, PartialOrd)]
pub struct Brain {
view_dist: i32,
mid_weights: [Vec<f32>; N_MID_WEIGHTS],
out_weights: [[f32; N_MID_WEIGHTS]; 5],
}
impl Brain {
pub fn random(view_dist: i32, rng: &mut Rng) -> Brain {
let view_dist = pos_round_up_2(i32::abs(view_dist));
let n_inputs = ((view_dist / 2) * (view_dist / 2) * 4) as usize - 2;
Brain {
view_dist,
mid_weights: repeat_array(|| {
iter::repeat_with(|| random_f32(rng))
.take(n_inputs)
.collect()
}),
out_weights: [
repeat_array(|| random_f32(rng)),
repeat_array(|| random_f32(rng)),
repeat_array(|| random_f32(rng)),
repeat_array(|| random_f32(rng)),
repeat_array(|| random_f32(rng)),
],
}
}
pub fn mutant(&self, rng: &mut Rng, amount: f64) -> Brain {
let amount = amount as f32;
let mut mutant = self.clone();
for neuron in mutant.mid_weights.iter_mut() {
for weight in neuron {
*weight += random_f32(rng) * amount;
}
}
for neuron in mutant.out_weights.iter_mut() {
for weight in neuron {
*weight += random_f32(rng) * amount;
}
}
mutant
}
pub fn compute_accel(&self, vel: Vector, track: &Racetrack) -> Vector {
let mut mid_iter = self.mid_weights.iter();
let mid_out = repeat_array(|| {
if let Some(neuron) = mid_iter.next() {
let quarter = (neuron.len() - 2) / 4;
let mut sum = 0.0;
let mut i = 0;
for x in (1..self.view_dist).step_by(2) {
for y in (1..self.view_dist).step_by(2) {
if let Some(true) = track.get(Vector::new(x, y)) {
sum += neuron[i];
}
if let Some(true) = track.get(Vector::new(-x, y)) {
sum += neuron[i + 1 * quarter];
}
if let Some(true) = track.get(Vector::new(-x, -y)) {
sum += neuron[i + 2 * quarter];
}
if let Some(true) = track.get(Vector::new(x, -y)) {
sum += neuron[i + 3 * quarter];
}
i += 1;
}
}
sum += vel.x as f32 * neuron[neuron.len() - 2];
sum += vel.y as f32 * neuron[neuron.len() - 1];
sum
} else {
unreachable!()
}
});
assert!(mid_iter.next().is_none());
let mut out = vec![
compute_out(&self.out_weights[0], &mid_out),
compute_out(&self.out_weights[1], &mid_out),
compute_out(&self.out_weights[2], &mid_out),
compute_out(&self.out_weights[3], &mid_out),
compute_out(&self.out_weights[4], &mid_out),
]
.into_iter();
let mut max = out.next().unwrap();
let mut max_i = 0;
for (i, choice) in out.enumerate() {
if choice > max {
max = choice;
max_i = i;
}
}
match max_i {
0 => Vector::new(1, 0),
1 => Vector::new(0, 1),
2 => Vector::new(-1, 0),
3 => Vector::new(0, -1),
4 => Vector::ORIGIN,
_ => unreachable!(),
}
}
}
fn pos_round_up_2(num: i32) -> i32 {
(num + 1) & !1
}
fn random_f32(rng: &mut Rng) -> f32 {
rng.forward() as f32 / Rng::RAND_MAX as f32 * 2.0 - 1.0
}
fn repeat_array<T, F: FnMut() -> T>(mut f: F) -> [T; N_MID_WEIGHTS] {
[
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
f(),
]
}
fn compute_out(neuron: &[f32; N_MID_WEIGHTS], inputs: &[f32; N_MID_WEIGHTS]) -> f32 {
neuron.into_iter().zip(inputs).map(|(&w, &i)| w * i).sum()
}