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|
mod config;
mod corpus;
mod layout;
use core::cmp::Ordering;
use rayon::prelude::*;
use rand::prelude::*;
use rand_pcg::*;
use corpus::*;
use layout::*;
fn main() {
let corpus = Corpus::load("chained_english_bigrams_1m.txt").unwrap();
println!("{}", corpus);
let starting_layout = Layout::from_verbose(
"
Y W F L M K P O , Q
U R S N H D T E A I
Z X C V J B G ' . /
",
)
.unwrap();
let mut rng = Pcg64::from_entropy();
type Fun = Vec<(Layout, Evaluation, f32)>;
let mut best = std::f32::INFINITY;
let mut layouts: Fun = (0..10)
.into_iter()
.map(|i| {
let layout;
if i >= 0 {
layout = Layout::new_random_from(&starting_layout, &mut rng);
} else {
layout = starting_layout;
}
let evl = corpus.evaluate_layout(&layout);
let fitness = evl.fitness();
return (layout, evl, fitness);
})
.collect();
let mut counter: u64 = 0;
while counter < 10000000000000 {
let new_layouts = layouts
.iter()
.enumerate()
.map(|(i, (kbd, _, _))| {
if i > 0 {
Layout::new_random_from(kbd, &mut rng)
} else {
kbd.clone()
}
})
.collect::<Vec<Layout>>();
new_layouts
.par_iter()
.map(|layout| {
let evl = corpus.evaluate_layout(layout);
let fitness = evl.fitness();
return (*layout, evl, fitness);
})
.collect_into_vec(&mut layouts);
layouts.sort_by(|(_, _, lfit), (_, _, rfit)| {
if lfit < rfit {
Ordering::Less
} else {
Ordering::Greater
}
});
if layouts[0].2 < best {
best = layouts[0].2;
println!("");
println!("================================================================================\nLayout:\n{}",
layouts[0].0
);
println!("{}\n{}", layouts[0].1, layouts[0].2);
}
counter += 1;
}
}
|
