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use rand::{prelude::*, seq::index::sample};
use rgb::{RGB, RGB8};
pub struct KMeans {
samples: Vec<RGB8>,
}
impl KMeans {
pub fn new(samples: Vec<RGB8>) -> Self {
Self { samples }
}
pub fn get_k_colors(&self, k: usize, max_iter: usize) -> Vec<RGB8> {
let mut centroids = self.get_centroid_seeds_simple(k);
for _ in 0..max_iter {
todo!()
}
centroids
.into_iter()
.map(|c| RGB8::new(c.r.round() as u8, c.g.round() as u8, c.b.round() as u8))
.collect()
}
/// Uses k-means++ algorithm (https://www.mathworks.com/help/stats/kmeans.html#bueq7aj-5)
fn get_centroid_seeds_simple(&self, k: usize) -> Vec<RGB<f32>> {
if k >= self.samples.len() {
return self.samples.iter().map(|&v| v.into()).collect();
}
let mut rng = thread_rng();
let mut centroids: Vec<RGB<f32>> =
vec![self.samples[rng.gen_range(0..self.samples.len())].into()];
while centroids.len() < k {
let next = *self
.samples
.iter()
.max_by(|&&v1, &&v2| {
let v1_closest_centroid = Self::closest_centroid(¢roids, v1.into());
let v2_closest_centroid = Self::closest_centroid(¢roids, v2.into());
vector_diff_2_norm(v1.into(), v1_closest_centroid)
.partial_cmp(&vector_diff_2_norm(v2.into(), v2_closest_centroid))
.unwrap()
})
.unwrap();
centroids.push(next.into());
}
centroids
}
fn closest_centroid(centroids: &[RGB<f32>], v: RGB<f32>) -> RGB<f32> {
*centroids
.iter()
.min_by(|&&c1, &&c2| {
vector_diff_2_norm(c1, v)
.partial_cmp(&vector_diff_2_norm(c2, v))
.unwrap()
})
.unwrap()
}
fn get_centroid_seeds_random(&self, k: usize) -> Vec<RGB<f32>> {
if k >= self.samples.len() {
return self.samples.iter().map(|&v| v.into()).collect();
}
sample(&mut thread_rng(), self.samples.len(), k)
.into_iter()
.map(|i| self.samples[i].into())
.collect()
}
}
fn vector_diff(v1: RGB<f32>, v2: RGB<f32>) -> RGB<f32> {
RGB::new(v1.r - v2.r, v1.g - v2.g, v1.b - v2.b)
}
fn vector_diff_2_norm(v1: RGB<f32>, v2: RGB<f32>) -> f32 {
let diff = vector_diff(v1, v2);
(diff.r.powi(2) + diff.g.powi(2) + diff.b.powi(2)).sqrt()
}
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