1 files changed, 54 insertions, 5 deletions

diff --git a/src/nih_kmeans.rs b/src/nih_kmeans.rs
index 98a9965..a34d528 100644
--- a/src/nih_kmeans.rs
+++ b/src/nih_kmeans.rs
@@ -1,3 +1,6 @@
+use std::collections::HashMap;
+
+#[cfg(rand)]
 use rand::{prelude::*, seq::index::sample};
 use rgb::{RGB, RGB8};
 
@@ -5,14 +8,40 @@ pub struct KMeans {
 	samples: Vec<RGB8>,
 }
 
+#[derive(Copy, Clone, Eq, PartialEq, Ord, PartialOrd, Hash)]
+struct HashableRGBF {
+	inner: (u32, u32, u32),
+}
+
+impl From<RGB<f32>> for HashableRGBF {
+	fn from(value: RGB<f32>) -> Self {
+		Self {
+			inner: (value.r.to_bits(), value.g.to_bits(), value.b.to_bits()),
+		}
+	}
+}
+
 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!()
+			let mut clusters: HashMap<HashableRGBF, Vec<RGB8>> = HashMap::new();
+
+			for &sample in &self.samples {
+				let closest_centroid = Self::closest_centroid(&centroids, sample.into());
+				clusters
+					.entry(closest_centroid.into())
+					.or_default()
+					.push(sample);
+			}
+			centroids = clusters
+				.into_values()
+				.map(|members| vector_avg(&members))
+				.collect()
 		}
 		centroids
 			.into_iter()
@@ -20,15 +49,19 @@ impl KMeans {
 			.collect()
 	}
 
-	/// Uses k-means++ algorithm (https://www.mathworks.com/help/stats/kmeans.html#bueq7aj-5)
+	/// Picks a point at random (if feature rand is enabled) for the first centroid, then iteratively adds the point furthest away from any centroid
+	/// A more complex solution is the probabilistic 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()];
+		#[cfg(rand)]
+		let index = thread_rng().gen_range(0..self.samples.len());
+		#[cfg(not(rand))]
+		let index = 0; //lol
+
+		let mut centroids: Vec<RGB<f32>> = vec![self.samples[index].into()];
 		while centroids.len() < k {
 			let next = *self
 				.samples
@@ -58,6 +91,7 @@ impl KMeans {
 			.unwrap()
 	}
 
+	#[cfg(rand)]
 	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();
@@ -78,3 +112,18 @@ 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()
 }
+
+fn vector_sum(acc: RGB<f32>, elem: RGB<f32>) -> RGB<f32> {
+	RGB::new(acc.r + elem.r, acc.g + elem.g, acc.b + elem.b)
+}
+
+fn vector_avg(vs: &[RGB8]) -> RGB<f32> {
+	let summed = vs.iter().fold(RGB::new(0.0, 0.0, 0.0), |acc, elem| {
+		vector_sum(acc, (*elem).into())
+	});
+	RGB::new(
+		summed.r / vs.len() as f32,
+		summed.g / vs.len() as f32,
+		summed.b / vs.len() as f32,
+	)
+}