Even cleaner

src/example.rs

@@ -103,13 +103,14 @@ fn main() {
         "green",
     ];
 
-    let kmeans = kmeans(elements, initial, &distance, &centroid, 1000).unwrap();
+    let (kmeans, nb_iterations) = kmeans(elements, initial, &distance, &centroid, 1000).unwrap();
+    let kmeans = kmeans.into_iter();
 
-    println!("Converged in {} iterations.", kmeans.1);
+    println!("Converged in {} iterations.", nb_iterations);
 
     let mut output = File::create("plot/dat.dat").unwrap();
 
-    for (index, (cluster, color)) in kmeans.0.iter().zip(colors.iter()).enumerate() {
+    for (index, (cluster, color)) in kmeans.iter().zip(colors.iter()).enumerate() {
         println!("Cluster {}: {} elements", index, cluster.len());
         for element in cluster {
             use std::io::Write;

src/lib.rs

@@ -1,3 +1,5 @@
+use std::slice::Iter;
+use std::iter::Zip;
 use std::collections::HashMap;
 
 #[derive(Debug)]
@@ -5,23 +7,20 @@ pub enum Error {
     TooManyIterations,
 }
 
-type Distance<T> = Fn(&T, &T) -> f64;
-type Centroid<T> = Fn(&Vec<T>) -> T;
-
 pub struct KmeansData<T: Clone + PartialEq> {
     pub elements: Vec<T>,
     pub labels: Vec<usize>,
 }
 
-pub struct Kmeans<'a, T: 'a + Clone + PartialEq> {
+pub struct Kmeans<T: Clone + PartialEq, D: Fn(&T, &T) -> f64, C: Fn(&Vec<T>) -> T> {
     data: KmeansData<T>,
     centroids: Vec<T>,
-    distance: &'a Distance<T>,
-    centroid: &'a Centroid<T>,
+    distance: D,
+    centroid: C,
 }
 
-impl<'a, T: 'a + Clone + PartialEq> Kmeans<'a, T> {
-    pub fn new(data: Vec<T>, centroids: Vec<T>, distance: &'a Distance<T>, centroid: &'a Centroid<T>) -> Kmeans<'a, T> {
+impl<T: Clone + PartialEq, D: Fn(&T, &T) -> f64, C: Fn(&Vec<T>) -> T> Kmeans<T, D, C> {
+    pub fn new(data: Vec<T>, centroids: Vec<T>, distance: D, centroid: C) -> Kmeans<T, D, C> {
 
         let len = data.len();
         Kmeans {
@@ -85,15 +84,42 @@ impl<'a, T: 'a + Clone + PartialEq> Kmeans<'a, T> {
 
         new_centroids
     }
+
+    pub fn iter(&self) -> Zip<Iter<T>, Iter<usize>> {
+        self.data.elements.iter().zip(self.data.labels.iter())
+    }
+
+    pub fn into_iter(self) -> Vec<Vec<T>> {
+        let mut map = HashMap::new();
+
+        for (element, label) in self.data.elements.iter().zip(self.data.labels.iter()) {
+            let mut centroid = map.entry(label).or_insert(vec![]);
+            centroid.push(element.clone());
+        }
+
+        let mut output = vec![];
+
+        for (_, value) in map {
+            let mut cluster = vec![];
+
+            for element in value {
+                cluster.push(element);
+            }
+
+            output.push(cluster);
+        }
+
+        output
+    }
 }
 
-pub fn kmeans<'a, T: 'a + Clone + PartialEq>(
+pub fn kmeans<T: Clone + PartialEq, D: Fn(&T, &T) -> f64, C: Fn(&Vec<T>) -> T>(
     elements: Vec<T>,
     initial: Vec<T>,
-    distance: &'a Distance<T>,
-    centroid: &'a Centroid<T>,
+    distance: D,
+    centroid: C,
     max_iteration: usize,
-) -> Result<(Vec<Vec<T>>,usize), Error> {
+) -> Result<(Kmeans<T, D, C>,usize), Error> {
 
     let mut clusters = Kmeans::new(elements, initial, distance, centroid);
 
@@ -109,24 +135,6 @@ pub fn kmeans<'a, T: 'a + Clone + PartialEq>(
 
     };
 
-    let mut map = HashMap::new();
-
-    for (element, label) in clusters.data.elements.iter().zip(clusters.data.labels.iter()) {
-        let mut centroid = map.entry(label).or_insert(vec![]);
-        centroid.push(element.clone());
-    }
-
-    let mut output = vec![];
-
-    for (_, value) in map {
-        let mut cluster = vec![];
-
-        for element in value {
-            cluster.push(element);
-        }
-
-        output.push(cluster);
-    }
-
-    Ok((output, iterations))
+    Ok((clusters, iterations))
 }
+