Cleaning

src/clusterable.rs

@@ -1,43 +0,0 @@
-use std::fmt::Debug;
-
-pub trait Clusterable where Self: Sized + Clone + PartialEq + Debug {
-    fn distance(&self, rhs: &Self) -> f64;
-    fn get_centroid<'a, I>(elements: I) -> Option<Self>
-        where I: Iterator<Item = &'a Self>, Self: 'a;
-}
-
-macro_rules! impl_clusterable {
-    ( $type: ty) => {
-        impl Clusterable for $type {
-            fn distance(&self, rhs: &Self) -> f64 {
-                if self > rhs {
-                    *self as f64 - *rhs as f64
-                } else {
-                    *rhs as f64 - *self as f64
-                }
-            }
-
-            fn get_centroid<'a, I>(elements: I) -> Option<Self>
-                where I: Iterator<Item = &'a Self>, Self: 'a {
-
-                let mut tmp = 0.0;
-                let mut count = 0.0;
-                for element in elements {
-                    tmp += element;
-                    count += 1.0;
-                }
-
-                if count > 0.0 {
-                    Some(tmp / count)
-                } else {
-                    None
-                }
-
-            }
-        }
-    }
-}
-
-impl_clusterable!(f32);
-impl_clusterable!(f64);
-

src/example.rs

@@ -5,7 +5,12 @@ use std::fmt::{Display, Formatter, Result};
 use std::fs::File;
 use rand::distributions::Range;
 use rand::distributions::normal::Normal;
-use generic_kmeans::{equal_kmeans, Clusterable};
+
+use generic_kmeans::kmeans;
+
+const CLUSTER_NUMBER: usize = 3;
+const VAR: f64 = 1.0;
+const MAX: f64 = 10.0;
 
 #[derive(PartialEq, Copy, Clone, Debug)]
 struct Vector2<T> {
@@ -28,38 +33,69 @@ impl<T: Display> Display for Vector2<T> {
     }
 }
 
-impl Clusterable for Vector2<f64> {
-    fn distance(&self, other: &Self) -> f64 {
-        (self.x - other.x) * (self.x - other.x) + (self.y - other.y) * (self.y - other.y)
+fn distance(v1: &Vector2<f64>, v2: &Vector2<f64>) -> f64 {
+    let dx = v2.x - v1.x;
+    let dy = v2.y - v1.y;
+    dx * dx + dy * dy
+}
+
+fn centroid(elements: &Vec<Vector2<f64>>) -> Vector2<f64> {
+    let mut v = Vector2::new(0.0, 0.0);
+
+    for element in elements {
+        v.x += element.x;
+        v.y += element.y;
     }
 
-    fn get_centroid<'a, I>(cluster: I) -> Option<Self>
-        where I: Iterator<Item = &'a Self>, Self: 'a {
+    v.x /= elements.len() as f64;
+    v.y /= elements.len() as f64;
 
-        let mut centroid = Vector2::new(0.0, 0.0);
-        let mut count = 0.0;
+    v
+}
 
-        for i in cluster {
-            centroid.x += i.x;
-            centroid.y += i.y;
-            count += 1.0;
-        }
+fn generate_points(centers: &mut Vec<(Vector2<f64>, Normal, Normal)>) -> Vec<Vector2<f64>> {
 
-        if count > 0.0 {
-            centroid.x /= count as f64;
-            centroid.y /= count as f64;
-            Some(centroid)
-        } else {
-            None
+    let mut rng = rand::thread_rng();
+    let mut output = vec![];
+
+    for &mut (_, x_rng, y_rng) in centers.iter_mut() {
+        for _ in 0..100 {
+            use rand::distributions::IndependentSample;
+            output.push(Vector2::new(x_rng.ind_sample(&mut rng), y_rng.ind_sample(&mut rng)));
         }
     }
+
+    output
+}
+
+fn generate_centers(number: usize) -> Vec<(Vector2<f64>, Normal, Normal)> {
+
+    let mut output = vec![];
+
+    let range = Range::new(0.0, MAX);
+    let mut rng = rand::thread_rng();
+
+    for _ in 0..number {
+
+        use rand::distributions::IndependentSample;
+        let center = Vector2::new(range.ind_sample(&mut rng), range.ind_sample(&mut rng));
+        output.push((center, Normal::new(center.x, VAR), Normal::new(center.y, VAR)));
+
+    }
+
+    output
 }
 
 fn main() {
 
-    const VAR: f64 = 1.0;
+    let mut centers = generate_centers(CLUSTER_NUMBER);
+    let elements = generate_points(&mut centers);
 
-    use rand::distributions::IndependentSample;
+    let initial = vec![
+        Vector2::new(0.0, 0.0),
+        Vector2::new(10.0, 0.0),
+        Vector2::new(0.0, 10.0),
+    ];
 
     let colors = vec![
         "blue",
@@ -67,39 +103,13 @@ fn main() {
         "green",
     ];
 
-    let range = Range::new(0.0, 10.0);
-    let mut rng = rand::thread_rng();
-    let cluster_number = 3;
+    let kmeans = kmeans(elements, initial, &distance, &centroid, 1000).unwrap();
 
-    let mut centers = vec![];
-    for _ in 0..cluster_number {
-        let center = Vector2::new(range.ind_sample(&mut rng), range.ind_sample(&mut rng));
-        centers.push((center, Normal::new(center.x, VAR), Normal::new(center.y, VAR)));
-    }
-
-    let mut elements = vec![];
-
-    for &mut (_, x_rng, y_rng) in centers.iter_mut() {
-
-        for _ in 0..100 {
-            elements.push(Vector2::new(x_rng.ind_sample(&mut rng), y_rng.ind_sample(&mut rng)));
-        }
-
-    }
-
-    let initialization = vec![
-        Vector2::new(0.0,0.0),
-        Vector2::new(10.0,0.0),
-        Vector2::new(0.0,10.0),
-    ];
-
-
-    let (clusters, nb_iterations) = equal_kmeans(initialization, elements, 100000).ok().unwrap();
-    let clusters = clusters.into_vec_vec();
+    println!("Converged in {} iterations.", kmeans.1);
 
     let mut output = File::create("plot/dat.dat").unwrap();
 
-    for (index, (cluster, color)) in clusters.iter().zip(colors.iter()).enumerate() {
+    for (index, (cluster, color)) in kmeans.0.iter().zip(colors.iter()).enumerate() {
         println!("Cluster {}: {} elements", index, cluster.len());
         for element in cluster {
             use std::io::Write;
@@ -107,11 +117,10 @@ fn main() {
         }
     }
 
-    println!("Finished in {} iterations", nb_iterations);
-
     let mut center_file = File::create("plot/centers.dat").unwrap();
     for (&(center, _, _), color) in centers.iter().zip(&colors) {
         use std::io::Write;
         writeln!(center_file, "{} {} {}", center.x, center.y, color).unwrap();
     }
+
 }

src/kmeans.rs

@@ -1,220 +0,0 @@
-use std;
-use std::collections::HashMap;
-use std::slice::Iter;
-use std::vec::IntoIter;
-use std::iter::Zip;
-use clusterable::Clusterable;
-
-pub struct Kmeans<T: Clusterable> {
-    centroids: Vec<T>,
-    elements: Vec<T>,
-    labels: Vec<usize>,
-    cluster_number: usize,
-}
-
-impl<T:Clusterable> Kmeans<T> {
-    pub fn new(centroids: Vec<T>, data: Vec<T>) -> Kmeans<T> {
-        let labels = Kmeans::build_labels(&centroids, &data);
-        let cluster_number = centroids.len();
-        Kmeans {
-            centroids: centroids,
-            elements: data,
-            labels: labels,
-            cluster_number: cluster_number
-        }
-    }
-
-    /// \returns True if converged
-    pub fn iterate(&mut self) -> bool {
-        // Update the centroids
-        let centroids = Kmeans::build_centroids(&self.elements, &self.labels, self.cluster_number);
-
-        if self.centroids == centroids {
-            true
-        } else {
-            self.centroids = centroids;
-            Kmeans::update_labels(&self.centroids, &self.elements, &mut self.labels);
-            false
-        }
-    }
-
-    pub fn build_labels(centroids: &Vec<T>, data: &Vec<T>) -> Vec<usize> {
-        debug_assert_ne!(0, centroids.len());
-
-        let mut output = vec![0; data.len()];
-        Kmeans::update_labels(centroids, data, &mut output);
-        output
-    }
-
-    pub fn update_labels(centroids: &Vec<T>, data: &Vec<T>, labels: &mut Vec<usize>) {
-        for (element, new_label) in data.iter().zip(labels.iter_mut()) {
-            *new_label = centroids
-                .iter()
-                .enumerate()
-                .min_by(|&(_, c1), &(_, c2)| {
-                    c1.distance(element).partial_cmp(&c2.distance(element)).unwrap()
-                }).unwrap().0;
-        }
-    }
-
-    pub fn build_centroids(data: &Vec<T>, labels: &Vec<usize>, cluster_number: usize) -> Vec<T> {
-
-        let mut centroids = vec![];
-        for label in 0..cluster_number {
-
-            let to_consider = data
-                .iter()
-                .enumerate()
-                .filter(|&(index, _)| labels[index] == label)
-                .map(|(_, element)| element);
-
-            if let Some(centroid) = T::get_centroid(to_consider) {
-                centroids.push(centroid);
-            }
-        }
-
-        centroids
-    }
-
-    pub fn iter(&self) -> Zip<Iter<T>, Iter<usize>> {
-        debug_assert_eq!(self.elements.len(), self.labels.len());
-        return self.elements.iter().zip(self.labels.iter());
-    }
-
-    pub fn into_iter(self) -> Zip<IntoIter<T>, IntoIter<usize>> {
-        debug_assert_eq!(self.elements.len(), self.labels.len());
-        return self.elements.into_iter().zip(self.labels.into_iter());
-    }
-
-    pub fn into_vec_vec(self) -> Vec<Vec<T>> {
-        let mut map = HashMap::new();
-
-        for (element, label) in self.into_iter() {
-            let mut entry = map.entry(label).or_insert(vec![]);
-            entry.push(element);
-        }
-
-        let mut output = vec![];
-
-        for (_, cluster) in map {
-            let mut vec = vec![];
-            for element in cluster {
-                vec.push(element);
-            }
-            output.push(vec);
-        }
-
-        output
-    }
-
-    pub fn to_vec_vec(&self) -> Vec<(Vec<T>, usize)> {
-        let mut map = HashMap::new();
-
-        for (element, label) in self.iter() {
-            let mut entry = map.entry(*label).or_insert(vec![]);
-            entry.push(element.clone());
-        }
-
-        let mut output = vec![];
-
-        for (label, cluster) in map {
-            let mut vec = vec![];
-            for element in cluster {
-                vec.push(element);
-            }
-            output.push((vec, label));
-        }
-
-        output
-    }
-
-}
-
-pub enum Error {
-    IterationsLimitExceeded,
-}
-
-pub fn kmeans<T: Clusterable>(centroids: Vec<T>, data: Vec<T>, max_iterations: usize)
-    -> Result<(Kmeans<T>, usize), Error> {
-
-    let mut kmeans = Kmeans::new(centroids, data);
-
-    for nb_iterations in 0..max_iterations {
-
-        let stable = kmeans.iterate();
-
-        if stable {
-            return Ok((kmeans, nb_iterations));
-        }
-
-    }
-
-    Err(Error::IterationsLimitExceeded)
-}
-
-pub fn equal_kmeans<T: Clusterable>(centroids: Vec<T>, data: Vec<T>, max_iterations: usize)
-    -> Result<(Kmeans<T>, usize), Error> {
-
-    let number_of_elements = data.len();
-    let (kmeans, nb_iterations) = kmeans(centroids, data, max_iterations)?;
-    let mut clusters = kmeans.to_vec_vec();
-
-    for &mut (ref mut cluster, ref mut label) in &mut clusters {
-        cluster.sort_by(|ref e1, ref e2| {
-            *&e1.distance(&kmeans.centroids[*label])
-                .partial_cmp(&e2.distance(&kmeans.centroids[*label])).unwrap()
-        });
-    }
-
-    let max = (number_of_elements as f64 / clusters.len() as f64).ceil() as usize;
-
-    let mut to_replace = vec![];
-    for &mut (ref mut cluster, _) in &mut clusters {
-        while cluster.len() > max {
-            to_replace.push(cluster.pop().unwrap());
-        }
-    }
-
-    for element in to_replace {
-        // Find the best non full cluster to place it in
-        let mut best_distance = std::f64::MAX;
-        let mut best_index = 0;
-
-        for (index, centroid) in kmeans.centroids.iter().enumerate() {
-
-            let new_distance = element.distance(centroid);
-            if new_distance < best_distance && clusters[index].0.len() < max {
-                best_distance = new_distance;
-                best_index = index;
-            }
-
-        }
-
-        clusters[best_index].0.push(element);
-    }
-
-    let mut elements = vec![];
-    let mut labels = vec![];
-    let mut centroids = vec![];
-
-    for (index, &(ref cluster, _)) in clusters.iter().enumerate() {
-        for element in cluster {
-            elements.push(element.clone());
-            labels.push(index);
-        }
-        centroids.push(Clusterable::get_centroid(cluster.iter()).unwrap());
-    }
-
-    let len = centroids.len();
-
-    // Build a new k-means from clusters
-    let new_kmeans = Kmeans {
-        centroids: centroids,
-        elements: elements,
-        labels: labels,
-        cluster_number: len,
-    };
-
-    Ok((new_kmeans, nb_iterations))
-
-}

src/lib.rs

@@ -1,10 +1,132 @@
-pub mod kmeans;
-pub mod clusterable;
+use std::collections::HashMap;
 
-pub use kmeans::Kmeans;
-pub use clusterable::Clusterable;
-pub use kmeans::Error;
-pub use kmeans::kmeans;
-pub use kmeans::equal_kmeans;
+#[derive(Debug)]
+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> {
+    data: KmeansData<T>,
+    centroids: Vec<T>,
+    distance: &'a Distance<T>,
+    centroid: &'a Centroid<T>,
+}
+
+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> {
+
+        let len = data.len();
+        Kmeans {
+            data: KmeansData { elements: data, labels: vec![0; len] },
+            centroids: centroids,
+            distance: distance,
+            centroid: centroid,
+        }
+
+    }
+
+    /// True if converged
+    pub fn iterate(&mut self) -> bool {
+        // Update the labels
+        self.update_labels();
+
+        // Update the centroids
+        let new_centroids = self.compute_centroids();
+
+        let ret = new_centroids == self.centroids;
+        self.centroids = new_centroids;
+        ret
+    }
+
+    fn update_labels(&mut self) {
+
+        let iterator = self.data.elements.iter().zip(self.data.labels.iter_mut());
+
+        for (ref element, ref mut label) in iterator {
+
+            let mut best_distance = std::f64::MAX;
+            let mut best_label = 0;
+
+            for (index, centroid) in self.centroids.iter().enumerate() {
+
+                let new_distance = (self.distance)(*element, centroid);
+                if new_distance < best_distance {
+                    best_distance = new_distance;
+                    best_label = index;
+                }
+            }
+
+            **label = best_label;
+        }
+
+    }
+
+    fn compute_centroids(&self) -> Vec<T> {
+        let mut centroids_map = HashMap::new();
+
+        for (element, label) in self.data.elements.iter().zip(self.data.labels.iter()) {
+            let mut centroid = centroids_map.entry(label).or_insert(vec![]);
+            centroid.push(element.clone());
+        }
+
+        let mut new_centroids = vec![];
+
+        for (_, value) in centroids_map {
+            new_centroids.push((self.centroid)(&value));
+        }
+
+        new_centroids
+    }
+}
+
+pub fn kmeans<'a, T: 'a + Clone + PartialEq>(
+    elements: Vec<T>,
+    initial: Vec<T>,
+    distance: &'a Distance<T>,
+    centroid: &'a Centroid<T>,
+    max_iteration: usize,
+) -> Result<(Vec<Vec<T>>,usize), Error> {
+
+    let mut clusters = Kmeans::new(elements, initial, distance, centroid);
+
+    let mut counter = 0;
+
+    let iterations = loop {
+
+        counter += 1;
+
+        if clusters.iterate() || counter > max_iteration {
+            break counter;
+        }
+
+    };
+
+    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))
+}