Update, clean, test, example

Cargo.toml

@@ -1,6 +1,11 @@
 [package]
-name = "kmeans"
+name = "generic_kmeans"
 version = "0.1.0"
 authors = ["Thomas Forgione <thomas@tforgione.fr>"]
 
 [dependencies]
+
+[[bin]]
+name = "example"
+path = "src/example.rs"
+

src/cluster.rs

@@ -0,0 +1,38 @@
+pub type Cluster<T> = Vec<T>;
+
+pub trait Clusterable where Self: Sized + Clone + PartialEq {
+    fn distance(&self, rhs: &Self) -> f64;
+    fn get_centroid(elements: &Vec<Self>) -> Option<Self>;
+}
+
+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(elements: &Vec<Self>) -> Option<Self> {
+
+                if elements.len() == 0 {
+                    return None;
+                }
+
+                let mut tmp = 0.0 as Self;
+                for element in elements {
+                    tmp += *element as Self;
+                }
+                Some(tmp / elements.len() as Self)
+
+            }
+        }
+    }
+}
+
+impl_clusterable!(f32);
+impl_clusterable!(f64);
+

src/example.rs

@@ -0,0 +1,82 @@
+extern crate generic_kmeans;
+
+use std::fmt::{Display, Formatter, Result};
+
+use generic_kmeans::{kmeans, Clusterable};
+
+#[derive(PartialEq, Clone)]
+struct Vector2<T> {
+    pub x: T,
+    pub y: T,
+}
+
+impl<T> Vector2<T> {
+    pub fn new(x: T, y: T) -> Vector2<T> {
+        Vector2 {
+            x: x,
+            y: y,
+        }
+    }
+}
+
+impl<T: Display> Display for Vector2<T> {
+    fn fmt(&self, formatter: &mut Formatter) -> Result {
+        write!(formatter, "({}, {})", self.x, self.y)
+    }
+}
+
+impl Clusterable for Vector2<f64> {
+    fn distance(&self, other: &Self) -> f64 {
+        (self.x - other.x) * (self.x - other.y) + (self.y - other.y) * (self.y - other.y)
+    }
+
+    fn get_centroid(cluster: &Vec<Vector2<f64>>) -> Option<Vector2<f64>> {
+
+        let len = cluster.len();
+
+        if len == 0 {
+            return None;
+        }
+
+        let mut centroid = Vector2::new(0.0, 0.0);
+
+        for i in cluster {
+            centroid.x += i.x;
+            centroid.y += i.y;
+        }
+
+        centroid.x /= len as f64;
+        centroid.y /= len as f64;
+
+        Some(centroid)
+    }
+}
+
+fn main() {
+
+    let elements = vec![
+        Vector2::new(8.0, 3.0),
+        Vector2::new(9.0, 3.0),
+        Vector2::new(9.0, 2.0),
+        Vector2::new(1.0, 8.0),
+        Vector2::new(2.0, 9.0),
+        Vector2::new(3.0, 8.0),
+    ];
+
+    let initial = vec![
+        Vector2::new(1.0, 10.0),
+        Vector2::new(10.0, 0.0),
+    ];
+
+    let (clusters, nb_iterations) = kmeans(initial, elements, 1000).ok().unwrap();
+
+    println!("{}", nb_iterations);
+
+    for (index, cluster) in clusters.iter().enumerate() {
+        println!("CLUSTER {}", index);
+
+        for element in cluster {
+            println!("\t{}", element);
+        }
+    }
+}

src/kmeans.rs

@@ -0,0 +1,48 @@
+use std;
+
+use kmeansdata::KmeansData;
+use cluster::{Cluster, Clusterable};
+
+pub struct Kmeans<T: Clusterable> {
+    pub centroids: Vec<T>,
+    pub data: KmeansData<T>,
+}
+
+impl<T:Clusterable> Kmeans<T> {
+    pub fn new(centroids: Vec<T>, data: Vec<Vec<T>>) -> Kmeans<T> {
+        Kmeans {
+            centroids: centroids,
+            data: KmeansData::from_clusters(data),
+        }
+    }
+
+    pub fn guess_centroids(data: Vec<Vec<T>>) -> Kmeans<T> {
+        let mut centroids = vec![];
+        for cluster in &data {
+            if let Some(centroid) = T::get_centroid(cluster) {
+            centroids.push(centroid);
+            }
+        }
+
+        Kmeans::new(centroids, data)
+
+    }
+
+    fn from_data(centroids: Vec<T>, data: KmeansData<T>) -> Kmeans<T> {
+        Kmeans {
+            centroids: centroids,
+            data: data,
+        }
+    }
+
+    pub fn next_iteration(self) -> (Kmeans<T>, bool) {
+        let (new_centroids, data) = self.data.iterate(&self.centroids);
+        let stable = new_centroids == self.centroids;
+        (Kmeans::from_data(new_centroids, data), stable)
+    }
+
+    pub fn iter(&self) -> std::slice::Iter<Cluster<T>> {
+        self.data.clusters()
+    }
+}
+

src/kmeansdata.rs

@@ -0,0 +1,168 @@
+use std;
+use std::marker::PhantomData;
+
+use cluster::{Clusterable, Cluster};
+
+
+pub struct KmeansData<T: Clusterable> {
+    clusters: Vec<Vec<T>>,
+}
+
+impl<T: Clusterable> KmeansData<T> {
+
+    pub fn from_clusters(data: Vec<Vec<T>>) -> KmeansData<T> {
+        KmeansData {
+            clusters: data,
+        }
+    }
+
+    pub fn new(centroids: &Vec<T>, data: KmeansDataIntoIter<T>) -> KmeansData<T> {
+
+        let mut clusters = vec![];
+
+        for _ in centroids {
+            clusters.push(Cluster::new());
+        }
+
+        let mut new_kmeans: KmeansData<T> = KmeansData {
+            clusters: clusters,
+        };
+
+        for element in data {
+
+            // Compute the distance
+            let mut distance = std::f64::MAX;
+            let mut index = 0;
+
+            for (new_index, centroid) in centroids.iter().enumerate() {
+                let new_distance = element.distance(centroid);
+
+                if new_distance < distance {
+                    distance = new_distance;
+                    index = new_index;
+                }
+            }
+
+            // Add element to the new kmeans
+            new_kmeans.clusters[index].push(element)
+
+        }
+
+        new_kmeans
+    }
+
+    pub fn add_cluster(&mut self) {
+        self.clusters.push(Cluster::new());
+    }
+
+    pub fn iterate(self, centroids: &Vec<T>) -> (Vec<T>, KmeansData<T>) {
+        // Compute the result with the given centroids
+        let result = KmeansData::new(&centroids, self.into_iter());
+
+        // Compute the new centroids
+        let mut new_centroids = vec![];
+
+        for cluster in &result.clusters {
+
+            if let Some(centroid) = T::get_centroid(cluster) {
+                new_centroids.push(centroid);
+            }
+
+        }
+
+        (new_centroids, result)
+    }
+
+    pub fn iter(&self) -> KmeansDataIter<T> {
+        KmeansDataIter {
+            global_iter: self.clusters.iter(),
+            local_iter: None,
+            _phantom: PhantomData,
+        }
+    }
+
+    pub fn clusters(&self) -> std::slice::Iter<Cluster<T>> {
+        self.clusters.iter()
+    }
+
+}
+
+pub struct KmeansDataIter<'a, T> where T:'a, T: Clusterable {
+    global_iter: std::slice::Iter<'a, std::vec::Vec<T>>,
+    local_iter:  Option<std::slice::Iter<'a, T>>,
+    _phantom: PhantomData<T>,
+}
+
+impl<'a, T: 'a + Clusterable> Iterator for KmeansDataIter<'a, T> {
+    type Item = &'a T;
+    fn next(&mut self) -> Option<&'a T> {
+        if let Some(ref mut local_iter) = self.local_iter {
+            match local_iter.next() {
+                Some(t) => Some(t),
+                None => {
+                    if let Some(next) = self.global_iter.next() {
+                        *local_iter = next.iter();
+                        match local_iter.next() {
+                            Some(t) => Some(t),
+                            None => None,
+                        }
+                    } else {
+                        None
+                    }
+                }
+            }
+        } else {
+            self.local_iter = match self.global_iter.next() {
+                None => None,
+                Some(t) => Some(t.iter()),
+            };
+            self.next()
+        }
+    }
+}
+
+pub struct KmeansDataIntoIter<T> where T: Clusterable {
+    global_iter: std::vec::IntoIter<std::vec::Vec<T>>,
+    local_iter:  Option<std::vec::IntoIter<T>>,
+    _phantom: PhantomData<T>,
+}
+
+impl<T: Clusterable> Iterator for KmeansDataIntoIter<T> {
+    type Item = T;
+    fn next(&mut self) -> Option<T> {
+        if let Some(ref mut local_iter) = self.local_iter {
+            match local_iter.next() {
+                Some(t) => Some(t),
+                None => {
+                    if let Some(next) = self.global_iter.next() {
+                        *local_iter = next.into_iter();
+                        match local_iter.next() {
+                            Some(t) => Some(t),
+                            None => None,
+                        }
+                    } else {
+                        None
+                    }
+                }
+            }
+        } else {
+            self.local_iter = match self.global_iter.next() {
+                None => None,
+                Some(t) => Some(t.into_iter()),
+            };
+            self.next()
+        }
+    }
+}
+
+impl<T: Clusterable> IntoIterator for KmeansData<T> {
+    type Item = T;
+    type IntoIter = KmeansDataIntoIter<T>;
+    fn into_iter(self) -> Self::IntoIter {
+        Self::IntoIter {
+            global_iter: self.clusters.into_iter(),
+            local_iter: None,
+            _phantom: PhantomData,
+        }
+    }
+}

src/lib.rs

@@ -1,190 +1,31 @@
+pub mod kmeans;
+pub mod kmeansdata;
+pub mod cluster;
 pub mod test;
 
-use std::marker::PhantomData;
+pub use kmeans::Kmeans;
+pub use kmeansdata::KmeansData;
+pub use cluster::{Cluster, Clusterable};
 
-pub trait Clusterable where Self: Sized {
-    fn distance(&self, rhs: &Self) -> f64;
-    fn get_centroid(elements: &Vec<Self>) -> Option<Self>;
+pub enum Error {
+    IterationsLimitExceeded,
 }
 
-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
-                }
-            }
+pub fn kmeans<T: Clusterable>(centroids: Vec<T>, data: Vec<T>, max_iterations: usize)
+    -> Result<(Kmeans<T>, usize), Error> {
 
-            fn get_centroid(elements: &Vec<Self>) -> Option<Self> {
+    let mut kmeans = Kmeans::new(centroids, vec![data]);
 
-                if elements.len() == 0 {
-                    return None;
-                }
+    for nb_iterations in 0..max_iterations {
 
-                let mut tmp = 0.0 as Self;
-                for element in elements {
-                    tmp += *element as Self;
-                }
-                Some(tmp / elements.len() as Self)
+        let (new_kmeans, stable) = kmeans.next_iteration();
+        kmeans = new_kmeans;
 
-            }
-        }
-    }
-}
-
-impl_clusterable!(f32);
-impl_clusterable!(f64);
-
-
-pub type Cluster<T> = Vec<T>;
-
-pub struct Kmeans<T: Clusterable> {
-    clusters: Vec<Vec<T>>,
-}
-
-impl<T: Clusterable> Kmeans<T> {
-
-    pub fn new(centroids: Vec<T>, data: KmeansIntoIter<T>) -> Kmeans<T> {
-
-        let mut clusters = vec![];
-
-        for _ in &centroids {
-            clusters.push(Cluster::new());
+        if stable {
+            return Ok((kmeans, nb_iterations));
         }
 
-        let mut new_kmeans: Kmeans<T> = Kmeans {
-            clusters: clusters,
-        };
-
-        for element in data {
-
-            // Compute the distance
-            let mut distance = std::f64::MAX;
-            let mut index = 0;
-
-            for (new_index, centroid) in centroids.iter().enumerate() {
-                let new_distance = element.distance(centroid);
-
-                if new_distance < distance {
-                    distance = new_distance;
-                    index = new_index;
-                }
-            }
-
-            // Add element to the new kmeans
-            new_kmeans.clusters[index].push(element)
-
-        }
-
-        new_kmeans
-    }
-
-    pub fn add_cluster(&mut self) {
-        self.clusters.push(Cluster::new());
-    }
-
-    pub fn iterate(self) -> Kmeans<T> {
-        let mut centroids = vec![];
-
-        // Compute the centroids
-        for cluster in &self.clusters {
-
-            if let Some(centroid) = T::get_centroid(cluster) {
-                centroids.push(centroid);
-            }
-
-        }
-
-        Kmeans::new(centroids, self.into_iter())
-    }
-
-    pub fn iter(&self) -> std::slice::Iter<Cluster<T>> {
-        self.clusters.iter()
     }
 
-}
-
-pub struct KmeansIter<'a, T> where T:'a, T: Clusterable {
-    global_iter: std::slice::Iter<'a, std::vec::Vec<T>>,
-    local_iter:  Option<std::slice::Iter<'a, T>>,
-    _phantom: PhantomData<T>,
-}
-
-impl<'a, T: 'a + Clusterable> Iterator for KmeansIter<'a, T> {
-    type Item = &'a T;
-    fn next(&mut self) -> Option<&'a T> {
-        if let Some(ref mut local_iter) = self.local_iter {
-            match local_iter.next() {
-                Some(t) => Some(t),
-                None => {
-                    if let Some(next) = self.global_iter.next() {
-                        *local_iter = next.iter();
-                        match local_iter.next() {
-                            Some(t) => Some(t),
-                            None => None,
-                        }
-                    } else {
-                        None
-                    }
-                }
-            }
-        } else {
-            self.local_iter = match self.global_iter.next() {
-                None => None,
-                Some(t) => Some(t.iter()),
-            };
-            self.next()
-        }
-    }
-}
-
-pub struct KmeansIntoIter<T> where T: Clusterable {
-    global_iter: std::vec::IntoIter<std::vec::Vec<T>>,
-    local_iter:  Option<std::vec::IntoIter<T>>,
-    _phantom: PhantomData<T>,
-}
-
-impl<T: Clusterable> Iterator for KmeansIntoIter<T> {
-    type Item = T;
-    fn next(&mut self) -> Option<T> {
-        if let Some(ref mut local_iter) = self.local_iter {
-            match local_iter.next() {
-                Some(t) => Some(t),
-                None => {
-                    if let Some(next) = self.global_iter.next() {
-                        *local_iter = next.into_iter();
-                        match local_iter.next() {
-                            Some(t) => Some(t),
-                            None => None,
-                        }
-                    } else {
-                        None
-                    }
-                }
-            }
-        } else {
-            self.local_iter = match self.global_iter.next() {
-                None => None,
-                Some(t) => Some(t.into_iter()),
-            };
-            self.next()
-        }
-    }
-}
-
-
-
-impl<T: Clusterable> IntoIterator for Kmeans<T> {
-    type Item = T;
-    type IntoIter = KmeansIntoIter<T>;
-    fn into_iter(self) -> Self::IntoIter {
-        Self::IntoIter {
-            global_iter: self.clusters.into_iter(),
-            local_iter: None,
-            _phantom: PhantomData,
-        }
-    }
+    Err(Error::IterationsLimitExceeded)
 }

src/test.rs

@@ -2,14 +2,12 @@
 mod test {
     #[test]
     fn iterators() {
-        use Kmeans;
+        use kmeansdata::KmeansData;
         let data = vec![4.0, 5.0, 11.0, 12.0, 13.0];
-        let kmeans = Kmeans {
-            clusters: vec![
-                vec![4.0, 5.0],
-                vec![11.0, 12.0, 13.0],
-            ],
-        };
+        let kmeans = KmeansData::from_clusters(vec![
+            vec![4.0, 5.0],
+            vec![11.0, 12.0, 13.0],
+        ]);
 
         for (val1, val2) in kmeans.into_iter().zip(data) {
             assert_eq!(val1, val2);
@@ -18,19 +16,29 @@ mod test {
 
     #[test]
     fn iterate() {
-        use Kmeans;
-        let kmeans = Kmeans {
-            clusters: vec![
-                vec![4.0, 5.0, 11.0, 12.0],
-                vec![13.0],
-            ],
-        };
-        let kmeans = kmeans.iterate();
-        let kmeans = kmeans.iterate();
-        let kmeans = kmeans.iterate();
-        let kmeans = kmeans.iterate();
+        use kmeans::Kmeans;
 
-        assert_eq!(kmeans.clusters[0], vec![4.0, 5.0]);
-        assert_eq!(kmeans.clusters[1], vec![11.0, 12.0, 13.0]);
+        let data = vec![
+            vec![4.0, 5.0, 11.0, 12.0],
+            vec![13.0],
+        ];
+
+        let solution = vec![
+            vec![4.0, 5.0],
+            vec![11.0, 12.0, 13.0],
+        ];
+
+        let mut kmeans = Kmeans::guess_centroids(data.clone());
+
+        for _ in 0..4 {
+            let (new_kmeans, stable) = kmeans.next_iteration();
+            kmeans = new_kmeans;
+        }
+
+        for (k1, k2) in kmeans.iter().zip(solution) {
+            for (i, j) in k1.iter().zip(&k2) {
+                assert_eq!(i, j);
+            }
+        }
     }
 }