commit c3aff5c77d9718d0ef1100d3483192ed481ba86d
Author: Thomas Forgione <thomas@tforgione.fr>
Date:   Fri Feb 16 15:34:13 2018 +0100

    First try

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..143b1ca
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+
+/target/
+**/*.rs.bk
+Cargo.lock
diff --git a/Cargo.toml b/Cargo.toml
new file mode 100644
index 0000000..f883c0a
--- /dev/null
+++ b/Cargo.toml
@@ -0,0 +1,6 @@
+[package]
+name = "kmeans"
+version = "0.1.0"
+authors = ["Thomas Forgione <thomas@tforgione.fr>"]
+
+[dependencies]
diff --git a/src/lib.rs b/src/lib.rs
new file mode 100644
index 0000000..26860e9
--- /dev/null
+++ b/src/lib.rs
@@ -0,0 +1,190 @@
+pub mod test;
+
+use std::marker::PhantomData;
+
+pub trait Clusterable where Self: Sized {
+    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);
+
+
+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());
+        }
+
+        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,
+        }
+    }
+}
diff --git a/src/test.rs b/src/test.rs
new file mode 100644
index 0000000..fdb2e52
--- /dev/null
+++ b/src/test.rs
@@ -0,0 +1,36 @@
+#[cfg(test)]
+mod test {
+    #[test]
+    fn iterators() {
+        use Kmeans;
+        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],
+            ],
+        };
+
+        for (val1, val2) in kmeans.into_iter().zip(data) {
+            assert_eq!(val1, val2);
+        }
+    }
+
+    #[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();
+
+        assert_eq!(kmeans.clusters[0], vec![4.0, 5.0]);
+        assert_eq!(kmeans.clusters[1], vec![11.0, 12.0, 13.0]);
+    }
+}