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use {Euclid, Point};
use point::Euclidean;
pub struct Kmeans<T> {
assignments: Vec<usize>,
centres: Vec<Euclid<T>>,
iterations: usize,
converged: bool,
}
impl<T> Kmeans<T>
where Euclid<T>: Point + Euclidean + Clone
{
pub fn new(data: &[Euclid<T>], k: usize) -> Kmeans<T> {
KmeansBuilder::new().kmeans(data, k)
}
pub fn clusters(&self) -> Vec<(Euclid<T>, Vec<usize>)> {
let mut ret = self.centres.iter().cloned().map(|c| (c, vec![])).collect::<Vec<_>>();
for (idx, &assign) in self.assignments.iter().enumerate() {
ret[assign].1.push(idx);
}
ret
}
pub fn converged(&self) -> Result<usize, usize> {
if self.converged {
Ok(self.iterations)
} else {
Err(self.iterations)
}
}
}
const DEFAULT_MAX_ITER: usize = 100;
const DEFAULT_TOL: f64 = 1e-6;
pub struct KmeansBuilder {
tol: f64,
max_iter: usize,
}
impl KmeansBuilder {
pub fn new() -> KmeansBuilder {
KmeansBuilder {
tol: DEFAULT_TOL,
max_iter: DEFAULT_MAX_ITER,
}
}
pub fn tolerance(self, tol: f64) -> KmeansBuilder {
KmeansBuilder { tol: tol, .. self }
}
pub fn max_iter(self, max_iter: usize) -> KmeansBuilder {
KmeansBuilder { max_iter: max_iter, .. self }
}
pub fn kmeans<T>(self, data: &[Euclid<T>], k: usize) -> Kmeans<T>
where Euclid<T>: Point + Euclidean + Clone
{
assert!(2 <= k && k < data.len());
let n = data.len();
let mut assignments = vec![!0; n];
let mut costs = vec![0.0; n];
let mut centres = data.iter().take(k).cloned().collect::<Vec<_>>();
let mut counts = vec![0; k];
update_assignments(data, &mut assignments, &mut counts, &mut costs, ¢res);
let mut objective = costs.iter().fold(0.0, |a, b| a + *b);
let mut converged = false;
let mut iter = 0;
while iter < self.max_iter {
update_centres(data, &assignments, &counts, &mut centres);
update_assignments(data, &mut assignments, &mut counts, &mut costs, ¢res);
let new_objective = costs.iter().fold(0.0, |a, b| a + *b);
if (new_objective - objective).abs() < self.tol {
converged = true;
break
}
objective = new_objective;
iter += 1
}
Kmeans {
assignments: assignments,
centres: centres,
iterations: iter,
converged: converged,
}
}
}
fn update_assignments<T>(data: &[Euclid<T>],
assignments: &mut [usize], counts: &mut [usize], costs: &mut [f64],
centres: &[Euclid<T>])
where Euclid<T>: Point + Euclidean + Clone
{
use std::f64::INFINITY as INF;
for place in counts.iter_mut() { *place = 0 }
for ((point, assign), cost) in data.iter().zip(assignments.iter_mut()).zip(costs.iter_mut()) {
let mut min_dist = INF;
let mut index = 0;
for (i, c) in centres.iter().enumerate() {
let dist = point.dist2(c);
if dist < min_dist {
min_dist = dist;
index = i;
}
}
*cost = min_dist;
*assign = index;
counts[index] += 1;
}
}
fn update_centres<T>(data: &[Euclid<T>],
assignments: &[usize], counts: &[usize],
centres: &mut [Euclid<T>])
where Euclid<T>: Point + Euclidean + Clone
{
for place in centres.iter_mut() { *place = <Euclid<T>>::zero() }
for (point, assign) in data.iter().zip(assignments.iter()) {
centres[*assign].add(point)
}
for (place, scale) in centres.iter_mut().zip(counts.iter()) {
place.scale(1.0 / *scale as f64)
}
}
#[cfg(test)]
mod tests {
use super::*;
use Euclid;
#[test]
fn smoke() {
let points = [Euclid([0.0, 0.0]),
Euclid([1.0, 0.5]),
Euclid([0.2, 0.2]),
Euclid([0.3, 0.8]),
Euclid([0.0, 1.0]),
];
let res = Kmeans::new(&points, 3);
let mut clusters = res.clusters();
for &mut (_, ref mut v) in &mut clusters {
v.sort()
}
clusters.sort_by(|a, b| a.1.cmp(&b.1));
assert_eq!(clusters,
[(Euclid([0.1, 0.1]), vec![0, 2]),
(Euclid([1.0, 0.5]), vec![1]),
(Euclid([0.15, 0.9]), vec![3, 4])]);
assert_eq!(res.converged(), Ok(2));
}
}