Struct KMeans 

pub struct KMeans { /* private fields */ }

A clustering-problem where each center can be any point in the metric space.

The metric space is the same space the points live in.

The cost of a cluster, given a center, is the sum of the squared Euclidean distances between the center and all points in the cluster. The center is automatically calculated to minimise the cost, which turns out to simply be the average of all point-positions in the cluster.

See the wikipedia-article on k-means-clustering for more information.

Implementations

impl KMeans

pub fn new(points: &[Point]) -> Result<Self, Error>

Construct a new k-means clustering instance from a slice of points.

Examples

use ndarray::array;
use exact_clustering::KMeans;

KMeans::new(&[array![0.0, 0.0], array![1.0, 2.0]]).unwrap();

Trait Implementations

impl Clone for KMeans

fn clone(&self) -> KMeans

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Cost for KMeans

fn num_points(&self) -> usize

Get the number of points that must be clustered.

fn cost(&mut self, cluster: Cluster) -> f64

Get the cost of a cluster.

fn approximate_clusterings(&mut self) -> Vec<(f64, Clustering)>

Quickly calculate a not-necessarily-optimal clustering.

fn total_cost(&mut self, clustering: &Clustering) -> f64

Get the total cost of a clustering.

fn optimal_clusterings(&mut self) -> Vec<(f64, Clustering)>

Calculate an optimal k-clustering for every 0 ≤ k ≤ self.num_points().

fn price_of_hierarchy(&mut self) -> (f64, Vec<Clustering>)

Calculate the price-of-hierarchy of the clustering-problem, together with an optimal hierarchy.

fn greedy_hierarchy(&mut self) -> Vec<(f64, Clustering)>

Calculate a greedy hierarchical clustering.

fn price_of_greedy(&mut self) -> (f64, Vec<Clustering>)

Calculate the cost-ratio of a greedy hierarchical clustering.

impl Debug for KMeans

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.