Struct KMedian 

pub struct KMedian { /* private fields */ }

A clustering-problem where each center must be one of the points that are to be clustered.

The cost is supplied using a distance-function between points. The cost of a cluster, given a center, is the sum of the distances between the center and all points in the cluster. The cost of a cluster will always be calculated by choosing the center yielding the smallest cost.

Implementations

impl KMedian

pub fn l2_squared(points: &[Point]) -> Result<Self, Error>

Create a k-median clustering instance using the squared L2-norm.

Examples

use ndarray::array;
use exact_clustering::KMedian;

KMedian::l2_squared(&[array![0.0, 0.0], array![1.0, 2.0]]).unwrap();

pub fn l2(points: &[Point]) -> Result<Self, Error>

Create a k-median clustering instance using the L2-norm.

Examples

use ndarray::array;
use exact_clustering::KMedian;

KMedian::l2(&[array![0.0, 0.0], array![1.0, 2.0]]).unwrap();

TODO: This is uncovered by tests.

pub fn l1(points: &[Point]) -> Result<Self, Error>

Create a k-median clustering instance using the L1-norm.

Examples

use ndarray::array;
use exact_clustering::KMedian;

KMedian::l1(&[array![0.0, 0.0], array![1.0, 2.0]]).unwrap();

pub fn weighted_l2_squared( weighted_points: &[WeightedPoint], ) -> Result<Self, Error>

Create a k-median clustering instance using the squared L2-norm.

Use KMedian::l2_squared instead if all your points have the same weight.

The distance between a weighted point (w, p) and the center (v, c) is the squared euclidean-distance between c and p, multiplied by w. For instance, the center of the cluster {(1, [0,0]), (2, [3,0])} will be (2, [3,0]), because the cost of choosing that center is 9, whereas the cost of choosing (1, [0,0]) as a center is 18.

Examples

use ndarray::array;
use exact_clustering::KMedian;

KMedian::weighted_l2_squared(&[(1.0, array![0.0, 0.0]), (2.0, array![1.0, 2.0])]).unwrap();

pub fn weighted_l2(weighted_points: &[WeightedPoint]) -> Result<Self, Error>

Create a k-median clustering instance using the L2-norm.

Use KMedian::l2 instead if all your points have the same weight.

The distance between a weighted point (w, p) and the center (v, c) is the euclidean-distance between c and p, multiplied by w.

Examples

use ndarray::array;
use exact_clustering::KMedian;

KMedian::weighted_l2(&[(1.0, array![0.0, 0.0]), (2.0, array![1.0, 2.0])]).unwrap();

TODO: This is uncovered by tests.

pub fn weighted_l1(weighted_points: &[WeightedPoint]) -> Result<Self, Error>

Create a k-median clustering instance using the L1-norm.

Use KMedian::l1 instead if all your points have the same weight.

The distance between a weighted point (w, p) and the center (v, c) is the taxicab-distance between c and p, multiplied by w. For instance, the center of the cluster {(1, [0,0]), (2, [3,0])} will be (2, [3,0]), because the cost of choosing that center is 3, whereas the cost of choosing (1, [0,0]) as a center is 6.

Examples

use ndarray::array;
use exact_clustering::KMedian;

KMedian::weighted_l1(&[(1.0, array![0.0, 0.0]), (2.0, array![1.0, 2.0])]).unwrap();

Trait Implementations

impl Clone for KMedian

fn clone(&self) -> KMedian

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Cost for KMedian

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

Get the total cost of a clustering.

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

Quickly calculate a not-necessarily-optimal 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 KMedian

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

Formats the value using the given formatter.