k_means

Aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean.

Functions

classifier

</>

pub fn classifier(data: List(List(Float)), k: Int) -> fn(
  List(Float),
) -> Int

Returns the function that classifies a point by identifying the index of the cluster it belongs to.

Points = [
  [0.0, 0.0],
  [4.0, 4.0],
  [9.0, 9.0],
  [4.3, 4.3],
  [9.9, 9.9],
  [4.4, 4.4],
  [0.1, 0.1],
],
K = 3,
F = emel@ml@k_means:clusters(Points, K),
F([4.7, 4.7]).
% 1

clusters

</>

pub fn clusters(points: List(List(Float)), k: Int) -> List(
  List(List(Float)),
)

The points get partitioned into k clusters in which each point belongs to the cluster with the nearest mean.

Points = [
  [0.0, 0.0],
  [4.0, 4.0],
  [9.0, 9.0],
  [4.3, 4.3],
  [9.9, 9.9],
  [4.4, 4.4],
  [0.1, 0.1],
],
K = 3,
emel@ml@k_means:clusters(Points, K).
% [
%   [[0.1, 0.1], [0.0, 0.0]],
%   [[4.0, 4.0], [4.3, 4.3], [4.4, 4.4]],
%   [[9.9, 9.9], [9.0, 9.0]],
% ]