Struct KmeansSpec 

pub struct KmeansSpec {
    pub n: usize,
    pub k: usize,
    pub f: usize,
    pub iters: usize,
    pub points: Symbol,
    pub centroids: Symbol,
    pub distance: ZipTerm,
    pub ties: Ties,
}

A recognized k-means clustering (Lloyd’s algorithm): n points of f integer features, row-major in points, around k centroids, row-major in centroids, which start as the first k points. Each of iters epochs assigns every point to its nearest centroid by the distance Σ_d term(point[d], centroid[d]) (ties as ties says), sums each centroid’s points and counts them, then sets every centroid to the integer mean of its points — 0 for a centroid with none.

Fields

n: usize

k: usize

f: usize

iters: usize

points: Symbol

centroids: Symbol

distance: ZipTerm

The distance a feature adds: ZipTerm::Read(0) the point’s, Read(1) the centroid’s.

ties: Ties

Implementations

impl KmeansSpec

pub fn run(&self, points: &[i64], ints: (i128, i128)) -> Result<Vec<i64>, Stop>

The final centroids (k × f, row-major) for the points points (n × f, row-major), as the program computes them; refused (with why) when a distance, a sum of distance terms in any order, or a feature sum over the points in any order leaves ints.

Trait Implementations

impl Clone for KmeansSpec

fn clone(&self) -> KmeansSpec

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for KmeansSpec

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

Formats the value using the given formatter.

impl PartialEq for KmeansSpec

fn eq(&self, other: &KmeansSpec) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl Eq for KmeansSpec

impl StructuralPartialEq for KmeansSpec