@thi.ng/k-means/api.d.ts

Configurable k-means & k-medians (with k-means++ initialization) for n-D vectors

import type { Fn, Fn0, Maybe } from "@thi.ng/api";
import type { IDistance } from "@thi.ng/distance";
import type { IRandom } from "@thi.ng/random";
import type { ReadonlyVec, Vec } from "@thi.ng/vectors";
export interface KMeansOpts {
    /**
     * Array of initial centroids (i.e. indices of selected points in `samples`
     * array given to {@link kmeans}) or a function producing such. If omitted,
     * {@link initKmeanspp} is used as default.
     */
    initial: number[] | KMeansInit<ReadonlyVec>;
    /**
     * Distance function/metric to use for finding nearest centroid.
     */
    dist: IDistance<ReadonlyVec>;
    /**
     * Sample dimensions. If omitted uses length of first sample vector.
     */
    dim: number;
    /**
     * Max. iteration count
     */
    maxIter: number;
    /**
     * PRNG instance to use for random centroid selection
     */
    rnd: IRandom;
    /**
     * Centroid refinement strategy (default: {@link means}).
     */
    strategy: CentroidStrategy;
    /**
     * Only used if no {@link KMeansOpts.initial} is given and the
     * {@link initKmeanspp} default is used. There the `exponent` is applied to
     * scale the distances to nearest centroid, which will be used to control
     * the weight distribution for choosing next centroid. A higher exponent
     * means that points with larger distances will be more prioritized in the
     * random selection.
     */
    exponent: number;
}
/**
 * k-means initialization function, e.g. {@link initKmeanspp}.
 */
export type KMeansInit<T extends ReadonlyVec> = (k: number, samples: T[], dist?: IDistance<ReadonlyVec>, rnd?: IRandom) => number[];
export type CentroidStrategy = Fn<number, {
    update: Fn<ReadonlyVec, void>;
    finish: Fn0<Maybe<Vec>>;
}>;
export interface Cluster {
    /**
     * Cluster ID
     */
    id: number;
    /**
     * Cluster centroid
     */
    centroid: ReadonlyVec;
    /**
     * Indices of original `samples` array belonging to this cluster
     */
    items: number[];
}
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