@thi.ng/k-means/mean-cut.d.ts

k-means & k-medians with customizable distance functions and centroid initializations for n-D vectors

import type { Fn } from "@thi.ng/api";
import type { ReadonlyVec } from "@thi.ng/vectors";
/**
 * Mean-cut clustering, also usable as {@link kmeans} /
 * {@link KMeansOpts.initial} centroid initialization. Returns up to `k`
 * centroids for given `samples`.
 *
 * @remarks
 * Only recommended for low-dimensional data.
 *
 * @param k
 * @param samples
 */
export declare const meanCut: <T extends ReadonlyVec>(k: number, samples: T[]) => import("@thi.ng/vectors").Vec<number>[];
/**
 * Median-cut clustering, also usable as {@link kmeans} /
 * {@link KMeansOpts.initial} centroid initialization. Returns up to `k`
 * centroids for given `samples`.
 *
 * @remarks
 * Only recommended for low-dimensional data.
 *
 * @param k
 * @param samples
 */
export declare const medianCut: <T extends ReadonlyVec>(k: number, samples: T[]) => import("@thi.ng/vectors").Vec<number>[];
/** @internal */
export declare const computeCutWith: (cut: Fn<ReadonlyVec, number>, samples: ReadonlyVec[], dim: number, depth: number) => ReadonlyVec[][];
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