/** @import { Metric } from "../metrics/index.js" */
/** @import { ParametersLSH } from "./index.js" */
/**
 * Locality Sensitive Hashing (LSH) for approximate nearest neighbor search.
 *
 * LSH uses hash functions that map similar items to the same buckets with high probability.
 * This implementation uses Random Projection hashing (SimHash-style) which works well for
 * cosine similarity and Euclidean distance.
 *
 * Key concepts:
 * - Multiple hash tables increase recall probability
 * - Each hash function projects data onto random hyperplanes
 * - Points on the same side of hyperplanes are hashed together
 * - Combines results from all tables for better accuracy
 *
 * Best suited for:
 * - High-dimensional data where exact methods fail
 * - Approximate nearest neighbor needs
 * - Large datasets where linear scan is too slow
 * - When some false positives/negatives are acceptable
 *
 * @class
 * @category KNN
 * @template {number[] | Float64Array} T
 * @extends KNN<T, ParametersLSH>
 * @see {@link https://en.wikipedia.org/wiki/Locality-sensitive_hashing}
 */
export class LSH<T extends number[] | Float64Array> extends KNN<T, ParametersLSH> {
  /**
   * Creates a new LSH index.
   *
   * @param {T[]} elements - Elements to index
   * @param {ParametersLSH} [parameters={}] - Configuration parameters
   */
  constructor(elements: T[], parameters?: ParametersLSH);
  _metric: Metric;
  _numHashTables: number;
  _numHashFunctions: number;
  _seed: number;
  _randomizer: Randomizer;
  /** @type {Map<string, number[]>[]} */
  _hashTables: Map<string, number[]>[];
  /** @type {Float64Array[][]} */
  _projections: Float64Array[][];
  /** @type {number[][]} */
  _offsets: number[][];
  /** @type {number} */
  _dim: number;
  /**
   * Initialize random projection vectors for all hash tables.
   * @private
   */
  private _initializeHashFunctions;
  /**
   * Compute hash signature for an element using random projections.
   * @private
   * @param {T} element
   * @param {number} tableIndex
   * @returns {string} Hash signature
   */
  private _computeHash;
  /**
   * Add elements to the LSH index.
   * @param {T[]} elements
   * @returns {this}
   */
  add(elements: T[]): this;
  /**
   * Search for k approximate nearest neighbors.
   * @param {T} query
   * @param {number} [k=5]
   * @returns {{ element: T; index: number; distance: number }[]}
   */
  search(
    query: T,
    k?: number,
  ): {
    element: T;
    index: number;
    distance: number;
  }[];
  /**
   * @param {number} i
   * @param {number} [k=5]
   * @returns {{ element: T; index: number; distance: number }[]}
   */
  search_by_index(
    i: number,
    k?: number,
  ): {
    element: T;
    index: number;
    distance: number;
  }[];
}
import type { ParametersLSH } from "./index.js";
import { KNN } from "./KNN.js";
import type { Metric } from "../metrics/index.js";
import { Randomizer } from "../util/index.js";
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