/** @import {InputType} from "../index.js" */
/** @import {Metric} from "../metrics/index.js" */
/** @import {ParametersSQDMDS} from "./index.js" */
/**
 * SQuadMDS (Stochastic Quartet MDS)
 *
 * A lean Stochastic Quartet MDS improving global structure preservation in
 * neighbor embedding like t-SNE and UMAP.
 *
 * @class
 * @template {InputType} T
 * @extends DR<T, ParametersSQDMDS>
 * @category Dimensionality Reduction
 */
export class SQDMDS<T extends InputType> extends DR<T, ParametersSQDMDS> {
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersSQDMDS>} [parameters]
   * @returns {T}
   */
  static transform<T_1 extends InputType>(X: T_1, parameters?: Partial<ParametersSQDMDS>): T_1;
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersSQDMDS>} [parameters]
   * @returns {Generator<T, T, void>}
   */
  static generator<T_1 extends InputType>(
    X: T_1,
    parameters?: Partial<ParametersSQDMDS>,
  ): Generator<T_1, T_1, void>;
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersSQDMDS>} [parameters]
   * @returns {Promise<T>}
   */
  static transform_async<T_1 extends InputType>(
    X: T_1,
    parameters?: Partial<ParametersSQDMDS>,
  ): Promise<T_1>;
  /**
   * SQuadMDS: a lean Stochastic Quartet MDS improving global structure preservation in neighbor embedding like t-SNE
   * and UMAP.
   *
   * @param {T} X
   * @param {Partial<ParametersSQDMDS>} [parameters]
   * @see {@link https://arxiv.org/pdf/2202.12087.pdf}
   */
  constructor(X: T, parameters?: Partial<ParametersSQDMDS>);
  init(): void;
  _add: ((...summands: Float64Array<ArrayBufferLike>[]) => Float64Array) | undefined;
  _sub_div:
    | ((
        x: Float64Array<ArrayBufferLike>,
        y: Float64Array<ArrayBufferLike>,
        div: number,
      ) => Float64Array)
    | undefined;
  _minus:
    | ((a: Float64Array<ArrayBufferLike>, b: Float64Array<ArrayBufferLike>) => Float64Array)
    | undefined;
  _mult: ((a: Float64Array<ArrayBufferLike>, v: number) => Float64Array) | undefined;
  _LR_init: number | undefined;
  _LR: number | undefined;
  _offset: number | undefined;
  _momentums: Matrix | undefined;
  _grads: Matrix | undefined;
  _indices: number[] | undefined;
  /** @type {(i: number, j: number, X: Matrix) => number} */
  _HD_metric: ((i: number, j: number, X: Matrix) => number) | undefined;
  /** @type {(i: number, j: number, X: Matrix) => number} */
  _HD_metric_exaggeration: ((i: number, j: number, X: Matrix) => number) | undefined;
  /**
   * Computes the projection.
   *
   * @param {number} [iterations=500] - Number of iterations. Default is `500`
   * @returns {T} The projection.
   */
  transform(iterations?: number): T;
  _decay_start: number | undefined;
  /**
   * Computes the projection.
   *
   * @param {number} [iterations=500] - Number of iterations. Default is `500`
   * @returns {Generator<T, T, void>} The intermediate steps of the projection.
   */
  generator(iterations?: number): Generator<T, T, void>;
  /**
   * Performs an optimization step.
   *
   * @private
   * @param {number} i - Acutal iteration.
   * @param {number} iterations - Number of iterations.
   */
  private _step;
  _distance_exaggeration: boolean | undefined;
  /**
   * Creates quartets of non overlapping indices.
   *
   * @private
   * @returns {Uint32Array[]}
   */
  private __quartets;
  /**
   * Computes and applies gradients, and updates momentum.
   *
   * @private
   * @param {boolean} distance_exaggeration
   */
  private _nestrov_iteration;
  /**
   * Computes the gradients.
   *
   * @param {Matrix} Y - The Projection.
   * @param {Matrix} grads - The gradients.
   * @param {boolean} [exaggeration=false] - Whether or not to use early exaggeration. Default is `false`
   * @param {boolean} [zero_grad=true] - Whether or not to reset the gradient in the beginning. Default is `true`
   * @returns {Matrix} The gradients.
   */
  _fill_MDS_grads(Y: Matrix, grads: Matrix, exaggeration?: boolean, zero_grad?: boolean): Matrix;
  /**
   * Quartet gradients for a projection.
   *
   * @private
   * @param {Matrix} Y - The acutal projection.
   * @param {number[]} quartet - The indices of the quartet.
   * @param {Float64Array} D_hd - The high-dimensional distances of the quartet.
   * @returns {Float64Array[]} The gradients for the quartet.
   */
  private _compute_quartet_grads;
  /**
   * Gradients for one element of the loss function's sum.
   *
   * @private
   * @param {Float64Array} a
   * @param {Float64Array} b
   * @param {Float64Array} c
   * @param {Float64Array} d
   * @param {number} d_ab
   * @param {number} d_ac
   * @param {number} d_ad
   * @param {number} d_bc
   * @param {number} d_bd
   * @param {number} d_cd
   * @param {number} p_ab
   * @param {number} sum_LD_dist
   * @returns {Float64Array[]}
   */
  private _ABCD_grads;
  /**
   * Inline!
   *
   * @param {number} d
   */
  __minus(
    d: number,
  ): (a: Float64Array<ArrayBufferLike>, b: Float64Array<ArrayBufferLike>) => Float64Array;
  /**
   * Inline!
   *
   * @param {number} d
   */
  __add(d: number): (...summands: Float64Array<ArrayBufferLike>[]) => Float64Array;
  /**
   * Inline!
   *
   * @param {number} d
   */
  __mult(d: number): (a: Float64Array<ArrayBufferLike>, v: number) => Float64Array;
  /**
   * Creates a new array `(x - y) / div`.
   *
   * @param {number} d
   */
  __sub_div(
    d: number,
  ): (
    x: Float64Array<ArrayBufferLike>,
    y: Float64Array<ArrayBufferLike>,
    div: number,
  ) => Float64Array;
}
import type { InputType } from "../index.js";
import type { ParametersSQDMDS } from "./index.js";
import { DR } from "./DR.js";
import { Matrix } from "../matrix/index.js";
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