/** @import {InputType} from "../index.js" */
/** @import {ParametersPCA} from "./index.js" */
/** @import {EigenArgs} from "../linear_algebra/index.js" */
/**
 * Principal Component Analysis (PCA)
 *
 * A linear dimensionality reduction technique that identifies the axes (principal components)
 * along which the variance of the data is maximized.
 *
 * @class
 * @template {InputType} T
 * @extends DR<T, ParametersPCA>
 * @category Dimensionality Reduction
 * @see {@link MDS} for another linear alternative
 *
 * @example
 * import * as druid from "@saehrimnir/druidjs";
 *
 * const X = [[1, 2], [3, 4], [5, 6]];
 * const pca = new druid.PCA(X, { d: 2 });
 * const Y = pca.transform();
 * // [[x1, y1], [x2, y2], [x3, y3]]
 */
export class PCA<T extends InputType> extends DR<T, ParametersPCA> {
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersPCA>} parameters
   * @returns {T}
   */
  static transform<T_1 extends InputType>(X: T_1, parameters: Partial<ParametersPCA>): T_1;
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersPCA>} parameters
   * @returns {Matrix}
   */
  static principal_components<T_1 extends InputType>(
    X: T_1,
    parameters: Partial<ParametersPCA>,
  ): Matrix;
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersPCA>} [parameters]
   * @returns {Generator<T, T, void>}
   */
  static generator<T_1 extends InputType>(
    X: T_1,
    parameters?: Partial<ParametersPCA>,
  ): Generator<T_1, T_1, void>;
  /**
   * @template {InputType} T
   * @param {T} X
   * @param {Partial<ParametersPCA>} [parameters]
   * @returns {Promise<T>}
   */
  static transform_async<T_1 extends InputType>(
    X: T_1,
    parameters?: Partial<ParametersPCA>,
  ): Promise<T_1>;
  /**
   * @param {T} X - The high-dimensional data.
   * @param {Partial<ParametersPCA>} [parameters] - Object containing parameterization of the DR method.
   */
  constructor(X: T, parameters?: Partial<ParametersPCA>);
  /**
   * Transforms the inputdata `X` to dimensionality `d`.
   *
   * @returns {Generator<T, T, void>} A generator yielding the intermediate steps of the projection.
   */
  generator(): Generator<T, T, void>;
  /**
   * Transforms the inputdata `X` to dimensionality `d`.
   *
   * @returns {T} - The projected data.
   */
  transform(): T;
  /**
   * Computes the `d` principal components of Matrix `X`.
   *
   * @returns {Matrix}
   */
  principal_components(): Matrix;
  V: Matrix | undefined;
}
import type { InputType } from "../index.js";
import type { ParametersPCA } from "./index.js";
import { DR } from "./DR.js";
import { Matrix } from "../matrix/index.js";
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