export { FASTMAP } from "./FASTMAP.js";
export { ISOMAP } from "./ISOMAP.js";
export { LDA } from "./LDA.js";
export { LLE } from "./LLE.js";
export { LSP } from "./LSP.js";
export { LTSA } from "./LTSA.js";
export { MDS } from "./MDS.js";
export { PCA } from "./PCA.js";
export { SAMMON } from "./SAMMON.js";
export { SMACOF } from "./SMACOF.js";
export { SQDMDS } from "./SQDMDS.js";
export { TopoMap } from "./TopoMap.js";
export { TriMap } from "./TriMap.js";
export { TSNE } from "./TSNE.js";
export { UMAP } from "./UMAP.js";
export type ParametersLSP = {
  /**
   * - number of neighbors to consider.
   */
  neighbors?: number | undefined;
  /**
   * - number of controlpoints
   */
  control_points?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersFASTMAP = {
  /**
   * - The dimensionality of the projection
   */
  d?: number | undefined;
  /**
   * - The metric which defines the distance between two points.
   */
  metric?: Metric | undefined;
  /**
   * - The seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersISOMAP = {
  /**
   * - The number of neighbors ISOMAP should use to project the data.
   */
  neighbors?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | undefined;
  /**
   * - Whether to use classical MDS or SMACOF for the final DR.
   */
  project?: "MDS" | "SMACOF" | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
  /**
   * - Parameters for the eigendecomposition algorithm.
   */
  eig_args?: Partial<EigenArgs> | undefined;
};
export type ParametersLDA = {
  /**
   * - The labels / classes for each data point.
   */
  labels: any[] | Float64Array;
  /**
   * - The dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - The seed for the random number generator.
   */
  seed?: number | undefined;
  /**
   * - Parameters for the eigendecomposition algorithm.
   */
  eig_args?: Partial<EigenArgs> | undefined;
};
export type ParametersLLE = {
  /**
   * - The number of neighbors for LLE.
   */
  neighbors?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
  /**
   * - Parameters for the eigendecomposition algorithm.
   */
  eig_args?: Partial<EigenArgs> | undefined;
};
export type ParametersLTSA = {
  /**
   * - The number of neighbors for LTSA.
   */
  neighbors?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
  /**
   * - Parameters for the eigendecomposition algorithm.
   */
  eig_args?: Partial<EigenArgs> | undefined;
};
export type ParametersMDS = {
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | "precomputed" | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
  /**
   * - Parameters for the eigendecomposition algorithm.
   */
  eig_args?: Partial<EigenArgs> | undefined;
};
export type ParametersPCA = {
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
  /**
   * - Parameters for the eigendecomposition algorithm.
   */
  eig_args?: Partial<EigenArgs> | undefined;
};
export type ParametersSAMMON<K extends keyof ChooseDR> = {
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | "precomputed" | undefined;
  /**
   * - Either "PCA" or "MDS", with which SAMMON initialiates the projection.
   */
  init_DR?: K | undefined;
  /**
   * - Parameters for the "init"-DR method.
   */
  init_parameters?: ChooseDR[K] | undefined;
  /**
   * - learning rate for gradient descent.
   */
  magic?: number | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersSMACOF = {
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | "precomputed" | undefined;
  /**
   * - maximum number of iterations.
   */
  iterations?: number | undefined;
  /**
   * - tolerance for stress difference.
   */
  epsilon?: number | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersSQDMDS = {
  d?: number | undefined;
  metric?: Metric | "precomputed" | undefined;
  /**
   * - Percentage of iterations using exaggeration phase.
   */
  decay_start?: number | undefined;
  /**
   * - Controls the decay of the learning parameter.
   */
  decay_cte?: number | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersTopoMap = {
  /**
   * = euclidean - The metric which defines the distance between
   * two points.
   */
  metric: Metric;
  /**
   * = 1212 - The seed for the random number generator.
   */
  seed: number;
};
export type ParametersTriMap = {
  /**
   * - scaling factor.
   */
  weight_adj?: number | undefined;
  /**
   * - number of inliers.
   */
  n_inliers?: number | undefined;
  /**
   * - number of outliers.
   */
  n_outliers?: number | undefined;
  /**
   * - number of random points.
   */
  n_random?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  tol?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersTSNE = {
  /**
   * - perplexity.
   */
  perplexity?: number | undefined;
  /**
   * - learning parameter.
   */
  epsilon?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points.
   */
  metric?: Metric | "precomputed" | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
export type ParametersUMAP = {
  /**
   * - size of the local neighborhood.
   */
  n_neighbors?: number | undefined;
  /**
   * - number of nearest neighbors connected in the local neighborhood.
   */
  local_connectivity?: number | undefined;
  /**
   * - controls how tightly points get packed together.
   */
  min_dist?: number | undefined;
  /**
   * - the dimensionality of the projection.
   */
  d?: number | undefined;
  /**
   * - the metric which defines the distance between two points in the high-dimensional space.
   */
  metric?: Metric | "precomputed" | undefined;
  /**
   * - The effective scale of embedded points.
   */
  _spread?: number | undefined;
  /**
   * - Interpolate between union and intersection.
   */
  _set_op_mix_ratio?: number | undefined;
  /**
   * - Weighting applied to negative samples.
   */
  _repulsion_strength?: number | undefined;
  /**
   * - The number of negative samples per positive sample.
   */
  _negative_sample_rate?: number | undefined;
  /**
   * - The number of training epochs.
   */
  _n_epochs?: number | undefined;
  /**
   * - The initial learning rate for the optimization.
   */
  _initial_alpha?: number | undefined;
  /**
   * - the seed for the random number generator.
   */
  seed?: number | undefined;
};
import type { Metric } from "../metrics/index.js";
import type { EigenArgs } from "../linear_algebra/index.js";
import type { ChooseDR } from "./SAMMON.js";
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