@jsmlt/jsmlt/distribution/unsupervised/base.js

JavaScript Machine Learning

"use strict";

Object.defineProperty(exports, "__esModule", {
  value: true
});
exports["default"] = void 0;

function _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError("Cannot call a class as a function"); } }

function _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if ("value" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }

function _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }

/**
 * Base class for clustering algorithms.
 */
var Clusterer =
/*#__PURE__*/
function () {
  function Clusterer() {
    _classCallCheck(this, Clusterer);
  }

  _createClass(Clusterer, [{
    key: "train",

    /**
     * Run the clustering algorithm on a dataset and obtain the cluster predictions per class.
     *
     * @abstract
     *
     * @param {Array.<Array.<number>>} X - Features per data point
     */
    value: function train(X) {
      throw new Error('Method must be implemented child class.');
    }
    /**
     * Assign clusters to samples.
     *
     * @param {Array.<Array.<number>>} X - Features per data point
     * @return {Array.<number>} Cluster indices assigned to input data points. For n input data
     *   points, an n-dimensional array containing the cluster assignments will be returned
     */

  }, {
    key: "cluster",
    value: function cluster(X) {
      throw new Error('Method must be implemented child class.');
    }
  }]);

  return Clusterer;
}();

exports["default"] = Clusterer;
module.exports = exports.default;