@jsmlt/jsmlt/distribution/kernel/gaussian.js

JavaScript Machine Learning

"use strict";

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

var _base = _interopRequireDefault(require("./base"));

var Arrays = _interopRequireWildcard(require("../arrays"));

function _getRequireWildcardCache() { if (typeof WeakMap !== "function") return null; var cache = new WeakMap(); _getRequireWildcardCache = function _getRequireWildcardCache() { return cache; }; return cache; }

function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } var cache = _getRequireWildcardCache(); if (cache && cache.has(obj)) { return cache.get(obj); } var newObj = {}; if (obj != null) { var hasPropertyDescriptor = Object.defineProperty && Object.getOwnPropertyDescriptor; for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) { var desc = hasPropertyDescriptor ? Object.getOwnPropertyDescriptor(obj, key) : null; if (desc && (desc.get || desc.set)) { Object.defineProperty(newObj, key, desc); } else { newObj[key] = obj[key]; } } } } newObj["default"] = obj; if (cache) { cache.set(obj, newObj); } return newObj; }

function _interopRequireDefault(obj) { return obj && obj.__esModule ? obj : { "default": obj }; }

function _typeof(obj) { if (typeof Symbol === "function" && typeof Symbol.iterator === "symbol") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj; }; } return _typeof(obj); }

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; }

function _possibleConstructorReturn(self, call) { if (call && (_typeof(call) === "object" || typeof call === "function")) { return call; } return _assertThisInitialized(self); }

function _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return self; }

function _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }

function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }

function _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }

/**
 * The Gaussian kernel, also known as the radial basis function (RBF) kernel
 */
var GaussianKernel =
/*#__PURE__*/
function (_Kernel) {
  _inherits(GaussianKernel, _Kernel);

  /**
   * Initialize the Gaussian kernel with user-specified parameters
   *
   * @param {number} [sigmaSquared = 1] - Normalization parameter for exponential
   */
  function GaussianKernel() {
    var _this;

    var sigmaSquared = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 1;

    _classCallCheck(this, GaussianKernel);

    _this = _possibleConstructorReturn(this, _getPrototypeOf(GaussianKernel).call(this));
    /**
     * Normalization parameter for exponential
     *
     * @type {number}
     */

    _this.sigmaSquared = sigmaSquared;
    return _this;
  }
  /**
   * @see {@link Kernel#apply}
   */


  _createClass(GaussianKernel, [{
    key: "apply",
    value: function apply(x, y) {
      // Gaussian
      var diff = Arrays.sum(x, Arrays.scale(y, -1));
      return Math.exp(-Arrays.dot(diff, diff) / (2 * this.sigmaSquared));
    }
  }]);

  return GaussianKernel;
}(_base["default"]);

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