think-bayes/cjs/GaussianPdf/index.js

An algorithm framework of probability and statistics for browser and Node.js environment.

"use strict";

require("core-js/modules/es.symbol");

require("core-js/modules/es.symbol.description");

require("core-js/modules/es.symbol.iterator");

require("core-js/modules/es.array.iterator");

require("core-js/modules/es.object.get-prototype-of");

require("core-js/modules/es.object.set-prototype-of");

require("core-js/modules/es.object.to-string");

require("core-js/modules/es.string.iterator");

require("core-js/modules/web.dom-collections.iterator");

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

var _Pdf2 = _interopRequireDefault(require("../Pdf"));

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); }

// import EvalGaussianPdf from '../EvalGaussianPdf';

/**
 * Represents the PDF of a Gaussian distribution.
 */
var GaussianPdf =
/*#__PURE__*/
function (_Pdf) {
  _inherits(GaussianPdf, _Pdf);

  /**
   * Constructs a Gaussian Pdf with given mu and sigma.
   * @param {number} mu mean
   * @param {number} sigma standard deviation
   */
  function GaussianPdf(mu, sigma) {
    var _this;

    _classCallCheck(this, GaussianPdf);

    _this = _possibleConstructorReturn(this, _getPrototypeOf(GaussianPdf).call(this));
    _this.mu = mu;
    _this.sigma = sigma;
    return _this;
  }
  /**
   * Evaluates this Pdf at x.
   * @returns float probability density
   */


  _createClass(GaussianPdf, [{
    key: "density",
    value: function density(x) {// return new EvalGaussianPdf(x, this.mu, this.sigma);
    }
  }]);

  return GaussianPdf;
}(_Pdf2.default);

exports.default = GaussianPdf;
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