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think-bayes

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

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"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.array.map"); require("core-js/modules/es.array.sort"); 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.regexp.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 _Pmf2 = _interopRequireDefault(require("../Pmf")); var _math = _interopRequireDefault(require("../math")); 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 _slicedToArray(arr, i) { return _arrayWithHoles(arr) || _iterableToArrayLimit(arr, i) || _nonIterableRest(); } function _nonIterableRest() { throw new TypeError("Invalid attempt to destructure non-iterable instance"); } function _iterableToArrayLimit(arr, i) { if (!(Symbol.iterator in Object(arr) || Object.prototype.toString.call(arr) === "[object Arguments]")) { return; } var _arr = []; var _n = true; var _d = false; var _e = undefined; try { for (var _i = arr[Symbol.iterator](), _s; !(_n = (_s = _i.next()).done); _n = true) { _arr.push(_s.value); if (i && _arr.length === i) break; } } catch (err) { _d = true; _e = err; } finally { try { if (!_n && _i["return"] != null) _i["return"](); } finally { if (_d) throw _e; } } return _arr; } function _arrayWithHoles(arr) { if (Array.isArray(arr)) return arr; } 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); } /** * Represents a joint distribution. * The values are sequences (usually tuples) * @param {string|array|object} values sequence of values * @param {string} name sequence of values */ var Joint = /*#__PURE__*/ function (_Pmf) { _inherits(Joint, _Pmf); function Joint() { _classCallCheck(this, Joint); return _possibleConstructorReturn(this, _getPrototypeOf(Joint).apply(this, arguments)); } _createClass(Joint, [{ key: "marginal", /** * Gets the marginal distribution of the indicated variable. * @param {number} i index of the variable we want * @returns Pmf */ value: function marginal(i, name) { var pmf = new _Pmf2.default(null, name); var _iteratorNormalCompletion = true; var _didIteratorError = false; var _iteratorError = undefined; try { for (var _iterator = this.items()[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) { var _step$value = _slicedToArray(_step.value, 2), vs = _step$value[0], prob = _step$value[1]; pmf.incr(vs[i], prob); } } catch (err) { _didIteratorError = true; _iteratorError = err; } finally { try { if (!_iteratorNormalCompletion && _iterator.return != null) { _iterator.return(); } } finally { if (_didIteratorError) { throw _iteratorError; } } } return pmf; } /** * Gets the conditional distribution of the indicated variable. * Distribution of vs[i], conditioned on vs[j] = val. * @param {number} i index of the variable we want * @param {number} j which variable is conditioned on * @param {*} val the value the jth variable has to have * @returns Pmf */ }, { key: "conditional", value: function conditional(i, j, val, name) { var pmf = new _Pmf2.default(null, name); var _iteratorNormalCompletion2 = true; var _didIteratorError2 = false; var _iteratorError2 = undefined; try { for (var _iterator2 = this.items()[Symbol.iterator](), _step2; !(_iteratorNormalCompletion2 = (_step2 = _iterator2.next()).done); _iteratorNormalCompletion2 = true) { var _step2$value = _slicedToArray(_step2.value, 2), vs = _step2$value[0], prob = _step2$value[1]; if (vs[j] !== val) continue; pmf.incr(vs[i], prob); } } catch (err) { _didIteratorError2 = true; _iteratorError2 = err; } finally { try { if (!_iteratorNormalCompletion2 && _iterator2.return != null) { _iterator2.return(); } } finally { if (_didIteratorError2) { throw _iteratorError2; } } } pmf.normalize(); return pmf; } /** * Returns the maximum-likelihood credible interval. * If percentage=90, computes a 90% CI containing the values * with the highest likelihoods. * @param {number} percentage float between 0 and 100 * @returns list of values from the suite */ }, { key: "maxLikeInterval", value: function maxLikeInterval() { var percentage = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : 90; var interval = []; var total = 0; var t = this.items().map(function (_ref) { var _ref2 = _slicedToArray(_ref, 2), x = _ref2[0], p = _ref2[1]; return [p, x]; }).sort(function (_ref3, _ref4) { var _ref5 = _slicedToArray(_ref3, 1), a = _ref5[0]; var _ref6 = _slicedToArray(_ref4, 1), b = _ref6[0]; return _math.default.compare(b, a); }); var _iteratorNormalCompletion3 = true; var _didIteratorError3 = false; var _iteratorError3 = undefined; try { for (var _iterator3 = t[Symbol.iterator](), _step3; !(_iteratorNormalCompletion3 = (_step3 = _iterator3.next()).done); _iteratorNormalCompletion3 = true) { var _step3$value = _slicedToArray(_step3.value, 2), prob = _step3$value[0], val = _step3$value[1]; interval.push(val); total = _math.default.add(total, prob); if (total >= _math.default.div(percentage, 100)) break; } } catch (err) { _didIteratorError3 = true; _iteratorError3 = err; } finally { try { if (!_iteratorNormalCompletion3 && _iterator3.return != null) { _iterator3.return(); } } finally { if (_didIteratorError3) { throw _iteratorError3; } } } return interval; } }]); return Joint; }(_Pmf2.default); exports.default = Joint; //# sourceMappingURL=index.js.map