think-bayes/cjs/Suite/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.from");

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.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 _utils = require("../utils");

var _helpers = require("../helpers");

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 _toConsumableArray(arr) { return _arrayWithoutHoles(arr) || _iterableToArray(arr) || _nonIterableSpread(); }

function _nonIterableSpread() { throw new TypeError("Invalid attempt to spread non-iterable instance"); }

function _iterableToArray(iter) { if (Symbol.iterator in Object(iter) || Object.prototype.toString.call(iter) === "[object Arguments]") return Array.from(iter); }

function _arrayWithoutHoles(arr) { if (Array.isArray(arr)) { for (var i = 0, arr2 = new Array(arr.length); i < arr.length; i++) { arr2[i] = arr[i]; } return arr2; } }

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 suite of hypotheses and their probabilities.
 * @param {string|array|object} values sequence of values
 * @param {string} name sequence of values
 */
var Suite =
/*#__PURE__*/
function (_Pmf) {
  _inherits(Suite, _Pmf);

  function Suite() {
    _classCallCheck(this, Suite);

    return _possibleConstructorReturn(this, _getPrototypeOf(Suite).apply(this, arguments));
  }

  _createClass(Suite, [{
    key: "update",

    /**
     * Updates each hypothesis based on the data.
     * @param {any} data any representation of the data
     * @returns the normalizing constant
     */
    value: function update(data) {
      var _iteratorNormalCompletion = true;
      var _didIteratorError = false;
      var _iteratorError = undefined;

      try {
        for (var _iterator = this.values()[Symbol.iterator](), _step; !(_iteratorNormalCompletion = (_step = _iterator.next()).done); _iteratorNormalCompletion = true) {
          var hypo = _step.value;
          var like = this.likelihood(data, hypo);
          this.mult(hypo, like);
        }
      } catch (err) {
        _didIteratorError = true;
        _iteratorError = err;
      } finally {
        try {
          if (!_iteratorNormalCompletion && _iterator.return != null) {
            _iterator.return();
          }
        } finally {
          if (_didIteratorError) {
            throw _iteratorError;
          }
        }
      }

      return this.normalize();
    }
    /**
     * Updates a suite of hypotheses based on new data.
     * Modifies the suite directly; if you want to keep the original, make a copy.
     * Note: unlike Update, LogUpdate does not normalize.
     * @param {any} any representation of the data
     */

  }, {
    key: "logUpdate",
    value: function logUpdate(data) {
      var _iteratorNormalCompletion2 = true;
      var _didIteratorError2 = false;
      var _iteratorError2 = undefined;

      try {
        for (var _iterator2 = this.values()[Symbol.iterator](), _step2; !(_iteratorNormalCompletion2 = (_step2 = _iterator2.next()).done); _iteratorNormalCompletion2 = true) {
          var hypo = _step2.value;
          var like = this.logLikelihood(data, hypo);
          this.incr(hypo, like);
        }
      } catch (err) {
        _didIteratorError2 = true;
        _iteratorError2 = err;
      } finally {
        try {
          if (!_iteratorNormalCompletion2 && _iterator2.return != null) {
            _iterator2.return();
          }
        } finally {
          if (_didIteratorError2) {
            throw _iteratorError2;
          }
        }
      }
    }
    /**
     * Updates each hypothesis based on the dataset.
     * This is more efficient than calling Update repeatedly because
     * it waits until the end to Normalize.
     * Modifies the suite directly; if you want to keep the original, make a copy.
     * @param {array|set} dataset a sequence of data
     * @returns the normalizing constant
     */

  }, {
    key: "updateSet",
    value: function updateSet(dataset) {
      for (var _i = 0, _arr = _toConsumableArray(dataset); _i < _arr.length; _i++) {
        var data = _arr[_i];
        var _iteratorNormalCompletion3 = true;
        var _didIteratorError3 = false;
        var _iteratorError3 = undefined;

        try {
          for (var _iterator3 = this.values()[Symbol.iterator](), _step3; !(_iteratorNormalCompletion3 = (_step3 = _iterator3.next()).done); _iteratorNormalCompletion3 = true) {
            var hypo = _step3.value;
            var like = this.likelihood(data, hypo);
            this.mult(hypo, like);
          }
        } catch (err) {
          _didIteratorError3 = true;
          _iteratorError3 = err;
        } finally {
          try {
            if (!_iteratorNormalCompletion3 && _iterator3.return != null) {
              _iterator3.return();
            }
          } finally {
            if (_didIteratorError3) {
              throw _iteratorError3;
            }
          }
        }
      }

      return this.normalize();
    }
    /**
     * Updates each hypothesis based on the dataset.
     * Modifies the suite directly; if you want to keep the original, make a copy.
     * @param {array|set} dataset a sequence of data
     */

  }, {
    key: "logUpdateSet",
    value: function logUpdateSet(dataset) {
      var _iteratorNormalCompletion4 = true;
      var _didIteratorError4 = false;
      var _iteratorError4 = undefined;

      try {
        for (var _iterator4 = dataset[Symbol.iterator](), _step4; !(_iteratorNormalCompletion4 = (_step4 = _iterator4.next()).done); _iteratorNormalCompletion4 = true) {
          var data = _step4.value;
          this.logUpdate(data);
        }
      } catch (err) {
        _didIteratorError4 = true;
        _iteratorError4 = err;
      } finally {
        try {
          if (!_iteratorNormalCompletion4 && _iterator4.return != null) {
            _iterator4.return();
          }
        } finally {
          if (_didIteratorError4) {
            throw _iteratorError4;
          }
        }
      }
    }
    /**
     * Computes the likelihood of the data under the hypothesis.
     * This method needs implement by children class
     * if not there is an `UnimplementedMethodException` would be throw
     * @param {any} data some representation of the data
     * @param {any} hypo some representation of the hypothesis
     * @returns likelihood
     */

  }, {
    key: "likelihood",
    value: function likelihood(data, hypo) {
      throw new _utils.UnimplementedMethodException();
    }
    /**
     * Computes the log likelihood of the data under the hypothesis.
     * This method needs implement by children class
     * if not there is an `UnimplementedMethodException` would be throw
     * @param {any} data some representation of the data
     * @param {any} hypo some representation of the hypothesis
     * @returns likelihood
     */

  }, {
    key: "logLikelihood",
    value: function logLikelihood(data, hypo) {
      throw new _utils.UnimplementedMethodException();
    }
    /**
     * Transforms from probabilities to odds.
     * Values with prob=0 are removed.
     */

  }, {
    key: "makeOdds",
    value: function makeOdds() {
      var _iteratorNormalCompletion5 = true;
      var _didIteratorError5 = false;
      var _iteratorError5 = undefined;

      try {
        for (var _iterator5 = this.items()[Symbol.iterator](), _step5; !(_iteratorNormalCompletion5 = (_step5 = _iterator5.next()).done); _iteratorNormalCompletion5 = true) {
          var _step5$value = _slicedToArray(_step5.value, 2),
              hypo = _step5$value[0],
              prob = _step5$value[1];

          if (prob) {
            this.set(hypo, (0, _helpers.odds)(prob));
          } else {
            this.remove(hypo);
          }
        }
      } catch (err) {
        _didIteratorError5 = true;
        _iteratorError5 = err;
      } finally {
        try {
          if (!_iteratorNormalCompletion5 && _iterator5.return != null) {
            _iterator5.return();
          }
        } finally {
          if (_didIteratorError5) {
            throw _iteratorError5;
          }
        }
      }
    }
    /**
     * Transforms from odds to probabilities.
     */

  }, {
    key: "makeProbs",
    value: function makeProbs() {
      var _iteratorNormalCompletion6 = true;
      var _didIteratorError6 = false;
      var _iteratorError6 = undefined;

      try {
        for (var _iterator6 = this.items()[Symbol.iterator](), _step6; !(_iteratorNormalCompletion6 = (_step6 = _iterator6.next()).done); _iteratorNormalCompletion6 = true) {
          var _step6$value = _slicedToArray(_step6.value, 2),
              hypo = _step6$value[0],
              _odds = _step6$value[1];

          this.set(hypo, (0, _helpers.probability)(_odds));
        }
      } catch (err) {
        _didIteratorError6 = true;
        _iteratorError6 = err;
      } finally {
        try {
          if (!_iteratorNormalCompletion6 && _iterator6.return != null) {
            _iterator6.return();
          }
        } finally {
          if (_didIteratorError6) {
            throw _iteratorError6;
          }
        }
      }
    }
  }]);

  return Suite;
}(_Pmf2.default);

exports.default = Suite;
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