@jsmlt/jsmlt/distribution/supervised/neighbors/knn.js

JavaScript Machine Learning

'use strict';

Object.defineProperty(exports, "__esModule", {
  value: true
});

var _extends = Object.assign || function (target) { for (var i = 1; i < arguments.length; i++) { var source = arguments[i]; for (var key in source) { if (Object.prototype.hasOwnProperty.call(source, key)) { target[key] = source[key]; } } } return target; };

var _createClass = 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); } } return function (Constructor, protoProps, staticProps) { if (protoProps) defineProperties(Constructor.prototype, protoProps); if (staticProps) defineProperties(Constructor, staticProps); return Constructor; }; }();

var _base = require('./base');

var _base2 = _interopRequireDefault(_base);

var _arrays = require('../../arrays');

var Arrays = _interopRequireWildcard(_arrays);

function _interopRequireWildcard(obj) { if (obj && obj.__esModule) { return obj; } else { var newObj = {}; if (obj != null) { for (var key in obj) { if (Object.prototype.hasOwnProperty.call(obj, key)) newObj[key] = obj[key]; } } newObj.default = obj; return newObj; } }

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

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

function _possibleConstructorReturn(self, call) { if (!self) { throw new ReferenceError("this hasn't been initialised - super() hasn't been called"); } return call && (typeof call === "object" || typeof call === "function") ? call : self; }

function _inherits(subClass, superClass) { if (typeof superClass !== "function" && superClass !== null) { throw new TypeError("Super expression must either be null or a function, not " + typeof superClass); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, enumerable: false, writable: true, configurable: true } }); if (superClass) Object.setPrototypeOf ? Object.setPrototypeOf(subClass, superClass) : subClass.__proto__ = superClass; } // Internal dependencies


/**
 * k-nearest neighbours learner. Classifies points based on the (possibly weighted) vote
 * of its k nearest neighbours (euclidian distance).
 */
var KNN = function (_Neighbors) {
  _inherits(KNN, _Neighbors);

  /**
   * Constructor. Initialize class members and store user-defined options.
   *
   * @param {Object} [optionsUser] - User-defined options for KNN
   * @param {number} [optionsUser.numNeighbours = 3] - Number of nearest neighbours to consider for
   *   the majority vote
   */
  function KNN() {
    var optionsUser = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};

    _classCallCheck(this, KNN);

    // Parse options
    var _this = _possibleConstructorReturn(this, (KNN.__proto__ || Object.getPrototypeOf(KNN)).call(this));

    var optionsDefault = {
      numNeighbours: 3
    };

    var options = _extends({}, optionsDefault, optionsUser);

    // Set options
    _this.numNeighbours = options.numNeighbours;
    return _this;
  }

  /**
   * @see {@link Classifier#train}
   */


  _createClass(KNN, [{
    key: 'train',
    value: function train(X, y) {
      if (X.length !== y.length) {
        throw new Error('Number of data points should match number of labels.');
      }

      // Store data points
      this.training = { X: X, y: y };
    }

    /**
     * @see {@link Classifier#predict}
     */

  }, {
    key: 'predict',
    value: function predict(X) {
      var _this2 = this;

      if (typeof this.training === 'undefined') {
        throw new Error('Model has to be trained in order to make predictions.');
      }

      if (X[0].length !== this.training.X[0].length) {
        throw new Error('Number of features of test data should match number of features of training data.');
      }

      // Make prediction for each data point
      var predictions = X.map(function (x) {
        return _this2.predictSample(x);
      });

      return predictions;
    }

    /**
     * Make a prediction for a single sample.
     *
     * @param {Array.<number>} sampleFeatures - Data point features
     * @return {mixed} Prediction. Label of class with highest prevalence among k nearest neighbours
     */

  }, {
    key: 'predictSample',
    value: function predictSample(sampleFeatures) {
      var _this3 = this;

      // Calculate distances to all other data points
      var distances = Arrays.zipWithIndex(this.training.X.map(function (x) {
        return Arrays.norm(Arrays.sum(sampleFeatures, Arrays.scale(x, -1)));
      }));

      // Sort training data points based on distance
      distances.sort(function (a, b) {
        if (a[0] > b[0]) return 1;
        if (a[0] < b[0]) return -1;
        return 0;
      });

      // Number of nearest neighbours to consider
      var k = Math.min(this.numNeighbours, distances.length);

      // Take top k distances
      var distancesTopKClasses = distances.slice(0, k).map(function (x) {
        return _this3.training.y[x[1]];
      });

      // Count the number of neighbours per class
      var votes = Arrays.valueCounts(distancesTopKClasses);

      // Get class index with highest number of votes
      var highest = -1;
      var highestLabel = -1;

      votes.forEach(function (vote) {
        if (vote[1] > highest) {
          highest = vote[1];
          highestLabel = vote[0];
        }
      });

      return highestLabel;
    }
  }]);

  return KNN;
}(_base2.default);

exports.default = KNN;
module.exports = exports['default'];