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

JavaScript Machine Learning

"use strict";

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/**
 * k-nearest neighbours learner. Classifies points based on the (possibly weighted) vote
 * of its k nearest neighbours (euclidian distance).
 */
var KNN =
/*#__PURE__*/
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 _this;

    var optionsUser = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};

    _classCallCheck(this, KNN);

    _this = _possibleConstructorReturn(this, _getPrototypeOf(KNN).call(this)); // Parse options

    var optionsDefault = {
      numNeighbours: 3
    };

    var options = _objectSpread({}, 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;
}(_base["default"]);

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