turftracker/frontend/node_modules/density-clustering/lib/DBSCAN.js

/**
 * DBSCAN - Density based clustering
 *
 * @author Lukasz Krawczyk <contact@lukaszkrawczyk.eu>
 * @copyright MIT
 */

/**
 * DBSCAN class construcotr
 * @constructor
 *
 * @param {Array} dataset
 * @param {number} epsilon
 * @param {number} minPts
 * @param {function} distanceFunction
 * @returns {DBSCAN}
 */
function DBSCAN(dataset, epsilon, minPts, distanceFunction) {
  /** @type {Array} */
  this.dataset = [];
  /** @type {number} */
  this.epsilon = 1;
  /** @type {number} */
  this.minPts = 2;
  /** @type {function} */
  this.distance = this._euclideanDistance;
  /** @type {Array} */
  this.clusters = [];
  /** @type {Array} */
  this.noise = [];

  // temporary variables used during computation

  /** @type {Array} */
  this._visited = [];
  /** @type {Array} */
  this._assigned = [];
  /** @type {number} */
  this._datasetLength = 0;

  this._init(dataset, epsilon, minPts, distanceFunction);
};

/******************************************************************************/
// public functions

/**
 * Start clustering
 *
 * @param {Array} dataset
 * @param {number} epsilon
 * @param {number} minPts
 * @param {function} distanceFunction
 * @returns {undefined}
 * @access public
 */
DBSCAN.prototype.run = function(dataset, epsilon, minPts, distanceFunction) {
  this._init(dataset, epsilon, minPts, distanceFunction);

  for (var pointId = 0; pointId < this._datasetLength; pointId++) {
    // if point is not visited, check if it forms a cluster
    if (this._visited[pointId] !== 1) {
      this._visited[pointId] = 1;

      // if closest neighborhood is too small to form a cluster, mark as noise
      var neighbors = this._regionQuery(pointId);

      if (neighbors.length < this.minPts) {
        this.noise.push(pointId);
      } else {
        // create new cluster and add point
        var clusterId = this.clusters.length;
        this.clusters.push([]);
        this._addToCluster(pointId, clusterId);

        this._expandCluster(clusterId, neighbors);
      }
    }
  }

  return this.clusters;
};

/******************************************************************************/
// protected functions

/**
 * Set object properties
 *
 * @param {Array} dataset
 * @param {number} epsilon
 * @param {number} minPts
 * @param {function} distance
 * @returns {undefined}
 * @access protected
 */
DBSCAN.prototype._init = function(dataset, epsilon, minPts, distance) {

  if (dataset) {

    if (!(dataset instanceof Array)) {
      throw Error('Dataset must be of type array, ' +
        typeof dataset + ' given');
    }

    this.dataset = dataset;
    this.clusters = [];
    this.noise = [];

    this._datasetLength = dataset.length;
    this._visited = new Array(this._datasetLength);
    this._assigned = new Array(this._datasetLength);
  }

  if (epsilon) {
    this.epsilon = epsilon;
  }

  if (minPts) {
    this.minPts = minPts;
  }

  if (distance) {
    this.distance = distance;
  }
};

/**
 * Expand cluster to closest points of given neighborhood
 *
 * @param {number} clusterId
 * @param {Array} neighbors
 * @returns {undefined}
 * @access protected
 */
DBSCAN.prototype._expandCluster = function(clusterId, neighbors) {

  /**
   * It's very important to calculate length of neighbors array each time,
   * as the number of elements changes over time
   */
  for (var i = 0; i < neighbors.length; i++) {
    var pointId2 = neighbors[i];

    if (this._visited[pointId2] !== 1) {
      this._visited[pointId2] = 1;
      var neighbors2 = this._regionQuery(pointId2);

      if (neighbors2.length >= this.minPts) {
        neighbors = this._mergeArrays(neighbors, neighbors2);
      }
    }

    // add to cluster
    if (this._assigned[pointId2] !== 1) {
      this._addToCluster(pointId2, clusterId);
    }
  }
};

/**
 * Add new point to cluster
 *
 * @param {number} pointId
 * @param {number} clusterId
 */
DBSCAN.prototype._addToCluster = function(pointId, clusterId) {
  this.clusters[clusterId].push(pointId);
  this._assigned[pointId] = 1;
};

/**
 * Find all neighbors around given point
 *
 * @param {number} pointId,
 * @param {number} epsilon
 * @returns {Array}
 * @access protected
 */
DBSCAN.prototype._regionQuery = function(pointId) {
  var neighbors = [];

  for (var id = 0; id < this._datasetLength; id++) {
    var dist = this.distance(this.dataset[pointId], this.dataset[id]);
    if (dist < this.epsilon) {
      neighbors.push(id);
    }
  }

  return neighbors;
};

/******************************************************************************/
// helpers

/**
 * @param {Array} a
 * @param {Array} b
 * @returns {Array}
 * @access protected
 */
DBSCAN.prototype._mergeArrays = function(a, b) {
  var len = b.length;

  for (var i = 0; i < len; i++) {
    var P = b[i];
    if (a.indexOf(P) < 0) {
      a.push(P);
    }
  }

  return a;
};

/**
 * Calculate euclidean distance in multidimensional space
 *
 * @param {Array} p
 * @param {Array} q
 * @returns {number}
 * @access protected
 */
DBSCAN.prototype._euclideanDistance = function(p, q) {
  var sum = 0;
  var i = Math.min(p.length, q.length);

  while (i--) {
    sum += (p[i] - q[i]) * (p[i] - q[i]);
  }

  return Math.sqrt(sum);
};

if (typeof module !== 'undefined' && module.exports) {
  module.exports = DBSCAN;
}