ducha-qianduan/node_modules/echarts/lib/util/KDTree.js

/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements.  See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership.  The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License.  You may obtain a copy of the License at
*
*   http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied.  See the License for the
* specific language governing permissions and limitations
* under the License.
*/

var quickSelect = require("./quickSelect");

/*
* Licensed to the Apache Software Foundation (ASF) under one
* or more contributor license agreements.  See the NOTICE file
* distributed with this work for additional information
* regarding copyright ownership.  The ASF licenses this file
* to you under the Apache License, Version 2.0 (the
* "License"); you may not use this file except in compliance
* with the License.  You may obtain a copy of the License at
*
*   http://www.apache.org/licenses/LICENSE-2.0
*
* Unless required by applicable law or agreed to in writing,
* software distributed under the License is distributed on an
* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
* KIND, either express or implied.  See the License for the
* specific language governing permissions and limitations
* under the License.
*/
function Node(axis, data) {
  this.left = null;
  this.right = null;
  this.axis = axis;
  this.data = data;
}
/**
 * @constructor
 * @alias module:echarts/data/KDTree
 * @param {Array} points List of points.
 * each point needs an array property to repesent the actual data
 * @param {Number} [dimension]
 *        Point dimension.
 *        Default will use the first point's length as dimensiont
 */


var KDTree = function (points, dimension) {
  if (!points.length) {
    return;
  }

  if (!dimension) {
    dimension = points[0].array.length;
  }

  this.dimension = dimension;
  this.root = this._buildTree(points, 0, points.length - 1, 0); // Use one stack to avoid allocation
  // each time searching the nearest point

  this._stack = []; // Again avoid allocating a new array
  // each time searching nearest N points

  this._nearstNList = [];
};
/**
 * Resursively build the tree
 */


KDTree.prototype._buildTree = function (points, left, right, axis) {
  if (right < left) {
    return null;
  }

  var medianIndex = Math.floor((left + right) / 2);
  medianIndex = quickSelect(points, left, right, medianIndex, function (a, b) {
    return a.array[axis] - b.array[axis];
  });
  var median = points[medianIndex];
  var node = new Node(axis, median);
  axis = (axis + 1) % this.dimension;

  if (right > left) {
    node.left = this._buildTree(points, left, medianIndex - 1, axis);
    node.right = this._buildTree(points, medianIndex + 1, right, axis);
  }

  return node;
};
/**
 * Find nearest point
 * @param  {Array} target Target point
 * @param  {Function} squaredDistance Squared distance function
 * @return {Array} Nearest point
 */


KDTree.prototype.nearest = function (target, squaredDistance) {
  var curr = this.root;
  var stack = this._stack;
  var idx = 0;
  var minDist = Infinity;
  var nearestNode = null;

  if (curr.data !== target) {
    minDist = squaredDistance(curr.data, target);
    nearestNode = curr;
  }

  if (target.array[curr.axis] < curr.data.array[curr.axis]) {
    // Left first
    curr.right && (stack[idx++] = curr.right);
    curr.left && (stack[idx++] = curr.left);
  } else {
    // Right first
    curr.left && (stack[idx++] = curr.left);
    curr.right && (stack[idx++] = curr.right);
  }

  while (idx--) {
    curr = stack[idx];
    var currDist = target.array[curr.axis] - curr.data.array[curr.axis];
    var isLeft = currDist < 0;
    var needsCheckOtherSide = false;
    currDist = currDist * currDist; // Intersecting right hyperplane with minDist hypersphere

    if (currDist < minDist) {
      currDist = squaredDistance(curr.data, target);

      if (currDist < minDist && curr.data !== target) {
        minDist = currDist;
        nearestNode = curr;
      }

      needsCheckOtherSide = true;
    }

    if (isLeft) {
      if (needsCheckOtherSide) {
        curr.right && (stack[idx++] = curr.right);
      } // Search in the left area


      curr.left && (stack[idx++] = curr.left);
    } else {
      if (needsCheckOtherSide) {
        curr.left && (stack[idx++] = curr.left);
      } // Search the right area


      curr.right && (stack[idx++] = curr.right);
    }
  }

  return nearestNode.data;
};

KDTree.prototype._addNearest = function (found, dist, node) {
  var nearestNList = this._nearstNList; // Insert to the right position
  // Sort from small to large

  for (var i = found - 1; i > 0; i--) {
    if (dist >= nearestNList[i - 1].dist) {
      break;
    } else {
      nearestNList[i].dist = nearestNList[i - 1].dist;
      nearestNList[i].node = nearestNList[i - 1].node;
    }
  }

  nearestNList[i].dist = dist;
  nearestNList[i].node = node;
};
/**
 * Find nearest N points
 * @param  {Array} target Target point
 * @param  {number} N
 * @param  {Function} squaredDistance Squared distance function
 * @param  {Array} [output] Output nearest N points
 */


KDTree.prototype.nearestN = function (target, N, squaredDistance, output) {
  if (N <= 0) {
    output.length = 0;
    return output;
  }

  var curr = this.root;
  var stack = this._stack;
  var idx = 0;
  var nearestNList = this._nearstNList;

  for (var i = 0; i < N; i++) {
    // Allocate
    if (!nearestNList[i]) {
      nearestNList[i] = {};
    }

    nearestNList[i].dist = 0;
    nearestNList[i].node = null;
  }

  var currDist = squaredDistance(curr.data, target);
  var found = 0;

  if (curr.data !== target) {
    found++;

    this._addNearest(found, currDist, curr);
  }

  if (target.array[curr.axis] < curr.data.array[curr.axis]) {
    // Left first
    curr.right && (stack[idx++] = curr.right);
    curr.left && (stack[idx++] = curr.left);
  } else {
    // Right first
    curr.left && (stack[idx++] = curr.left);
    curr.right && (stack[idx++] = curr.right);
  }

  while (idx--) {
    curr = stack[idx];
    var currDist = target.array[curr.axis] - curr.data.array[curr.axis];
    var isLeft = currDist < 0;
    var needsCheckOtherSide = false;
    currDist = currDist * currDist; // Intersecting right hyperplane with minDist hypersphere

    if (found < N || currDist < nearestNList[found - 1].dist) {
      currDist = squaredDistance(curr.data, target);

      if ((found < N || currDist < nearestNList[found - 1].dist) && curr.data !== target) {
        if (found < N) {
          found++;
        }

        this._addNearest(found, currDist, curr);
      }

      needsCheckOtherSide = true;
    }

    if (isLeft) {
      if (needsCheckOtherSide) {
        curr.right && (stack[idx++] = curr.right);
      } // Search in the left area


      curr.left && (stack[idx++] = curr.left);
    } else {
      if (needsCheckOtherSide) {
        curr.left && (stack[idx++] = curr.left);
      } // Search the right area


      curr.right && (stack[idx++] = curr.right);
    }
  } // Copy to output


  for (var i = 0; i < found; i++) {
    output[i] = nearestNList[i].node.data;
  }

  output.length = found;
  return output;
};

var _default = KDTree;
module.exports = _default;