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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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/**
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* AUTO-GENERATED FILE. DO NOT MODIFY.
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*/
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/*
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* Licensed to the Apache Software Foundation (ASF) under one
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* or more contributor license agreements. See the NOTICE file
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* distributed with this work for additional information
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* regarding copyright ownership. The ASF licenses this file
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* to you under the Apache License, Version 2.0 (the
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* "License"); you may not use this file except in compliance
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* with the License. You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing,
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* software distributed under the License is distributed on an
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* "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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* KIND, either express or implied. See the License for the
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* specific language governing permissions and limitations
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* under the License.
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*/
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import quickSelect from './quickSelect.js';
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var KDTreeNode = /** @class */function () {
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function KDTreeNode(axis, data) {
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this.axis = axis;
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this.data = data;
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}
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return KDTreeNode;
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}();
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/**
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* @constructor
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* @alias module:echarts/data/KDTree
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* @param {Array} points List of points.
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* each point needs an array property to represent the actual data
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* @param {Number} [dimension]
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* Point dimension.
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* Default will use the first point's length as dimension.
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*/
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var KDTree = /** @class */function () {
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function KDTree(points, dimension) {
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// Use one stack to avoid allocation
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// each time searching the nearest point
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this._stack = [];
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// Again avoid allocating a new array
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// each time searching nearest N points
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this._nearstNList = [];
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if (!points.length) {
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return;
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}
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if (!dimension) {
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dimension = points[0].array.length;
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}
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this.dimension = dimension;
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this.root = this._buildTree(points, 0, points.length - 1, 0);
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}
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/**
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* Recursively build the tree.
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*/
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KDTree.prototype._buildTree = function (points, left, right, axis) {
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if (right < left) {
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return null;
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}
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var medianIndex = Math.floor((left + right) / 2);
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medianIndex = quickSelect(points, left, right, medianIndex, function (a, b) {
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return a.array[axis] - b.array[axis];
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});
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var median = points[medianIndex];
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var node = new KDTreeNode(axis, median);
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axis = (axis + 1) % this.dimension;
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if (right > left) {
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node.left = this._buildTree(points, left, medianIndex - 1, axis);
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node.right = this._buildTree(points, medianIndex + 1, right, axis);
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}
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return node;
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};
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;
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/**
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* Find nearest point
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* @param target Target point
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* @param squaredDistance Squared distance function
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* @return Nearest point
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*/
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KDTree.prototype.nearest = function (target, squaredDistance) {
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var curr = this.root;
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var stack = this._stack;
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var idx = 0;
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var minDist = Infinity;
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var nearestNode = null;
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if (curr.data !== target) {
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minDist = squaredDistance(curr.data, target);
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nearestNode = curr;
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}
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if (target.array[curr.axis] < curr.data.array[curr.axis]) {
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// Left first
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curr.right && (stack[idx++] = curr.right);
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curr.left && (stack[idx++] = curr.left);
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} else {
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// Right first
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curr.left && (stack[idx++] = curr.left);
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curr.right && (stack[idx++] = curr.right);
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}
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while (idx--) {
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curr = stack[idx];
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var currDist = target.array[curr.axis] - curr.data.array[curr.axis];
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var isLeft = currDist < 0;
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var needsCheckOtherSide = false;
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currDist = currDist * currDist;
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// Intersecting right hyperplane with minDist hypersphere
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if (currDist < minDist) {
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currDist = squaredDistance(curr.data, target);
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if (currDist < minDist && curr.data !== target) {
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minDist = currDist;
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nearestNode = curr;
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}
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needsCheckOtherSide = true;
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}
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if (isLeft) {
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if (needsCheckOtherSide) {
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curr.right && (stack[idx++] = curr.right);
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}
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// Search in the left area
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curr.left && (stack[idx++] = curr.left);
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} else {
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if (needsCheckOtherSide) {
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curr.left && (stack[idx++] = curr.left);
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}
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// Search the right area
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curr.right && (stack[idx++] = curr.right);
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}
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}
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return nearestNode.data;
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};
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;
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KDTree.prototype._addNearest = function (found, dist, node) {
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var nearestNList = this._nearstNList;
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var i = found - 1;
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// Insert to the right position
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// Sort from small to large
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for (; i > 0; i--) {
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if (dist >= nearestNList[i - 1].dist) {
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break;
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} else {
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nearestNList[i].dist = nearestNList[i - 1].dist;
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nearestNList[i].node = nearestNList[i - 1].node;
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}
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}
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nearestNList[i].dist = dist;
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nearestNList[i].node = node;
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};
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;
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/**
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* Find nearest N points
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* @param target Target point
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* @param N
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* @param squaredDistance Squared distance function
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* @param output Output nearest N points
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*/
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KDTree.prototype.nearestN = function (target, N, squaredDistance, output) {
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if (N <= 0) {
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output.length = 0;
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return output;
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}
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var curr = this.root;
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var stack = this._stack;
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var idx = 0;
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var nearestNList = this._nearstNList;
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for (var i = 0; i < N; i++) {
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// Allocate
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if (!nearestNList[i]) {
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nearestNList[i] = {
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dist: 0,
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node: null
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};
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}
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nearestNList[i].dist = 0;
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nearestNList[i].node = null;
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}
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var currDist = squaredDistance(curr.data, target);
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var found = 0;
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if (curr.data !== target) {
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found++;
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this._addNearest(found, currDist, curr);
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}
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if (target.array[curr.axis] < curr.data.array[curr.axis]) {
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// Left first
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curr.right && (stack[idx++] = curr.right);
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curr.left && (stack[idx++] = curr.left);
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} else {
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// Right first
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curr.left && (stack[idx++] = curr.left);
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curr.right && (stack[idx++] = curr.right);
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}
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while (idx--) {
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curr = stack[idx];
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var currDist_1 = target.array[curr.axis] - curr.data.array[curr.axis];
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var isLeft = currDist_1 < 0;
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var needsCheckOtherSide = false;
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currDist_1 = currDist_1 * currDist_1;
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// Intersecting right hyperplane with minDist hypersphere
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if (found < N || currDist_1 < nearestNList[found - 1].dist) {
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currDist_1 = squaredDistance(curr.data, target);
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if ((found < N || currDist_1 < nearestNList[found - 1].dist) && curr.data !== target) {
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if (found < N) {
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found++;
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}
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this._addNearest(found, currDist_1, curr);
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}
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needsCheckOtherSide = true;
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}
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if (isLeft) {
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if (needsCheckOtherSide) {
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curr.right && (stack[idx++] = curr.right);
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}
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// Search in the left area
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curr.left && (stack[idx++] = curr.left);
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} else {
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if (needsCheckOtherSide) {
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curr.left && (stack[idx++] = curr.left);
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}
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// Search the right area
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curr.right && (stack[idx++] = curr.right);
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}
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}
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// Copy to output
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for (var i = 0; i < found; i++) {
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output[i] = nearestNList[i].node.data;
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}
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output.length = found;
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return output;
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};
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return KDTree;
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}();
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export default KDTree;
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