如何快速检索包含某个空间点的所有球体
How to fast retrieve all spheres containing a certain spatial point
假设我有一个 N 维的球数组 space。每个球都有一定的半径和位置。仅举一个二维平面中圆圈的简化示例如下：
- 中心在
(0, 0)，半径：5.5
- 中心在
(3.5, 4)，半径：12.1
- 中心在
(10.5, -3.2) 半径：3.1
...
希望它能解释清楚。这个列表可能非常大。每个球（在 2D 中，它是一个圆）都有一定的半径和位置。球可以重叠（嗯，几何对象，而不是物理对象）。
我的挑战是，给定 space 中的一个点 P（例如，(12, 4) 在 2D 示例中），我想快速搜索给定的所有球包含点 P 的列表（可能非常大的列表）（P 和球中心之间的距离小于该球的半径）。
由于球列表很长，我无法负担每次搜索都扫描列表的费用。 我需要此列表中的一些索引。然而，点P的位置是任意的。请问有没有什么索引方法可以支持这种搜索？
非常感谢！
如果可以对最大半径进行一些限制，则可以使用修改后的 Vantage Point Tree。
通常情况下，VP Tree 会划分 n 维点，您可以在 Log N 时间内查询离您的搜索点最近的点。
您必须对其进行修改以存储半径，然后递归地进行查询以不断找到距离您的搜索点最近的第 n 个邻居，然后验证搜索点是否在半径内，直到您的“最大半径”距离阈值超过了。
这是 JS 中的标准 VP 树实现：
https://codepen.io/louisricci/pen/JYaXKb?editors=001
还有其他度量树算法，如 KD Tree 和 Ball Tree 做类似的工作。
https://towardsdatascience.com/tree-algorithms-explained-ball-tree-algorithm-vs-kd-tree-vs-brute-force-9746debcd940
上面链接的 VP Tree 代码笔的源代码，对您来说可能是一个很好的起点。
"use strict";
const VPLearn = (function() {
const VP_LEAF_SIZE = 20;
const VP_CONSTANT = 1.7;
const Z_SCORE_95 = 1.96;
const CONFIDENCE_INTERVAL_10 = 0.10;
const SAMPLE_CONSTANT = ((Z_SCORE_95 * Z_SCORE_95) * 0.5 * (1 - 0.5)) / (CONFIDENCE_INTERVAL_10 * CONFIDENCE_INTERVAL_10);
function find(arr, d) {
var l = 0, h = arr.length - 1, i;
while(l <= h) {
i = (l + h) >> 1;
if(arr[i].d < d) {
l = ++i;
} else if(arr[i].d > d) {
h = --i;
} else {
return i;
}
}
return -(++l);
}
class VPValue {
/**
@param {!number} index
@param {!Array.<number>} value
@param {number=} distance
*/
constructor(index, value, distance) {
this.i = index;
this.v = value;
this.d = distance||Infinity;
}
}
class VPNode {
/**
@param {number=} index
@param {number=} radius
@param {VPNode=} near
@param {VPNode=} far
@param {Array.<VPValue>=} values
*/
constructor(index, radius, near, far, values) {
this.i = index||0;
this.r = radius||0;
this.n = near||null;
this.f = far||null;
this.v = values||null;
}
}
class CustomHeap {
/**
@param {!number} size
*/
constructor(size) {
this.size = size;
this.data = [];
}
peek() {
return this.data[0];
}
/**
@param {!VPValue} el
*/
add(el) {
if(this.size === this.data.length && this.peek().d <= el.d) {
return;
}
let pos = this.data.length,
parent;
this.data.push(el);
while(pos > 0) { // bubble up
parent = (pos - 1) >> 1;
if(this.data[parent].d < this.data[pos].d) {
const tmp = this.data[parent];
this.data[parent] = this.data[pos];
this.data[pos] = tmp;
pos = parent;
} else {
break;
}
}
if(this.size < this.data.length) {
this.remove();
}
}
/**
@return {?VPValue}
*/
remove() {
if(this.data.length === 0) {
return null;
}
const result = this.data[0],
head = this.data.pop();
if(this.data.length > 0) {
this.data[0] = head;
let pos = 0,
last = this.data.length - 1,
l, r, m;
do { // bubble down
l = (pos << 1) + 1;
r = l + 1;
m = pos;
if(l <= last && this.data[m].d < this.data[l].d) {
m = l;
}
if(r <= last && this.data[m].d < this.data[r].d) {
m = r;
}
if(m !== pos) {
const tmp = this.data[m];
this.data[m] = this.data[pos];
this.data[pos] = tmp;
pos = m;
} else {
break;
}
} while(true);
}
return result;
}
}
class CustomHeap2 {
/**
@param {!number} size
*/
constructor(size) {
this.size = size;
this.data = [];
}
peek() {
return this.data[this.data.length - 1];
}
/**
@param {!VPValue} el
*/
add(el) {
if(this.size === this.data.length && this.peek().d <= el.d) {
return;
}
const index = find(this.data, el.d);
this.data.splice((index < 0 ? ~index : index), 0, el);
if(this.size < this.data.length) {
this.remove();
}
}
/**
@return {?VPValue}
*/
remove() {
const result = this.data.pop();
return result;
}
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function magnitude(v) {
return Math.sqrt(v.reduce((p, c) => p + c * c, 0));
}
/**
@param {!Array.<number>} v
@return {!Arra.<number>}
*/
function scale(v, s) {
return v.map(vi => s * vi);
}
/**
@param {!Array.<number>} v
@return {!Arra.<number>}
*/
function weight(v, w) {
return v.map((vi, i) => w[i] * vi);
}
/**
@param {!Array.<number>} v
@return {!Arra.<number>}
*/
function normalize(v) {
const dist = magnitude(v);
return v.map(vi => vi / dist);
}
/**
@param {!Array.<number>} v1
@param {!Array.<number>} v2
@return {!number}
*/
function distanceSquared(v1, v2) {
let dist = 0;
for(let i=0; i<v1.length; i++) {
const diff = v1[i] - v2[i];
dist += diff * diff;
}
return dist;
}
/**
@param {!Array.<number>} v1
@param {!Array.<number>} v2
@return {!number}
*/
function distance(v1, v2) {
return Math.sqrt(distanceSquared(v1, v2));
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function mean(v) {
return v.reduce((p, c) => p + c) / v.length;
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function sigmaSquared(v) {
const mu = mean(v);
return v.reduce((p, c) => p + Math.pow(c - mu, 2), 0);
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function standardDeviation(v) {
const mu = mean(v);
return Math.sqrt(v.reduce((p, c) => p + Math.pow(c - mu, 2), 0) / v.length);
}
/**
@param {!number} populationCount
@return {!number}
*/
function calcSampleSize(populationCount) {
return (SAMPLE_CONSTANT / (1 + ((SAMPLE_CONSTANT - 1) / populationCount)))|0;
}
/**
@param {!number} populationCount
@param {!number} size
@return {!Array.<number>}
*/
function sample(populationCount, size) {
const set = new Set();
for(let i=0, pc=populationCount; i<size; i++) {
set.add(Math.floor(Math.random() * pc)|0);
}
return Array.from(set);
}
/**
@param {!Array.<Array.<number>>} vList
@return {!Array.<number>}
*/
function centeroid(vList) {
const sum = Array(vList[0].length).fill(0);
vList.forEach(vi => vi.forEach((vij, j) => sum[j] += vij));
return scale(sum, 1 / vList.length);
}
/**
@param {!Array.<Array.<number>>} vList
@return {!Array.<VPValue>}
*/
function pickVantagePoints(vList) {
const sampleSize = Math.min(vList.length, calcSampleSize(vList.length)),
count = Math.max(1, Math.log(vList.length) / Math.log(VP_CONSTANT))|0,
vpSet = new Set(),
vpList = [];
let bestIndex, bestValue;
// pick root vantage point, candidate with largest std dev from samples
const candidates = sample(vList.length, sampleSize);
bestIndex = -1;
bestValue = 0;
for(let i=0; i<candidates.length; i++) {
const index = candidates[i],
candidate = vList[index],
stdDev = sigmaSquared(sample(vList.length, sampleSize).map(vi => distance(vList[vi], candidate)));
if(stdDev >= bestValue) {
bestIndex = index;
bestValue = stdDev;
}
}
vpSet.add(bestIndex);
vpList.push(new VPValue(bestIndex, vList[bestIndex]));
// pick 2nd vantage point, furthest sample from 1st vantage point
if(count > 1) {
bestIndex = -1;
bestValue = 0;
sample(vList.length, sampleSize).forEach(vi => {
const dist = distanceSquared(vpList[0].v, vList[vi]);
if(dist >= bestValue) {
bestIndex = vi;
bestValue = dist;
}
});
vpSet.add(bestIndex);
vpList.push(new VPValue(bestIndex, vList[bestIndex]));
}
// pick remaining vantage points, furthest from other vantage points
while(vpSet.size < count) {
bestIndex = -1;
bestValue = 0;
sample(vList.length, sampleSize).forEach(vi => {
const meanDistSq = mean(vpList.map(vpi => distanceSquared(vpi.v, vList[vi])));
if(meanDistSq >= bestValue) {
bestIndex = vi;
bestValue = meanDistSq;
}
});
if(!vpSet.has(bestIndex)) {
vpSet.add(bestIndex);
vpList.push(new VPValue(bestIndex, vList[bestIndex]));
}
}
//
return vpList;
}
/**
@param {!Array.<VPValue>} vpList
@param {!Array.<Array.<number>>} vList
@return {!VPNode}
*/
function buildVantagePointTree(vpList, vList) {
const vnList = vList.map((vi, i) => new VPValue(i, vi)),
root = new VPNode(),
queue = [root, vnList];
let qIndex = 0, node, list, bestIndex, bestValue, pivot, radius, near, far;
// partition data
while(qIndex < queue.length) {
node = queue[qIndex++];
list = queue[qIndex++];
// leaf node
if(list.length <= VP_LEAF_SIZE) {
node.v = list;
continue;
}
// root node
if(node === root) {
bestIndex = 0;
} else { // choose best vp
bestIndex = -1;
bestValue = -1;
vpList.forEach((vpi, i) => {
const vpiValue = vpi.v,
vpIndex = i,
samples = sample(list.length, calcSampleSize(list.length)),
tmp = samples.map(idx => distance(list[idx].v, vpiValue)),
stdDev = sigmaSquared(tmp);
if(stdDev > bestValue) {
bestIndex = vpIndex;
bestValue = stdDev;
}
});
}
// calc distances from vp to all elements
list.forEach(vni => vni.d = distance(vni.v, vpList[bestIndex].v));
// sort highest to lowest distance
list.sort((a, b) => b.d - a.d);
// pivot
pivot = Math.max(0, (list.length >> 1) - 1);
radius = list[pivot].d;
while(pivot + 1 < list.length && radius === list[pivot + 1].d) {
pivot++;
}
// near and far partitions
// far are all GREATER THAN OR EQUAL TO RADIUS
far = list.slice(0, pivot + 1);
// near are all LESS THAN RADIUS
near = list.slice(pivot + 1);
// node
node.i = bestIndex;
node.r = radius;
if(far.length > 0) {
node.f = new VPNode();
queue.push(node.f, far);
}
if(near.length > 0) {
node.n = new VPNode();
queue.push(node.n, near);
}
}
return root;
}
class VPLearn {
/**
@param {boolean=} normalizeInputs Normalize all input vectors
@param {Array.<number>=} customWeights Multiply components of input vectors by corresponding weight
*/
constructor(normalizeInputs, customWeights) {
this._inputs = [];
this._targets = [];
this._vantagePoints = [];
this._tree = null;
this._weights = customWeights || null;
this._normalize = normalizeInputs || false;
}
/**
Add an input-target pair.
@param {Array.<number>} input Training vector
@param {*} target Training target
*/
train(input, target) {
if(this._weights !== null) {
input = weight(input, this._weights);
}
if(this._normalize === true) {
input = normalize(input);
}
this._inputs.push(input);
this._targets.push(target);
this._tree = null;
}
/**
Add multiple training objects.
@param {{input:Array.<number>, target:*}} trainingData
*/
trainRange(trainingData) {
trainingData.forEach(data => this.train(data.input, data.target));
}
/**
Compile training data into a vantage point tree.
*/
compile() {
this._vantagePoints = pickVantagePoints(this._inputs);
this._tree = buildVantagePointTree(this._vantagePoints, this._inputs);
}
/**
Find the nearest input values and return the corresponding targets.
@param {Array.<number> input Search vector
@param {number=} nearest Nth nearest results
@param {Array.<{i:number, v:Array.<number>, d:number}>} inputResults OUT array of nth nearest tree leafs: (i)ndex, (v)alue, and (d)istance from input
@returm {Array.<*>} Nearest target values
*/
query(input, nearest = 1, inputResults = null) {
if(this._weights !== null) {
input = weight(input, this._weights);
}
if(this._normalize === true) {
input = normalize(input);
}
if(this._tree === null) {
this.compile();
}
const vpList = this._vantagePoints.map(vpi => new VPValue(vpi.i, vpi.v)),
stack = [this._tree],
nth = new CustomHeap(nearest),
result = [];
let tau = Number.MAX_VALUE,
node, vp, dist, tmp;
while(stack.length > 0) {
node = stack.pop();
// value node
if(node.v !== null) {
node.v.forEach(vi => {
vi.d = distance(input, vi.v);
nth.add(vi);
if(nth.data.length === nearest) {
tau = nth.peek().d;
}
});
continue;
}
// vantage point
vp = vpList[node.i];
if(vp.d !== Infinity) {
dist = vp.d;
} else {
vp.d = dist = distance(input, vp.v);
}
if(dist < node.r) {
if(dist < node.r + tau && node.n !== null) {
stack.push(node.n);
}
if(dist >= node.r - tau && node.f !== null) {
stack.push(node.f);
}
} else {
if(dist >= node.r - tau && node.f !== null) {
stack.push(node.f);
}
if(dist < node.r + tau && node.n !== null) {
stack.push(node.n);
}
}
}
// return the target values in closest to furtherest order of the corresponding input
if(Array.isArray(inputResults)) {
while((tmp = nth.remove()) !== null) {
result.push(JSON.parse(JSON.stringify(this._targets[tmp.i])));
inputResults.push(JSON.parse(JSON.stringify(tmp)));
}
inputResults.reverse();
return result.reverse();
} else {
while((tmp = nth.remove()) !== null) {
result.push(JSON.parse(JSON.stringify(this._targets[tmp.i])));
}
return result.reverse();
}
}
}
return VPLearn;
})();
document.body.innerHTML += '<pre style="overflow:scroll; height:10em;"></pre>';
const outp = document.querySelector("pre");
const compass = new VPLearn();
compass.trainRange([
{input: [0], target:"N"},
{input: [22.5], target:"NNE"},
{input: [45], target:"NE"},
{input: [77.5], target:"ENE"},
{input: [90], target:"E"},
{input: [112.5], target:"ESE"},
{input: [135], target:"SE"},
{input: [157.5], target:"SSE"},
{input: [180], target:"S"},
{input: [202.5], target:"SSW"},
{input: [225], target:"SW"},
{input: [247.5], target:"WSW"},
{input: [270], target:"W"},
{input: [292.5], target:"WNW"},
{input: [315], target:"NW"},
{input: [337.5], target:"WNW"},
{input: [360], target:"N"}
]);
compass.compile();
outp.innerHTML += "Compass (Degrees -> Direction)\n";
for(let i=0; i<=360; i++) {
outp.innerHTML += `${i} -> ${compass.query([i])[0]}\n`;
}
const colors = new VPLearn();
colors.trainRange([
{input: [240,248,255], target: "aliceblue"},
{input: [250,235,215], target: "antiquewhite"},
{input: [0,255,255], target: "aqua"},
{input: [127,255,212], target: "aquamarine"},
{input: [240,255,255], target: "azure"},
{input: [245,245,220], target: "beige"},
{input: [255,228,196], target: "bisque"},
{input: [0,0,0], target: "black"},
{input: [255,235,205], target: "blanchedalmond"},
{input: [0,0,255], target: "blue"},
{input: [138,43,226], target: "blueviolet"},
{input: [165,42,42], target: "brown"},
{input: [222,184,135], target: "burlywood"},
{input: [95,158,160], target: "cadetblue"},
{input: [127,255,0], target: "chartreuse"},
{input: [210,105,30], target: "chocolate"},
{input: [255,127,80], target: "coral"},
{input: [100,149,237], target: "cornflowerblue"},
{input: [255,248,220], target: "cornsilk"},
{input: [220,20,60], target: "crimson"},
{input: [0,255,255], target: "cyan"},
{input: [0,0,139], target: "darkblue"},
{input: [0,139,139], target: "darkcyan"},
{input: [184,134,11], target: "darkgoldenrod"},
{input: [169,169,169], target: "darkgray"},
{input: [0,100,0], target: "darkgreen"},
{input: [189,183,107], target: "darkkhaki"},
{input: [139,0,139], target: "darkmagenta"},
{input: [85,107,47], target: "darkolivegreen"},
{input: [255,140,0], target: "darkorange"},
{input: [153,50,204], target: "darkorchid"},
{input: [139,0,0], target: "darkred"},
{input: [233,150,122], target: "darksalmon"},
{input: [143,188,143], target: "darkseagreen"},
{input: [72,61,139], target: "darkslateblue"},
{input: [47,79,79], target: "darkslategray"},
{input: [0,206,209], target: "darkturquoise"},
{input: [148,0,211], target: "darkviolet"},
{input: [255,20,147], target: "deeppink"},
{input: [0,191,255], target: "deepskyblue"},
{input: [105,105,105], target: "dimgray"},
{input: [30,144,255], target: "dodgerblue"},
{input: [178,34,34], target: "firebrick"},
{input: [255,250,240], target: "floralwhite"},
{input: [34,139,34], target: "forestgreen"},
{input: [255,0,255], target: "fuchsia"},
{input: [220,220,220], target: "gainsboro"},
{input: [248,248,255], target: "ghostwhite"},
{input: [255,215,0], target: "gold"},
{input: [218,165,32], target: "goldenrod"},
{input: [128,128,128], target: "gray"},
{input: [0,128,0], target: "green"},
{input: [173,255,47], target: "greenyellow"},
{input: [240,255,240], target: "honeydew"},
{input: [255,105,180], target: "hotpink"},
{input: [205,92,92], target: "indianred"},
{input: [75,0,130], target: "indigo"},
{input: [255,255,240], target: "ivory"},
{input: [240,230,140], target: "khaki"},
{input: [230,230,250], target: "lavender"},
{input: [255,240,245], target: "lavenderblush"},
{input: [124,252,0], target: "lawngreen"},
{input: [255,250,205], target: "lemonchiffon"},
{input: [173,216,230], target: "lightblue"},
{input: [240,128,128], target: "lightcoral"},
{input: [224,255,255], target: "lightcyan"},
{input: [250,250,210], target: "lightgoldenrodyellow"},
{input: [144,238,144], target: "lightgreen"},
{input: [211,211,211], target: "lightgrey"},
{input: [255,182,193], target: "lightpink"},
{input: [255,160,122], target: "lightsalmon"},
{input: [32,178,170], target: "lightseagreen"},
{input: [135,206,250], target: "lightskyblue"},
{input: [119,136,153], target: "lightslategray"},
{input: [176,196,222], target: "lightsteelblue"},
{input: [255,255,224], target: "lightyellow"},
{input: [0,255,0], target: "lime"},
{input: [50,205,50], target: "limegreen"},
{input: [250,240,230], target: "linen"},
{input: [255,0,255], target: "magenta"},
{input: [128,0,0], target: "maroon"},
{input: [102,205,170], target: "mediumaquamarine"},
{input: [0,0,205], target: "mediumblue"},
{input: [186,85,211], target: "mediumorchid"},
{input: [147,112,219], target: "mediumpurple"},
{input: [60,179,113], target: "mediumseagreen"},
{input: [123,104,238], target: "mediumslateblue"},
{input: [0,250,154], target: "mediumspringgreen"},
{input: [72,209,204], target: "mediumturquoise"},
{input: [199,21,133], target: "mediumvioletred"},
{input: [25,25,112], target: "midnightblue"},
{input: [245,255,250], target: "mintcream"},
{input: [255,228,225], target: "mistyrose"},
{input: [255,228,181], target: "moccasin"},
{input: [255,222,173], target: "navajowhite"},
{input: [0,0,128], target: "navy"},
{input: [253,245,230], target: "oldlace"},
{input: [128,128,0], target: "olive"},
{input: [107,142,35], target: "olivedrab"},
{input: [255,165,0], target: "orange"},
{input: [255,69,0], target: "orangered"},
{input: [218,112,214], target: "orchid"},
{input: [238,232,170], target: "palegoldenrod"},
{input: [152,251,152], target: "palegreen"},
{input: [175,238,238], target: "paleturquoise"},
{input: [219,112,147], target: "palevioletred"},
{input: [255,239,213], target: "papayawhip"},
{input: [255,218,185], target: "peachpuff"},
{input: [205,133,63], target: "peru"},
{input: [255,192,203], target: "pink"},
{input: [221,160,221], target: "plum"},
{input: [176,224,230], target: "powderblue"},
{input: [128,0,128], target: "purple"},
{input: [255,0,0], target: "red"},
{input: [188,143,143], target: "rosybrown"},
{input: [65,105,225], target: "royalblue"},
{input: [139,69,19], target: "saddlebrown"},
{input: [250,128,114], target: "salmon"},
{input: [244,164,96], target: "sandybrown"},
{input: [46,139,87], target: "seagreen"},
{input: [255,245,238], target: "seashell"},
{input: [160,82,45], target: "sienna"},
{input: [192,192,192], target: "silver"},
{input: [135,206,235], target: "skyblue"},
{input: [106,90,205], target: "slateblue"},
{input: [112,128,144], target: "slategray"},
{input: [255,250,250], target: "snow"},
{input: [0,255,127], target: "springgreen"},
{input: [70,130,180], target: "steelblue"},
{input: [210,180,140], target: "tan"},
{input: [0,128,128], target: "teal"},
{input: [216,191,216], target: "thistle"},
{input: [255,99,71], target: "tomato"},
{input: [64,224,208], target: "turquoise"},
{input: [238,130,238], target: "violet"},
{input: [245,222,179], target: "wheat"},
{input: [255,255,255], target: "white"},
{input: [245,245,245], target: "whitesmoke"},
{input: [255,255,0], target: "yellow"},
{input: [154,205,50], target: "yellowgreen"}
]);
colors.compile();
console.log(colors._tree);
console.log(colors._vantagePoints);
document.body.innerHTML += '<input id="cvR" type="number" value="255"/><input id="cvG" type="number" value="255"/><input id="cvB" type="number" value="255"/><input type="button" id="btnCV" value="Top 2 Colors"/><div id="CV"></div>';
const MAX_COLOR_DIST = Math.sqrt(255*255 + 255*255 + 255*255);
document.querySelector("#btnCV").addEventListener("click", e => {
const rgb = [
parseInt(document.querySelector("#cvR").value),
parseInt(document.querySelector("#cvG").value),
parseInt(document.querySelector("#cvB").value)
];
const meta = [];
const result = colors.query(rgb, 2, meta);
document.querySelector("#CV").innerHTML = `<div style="border:4px solid rgb(${rgb});" >rgb(${rgb}) : "${result.toString()} ${(MAX_COLOR_DIST - meta[0].d) * 100 / MAX_COLOR_DIST}%, ${(MAX_COLOR_DIST - meta[1].d) * 100 / MAX_COLOR_DIST}%"</div>`;
}, false);
假设我有一个 N 维的球数组 space。每个球都有一定的半径和位置。仅举一个二维平面中圆圈的简化示例如下：
- 中心在
(0, 0)，半径：5.5 - 中心在
(3.5, 4)，半径：12.1 - 中心在
(10.5, -3.2)半径：3.1...
希望它能解释清楚。这个列表可能非常大。每个球（在 2D 中，它是一个圆）都有一定的半径和位置。球可以重叠（嗯，几何对象，而不是物理对象）。
我的挑战是，给定 space 中的一个点 P（例如，(12, 4) 在 2D 示例中），我想快速搜索给定的所有球包含点 P 的列表（可能非常大的列表）（P 和球中心之间的距离小于该球的半径）。
由于球列表很长，我无法负担每次搜索都扫描列表的费用。 我需要此列表中的一些索引。然而，点P的位置是任意的。请问有没有什么索引方法可以支持这种搜索？
非常感谢！
如果可以对最大半径进行一些限制，则可以使用修改后的 Vantage Point Tree。
通常情况下，VP Tree 会划分 n 维点，您可以在 Log N 时间内查询离您的搜索点最近的点。
您必须对其进行修改以存储半径，然后递归地进行查询以不断找到距离您的搜索点最近的第 n 个邻居，然后验证搜索点是否在半径内，直到您的“最大半径”距离阈值超过了。
这是 JS 中的标准 VP 树实现： https://codepen.io/louisricci/pen/JYaXKb?editors=001
还有其他度量树算法，如 KD Tree 和 Ball Tree 做类似的工作。 https://towardsdatascience.com/tree-algorithms-explained-ball-tree-algorithm-vs-kd-tree-vs-brute-force-9746debcd940
上面链接的 VP Tree 代码笔的源代码，对您来说可能是一个很好的起点。
"use strict";
const VPLearn = (function() {
const VP_LEAF_SIZE = 20;
const VP_CONSTANT = 1.7;
const Z_SCORE_95 = 1.96;
const CONFIDENCE_INTERVAL_10 = 0.10;
const SAMPLE_CONSTANT = ((Z_SCORE_95 * Z_SCORE_95) * 0.5 * (1 - 0.5)) / (CONFIDENCE_INTERVAL_10 * CONFIDENCE_INTERVAL_10);
function find(arr, d) {
var l = 0, h = arr.length - 1, i;
while(l <= h) {
i = (l + h) >> 1;
if(arr[i].d < d) {
l = ++i;
} else if(arr[i].d > d) {
h = --i;
} else {
return i;
}
}
return -(++l);
}
class VPValue {
/**
@param {!number} index
@param {!Array.<number>} value
@param {number=} distance
*/
constructor(index, value, distance) {
this.i = index;
this.v = value;
this.d = distance||Infinity;
}
}
class VPNode {
/**
@param {number=} index
@param {number=} radius
@param {VPNode=} near
@param {VPNode=} far
@param {Array.<VPValue>=} values
*/
constructor(index, radius, near, far, values) {
this.i = index||0;
this.r = radius||0;
this.n = near||null;
this.f = far||null;
this.v = values||null;
}
}
class CustomHeap {
/**
@param {!number} size
*/
constructor(size) {
this.size = size;
this.data = [];
}
peek() {
return this.data[0];
}
/**
@param {!VPValue} el
*/
add(el) {
if(this.size === this.data.length && this.peek().d <= el.d) {
return;
}
let pos = this.data.length,
parent;
this.data.push(el);
while(pos > 0) { // bubble up
parent = (pos - 1) >> 1;
if(this.data[parent].d < this.data[pos].d) {
const tmp = this.data[parent];
this.data[parent] = this.data[pos];
this.data[pos] = tmp;
pos = parent;
} else {
break;
}
}
if(this.size < this.data.length) {
this.remove();
}
}
/**
@return {?VPValue}
*/
remove() {
if(this.data.length === 0) {
return null;
}
const result = this.data[0],
head = this.data.pop();
if(this.data.length > 0) {
this.data[0] = head;
let pos = 0,
last = this.data.length - 1,
l, r, m;
do { // bubble down
l = (pos << 1) + 1;
r = l + 1;
m = pos;
if(l <= last && this.data[m].d < this.data[l].d) {
m = l;
}
if(r <= last && this.data[m].d < this.data[r].d) {
m = r;
}
if(m !== pos) {
const tmp = this.data[m];
this.data[m] = this.data[pos];
this.data[pos] = tmp;
pos = m;
} else {
break;
}
} while(true);
}
return result;
}
}
class CustomHeap2 {
/**
@param {!number} size
*/
constructor(size) {
this.size = size;
this.data = [];
}
peek() {
return this.data[this.data.length - 1];
}
/**
@param {!VPValue} el
*/
add(el) {
if(this.size === this.data.length && this.peek().d <= el.d) {
return;
}
const index = find(this.data, el.d);
this.data.splice((index < 0 ? ~index : index), 0, el);
if(this.size < this.data.length) {
this.remove();
}
}
/**
@return {?VPValue}
*/
remove() {
const result = this.data.pop();
return result;
}
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function magnitude(v) {
return Math.sqrt(v.reduce((p, c) => p + c * c, 0));
}
/**
@param {!Array.<number>} v
@return {!Arra.<number>}
*/
function scale(v, s) {
return v.map(vi => s * vi);
}
/**
@param {!Array.<number>} v
@return {!Arra.<number>}
*/
function weight(v, w) {
return v.map((vi, i) => w[i] * vi);
}
/**
@param {!Array.<number>} v
@return {!Arra.<number>}
*/
function normalize(v) {
const dist = magnitude(v);
return v.map(vi => vi / dist);
}
/**
@param {!Array.<number>} v1
@param {!Array.<number>} v2
@return {!number}
*/
function distanceSquared(v1, v2) {
let dist = 0;
for(let i=0; i<v1.length; i++) {
const diff = v1[i] - v2[i];
dist += diff * diff;
}
return dist;
}
/**
@param {!Array.<number>} v1
@param {!Array.<number>} v2
@return {!number}
*/
function distance(v1, v2) {
return Math.sqrt(distanceSquared(v1, v2));
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function mean(v) {
return v.reduce((p, c) => p + c) / v.length;
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function sigmaSquared(v) {
const mu = mean(v);
return v.reduce((p, c) => p + Math.pow(c - mu, 2), 0);
}
/**
@param {!Array.<number>} v
@return {!number}
*/
function standardDeviation(v) {
const mu = mean(v);
return Math.sqrt(v.reduce((p, c) => p + Math.pow(c - mu, 2), 0) / v.length);
}
/**
@param {!number} populationCount
@return {!number}
*/
function calcSampleSize(populationCount) {
return (SAMPLE_CONSTANT / (1 + ((SAMPLE_CONSTANT - 1) / populationCount)))|0;
}
/**
@param {!number} populationCount
@param {!number} size
@return {!Array.<number>}
*/
function sample(populationCount, size) {
const set = new Set();
for(let i=0, pc=populationCount; i<size; i++) {
set.add(Math.floor(Math.random() * pc)|0);
}
return Array.from(set);
}
/**
@param {!Array.<Array.<number>>} vList
@return {!Array.<number>}
*/
function centeroid(vList) {
const sum = Array(vList[0].length).fill(0);
vList.forEach(vi => vi.forEach((vij, j) => sum[j] += vij));
return scale(sum, 1 / vList.length);
}
/**
@param {!Array.<Array.<number>>} vList
@return {!Array.<VPValue>}
*/
function pickVantagePoints(vList) {
const sampleSize = Math.min(vList.length, calcSampleSize(vList.length)),
count = Math.max(1, Math.log(vList.length) / Math.log(VP_CONSTANT))|0,
vpSet = new Set(),
vpList = [];
let bestIndex, bestValue;
// pick root vantage point, candidate with largest std dev from samples
const candidates = sample(vList.length, sampleSize);
bestIndex = -1;
bestValue = 0;
for(let i=0; i<candidates.length; i++) {
const index = candidates[i],
candidate = vList[index],
stdDev = sigmaSquared(sample(vList.length, sampleSize).map(vi => distance(vList[vi], candidate)));
if(stdDev >= bestValue) {
bestIndex = index;
bestValue = stdDev;
}
}
vpSet.add(bestIndex);
vpList.push(new VPValue(bestIndex, vList[bestIndex]));
// pick 2nd vantage point, furthest sample from 1st vantage point
if(count > 1) {
bestIndex = -1;
bestValue = 0;
sample(vList.length, sampleSize).forEach(vi => {
const dist = distanceSquared(vpList[0].v, vList[vi]);
if(dist >= bestValue) {
bestIndex = vi;
bestValue = dist;
}
});
vpSet.add(bestIndex);
vpList.push(new VPValue(bestIndex, vList[bestIndex]));
}
// pick remaining vantage points, furthest from other vantage points
while(vpSet.size < count) {
bestIndex = -1;
bestValue = 0;
sample(vList.length, sampleSize).forEach(vi => {
const meanDistSq = mean(vpList.map(vpi => distanceSquared(vpi.v, vList[vi])));
if(meanDistSq >= bestValue) {
bestIndex = vi;
bestValue = meanDistSq;
}
});
if(!vpSet.has(bestIndex)) {
vpSet.add(bestIndex);
vpList.push(new VPValue(bestIndex, vList[bestIndex]));
}
}
//
return vpList;
}
/**
@param {!Array.<VPValue>} vpList
@param {!Array.<Array.<number>>} vList
@return {!VPNode}
*/
function buildVantagePointTree(vpList, vList) {
const vnList = vList.map((vi, i) => new VPValue(i, vi)),
root = new VPNode(),
queue = [root, vnList];
let qIndex = 0, node, list, bestIndex, bestValue, pivot, radius, near, far;
// partition data
while(qIndex < queue.length) {
node = queue[qIndex++];
list = queue[qIndex++];
// leaf node
if(list.length <= VP_LEAF_SIZE) {
node.v = list;
continue;
}
// root node
if(node === root) {
bestIndex = 0;
} else { // choose best vp
bestIndex = -1;
bestValue = -1;
vpList.forEach((vpi, i) => {
const vpiValue = vpi.v,
vpIndex = i,
samples = sample(list.length, calcSampleSize(list.length)),
tmp = samples.map(idx => distance(list[idx].v, vpiValue)),
stdDev = sigmaSquared(tmp);
if(stdDev > bestValue) {
bestIndex = vpIndex;
bestValue = stdDev;
}
});
}
// calc distances from vp to all elements
list.forEach(vni => vni.d = distance(vni.v, vpList[bestIndex].v));
// sort highest to lowest distance
list.sort((a, b) => b.d - a.d);
// pivot
pivot = Math.max(0, (list.length >> 1) - 1);
radius = list[pivot].d;
while(pivot + 1 < list.length && radius === list[pivot + 1].d) {
pivot++;
}
// near and far partitions
// far are all GREATER THAN OR EQUAL TO RADIUS
far = list.slice(0, pivot + 1);
// near are all LESS THAN RADIUS
near = list.slice(pivot + 1);
// node
node.i = bestIndex;
node.r = radius;
if(far.length > 0) {
node.f = new VPNode();
queue.push(node.f, far);
}
if(near.length > 0) {
node.n = new VPNode();
queue.push(node.n, near);
}
}
return root;
}
class VPLearn {
/**
@param {boolean=} normalizeInputs Normalize all input vectors
@param {Array.<number>=} customWeights Multiply components of input vectors by corresponding weight
*/
constructor(normalizeInputs, customWeights) {
this._inputs = [];
this._targets = [];
this._vantagePoints = [];
this._tree = null;
this._weights = customWeights || null;
this._normalize = normalizeInputs || false;
}
/**
Add an input-target pair.
@param {Array.<number>} input Training vector
@param {*} target Training target
*/
train(input, target) {
if(this._weights !== null) {
input = weight(input, this._weights);
}
if(this._normalize === true) {
input = normalize(input);
}
this._inputs.push(input);
this._targets.push(target);
this._tree = null;
}
/**
Add multiple training objects.
@param {{input:Array.<number>, target:*}} trainingData
*/
trainRange(trainingData) {
trainingData.forEach(data => this.train(data.input, data.target));
}
/**
Compile training data into a vantage point tree.
*/
compile() {
this._vantagePoints = pickVantagePoints(this._inputs);
this._tree = buildVantagePointTree(this._vantagePoints, this._inputs);
}
/**
Find the nearest input values and return the corresponding targets.
@param {Array.<number> input Search vector
@param {number=} nearest Nth nearest results
@param {Array.<{i:number, v:Array.<number>, d:number}>} inputResults OUT array of nth nearest tree leafs: (i)ndex, (v)alue, and (d)istance from input
@returm {Array.<*>} Nearest target values
*/
query(input, nearest = 1, inputResults = null) {
if(this._weights !== null) {
input = weight(input, this._weights);
}
if(this._normalize === true) {
input = normalize(input);
}
if(this._tree === null) {
this.compile();
}
const vpList = this._vantagePoints.map(vpi => new VPValue(vpi.i, vpi.v)),
stack = [this._tree],
nth = new CustomHeap(nearest),
result = [];
let tau = Number.MAX_VALUE,
node, vp, dist, tmp;
while(stack.length > 0) {
node = stack.pop();
// value node
if(node.v !== null) {
node.v.forEach(vi => {
vi.d = distance(input, vi.v);
nth.add(vi);
if(nth.data.length === nearest) {
tau = nth.peek().d;
}
});
continue;
}
// vantage point
vp = vpList[node.i];
if(vp.d !== Infinity) {
dist = vp.d;
} else {
vp.d = dist = distance(input, vp.v);
}
if(dist < node.r) {
if(dist < node.r + tau && node.n !== null) {
stack.push(node.n);
}
if(dist >= node.r - tau && node.f !== null) {
stack.push(node.f);
}
} else {
if(dist >= node.r - tau && node.f !== null) {
stack.push(node.f);
}
if(dist < node.r + tau && node.n !== null) {
stack.push(node.n);
}
}
}
// return the target values in closest to furtherest order of the corresponding input
if(Array.isArray(inputResults)) {
while((tmp = nth.remove()) !== null) {
result.push(JSON.parse(JSON.stringify(this._targets[tmp.i])));
inputResults.push(JSON.parse(JSON.stringify(tmp)));
}
inputResults.reverse();
return result.reverse();
} else {
while((tmp = nth.remove()) !== null) {
result.push(JSON.parse(JSON.stringify(this._targets[tmp.i])));
}
return result.reverse();
}
}
}
return VPLearn;
})();
document.body.innerHTML += '<pre style="overflow:scroll; height:10em;"></pre>';
const outp = document.querySelector("pre");
const compass = new VPLearn();
compass.trainRange([
{input: [0], target:"N"},
{input: [22.5], target:"NNE"},
{input: [45], target:"NE"},
{input: [77.5], target:"ENE"},
{input: [90], target:"E"},
{input: [112.5], target:"ESE"},
{input: [135], target:"SE"},
{input: [157.5], target:"SSE"},
{input: [180], target:"S"},
{input: [202.5], target:"SSW"},
{input: [225], target:"SW"},
{input: [247.5], target:"WSW"},
{input: [270], target:"W"},
{input: [292.5], target:"WNW"},
{input: [315], target:"NW"},
{input: [337.5], target:"WNW"},
{input: [360], target:"N"}
]);
compass.compile();
outp.innerHTML += "Compass (Degrees -> Direction)\n";
for(let i=0; i<=360; i++) {
outp.innerHTML += `${i} -> ${compass.query([i])[0]}\n`;
}
const colors = new VPLearn();
colors.trainRange([
{input: [240,248,255], target: "aliceblue"},
{input: [250,235,215], target: "antiquewhite"},
{input: [0,255,255], target: "aqua"},
{input: [127,255,212], target: "aquamarine"},
{input: [240,255,255], target: "azure"},
{input: [245,245,220], target: "beige"},
{input: [255,228,196], target: "bisque"},
{input: [0,0,0], target: "black"},
{input: [255,235,205], target: "blanchedalmond"},
{input: [0,0,255], target: "blue"},
{input: [138,43,226], target: "blueviolet"},
{input: [165,42,42], target: "brown"},
{input: [222,184,135], target: "burlywood"},
{input: [95,158,160], target: "cadetblue"},
{input: [127,255,0], target: "chartreuse"},
{input: [210,105,30], target: "chocolate"},
{input: [255,127,80], target: "coral"},
{input: [100,149,237], target: "cornflowerblue"},
{input: [255,248,220], target: "cornsilk"},
{input: [220,20,60], target: "crimson"},
{input: [0,255,255], target: "cyan"},
{input: [0,0,139], target: "darkblue"},
{input: [0,139,139], target: "darkcyan"},
{input: [184,134,11], target: "darkgoldenrod"},
{input: [169,169,169], target: "darkgray"},
{input: [0,100,0], target: "darkgreen"},
{input: [189,183,107], target: "darkkhaki"},
{input: [139,0,139], target: "darkmagenta"},
{input: [85,107,47], target: "darkolivegreen"},
{input: [255,140,0], target: "darkorange"},
{input: [153,50,204], target: "darkorchid"},
{input: [139,0,0], target: "darkred"},
{input: [233,150,122], target: "darksalmon"},
{input: [143,188,143], target: "darkseagreen"},
{input: [72,61,139], target: "darkslateblue"},
{input: [47,79,79], target: "darkslategray"},
{input: [0,206,209], target: "darkturquoise"},
{input: [148,0,211], target: "darkviolet"},
{input: [255,20,147], target: "deeppink"},
{input: [0,191,255], target: "deepskyblue"},
{input: [105,105,105], target: "dimgray"},
{input: [30,144,255], target: "dodgerblue"},
{input: [178,34,34], target: "firebrick"},
{input: [255,250,240], target: "floralwhite"},
{input: [34,139,34], target: "forestgreen"},
{input: [255,0,255], target: "fuchsia"},
{input: [220,220,220], target: "gainsboro"},
{input: [248,248,255], target: "ghostwhite"},
{input: [255,215,0], target: "gold"},
{input: [218,165,32], target: "goldenrod"},
{input: [128,128,128], target: "gray"},
{input: [0,128,0], target: "green"},
{input: [173,255,47], target: "greenyellow"},
{input: [240,255,240], target: "honeydew"},
{input: [255,105,180], target: "hotpink"},
{input: [205,92,92], target: "indianred"},
{input: [75,0,130], target: "indigo"},
{input: [255,255,240], target: "ivory"},
{input: [240,230,140], target: "khaki"},
{input: [230,230,250], target: "lavender"},
{input: [255,240,245], target: "lavenderblush"},
{input: [124,252,0], target: "lawngreen"},
{input: [255,250,205], target: "lemonchiffon"},
{input: [173,216,230], target: "lightblue"},
{input: [240,128,128], target: "lightcoral"},
{input: [224,255,255], target: "lightcyan"},
{input: [250,250,210], target: "lightgoldenrodyellow"},
{input: [144,238,144], target: "lightgreen"},
{input: [211,211,211], target: "lightgrey"},
{input: [255,182,193], target: "lightpink"},
{input: [255,160,122], target: "lightsalmon"},
{input: [32,178,170], target: "lightseagreen"},
{input: [135,206,250], target: "lightskyblue"},
{input: [119,136,153], target: "lightslategray"},
{input: [176,196,222], target: "lightsteelblue"},
{input: [255,255,224], target: "lightyellow"},
{input: [0,255,0], target: "lime"},
{input: [50,205,50], target: "limegreen"},
{input: [250,240,230], target: "linen"},
{input: [255,0,255], target: "magenta"},
{input: [128,0,0], target: "maroon"},
{input: [102,205,170], target: "mediumaquamarine"},
{input: [0,0,205], target: "mediumblue"},
{input: [186,85,211], target: "mediumorchid"},
{input: [147,112,219], target: "mediumpurple"},
{input: [60,179,113], target: "mediumseagreen"},
{input: [123,104,238], target: "mediumslateblue"},
{input: [0,250,154], target: "mediumspringgreen"},
{input: [72,209,204], target: "mediumturquoise"},
{input: [199,21,133], target: "mediumvioletred"},
{input: [25,25,112], target: "midnightblue"},
{input: [245,255,250], target: "mintcream"},
{input: [255,228,225], target: "mistyrose"},
{input: [255,228,181], target: "moccasin"},
{input: [255,222,173], target: "navajowhite"},
{input: [0,0,128], target: "navy"},
{input: [253,245,230], target: "oldlace"},
{input: [128,128,0], target: "olive"},
{input: [107,142,35], target: "olivedrab"},
{input: [255,165,0], target: "orange"},
{input: [255,69,0], target: "orangered"},
{input: [218,112,214], target: "orchid"},
{input: [238,232,170], target: "palegoldenrod"},
{input: [152,251,152], target: "palegreen"},
{input: [175,238,238], target: "paleturquoise"},
{input: [219,112,147], target: "palevioletred"},
{input: [255,239,213], target: "papayawhip"},
{input: [255,218,185], target: "peachpuff"},
{input: [205,133,63], target: "peru"},
{input: [255,192,203], target: "pink"},
{input: [221,160,221], target: "plum"},
{input: [176,224,230], target: "powderblue"},
{input: [128,0,128], target: "purple"},
{input: [255,0,0], target: "red"},
{input: [188,143,143], target: "rosybrown"},
{input: [65,105,225], target: "royalblue"},
{input: [139,69,19], target: "saddlebrown"},
{input: [250,128,114], target: "salmon"},
{input: [244,164,96], target: "sandybrown"},
{input: [46,139,87], target: "seagreen"},
{input: [255,245,238], target: "seashell"},
{input: [160,82,45], target: "sienna"},
{input: [192,192,192], target: "silver"},
{input: [135,206,235], target: "skyblue"},
{input: [106,90,205], target: "slateblue"},
{input: [112,128,144], target: "slategray"},
{input: [255,250,250], target: "snow"},
{input: [0,255,127], target: "springgreen"},
{input: [70,130,180], target: "steelblue"},
{input: [210,180,140], target: "tan"},
{input: [0,128,128], target: "teal"},
{input: [216,191,216], target: "thistle"},
{input: [255,99,71], target: "tomato"},
{input: [64,224,208], target: "turquoise"},
{input: [238,130,238], target: "violet"},
{input: [245,222,179], target: "wheat"},
{input: [255,255,255], target: "white"},
{input: [245,245,245], target: "whitesmoke"},
{input: [255,255,0], target: "yellow"},
{input: [154,205,50], target: "yellowgreen"}
]);
colors.compile();
console.log(colors._tree);
console.log(colors._vantagePoints);
document.body.innerHTML += '<input id="cvR" type="number" value="255"/><input id="cvG" type="number" value="255"/><input id="cvB" type="number" value="255"/><input type="button" id="btnCV" value="Top 2 Colors"/><div id="CV"></div>';
const MAX_COLOR_DIST = Math.sqrt(255*255 + 255*255 + 255*255);
document.querySelector("#btnCV").addEventListener("click", e => {
const rgb = [
parseInt(document.querySelector("#cvR").value),
parseInt(document.querySelector("#cvG").value),
parseInt(document.querySelector("#cvB").value)
];
const meta = [];
const result = colors.query(rgb, 2, meta);
document.querySelector("#CV").innerHTML = `<div style="border:4px solid rgb(${rgb});" >rgb(${rgb}) : "${result.toString()} ${(MAX_COLOR_DIST - meta[0].d) * 100 / MAX_COLOR_DIST}%, ${(MAX_COLOR_DIST - meta[1].d) * 100 / MAX_COLOR_DIST}%"</div>`;
}, false);