StackGenVis: Alignment of Data, Algorithms, and Models for Stacking Ensemble Learning Using Performance Metrics https://doi.org/10.1109/TVCG.2020.3030352
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StackGenVis/frontend/node_modules/point-cluster/kd.js

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/**
* kd-tree based clustering
*
* Based on kdbush package
*/
'use strict'
module.exports = function clusterKD (points, ids, levels, weights, options) {
let ptr = 0
let n = ids.length
let nodeSize = options.nodeSize
sort(0, n - 1, 0)
function sort(left, right, level) {
let count = right - left
weights[ptr] = count
levels[ptr++] = level
if (count <= nodeSize) return;
let m = Math.floor((left + right) / 2);
select(m, left, right, level % 2);
sort(left, m - 1, level + 1);
sort(m + 1, right, level + 1);
}
function select(k, left, right, inc) {
while (right > left) {
if (right - left > 600) {
let n = right - left + 1;
let m = k - left + 1;
let z = Math.log(n);
let s = 0.5 * Math.exp(2 * z / 3);
let sd = 0.5 * Math.sqrt(z * s * (n - s) / n) * (m - n / 2 < 0 ? -1 : 1);
let newLeft = Math.max(left, Math.floor(k - m * s / n + sd));
let newRight = Math.min(right, Math.floor(k + (n - m) * s / n + sd));
select(k, newLeft, newRight, inc);
}
let t = points[2 * k + inc];
let i = left;
let j = right;
swapItem(left, k);
if (points[2 * right + inc] > t) swapItem(left, right);
while (i < j) {
swapItem(i, j);
i++;
j--;
while (points[2 * i + inc] < t) i++;
while (points[2 * j + inc] > t) j--;
}
if (points[2 * left + inc] === t) swapItem(left, j);
else {
j++;
swapItem(j, right);
}
if (j <= k) left = j + 1;
if (k <= j) right = j - 1;
}
}
function swapItem(i, j) {
swap(ids, i, j);
swap(points, 2 * i, 2 * j);
swap(points, 2 * i + 1, 2 * j + 1);
}
function swap(arr, i, j) {
let tmp = arr[i];
arr[i] = arr[j];
arr[j] = tmp;
}
}