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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| README.md | 4 years ago | |
| kdbush.js | 4 years ago | |
| kdbush.min.js | 4 years ago | |
| package.json | 4 years ago | |
README.md
KDBush

A very fast static spatial index for 2D points based on a flat KD-tree. Compared to RBush:
- points only — no rectangles
- static — you can't add/remove items
- indexing is 5-8 times faster
const index = new KDBush(points); // make an index
const ids1 = index.range(10, 10, 20, 20); // bbox search - minX, minY, maxX, maxY
const ids2 = index.within(10, 10, 5); // radius search - x, y, radius
Install
Install using NPM (npm install kdbush) or Yarn (yarn add kdbush), then:
// import as a ES module
import KDBush from 'kdbush';
// or require in Node / Browserify
const KDBush = require('kdbush');
Or use a browser build directly:
<script src="https://unpkg.com/kdbush@2.0.0/kdbush.min.js"></script>
API
new KDBush(points[, getX, getY, nodeSize, arrayType])
Creates an index from the given points.
points: Input array of points.getX,getY: Functions to getxandyfrom an input point. By default, it assumes[x, y]format.nodeSize: Size of the KD-tree node,64by default. Higher means faster indexing but slower search, and vise versa.arrayType: Array type to use for storing coordinate values.Float64Arrayby default, but if your coordinates are integer values,Int32Arraymakes things a bit faster.
const index = kdbush(points, p => p.x, p => p.y, 64, Int32Array);
index.range(minX, minY, maxX, maxY)
Finds all items within the given bounding box and returns an array of indices that refer to the items in the original points input array.
const results = index.range(10, 10, 20, 20).map(id => points[id]);
index.within(x, y, radius)
Finds all items within a given radius from the query point and returns an array of indices.
const results = index.within(10, 10, 5).map(id => points[id]);