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/convex-hull/README.md

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convex-hull
===========
This module is a wrapper over various convex hull modules which exposes a simple interface for computing convex hulls of point sets in any dimension.
# Example
```javascript
var ch = require('convex-hull')
var points = [
[0,0],
[1,0],
[0,1],
[0.15,0.15],
[0.5, 0.5]
]
//Picture:
//
// [0,1] *
// |\
// | \
// | \
// | \
// | \
// | \
// | \
// | * [0.5,0.5]
// | \
// | \
// | \
// | \
// | \
// | * \
// | [0.15,0.15] \
// [0,0] *---------------* [1,0]
//
console.log(ch(points))
```
Output:
```javascript
[[0, 1], [1, 2], [2, 0]]
```
# Install
```
npm install convex-hull
```
If you want to use it in a webpage, use [browserify](http://browserify.org).
# API
#### `require('convex-hull')(points)`
Computes the convex hull of `points`
* `points` is an array of points encoded as `d` length arrays
**Returns** A polytope encoding the convex hull of the point set.
**Time complexity** The procedure takes O(n^floor(d/2) + n log(n)) time.
**Note** This module is a wrapper over incremental-convex-hull and monotone-convex-hull for convenience. It will select an optimal algorithm for whichever dimension is appropriate.
# Credits
(c) 2014 Mikola Lysenko. MIT License