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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72 lines
1.4 KiB
72 lines
1.4 KiB
4 years ago
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convex-hull
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===========
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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.
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# Example
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```javascript
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var ch = require('convex-hull')
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var points = [
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[0,0],
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[1,0],
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[0,1],
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[0.15,0.15],
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[0.5, 0.5]
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]
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//Picture:
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//
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// [0,1] *
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// |\
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// | \
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// | \
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// | \
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// | \
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// | \
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// | \
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// | * [0.5,0.5]
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// | \
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// | \
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// | \
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// | \
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// | \
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// | * \
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// | [0.15,0.15] \
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// [0,0] *---------------* [1,0]
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//
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console.log(ch(points))
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```
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Output:
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```javascript
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[[0, 1], [1, 2], [2, 0]]
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```
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# Install
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```
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npm install convex-hull
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```
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If you want to use it in a webpage, use [browserify](http://browserify.org).
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# API
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#### `require('convex-hull')(points)`
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Computes the convex hull of `points`
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* `points` is an array of points encoded as `d` length arrays
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**Returns** A polytope encoding the convex hull of the point set.
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**Time complexity** The procedure takes O(n^floor(d/2) + n log(n)) time.
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**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.
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# Credits
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(c) 2014 Mikola Lysenko. MIT License
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