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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Angelos Chatzimparmpas
e069030893
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3 years ago | |
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| .eslintrc.json | 3 years ago | |
| .travis.yml | 3 years ago | |
| index.js | 3 years ago | |
| package.json | 3 years ago | |
| readme.md | 3 years ago | |
| test.js | 3 years ago | |
readme.md
array-normalize 
Normalize array to unit length, that is 0..1 range. See feature scaling.
const normalize = require('array-normalize')
normalize([0, 50, 100]) // [0, .5, 1]
normalize([0, 0, .1, .2, 1, 2], 2) // [0, 0, .1, .1, 1, 1]
normalize([0, .25, 1, .25], 2, [0, .5, 1, .5]) // [0, .5, 1, .5])
API
array = normalize(array, dimensions=1, bounds?)
Normalizes n-dimensional array in-place using dimensions as stride, ie. for 1d array the expected data layout is [x, x, x, ...] for 2d is [x, y, x, y, ...], etc.
Every dimension is normalized independently, eg. 2d array is normalized to unit square [0, 0, 1, 1].
Optional bounds box can predefine min/max to optimize calculations.