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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23 lines
1.1 KiB
23 lines
1.1 KiB
# array-normalize [](http://github.com/badges/stability-badges) [](https://travis-ci.org/dfcreative/array-normalize)
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Normalize array to unit length, that is 0..1 range. See [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling).
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[](https://npmjs.org/package/array-normalize/)
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```js
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const normalize = require('array-normalize')
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normalize([0, 50, 100]) // [0, .5, 1]
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normalize([0, 0, .1, .2, 1, 2], 2) // [0, 0, .1, .1, 1, 1]
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normalize([0, .25, 1, .25], 2, [0, .5, 1, .5]) // [0, .5, 1, .5])
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```
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## API
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### array = normalize(array, dimensions=1, bounds?)
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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.
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Every dimension is normalized independently, eg. 2d array is normalized to unit square `[0, 0, 1, 1]`.
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Optional `bounds` box can predefine min/max to optimize calculations.
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