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ml-array-rescale
Advanced tools
The ml-array-rescale npm package is used to rescale arrays to a specified range. This can be particularly useful in data preprocessing for machine learning tasks, where normalizing or standardizing data is a common step.
Rescale array to a specified range
This feature allows you to rescale an array to a specified range. In this example, the array [1, 2, 3, 4, 5] is rescaled to the range [0, 1].
const rescale = require('ml-array-rescale');
const array = [1, 2, 3, 4, 5];
const rescaledArray = rescale(array, {min: 0, max: 1});
console.log(rescaledArray); // Output: [0, 0.25, 0.5, 0.75, 1]
Rescale array with default range
This feature allows you to rescale an array to the default range [0, 1] if no range is specified. In this example, the array [10, 20, 30, 40, 50] is rescaled to the range [0, 1].
const rescale = require('ml-array-rescale');
const array = [10, 20, 30, 40, 50];
const rescaledArray = rescale(array);
console.log(rescaledArray); // Output: [0, 0.25, 0.5, 0.75, 1]
The scikit-learn package is a comprehensive machine learning library for Python that includes a preprocessing module with functions for scaling and normalizing data. It is more feature-rich compared to ml-array-rescale, offering a wide range of machine learning algorithms and utilities.
The minmaxscaler npm package provides similar functionality to ml-array-rescale, allowing you to scale arrays to a specified range. However, it is less popular and less frequently updated compared to ml-array-rescale.
Rescale an array into a range.
$ npm install --save ml-array-rescale
rescale(input[, options])
Rescales the values in the input
so they fit between two new values.
Options:
min
: the new minimum value (default: 0)max
: the new maximum value (default: 1)output
: an array to use for output. You can pass input
here to get an in-place modification.import rescale from 'ml-array-rescale';
const result = rescale([0, 1, 2, 3, 4]);
// [0, 0.25, 0.5, 0.75, 1]
FAQs
Rescale an array into a range
We found that ml-array-rescale demonstrated a not healthy version release cadence and project activity because the last version was released a year ago. It has 4 open source maintainers collaborating on the project.
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