array-normalize - npm Package Compare versions

Comparing version 1.1.1 to 1.1.2

.npmignore

index.js

		'use strict'

		var getBounds = require('array-bounds')

		module.exports = normalize;

		function normalize (arr, dim) {
		function normalize (arr, dim, bounds) {
		if (!arr \|\| arr.length == null) throw Error('Argument should be an array')

		if (dim == null) dim = 1
		if (bounds == null) bounds = getBounds(arr, dim)

		for (var offset = 0; offset < dim; offset++) {
		var max = -Infinity, min = Infinity, i = offset, l = arr.length;
		var max = bounds[dim + offset], min = bounds[offset], i = offset, l = arr.length;

		for (; i < l; i+=dim) {
		if (arr[i] > max) max = arr[i];
		if (arr[i] < min) min = arr[i];
		}

		if (max === Infinity && min === -Infinity) {
		@@ -19,0 +17,0 @@ for (i = offset; i < l; i+=dim) {

package.json

		{
		"name": "array-normalize",
		"version": "1.1.1",
		"version": "1.1.2",
		"description": "Normalize array (possibly n-dimensional) to zero mean and unit variance",
		@@ -25,3 +25,6 @@ "main": "index.js",
		},
		"homepage": "https://github.com/dfcreative/array-normalize#readme"
		"homepage": "https://github.com/dfcreative/array-normalize#readme",
		"dependencies": {
		"array-bounds": "^1.0.0"
		}
		}

readme.md

		# array-normalize [![experimental](https://img.shields.io/badge/stability-unstable-yellow.svg)](http://github.com/badges/stability-badges) [![Build Status](https://img.shields.io/travis/dfcreative/array-normalize.svg)](https://travis-ci.org/dfcreative/array-normalize)

		Normalize array in-place to zero mean and unit variance.
		Normalize array to unit length, that is 0..1 range. See [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling).

		@@ -15,4 +15,4 @@ [![npm install array-normalize](https://nodei.co/npm/array-normalize.png?mini=true)](https://npmjs.org/package/array-normalize/)

		### array = normalize(array, n=1)
		### array = normalize(array, n=1, bounds?)

		Normalizes n-dimensional array in-place using dimensions `n` 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, so 2d array is normalized to unit square `[0, 0, 1, 1]`.
		Normalizes n-dimensional array in-place using dimensions `n` 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, so 2d array is normalized to unit square `[0, 0, 1, 1]`. Optionally pass `bounds` box if you know min/max values to optimize calculations.

test.js

		@@ -5,2 +5,3 @@ 'use strict'
		const assert = require('assert')
		const getBounds = require('array-bounds')

		@@ -18,1 +19,4 @@ let a = [-Infinity, -1, 0, 1, Infinity]
		assert.deepEqual(norm(d.slice(), 2), [0, 0, .1, .1, 1, 1])

		let e = [0, 0, .1, .2, 1, 2]
		assert.deepEqual(norm(e.slice(), 2, getBounds(e, 2)), [0, 0, .1, .1, 1, 1])

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