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See [feature scaling](https://en.wikipedia.org/wiki/Feature_scaling).\r\n\r\n[![npm install array-normalize](https://nodei.co/npm/array-normalize.png?mini=true)](https://npmjs.org/package/array-normalize/)\r\n\r\n```js\r\nconst normalize = require('array-normalize')\r\n\r\nnormalize([0, 50, 100]) // [0, .5, 1]\r\nnormalize([0, 0, .1, .2, 1, 2], 2) // [0, 0, .1, .1, 1, 1]\r\nnormalize([0, .25, 1, .25], 2, [0, .5, 1, .5]) // [0, .5, 1, .5])\r\n```\r\n\r\n## API\r\n\r\n### array = normalize(array, stride=1, bounds?)\r\n\r\nNormalizes n-dimensional array in-place using optional stride for n-dimensions, ie. for 2d data layout is `[x, y, x, y, ...]`.\r\n\r\nEvery dimension is normalized independently, eg. 2d array is normalized to unit square `[0, 0, 1, 1]`.\r\n\r\nOptional `bounds` box can predefine min/max to skip bounds detection.\r\n\r\n<p align=\"center\">ॐ</p>\r\n","maintainers":[{"name":"dfcreative","email":"df.creative@gmail.com"},{"name":"dy","email":"df.creative@gmail.com"}],"time":{"modified":"2022-06-13T03:28:36.695Z","created":"2017-01-24T13:31:50.006Z","1.0.0":"2017-01-24T13:31:50.006Z","1.0.1":"2017-01-24T13:39:55.412Z","1.1.0":"2017-05-31T22:46:26.175Z","1.1.1":"2017-05-31T23:06:16.717Z","1.1.2":"2017-06-01T00:29:57.828Z","1.1.3":"2017-07-12T15:59:05.904Z","1.1.4":"2019-10-28T21:53:33.179Z","2.0.0":"2020-12-22T00:05:00.109Z"},"homepage":"https://github.com/dy/array-normalize#readme","keywords":["array","normalize","normal","statistics","samples"],"repository":{"type":"git","url":"git+https://github.com/dy/array-normalize.git"},"author":{"name":"Dima Iv","email":"dfcreative@gmail.com"},"bugs":{"url":"https://github.com/dy/array-normalize/issues"},"license":"MIT","readmeFilename":"readme.md"}