https://github.com/atlassubbed/atlas-dataset

https://github.com/atlassubbed/atlas-dataset

Last synced: 21 days ago
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• Host: GitHub
• URL: https://github.com/atlassubbed/atlas-dataset
• Owner: atlassubbed
• License: other
• Created: 2018-07-15T00:42:16.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-07-16T21:58:47.000Z (over 6 years ago)
• Last Synced: 2024-11-05T18:15:02.172Z (2 months ago)
• Language: JavaScript
• Size: 42 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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README

        
# atlas-dataset

Calculate mean, standard deviation, sum for a set of data points.

[![Travis](https://img.shields.io/travis/atlassubbed/atlas-dataset.svg)](https://travis-ci.org/atlassubbed/atlas-dataset)

---

## install

```

npm install --save atlas-dataset

```

## why

A minimal wrapper, allowing for *basic* statistical inspection of an array of numbers. All linear and polylogarithmic calculations are cached on-demand and reused.

## examples

#### instantiate a new `Dataset`

For these examples, we'll be using an array of 1,000,000 random floats between zero and one. When you instantiate a new `Dataset`, the array is shallow copied to avoid manipulating the original:

```javascript

const Dataset = require("atlas-dataset");

const arr = [];

for (let i = 1e6; i--;){

  arr.push(Math.random())

}

const set = new Dataset(arr)

```

#### calculate values

```javascript

...

console.log(`size: ${set.size()} sum: ${set.sum()}`)

console.log(`value: ${set.mean()} +/- ${set.stddev()}`)

console.log(`median: ${set.median()} +/- ${set.mad()}`)

// size: 1000000 sum: 500128.4297823687

// mean: 0.5001284297823687 +/- 0.2884814684388095

// median: 0.4996962409854201 +/- 0.24966274565483493

```

#### updating the data

```javascript

...

set.add(Math.random());

console.log(`size: ${set.size()} sum: ${set.sum()}`)

console.log(`value: ${set.mean()} +/- ${set.stddev()}`)

console.log(`median: ${set.median()} +/- ${set.mad()}`)

// size: 1000001 sum: 500128.76868722256

// mean: 0.500128268558954 +/- 0.2884813692496768

// median: 0.4996961275123013 +/- 0.2496624248162307

```

## caveats

In the examples, `arr` is not normally distributed, nor are we caring about the amount of significant figures in the result.

## todo

For efficiently updating calculations, derive a recurrence relation for each quantity, `V`:

```

V(X_n+1) = f(V(X_n), x_n+1)

```

For example `Mu_n+1 = (Mu_n*n + x_n+1)/(n+1)` and updating the `size` and `sum` is trivial. Recomputing `stddev` is easy using the forumla: `s^2 =  - ^2`. Just square `s_n`, compute `s_n+1^2` then take the square root. Updating the mean square is the exact same thing as updating the mean, we just replace `x_n+1` with `x_n+1^2` in the numerator.