# encog-node

> A Node.js port of the Encog Machine Learning Framework

Latest version **0.3.0** (published 2017-04-26) · 0 weekly downloads

## Install

```sh
npm install encog-node
pnpm add encog-node
yarn add encog-node
bun add encog-node
```

## Health

**Score 10/100 (F)** — status: abandoned.

Positive: no vulnerabilities.

Warnings: low downloads; no types; no esm support; low quality score; pre 1.0.

Negative: abandoned; low maintenance score.

## Facts

| | |
|---|---|
| Version | 0.3.0 |
| Published | 2017-04-26 |
| First published | 2013-09-01 |
| Weekly downloads | 0 |
| TypeScript types | none |
| Module format | CommonJS |
| Node | >=5.0.0 |
| Dependencies | 2 |
| Known vulnerabilities | 0 |
| Install scripts | no |
| GitHub stars | 7 |
| Author | Thimira Amaratunga |
| Maintainers | thimira |
| Keywords | encog-node, encog, machine, learning, framework, neural, network, artificial, intelligence, intelligent, algorithms, genetic, classification, flocking, javascript, wrapper, port |

## Links

- npm: https://www.npmjs.com/package/encog-node
- Repository: https://github.com/Thimira/encog-node
- Homepage: https://github.com/Thimira/encog-node#readme
- Issues: https://github.com/Thimira/encog-node/issues
- npm.io page: https://npm.io/package/encog-node

## Dependencies (2)

- [lodash](https://npm.io/package/lodash.md) ^4.17.4
- [require-dir](https://npm.io/package/require-dir.md) ^0.3.1

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## Recent versions

- 0.3.0 (latest) — 2017-04-26
- 0.1.2 — 2016-05-01
- 0.1.1 — 2016-04-23
- 0.1.0 — 2016-04-23
- 0.0.1 — 2013-09-01

## README

# Encog-Node

Encog-Node is a Node.js port of the popular Encog Machine Learning Framework by Jeff Heaton.

All credits of the framework should go to Jeff Heaton - http://www.heatonresearch.com/encog/

Currently based on the encog-javascript v1.0 - https://github.com/encog/encog-javascript

## Installation

    npm install encog-node

## Usage

Just require the library and all of ENCOG namespace will be available to you,

```js
var ENCOG = require('encog-node');
```

### Example

The example code below will build a simple XOR Neural Network, the code is included in `examples\xor.js`

```js
var ENCOG = require('encog-node');

var XOR_INPUT = [
    [0, 0],
    [1, 0],
    [0, 1],
    [1, 1]
];

var XOR_IDEAL = [
    [0],
    [1],
    [1],
    [0]
];

var network = ENCOG.networks.basic.create([
    ENCOG.layers.basic.create(ENCOG.activationFunctions.sigmoid.create(), 2, 1),
    ENCOG.layers.basic.create(ENCOG.activationFunctions.sigmoid.create(), 3, 1),
    ENCOG.layers.basic.create(ENCOG.activationFunctions.sigmoid.create(), 1, 0)
]);

network.randomize();

var train = ENCOG.trainers.propagation.create(network, ENCOG.errorFunctions.linear.create(), XOR_INPUT, XOR_IDEAL, "RPROP", 0, 0);

var iteration = 1;

do {
    train.iteration();
    var trainResultString = "Training Iteration #" + iteration + ", Error: " + train.error;
    console.log(trainResultString + "\n");
    iteration++;
} while (iteration < 1000 && train.error > 0.01);

var input = [0, 0];
var output = [];

console.log("Testing neural network: \n");

for (var i = 0; i < XOR_INPUT.length; i++) {
    output = network.compute(XOR_INPUT[i]);
    var testResultString = "Input: " + String(XOR_INPUT[i][0]) +
        " ; " + String(XOR_INPUT[i][1]) +
        "   Output: " + String(output[0]) +
        "   Ideal: " + String(XOR_IDEAL[i][0]);
    console.log(testResultString + "\n");
}
```

Will display,

    >node index.js
    Training Iteration #1, Error: 0.33306242864283925
    Training Iteration #2, Error: 0.30684930995968274
    Training Iteration #3, Error: 0.2816136873215376
    Training Iteration #4, Error: 0.2614275886340755
    ..........
    ..........
    ..........
    Training Iteration #44, Error: 0.010807377445510056
    Training Iteration #45, Error: 0.005187735146628829
    Testing neural network
    Input: 0 ; 0   Output: 0.000056493461985276595   Ideal: 0
    Input: 1 ; 0   Output: 0.9995493238264583   Ideal: 1
    Input: 0 ; 1   Output: 0.9987763730629743   Ideal: 1
    Input: 1 ; 1   Output: 0.08974271940228784   Ideal: 0

### Running included examples

The examples are included in the `examples` folder
The XOR example can be simply run by,

```js
var ENCOG = require('encog-node');

ENCOG.examples.xor();
```

The Iris flower data set example can be run by,

```js
var ENCOG = require('encog-node');

ENCOG.examples.iris();
```

## Node.js version compatibility

Should work on all Node.js versions. Tested up to Node.js `v6.3.0`

## Credits

Credits should go to [Jeff Heaton](https://github.com/jeffheaton) for the original Encog Machine Learning Framework - http://www.heatonresearch.com/about/

The capabilities of the framework are explained here by the author : http://www.codeproject.com/Articles/477689/JavaScript-Machine-Learning-and-Neural-Networks-wi

### Contributors

  - [Thimira Amaratunga](https://github.com/Thimira) - Originally ported Encog to Node.js
  - [Rui Cardoso](https://github.com/redsoul)

---
_Source: https://npm.io/package/encog-node · Machine-readable twin of the npm.io package page. Health data is recomputed on every publish._