@jsmlt/jsmlt
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JavaScript Machine Learning
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# JSMLT
[](https://www.npmjs.com/package/@jsmlt/jsmlt)
[](https://www.npmjs.com/package/@jsmlt/jsmlt)
[](https://github.com/jsmlt/jsmlt)
[<img alt="JSMLT" src="https://avatars0.githubusercontent.com/u/31863813?v=4&s=160" height="160px" align="right"/>](https://github.com/jsmlt/jsmlt/)
The JavaScript Machine Learning Toolkit, or JSMLT, is an open source JavaScript library for education in machine learning. It implements several well-known supervised learning algorithms in an understandable, modular and well-commented way. Furthermore, visualization examples are provided which allow you to explore the way different machine learning algorithms work. Ultimately, JSMLT is intended to provide students with a better learning experience when studying machine learning algorithms.
If you want to explore a visualization of the machine learning algorithms in JSMLT, check out http://visualml.io. It provides an interactive environment for using JSMLT's algorithms.
# Getting started
This short guide will help you get started with JSMLT.
## Installation
> We're assuming you've got Node.js and npm installed. If you haven't, you should: download and install it from [nodejs.org](https://nodejs.org/en/).
To install JSMLT into your npm project via npm, run
```
$ npm install @jsmlt/jsmlt
```
## A simple example
In this small example, we're going to train an SVM on a small example dataset. The code example below starts with loading JSMLT, creating some dummy training and test data, and running an SVM classifier on it. It's pretty simple!
> If you want to run this example without having to set up anything by yourself, check out the [JSMLT examples repository](https://github.com/jsmlt/examples). It includes the example below, and requires no further setup: it's ready to run!
```js
// Import JSMLT library
var jsmlt = require('@jsmlt/jsmlt');
// Training data
train_X = [[-1,-1], [-1,1], [1,1], [1,-1]];
train_y = [0, 0, 1, 1];
// Testing data
test_X = [[1,2], [1,-2], [-1,-2], [-1,2]];
// Create and train classifier
var clf = new jsmlt.Supervised.SVM.SVM({
kernel: new jsmlt.Kernel.Linear(),
});
clf.train(train_X, train_y);
// Make predictions on test data
console.log(clf.predict(test_X));
```
Running this simple example will output the classification result `[1,1,0,0]`, meaning it classified the first two points as 0, and the second two points as 1.
# API
> The entire API documentation can be found [here](http://visualml.io/jsmlt/docs/identifiers.html). You can also build the documentation locally by downloading and installing JSMLT and running `npm run-script build-documentation`: the documentation will then be available in the `docs` folder.
### Supervised learning algorithms
- JSMLT.Supervised.SVM.[SVM](http://visualml.io/jsmlt/docs/class/src/supervised/svm/svm.js~SVM.html): Support Vector Machine classifier
- JSMLT.Supervised.Linear.[Perceptron](http://visualml.io/jsmlt/docs/class/src/supervised/linear/perceptron.js~Perceptron.html): Perceptron classifier
- JSMLT.Supervised.Neighbors.[KNN](http://visualml.io/jsmlt/docs/class/src/supervised/neighbors/knn.js~KNN.html): k-nearest neighbors classifier
- JSMLT.Supervised.Neighbors.[LogisticRegression](http://visualml.io/jsmlt/docs/class/src/supervised/linear/logistic_regression.js~LogisticRegression.html): Logistic Regression classifier
### Kernels
- JSMLT.Kernel.[LinearKernel](http://visualml.io/jsmlt/docs/class/src/kernel/linear.js~LinearKernel.html): Linear kernel
- JSMLT.Kernel.[GaussianKernel](http://visualml.io/jsmlt/docs/class/src/kernel/gaussian.js~GaussianKernel.html): Gaussian (RBF) kernel
### Preprocessing
- JSMLT.Preprocessing.[LabelEncoder](http://visualml.io/jsmlt/docs/class/src/preprocessing/labelencoder.js~LabelEncoder.html): Encode string or other type of labels to integers
### Model selection
- JSMLT.ModelSelection.[trainTestSplit](http://visualml.io/jsmlt/docs/function/index.html#static-function-trainTestSplit): Data set splitting
### Datasets
- JSMLT.Datasets.[loadIris](http://visualml.io/jsmlt/docs/function/index.html#static-function-loadIris): Iris dataset loading
### Validation
- JSMLT.Validation.Metrics.[accuracy](http://visualml.io/jsmlt/docs/function/index.html#static-function-accuracy): Accuracy metric for validation
### Classification boundaries
- JSMLT.Classification.[Boundaries](http://visualml.io/jsmlt/docs/class/src/classification/boundaries.js~Boundaries.html): Classification boundaries for trained classifier
# Development
JSMLT is maintained by [Jesper van Engelen](https://github.com/engelen), and is in active development. It is currently not ready to be used in any production environments.