ml

Machine learning tools

Introduction

This library is a compilation of the tools developed in the mljs organization.
It is mainly maintained for use in the browser. If you are working with Node.js, you might prefer to add to your dependencies only the libraries that you need, as they are usually published to npm more often.
We prefix all our npm package names with ml- (eg. ml-matrix) so they are easy to find.

Installation

Node.JS

$ npm install ml

Bower

$ bower install ml

Using our CDN in a web page

<script src="https://www.lactame.com/lib/ml/1.1.1/ml.js"></script>

Usage

Node.JS

var ML = require('ml');

Browser with AMD

require(['path/to/ml/dist/ml.min'], function (ML) {
    // ML.Math ...
});

Browser as global

<script src="path/to/ml/dist/ml.min.js" />
<script>
    // ML.Math ...
</script>

Tools

• Root packages
  • Array Utils: ML.ArrayUtils
  • Bit array operations: ML.BitArray
  • Hash table: ML.HashTable
  • Matrix: ML.Matrix
  • Pad array: ML.PadArray
  • Regression: ML.Regression
• Math: ML.Math
  • Distance: ML.Math.Distance
  • Similarity: ML.Math.Similarity
  • Savitzky-Golay filter: ML.Math.SG
  • Matrix: ML.Matrix
  • Sparse matrix: ML.SparseMatrix
  • Kernels: ML.Kernel
• Stat: ML.Stat
  • Array: ML.Stat.array
  • Matrix: ML.Stat.matrix
  • Principal component analysis (PCA): ML.Stat.PCA
  • Performance: ML.Stat.Performance
• Random number generation: ML.RNG
  • XORShift-add: ML.RNG.XSadd
• Clustering: ML.Clust
  • Hierarchical clustering: ML.Clust.hclust
  • K-means: ML.Clust.kmeans
• Supervised learning: ML.SL
  • Support vector machines: ML.SL.SVM
  • Naive Bayes: ML.SL.NaiveBayes
  • K-Nearest Neighbor: ML.SL.KNN
  • Partial least squares (PLS): ML.SL.PLS
  • Cross-validation utility: ML.SL.CrossValidation (alias ML.SL.CV)
• Neural networks: ML.NN
  • Self-organizing map / Kohonen networks: ML.NN.SOM
  • Feedforward Neural Networks: ML.NN.FNN

License

MIT