Package Exports
- ml
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Readme
ml
Machine learning tools in JavaScript
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 mlBower
$ bower install mlUsing our CDN in a web page
<script src="https://www.lactame.com/lib/ml/2.0.0/ml.min.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 - Binary search:
ML.binarySearch - Number comparison functions for sorting:
ML.numSort
- Array Utils:
- Math:
ML.Math- Distance:
ML.Math.Distance - Similarity:
ML.Math.Similarity - DistanceMatrix:
ML.Math.DistanceMatrix - Savitzky-Golay filter:
ML.Math.SG - Savitzky-Golay generalized:
ML.Math.SGG - Matrix:
ML.Math.Matrix(alias ofML.Matrix) - Sparse matrix:
ML.Math.SparseMatrix - Kernels:
ML.Math.Kernel
- Distance:
- Stat:
ML.Stat- Array:
ML.Stat.array - Matrix:
ML.Stat.matrix - Principal component analysis (PCA):
ML.Stat.PCA - Performance:
ML.Stat.Performance
- Array:
- Optimization:
ML.Optimization- BellOptimizer:
ML.Optimization.BellOptimizer - LevenbergMarquardt:
ML.Optimization.LevenbergMarquardt
- BellOptimizer:
- Random number generation:
ML.RNG- XORShift-add:
ML.RNG.XSadd
- XORShift-add:
- Clustering:
ML.Clust- Hierarchical clustering:
ML.Clust.hclust - K-means:
ML.Clust.kmeans
- Hierarchical clustering:
- 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(aliasML.SL.CV)
- Support vector machines:
- Neural networks:
ML.NN