ml.js - 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.

To include the ml.js library in a web page:

<script src="https://www.lactame.com/lib/ml/3.2.0/ml.min.js"></script>

It will be available as the global ML variable. The package is in UMD format and can be "required" within webpack or requireJS.

List of included libraries

Unsupervised learning

• Principal component analysis (PCA): ML.PCA
• Hierarchical clustering: ML.HClust
• K-means clustering: ML.KMeans

Supervised learning

• Support vector machines: ML.SVM
• Naive Bayes: ML.NaiveBayes
• K-Nearest Neighbor (KNN): ML.KNN
• Partial least squares (PLS): ML.PLS
• Cross-validation: ML.CrossValidation
• Confusion matrix: ML.ConfusionMatrix
• Decision tree classifier: ML.DecisionTreeClassifier
• Random forest classifier: ML.RandomForestClassifier

Artificial neural networks (ANN)

• Feedforward Neural Networks: ML.FNN
• Self-organizing map / Kohonen networks: ML.SOM

Regression

• Simple linear regression: ML.SimpleLinearRegression
• Polynomial regression: ML.PolynomialRegression
• Multivariate linear regression: ML.MultivariateLinearRegression
• Power regression: ML.PowerRegression
• Exponential regression: ML.ExponentialRegression
• Theil-Sen regression: ML.TheilSenRegression
• Robust polynomial regression: ML.RobustPolynomialRegression
• Decision tree regression: ML.DecisionTreeRegression
• Random forest regression: ML.RandomForestRegression

Optimization

• Levenberg-Marquardt: ML.levenbergMarquardt

Math

• Matrix: ML.Matrix (Matrix class)
• Singular value decomposition (SVD): ML.SVD
• Eigenvalue decomposition (EVD): ML.EVD
• Cholesky decomposition: ML.CholeskyDecomposition
• Lu decomposition: ML.LuDecomposition
• Qr decomposition: ML.QrDecomposition
• Sparse matrix: ML.SparseMatrix
• Kernels: ML.Kernel
• Distance functions: ML.Distance
• Similarity functions: ML.Similarity
• Distance matrix: ML.distanceMatrix
• XORShift-add RNG: ML.XSadd

Statistics

• Performance (ROC curve): ML.Performance

Data preprocessing

• Principal component analysis (PCA): ML.PrincipalComponentAnalysis
• Savitzky-Golay filter: ML.savitzkyGolay
• Savitzky-Golay generalized: ML.savitzkyGolayGeneralized

Utility

• Bit array operations: ML.BitArray
• Hash table: ML.HashTable
• Pad array: ML.padArray
• Binary search: ML.binarySearch
• Number comparison functions for sorting: ML.numSort

License

MIT