开源软件名称：

mljs/ml

开源软件地址：

https://github.com/mljs/ml

开源编程语言：

JavaScript 100.0%

开源软件介绍：

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/6.0.0/ml.min.js"></script>

It will be available as the global ML variable. The package is in UMD format.

List of included libraries

Unsupervised learning

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

Supervised learning

Naive Bayes: ML.NaiveBayes
K-Nearest Neighbor (KNN): ML.KNN
Partial least squares (PLS): ML.PLS
K-OPLS: ML.KOPLS
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
Fast Combinatorial Non-negative Least Squares: ML.FCNNLS

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
non-negative Generalized Morphological Component Analysis ML.nGMCA

`ML.Array`

`ML.ArrayXY`

Functions dealing with an object containing 2 properties x and y, both arrays.

Example:

let result = ML.ArrayXY.sortX({ x: [2, 3, 1], y: [4, 6, 2] });
// result = {x: [1,2,3], y: [2,4,6]}

ML.ArrayXY.weightedMerge: Merge abscissa values on similar ordinates and weight the group of abscissa
ML.ArrayXY.maxMerge: Merge abscissa values on similar ordinates and keeps the abscissa with bigger ordinate value
ML.ArrayXY.closestX: Get the closest point for a specific abscissa value
ML.ArrayXY.centroidsMerge: Merge abscissa values if the ordinate value is in a list of centroids
ML.ArrayXY.sortX: Sort a set of point based on the abscissas values
ML.ArrayXY.maxY: Sort a set of point based on the abscissas values
ML.ArrayXY.uniqueX: Ensure that x values are unique