Description
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. mlmatrix) so they are easy to find.
To include the ml.js library in a web page:
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README
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. mlmatrix) so they are easy to find.
To include the ml.js library in a web page:
<script src="https://www.lactame.com/lib/ml/4.0.0/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
 Kmeans clustering:
ML.KMeans
Supervised learning
 Naive Bayes:
ML.NaiveBayes
 KNearest Neighbor (KNN):
ML.KNN
 Partial least squares (PLS):
ML.PLS
 KOPLS:
ML.KOPLS
 Crossvalidation:
ML.CrossValidation
 Confusion matrix:
ML.ConfusionMatrix
 Decision tree classifier:
ML.DecisionTreeClassifier
 Random forest classifier:
ML.RandomForestClassifier
Artificial neural networks (ANN)
Regression
 Simple linear regression:
ML.SimpleLinearRegression
 Polynomial regression:
ML.PolynomialRegression
 Multivariate linear regression:
ML.MultivariateLinearRegression
 Power regression:
ML.PowerRegression
 Exponential regression:
ML.ExponentialRegression
 TheilSen regression:
ML.TheilSenRegression
 Robust polynomial regression:
ML.RobustPolynomialRegression
 Decision tree regression:
ML.DecisionTreeRegression
 Random forest regression:
ML.RandomForestRegression
Optimization
 LevenbergMarquardt:
ML.levenbergMarquardt
 Fast Combinatorial Nonnegative 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
 XORShiftadd RNG:
ML.XSadd
ML.Array
ML.Array.min
ML.Array.max
ML.Array.median
ML.Array.mean
ML.Array.mode
ML.Array.normed
ML.Array.rescale
ML.Array.sequentialFill
ML.Array.standardDeviation
ML.Array.variance
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
Statistics
 Performance (ROC curve):
ML.Performance
Data preprocessing
 Principal component analysis (PCA):
ML.PCA
 SavitzkyGolay filter:
ML.savitzkyGolay
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
 Random number generation:
ML.Random
License
[MIT](./LICENSE)
*Note that all licence references and agreements mentioned in the ml.js README section above
are relevant to that project's source code only.