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MLweb

MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.

MLweb includes the following three components:

  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)

Documentation

Documentation for LALOLib and ML.js is available here.

LALOLab comes with an online help including the list of all functions and many examples.

Note to users

This repository is mostly intended for developers wishing to modify or extend these tools. Ready-to-use versions of the tools are available online at:

or as modules (see the documentation for details) at:

Functions provided by LALOLib

  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming

See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.

Machine learning capabilities provided by ML.js

Classification

  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees

Regression

  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg

Clustering

  • K-means,
  • Spectral clustering

Dimensionality reduction

  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment

Installation

Download the source files from here or by cloning this repository and run

cd lalolab
make

to build the libraries in the lalolab/ folder:

lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with

firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.

About

Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.

mlweb.loria.fr

Topics

javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine

Resources

Readme
GPL-2.0 license
Activity

Stars

86 stars

Watchers

9 watching

Forks

22 forks
Report repository

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Used by

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MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
Stars
86 stars
Watchers
9 watching
Forks
22 forks
Report repository
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GitHub - lauerfab/MLweb: Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab. · GitHub
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MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
Stars
86 stars
Watchers
9 watching
Forks
22 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
// Highlight search terms from Google/DuckDuckGo/Bing referrer
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(function(){
try {
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MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
Stars
86 stars
Watchers
9 watching
Forks
22 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
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if (clean !== a.href) a.href = clean;
});
}
});
});
});
observer.observe(document.body, { childList: true, subtree: true });
})();
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '
GitHub - lauerfab/MLweb: Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab. · GitHub
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MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
Stars
86 stars
Watchers
9 watching
Forks
22 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
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function tryTheater() {
var btn = document.querySelector('button[aria-label="Theater mode"], ytd-player #player button[title="Theater mode"]');
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(function(){
try {
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MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
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86 stars
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9 watching
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22 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Remove or un-stick sticky/fixed headers that block content
(function() {
function unstick() {
document.querySelectorAll('header, nav, [role="banner"], .header, .navbar, .sticky, .fixed-top, [style*="position: fixed"], [style*="position:sticky"]').forEach(function(el) {
if (el.style.position === 'fixed' || el.style.position === 'sticky' || getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {
el.style.position = 'static';
el.style.top = 'auto';
el.style.zIndex = 'auto';
}
});
}
unstick();
var observer = new MutationObserver(unstick);
observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });
})();
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '
GitHub - lauerfab/MLweb: Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab. · GitHub
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MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
Stars
86 stars
Watchers
9 watching
Forks
22 forks
Report repository
Releases
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Used by
Contributors
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, 'i');
if (__m === '*' || __re.test(location.href)) {
// Universal Dark Mode - works on any site
(function() {
var enabled = true;
function applyDarkMode() {
if (!enabled) return;
// Create style element if it doesn't exist
var style = document.getElementById('universal-dark-mode-style');
if (!style) {
style = document.createElement('style');
style.id = 'universal-dark-mode-style';
document.head.appendChild(style);
}
// Dark mode CSS - inverts colors but preserves images/video
style.textContent = '
/* Invert everything except media */
html {
filter: invert(1) hue-rotate(180deg) !important;
background: #1a1a2e !important;
}
/* Restore images, videos, iframes, canvas */
img, video, iframe, canvas, svg, picture, [style*="background-image"] {
filter: invert(1) hue-rotate(180deg) !important;
}
/* Preserve specific elements that should not be inverted */
.no-dark-mode, .no-dark-mode *,
[data-theme="light"], [data-theme="light"],
.ace_editor, .ace_editor *,
.CodeMirror, .CodeMirror *,
.monaco-editor, .monaco-editor *,
.markdown-body pre, .markdown-body pre *,
.highlight, .highlight *,
pre code, pre code * {
filter: none !important;
}
/* Fix common UI elements */
.modal, .popup, .dropdown-menu, .tooltip, .popover {
filter: invert(1) hue-rotate(180deg) !important;
background: #2d2d44 !important;
border-color: #444 !important;
}
/* Scrollbars */
::-webkit-scrollbar { background: #1a1a2e !important; }
::-webkit-scrollbar-thumb { background: #444 !important; }
::-webkit-scrollbar-thumb:hover { background: #555 !important; }
/* Selection */
::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }
';
}
function removeDarkMode() {
var style = document.getElementById('universal-dark-mode-style');
if (style) style.remove();
}
// Toggle with Alt+Shift+D
document.addEventListener('keydown', function(e) {
if (e.altKey && e.shiftKey && e.key === 'D') {
e.preventDefault();
enabled = !enabled;
if (enabled) {
applyDarkMode();
console.log('[Universal Dark Mode] Enabled');
} else {
removeDarkMode();
console.log('[Universal Dark Mode] Disabled');
}
}
});
// Apply on load
applyDarkMode();
// Re-apply on dynamic content
var observer = new MutationObserver(function(mutations) {
if (enabled && !document.getElementById('universal-dark-mode-style')) {
applyDarkMode();
}
});
observer.observe(document.head, { childList: true });
console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');
})();
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();
GitHub - lauerfab/MLweb: Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab. · GitHub
Skip to content
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Latest commit
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136 Commits
Folders and files
NameName
Last commit message
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LICENSE
LICENSE
README.md
README.md
Repository files navigation
MLweb
MLweb is an open-source project that aims at bringing machine learning capabilities to web pages and web applications. See the official website for more information.
MLweb includes the following three components:
  • ML.js: a javascript library for machine learning
  • LALOLib: a javascript library for scientific computing (linear algebra, statistics, optimization)
  • LALOLab: an online Matlab-like development environment (try it at http://mlweb.loria.fr/lalolab/)
Documentation
Documentation for LALOLib and ML.js is available here.
LALOLab comes with an online help including the list of all functions and many examples.
Note to users
This repository is mostly intended for developers wishing to modify or extend these tools.
Ready-to-use versions of the tools are available online at:
or as modules (see the documentation for details) at:
Functions provided by LALOLib
  • Linear algerbra: basic vector and matrix operations, linear system solvers, matrix factorizations (QR, Cholesky), eigendecomposition, singular value decomposition, conjugate gradient sparse linear system solver, complex numbers/matrices, discrete Fourier transform... )
  • Statistics: random numbers, sampling from and estimating standard distributions
  • Optimization: steepest descent, BFGS, linear programming (thanks to glpk.js), quadratic programming
See this benchmark for a comparison of LALOLib with other linear algebra javascript libraries.
Machine learning capabilities provided by ML.js
Classification
  • K-nearest neighbors,
  • Linear/quadratic discriminant analysis,
  • Naive Bayes classifier,
  • Logistic regression,
  • Perceptron,
  • Multi-layer perceptron,
  • Support vector machines,
  • Multi-class support vector machines,
  • Decision trees
Regression
  • Least squares,
  • Least absolute devations,
  • K-nearest neighbors,
  • Ridge regression,
  • LASSO,
  • LARS,
  • Orthogonal least squares,
  • Multi-layer perceptron,
  • Kernel ridge regression,
  • Support vector regression,
  • K-LinReg
Clustering
  • K-means,
  • Spectral clustering
Dimensionality reduction
  • Principal component analysis,
  • Locally linear embedding,
  • Local tangent space alignment
Installation
Download the source files from here or by cloning this repository and run
cd lalolab
make

to build the libraries in the lalolab/ folder:
lalolib.js and lalolibworker.js --> for LALOLib
ml.js and mlworker.js --> for ML.js

Then, you can launch LALOLab by opening lalolab/index.html in a browser, for instance with
firefox index.html

Note to Chrome users: you need to use the --allow-file-access-from-files flag on Chrome command line. For Chromium under Linux, you can use the convenient script lalolab/chromelab.
About
Machine learning and scientific computing (linear algebra, statistics, optimization) javascript libraries, with an online lab.
mlweb.loria.fr
Topics
javascript-librarylinear-algebramachine-learningregressionstatisticssupport-vector-machine
Resources
Readme
GPL-2.0 license
Activity
Stars
86 stars
Watchers
9 watching
Forks
22 forks
Report repository
Releases
Packages
Used by
Contributors
Languages