Projects running under mac os x.
Showing Items 1-20 of 48 on page 1 of 3: 1 2 3 Next

Logo MLweb 1.2

by lauerfab - February 23, 2018, 15:40:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15659 views, 3698 downloads, 3 subscriptions

About: MLweb is an open source project that aims at bringing machine learning capabilities into web pages and web applications, while maintaining all computations on the client side. It includes (i) a javascript library to enable scientific computing within web pages, (ii) a javascript library implementing machine learning algorithms for classification, regression, clustering and dimensionality reduction, (iii) a web application providing a matlab-like development environment.

  • Add bibtex entry of corresponding Neurocomputing paper
  • Create javascript modules to avoid global scope pollution in web pages

Logo Armadillo library 8.400

by cu24gjf - February 20, 2018, 03:26:16 CET [ Project Homepage BibTeX Download ] 132488 views, 25160 downloads, 5 subscriptions

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About: Armadillo is a high quality C++ linear algebra library, aiming towards a good balance between speed and ease of use. The function syntax is deliberately similar to MATLAB. Useful for algorithm development directly in C++, or quick conversion of research code into production environments (eg. software & hardware products).

  • faster handling of band matrices by solve() and chol()
  • faster incremental construction of sparse matrices via element access operators
  • faster diagonal views in sparse matrices
  • faster handling of sparse matrices by repmat()
  • faster loading of CSV files
  • faster gmm_diag class, for Gaussian mixture models with diagonal covariance matrices
  • speedups via expanded use of OpenMP by many element-wise functions
  • expanded kron() to handle sparse matrices
  • expanded index_min() and index_max() to handle cubes
  • expanded SpMat to save/load sparse matrices in coord format
  • expanded .save() to allow appending new datasets to existing HDF5 files
  • expanded .save()/.load() to allow specification of datasets within HDF5 files
  • expanded .each_slice() to optionally use OpenMP for multi-threaded execution
  • expanded clamp() to handle cubes
  • added submatrix & subcube iterators
  • added normpdf(), normcdf(), mvnrnd()
  • added chi2rnd(), wishrnd(), iwishrnd()
  • added gmm_full class, for Gaussian mixture models with full covariance matrices
  • added affmul() to simplify application of affine transformations
  • added intersect() for finding common elements in two vectors/matrices


by billandreo - October 31, 2017, 16:01:32 CET [ Project Homepage BibTeX Download ] 2422 views, 2264 downloads, 2 subscriptions

About: This is a tool for retrieving nearest neighbors and clustering of large categorical data sets represented in transactional form. The clustering is achieved via a locality-sensitive hashing of categorical datasets for speed and scalability.


Initial Announcement on

About: An open-source framework for benchmarking of feature selection algorithms and cost functions.


Initial Announcement on

Logo Salad 0.6.1

by chwress - June 17, 2016, 11:26:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 19617 views, 3558 downloads, 3 subscriptions

About: A Content Anomaly Detector based on n-Grams


A teeny tiny fix to correctly handle input strings shorter than a registers width

Logo Harry 0.4.2

by konrad - April 16, 2016, 10:50:38 CET [ Project Homepage BibTeX Download ] 15956 views, 3315 downloads, 3 subscriptions

About: A Tool for Measuring String Similarity


This release fixes the incorrect implementation of the bag distance.

Logo QMiner 5.0.0

by blazfortuna - April 8, 2016, 14:17:58 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5460 views, 998 downloads, 2 subscriptions

About: Analytic engine for real-time large-scale streams containing structured and unstructured data.


Initial Announcement on

Logo Toupee 0.1

by nitbix - March 7, 2016, 20:29:59 CET [ Project Homepage BibTeX Download ] 2339 views, 571 downloads, 3 subscriptions

About: A Python based library for running experiments with Deep Learning and Ensembles on GPUs.


Initial Announcement on

Logo Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 30713 views, 6340 downloads, 3 subscriptions

About: The Libra Toolkit is a collection of algorithms for learning and inference with discrete probabilistic models, including Bayesian networks, Markov networks, dependency networks, sum-product networks, arithmetic circuits, and mixtures of trees.


Version 1.1.2d (12/29/2015):

  • Minor fixes to scripts
  • Published in JMLR ML-OSS!

Logo Lua MapReduce v0.4.0

by pakozm - December 23, 2015, 09:32:11 CET [ Project Homepage BibTeX Download ] 12167 views, 2534 downloads, 3 subscriptions

About: Lua-MapReduce framework implemented in Lua using luamongo driver and MongoDB as storage. It follows Iterative MapReduce for training of Machine Learning statistical models.

  • Updated to work with luamongo v0.5.0, which uses C++ driver legacy-1.1.0

Logo A Pattern Recognizer In Lua with ANNs v0.4.1

by pakozm - December 3, 2015, 15:01:36 CET [ Project Homepage BibTeX Download ] 12403 views, 2767 downloads, 2 subscriptions

About: APRIL-ANN toolkit (A Pattern Recognizer In Lua with Artificial Neural Networks). This toolkit incorporates ANN algorithms (as dropout, stacked denoising auto-encoders, convolutional neural networks), with other pattern recognition methods as hidden makov models (HMMs) among others.

  • Updated home repository link to follow april-org github organization.
  • Improved serialize/deserialize functions, reimplemented all the serialization procedure.
  • Added exceptions support to LuaPkg and APRIL-ANN, allowing to capture C++ errors into Lua code.
  • Added set class.
  • Added series class.
  • Added data_frame class, similar to Python Pandas DataFrame.
  • Serialization and deserilization have been updated with more robust and reusable API, implemented in util.serialize() and util.deserialize() functions.
  • Added matrix.ext.broadcast utility (similar to broadcast in numpy).
  • Added ProbablisitcMatrixANNComponent, which allow to implement probabilistic mixtures of posteriors and/or likelihoods.
  • Added batch normalization ANN component.
  • Allowing matrix.join to add new axis.
  • Added methods prod(), cumsum() and cumprod() at matrix classes.
  • Added methods count_eq() and count_neq() at matrix classes.
  • Serializable objects API have been augmented with methods ctor_name() and
    ctor_params() in Lua, refered to luaCtorName() and luaCtorParams() in C++.
  • Added to dynamic cast C++ objects pushed into Lua, allowing to convert base class objects into any of its derived classes.
  • Added matrix.sparse as valid values for targets in ann.loss.mse and
  • Changed matrix metamethods __index and __newindex, allowing to use
    matrix objects with standard Lua operator[].
  • Added matrix.masked_fill and matrix.masked_copy matrix.
  • Added matrix.indexed_fill and matrix.indexed_copy matrix.
  • Added ann.components.probabilistic_matrix, and its corresponding specializations ann.components.left_probabilistic_matrix and
  • Added operator[] in the right side of matrix operations.
  • Added ann.components.transpose.
  • Added max_gradients_norm in traianble.supervised_trainer, to avoid gradients exploding.
  • Added ann.components.actf.sparse_logistic a logistic activation function with sparsity penalty.
  • Simplified math.add, math.sub, ... and other math extensions for reductions, their original behavior can be emulated by using bind function.
  • Added bind function to freeze any positional argument of any Lua function.
  • Function stats.boot uses multiple_unpack to allow a table of sizes and the generation of multiple index matrices.
  • Added multiple_unpack Lua function.
  • Added __tostring metamethod to numeric memory blocks in Lua.
  • Added dataset.token.sparse_matrix, a dataset which allow to traverse by rows a sparse matrix instance.
  • Added, a builder which uses the Dictionary-of-Keys format to construct a sparse matrix from scratch.
  • Added method data to numeric matrix classes.
  • Added methods values, indices, first_index to sparse matrix class.
  • Fixed bugs when reading bad formed CSV files.
  • Fixed bugs at statistical distributions.
  • FloatRGB bug solved on equal (+=, -=, ...) operators. This bug affected ImageRGB operations such as resize.
  • Solved problems when chaining methods in Lua, some objects end to be garbage collected.
  • Improved support of strings in auto-completion (rlcompleter package).
  • Solved bug at SparseMatrix<T> when reading it from a file.
  • Solved bug in Image<T>::rotate90_cw methods.
  • Solved bug in SparseMatrix::toDense() method.


  • Better LuaTable accessors, using [] operator.
  • Implementation of matrix __index, __newindex and __call metamethods in C++.
  • Implementation of matProd(), matCumSum() and matCumProd() functions.
  • Implementation of matCountEq() and matCountNeq() functions for
  • Updated matrix_ext_operations.h to change API of matrix operations. All functions have been overloaded to accept an in-place operation and another version which receives a destination matrix.
  • Adding iterators to language models.
  • Added MatrixScalarMap2 which receives as input2 a SparaseMatrix instance. This functions needs to be generalized to work with CPU and CUDA.
  • The method SparseMatrix<T>::fromDenseMatrix() uses a DOKBuilder object to build the sparse matrix.
  • The conversion of a Matrix<T> into a SparseMatrix<T> has been changed from a constructor overload to the static method
  • Added support for IPyLua.
  • Optimized matrix access for confusion matrix.
  • Minor changes in class.lua.
  • Improved binding to avoid multiple object copies when pushing C++ objects.
  • Added Git commit hash and compilation time.

Logo PyScriptClassifier 0.3.0

by cjb60 - November 25, 2015, 04:07:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 5275 views, 1194 downloads, 2 subscriptions

About: Easily prototype WEKA classifiers and filters using Python scripts.



  • Filters have now been implemented.
  • Classifier and filter classes satisfy base unit tests.


  • Can now choose to save the script in the model using the -save flag.


  • Added Python 3 support.
  • Added uses decorator to prevent non-essential arguments from being passed.
  • Fixed nasty bug where imputation, binarisation, and standardisation would not actually be applied to test instances.
  • GUI in WEKA now displays the exception as well.
  • Fixed bug where single quotes in attribute values could mess up args creation.
  • ArffToPickle now recognises class index option and arguments.
  • Fix nasty bug where filters were not being saved and were made from scratch from test data.


  • ArffToArgs gets temporary folder in a platform-independent way, instead of assuming /tmp/.
  • Can now save args in ArffToPickle using save.


  • Initial release.

Logo bandicoot 0.4

by yvesalexandre - November 20, 2015, 17:08:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3017 views, 641 downloads, 2 subscriptions

About: An open-source Python toolbox to analyze mobile phone metadata.


Initial Announcement on

Logo JMLR Darwin 1.9

by sgould - September 8, 2015, 06:50:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 75350 views, 15345 downloads, 4 subscriptions

About: A platform-independent C++ framework for machine learning, graphical models, and computer vision research and development.


Version 1.9:

  • Replaced drwnInPaint class with drwnImageInPainter class and added inPaint application
  • Added function to read CIFAR-10 and CIFAR-100 style datasets (see
  • Added drwnMaskedPatchMatch, drwnBasicPatchMatch, drwnSelfPatchMatch and basicPatchMatch application
  • drwnPatchMatchGraph now allows multiple matches to the same image
  • Upgraded wxWidgets to 3.0.2 (problems on Mac OS X)
  • Switched Mac OS X compilation to libc++ instead of libstdc++
  • Added Python scripts for running experiments and regression tests
  • Refactored drwnGrabCutInstance class to support both GMM and colour histogram model
  • Added cacheSortIndex to drwnDecisionTree for trading-off speed versus memory usage
  • Added mexLoadPatchMatchGraph for loading drwnPatchMatchGraph objects into Matlab
  • Improved documentation, other bug fixes and performance improvements

Logo XGBoost v0.4.0

by crowwork - May 12, 2015, 08:57:16 CET [ Project Homepage BibTeX Download ] 21048 views, 3705 downloads, 3 subscriptions

About: xgboost: eXtreme Gradient Boosting It is an efficient and scalable implementation of gradient boosting framework. The package includes efficient linear model solver and tree learning algorithm. The package can automatically do parallel computation with OpenMP, and it can be more than 10 times faster than existing gradient boosting packages such as gbm or sklearn.GBM . It supports various objective functions, including regression, classification and ranking. The package is made to be extensible, so that user are also allowed to define there own objectives easily. The newest version of xgboost now supports distributed learning on various platforms such as hadoop, mpi and scales to even larger problems

  • Distributed version of xgboost that runs on YARN, scales to billions of examples

  • Direct save/load data and model from/to S3 and HDFS

  • Feature importance visualization in R module, by Michael Benesty

  • Predict leaf index

  • Poisson regression for counts data

  • Early stopping option in training

  • Native save load support in R and python

  • xgboost models now can be saved using save/load in R

  • xgboost python model is now pickable

  • sklearn wrapper is supported in python module

  • Experimental External memory version

Logo JMLR Sally 1.0.0

by konrad - March 26, 2015, 17:01:35 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 60284 views, 11314 downloads, 3 subscriptions

About: A Tool for Embedding Strings in Vector Spaces


Support for explicit selection of granularity added. Several minor bug fixes. We have reached 1.0

Logo libcmaes 0.9.5

by beniz - March 9, 2015, 09:05:22 CET [ Project Homepage BibTeX Download ] 15792 views, 3003 downloads, 3 subscriptions

About: Libcmaes is a multithreaded C++11 library (with Python bindings) for high performance blackbox stochastic optimization of difficult, possibly non-linear and non-convex functions, using the CMA-ES algorithm for Covariance Matrix Adaptation Evolution Strategy. Libcmaes is useful to minimize / maximize any function, without information regarding gradient or derivability.


This is a major release, with several novelties, improvements and fixes, among which:

  • step-size two-point adaptaion scheme for improved performances in some settings, ref #88

  • important bug fixes to the ACM surrogate scheme, ref #57, #106

  • simple high-level workflow under Python, ref #116

  • improved performances in high dimensions, ref #97

  • improved profile likelihood and contour computations, including under geno/pheno transforms, ref #30, #31, #48

  • elitist mechanism for forcing best solutions during evolution, ref 103

  • new legacy plotting function, ref #110

  • optional initial function value, ref #100

  • improved C++ API, ref #89

  • Python bindings support with Anaconda, ref #111

  • configure script now tries to detect numpy when building Python bindings, ref #113

  • Python bindings now have embedded documentation, ref #114

  • support for Travis continuous integration, ref #122

  • lower resolution random seed initialization

Logo JMLR RL library 3.00.00

by frezza - January 13, 2015, 04:15:16 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6249 views, 1815 downloads, 2 subscriptions

About: A template based C++ reinforcement learning library


Initial Announcement on

Logo linearizedGP 1.0

by dsteinberg - November 28, 2014, 07:02:54 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3749 views, 769 downloads, 1 subscription

About: Gaussian processes with general nonlinear likelihoods using the unscented transform or Taylor series linearisation.


Initial Announcement on

Logo pySPACE 1.2

by krell84 - October 29, 2014, 15:36:28 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 9110 views, 1692 downloads, 1 subscription

About: pySPACE is the abbreviation for "Signal Processing and Classification Environment in Python using YAML and supporting parallelization". It is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g. SVM, LDA). pySPACE incorporates the concept of node and node chains of the MDP framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text- configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.


improved testing, improved documentation, windows compatibility, more algorithms

Showing Items 1-20 of 48 on page 1 of 3: 1 2 3 Next