About: Armadillo is a high quality C++ library for linear algebra & scientific computing, 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 sparse matrices by kron() and repmat()
• faster transpose of sparse matrices
• faster element access in sparse matrices
• faster row iterators for sparse matrices
• faster handling of compound expressions by trace()
• more efficient handling of aliasing in submatrix views
• expanded normalise() to handle sparse matrices
• expanded .transform() and .for_each() to handle sparse matrices
• added reverse() for reversing order of elements
• added repelem() for replicating elements
• added roots() for finding the roots of a polynomial

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

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 mloss.org.

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

Initial Announcement on mloss.org.

About: A Content Anomaly Detector based on n-Grams

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

About: A Tool for Measuring String Similarity

This release fixes the incorrect implementation of the bag distance.

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

Initial Announcement on mloss.org.

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

Initial Announcement on mloss.org.

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!

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

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 cast.to 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
  ann.loss.cross_entropy.
• 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
  ann.components.right_probabilistic_matrix.
• 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 matrix.sparse.builders.dok, 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.

C/C++

• 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
  Matrix<T>.
• 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
  SparseMatrix<T>::fromDenseMatrix().
• 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.

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

0.3.0

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

0.2.1

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

0.2.0

• 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.

0.1.1

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

0.1.0

• Initial release.

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

Initial Announcement on mloss.org.

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

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

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

Initial Announcement on mloss.org.

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