Projects supporting the ascii data format.
Showing Items 1-20 of 45 on page 1 of 3: 1 2 3 Next

Logo JMLR MLPACK 3.0.2

by rcurtin - June 9, 2018, 18:03:57 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 228938 views, 40085 downloads, 0 subscriptions

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About: A fast, flexible C++ machine learning library, with bindings to other languages.

Changes:

Released June 8th, 2018.

  • Documentation generation fixes for Python bindings (#1421).
  • Fix build error for man pages if command-line bindings are not being built (#1424).
  • Add shuffle parameter and Shuffle() method to KFoldCV (#1412). This will shuffle the data when the object is constructed, or when Shuffle() is called.
  • Added neural network layers: AtrousConvolution (#1390), Embedding (#1401), and LayerNorm (layer normalization) (#1389).
  • Add Pendulum environment for reinforcement learning (#1388) and update Mountain Car environment (#1394).

Logo Armadillo library 8.500

by cu24gjf - April 23, 2018, 17:29:44 CET [ Project Homepage BibTeX Download ] 278046 views, 67586 downloads, 0 subscriptions

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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).

Changes:
  • 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

Logo Somoclu 1.7.5

by peterwittek - March 1, 2018, 23:30:34 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 94265 views, 17287 downloads, 0 subscriptions

About: Somoclu is a massively parallel implementation of self-organizing maps. It relies on OpenMP for multicore execution, MPI for distributing the workload, and it can be accelerated by CUDA on a GPU cluster. A sparse kernel is also included, which is useful for training maps on vector spaces generated in text mining processes. Apart from a command line interface, Python, Julia, R, and MATLAB are supported.

Changes:
  • New: A Makefile for mingw to build on Windows.
  • Changed: PR #94 added a much more efficient sparse kernel.
  • Changed: boilerplate code for Julia greatly improved.
  • Changed: Code cleanup, pre-processor macros simplified.
  • Changed: Adapted to Seaborn API changes in plotting heatmaps.

Logo MLweb 1.2

by lauerfab - February 23, 2018, 15:40:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 47660 views, 10991 downloads, 0 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.

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

Logo JMLR MSVMpack 1.5.1

by lauerfab - March 9, 2017, 12:29:37 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 63156 views, 15341 downloads, 0 subscriptions

About: MSVMpack is a Multi-class Support Vector Machine (M-SVM) package. It is dedicated to SVMs which can handle more than two classes without relying on decomposition methods and implements the four M-SVM models from the literature: Weston and Watkins M-SVM, Crammer and Singer M-SVM, Lee, Lin and Wahba M-SVM, and the M-SVM2 of Guermeur and Monfrini.

Changes:
  • Fix compilation error with recent gcc

Logo Multiagent Decision Process Toolbox 0.4

by faoliehoek - June 2, 2016, 17:38:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 15198 views, 3082 downloads, 0 subscriptions

About: The Multiagent decision process (MADP) Toolbox is a free C++ software toolbox for scientific research in decision-theoretic planning and learning in multiagent systems.

Changes:

-Includes freshly written spirit parser for .pomdp files. -Includes new code for pruning POMDP vectors; obviates dependence on Cassandra's code and old LP solve version. -Includes new factor graph solution code -Generalized firefighting CGBG domain added -Simulation class for Factored Dec-POMDPs and TOI Dec-MDPs -Approximate BG clustering methods and kGMAA with clustering.


Logo hca 0.63

by wbuntine - April 26, 2016, 15:35:03 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 68336 views, 9265 downloads, 0 subscriptions

About: Multi-core non-parametric and bursty topic models (HDP-LDA, DCMLDA, and other variants of LDA) implemented in C using efficient Gibbs sampling, with hyperparameter sampling and other flexible controls.

Changes:

Corrected the new normalised Gamma model for topics so it works with multicore. Improvements to documentation. Added an asymptotic version of the generalised Stirling numbers so it longer fails when they run out of bounds on bigger data.


Logo Libra 1.1.2d

by lowd - February 4, 2016, 08:51:50 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 62658 views, 13050 downloads, 0 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.

Changes:

Version 1.1.2d (12/29/2015):

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

Logo A Pattern Recognizer In Lua with ANNs v0.4.1

by pakozm - December 3, 2015, 15:01:36 CET [ Project Homepage BibTeX Download ] 26815 views, 6536 downloads, 0 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.

Changes:
  • 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.

Logo SALSA.jl 0.0.5

by jumutc - September 28, 2015, 17:28:56 CET [ Project Homepage BibTeX Download ] 7485 views, 1882 downloads, 0 subscriptions

About: SALSA (Software lab for Advanced machine Learning with Stochastic Algorithms) is an implementation of the well-known stochastic algorithms for Machine Learning developed in the high-level technical computing language Julia. The SALSA software package is designed to address challenges in sparse linear modelling, linear and non-linear Support Vector Machines applied to large data samples with user-centric and user-friendly emphasis.

Changes:

Initial Announcement on mloss.org.


Logo Universal Java Matrix Package 0.3.0

by arndt - July 31, 2015, 14:23:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 28267 views, 5380 downloads, 0 subscriptions

About: The Universal Java Matrix Package (UJMP) is a data processing tool for Java. Unlike JAMA and Colt, it supports multi-threading and is therefore much faster on current hardware. It does not only support matrices with double values, but instead handles every type of data as a matrix through a common interface, e.g. CSV files, Excel files, images, WAVE audio files, tables in SQL data bases, and much more.

Changes:

Updated to version 0.3.0


Logo JMLR libDAI 0.3.2

by jorism - July 17, 2015, 15:59:55 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 94631 views, 18488 downloads, 0 subscriptions

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About: libDAI provides free & open source implementations of various (approximate) inference methods for graphical models with discrete variables, including Bayesian networks and Markov Random Fields.

Changes:

Release 0.3.2 fixes various bugs and adds GLC (Generalized Loop Corrections) written by Siamak Ravanbakhsh.


Logo LMW Tree 1.0

by cdevries - May 30, 2015, 11:42:23 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8527 views, 2001 downloads, 0 subscriptions

About: Learning M-Way Tree - Web Scale Clustering - EM-tree, K-tree, k-means, TSVQ, repeated k-means, clustering, random projections, random indexing, hashing, bit signatures

Changes:

Initial Announcement on mloss.org.


Logo ClusterEval 1.1

by cdevries - May 18, 2015, 22:01:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 18090 views, 3696 downloads, 0 subscriptions

About: Cluster quality Evaluation software. Implements cluster quality metrics based on ground truths such as Purity, Entropy, Negentropy, F1 and NMI. It includes a novel approach to correct for pathological or ineffective clusterings called 'Divergence from a Random Baseline'.

Changes:

Moved project to GitHub.


Logo fertilized forests 1.0beta

by Chrisl_S - January 23, 2015, 16:04:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 8760 views, 2531 downloads, 0 subscriptions

About: The fertilized forests project has the aim to provide an easy to use, easy to extend, yet fast library for decision forests. It summarizes the research in this field and provides a solid platform to extend it. Offering consistent interfaces to C++, Python and Matlab and being available for all major compilers gives the user high flexibility for using the library.

Changes:

Initial Announcement on mloss.org.


Logo JEMLA 1.0

by bathaeian - January 4, 2015, 08:34:49 CET [ Project Homepage BibTeX Download ] 6700 views, 2078 downloads, 0 subscriptions

About: Java package for calculating Entropy for Machine Learning Applications. It has implemented several methods of handling missing values. So it can be used as a lab for examining missing values.

Changes:

Discretizing numerical values is added to calculate mode of values and fractional replacement of missing ones. class diagram is on the web http://profs.basu.ac.ir/bathaeian/free_space/jemla.rar


Logo libAGF 0.9.8

by Petey - December 6, 2014, 02:35:39 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 41405 views, 8643 downloads, 0 subscriptions

About: C++ software for statistical classification, probability estimation and interpolation/non-linear regression using variable bandwidth kernel estimation.

Changes:

New in Version 0.9.8:

  • bug fixes: svm file conversion works properly and is more general

  • non-hierarchical multi-borders has 3 options for solving for the conditional probabilities: matrix inversion, voting, and matrix inversion over-ridden by voting, with re-normalization

  • multi-borders now works with external binary classifiers

  • random numbers resolve a tie when selecting classes based on probabilities

  • pair of routines, sort_discrete_vectors and search_discrete_vectors, for classification based on n-d binning (still experimental)

  • command options have been changed with many new additions, see QUICKSTART file or run the relevant commands for details


Logo QSMM 1.16

by olegvol - July 29, 2014, 19:37:31 CET [ Project Homepage BibTeX Download ] 7268 views, 1971 downloads, 0 subscriptions

About: The implementation of adaptive probabilistic mappings.

Changes:

Initial Announcement on mloss.org.


Logo JMLR Continuous Time Bayesian Network Reasoning and Learning Engine 1.1.1

by cshelton - December 9, 2013, 18:44:02 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 50916 views, 14858 downloads, 0 subscriptions

About: The CTBN-RLE is a C++ package of executables and libraries for inference and learning algorithms for continuous time Bayesian networks (CTBNs).

Changes:

compilation problems fixed


Logo JMLR CARP 3.3

by volmeln - November 7, 2013, 15:48:06 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 49127 views, 13200 downloads, 0 subscriptions

About: CARP: The Clustering Algorithms’ Referee Package

Changes:

Generalized overlap error and some bugs have been fixed


Showing Items 1-20 of 45 on page 1 of 3: 1 2 3 Next