20 projects found that use python as the programming language.
Showing Items 1-20 of 119 on page 1 of 6: 1 2 3 4 5 6 Next

Logo Somoclu 1.7.4

by peterwittek - June 6, 2017, 15:48:11 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 27544 views, 5002 downloads, 3 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: Verbosity parameter in the command-line, Python, MATLAB, and Julia interfaces.
  • Changed: Calculation of U-matrix parallelized.
  • Changed: Moved feeding data to train method in the Python interface.
  • Fixed: The random seed was set to 0 for testing purposes. This is now changed to a wall-time based initialization.
  • Fixed: Sparse matrix reader made more robust.
  • Fixed: Compatibility with kohonen 3 resolved.
  • Fixed: Compatibility with Matplotlib 2 resolved.

Logo glyph 0.3.2

by mquade - June 1, 2017, 20:51:52 CET [ Project Homepage BibTeX Download ] 450 views, 122 downloads, 1 subscription

About: glyph is a python 3 library based on deap providing abstraction layers for symbolic regression problems.

Changes:

Initial Announcement on mloss.org.


Logo pycobra regression analysis and ensemble toolkit 0.1.0

by bhargavvader - April 19, 2017, 15:04:14 CET [ Project Homepage BibTeX Download ] 668 views, 122 downloads, 2 subscriptions

About: pycobra is a python toolkit to help with regression analysis and visualisation. It provides an implementation of the COBRA predictor aggregation algorithm.

Changes:

Initial Announcement on mloss.org.


Logo Theano 0.9.0

by jaberg - April 10, 2017, 20:30:17 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 33088 views, 5560 downloads, 3 subscriptions

About: A Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Dynamically generates CPU and GPU modules for good performance. Deep Learning Tutorials illustrate deep learning with Theano.

Changes:

Theano 0.9.0 (20th of March, 2017)

Highlights (since 0.8.0):

* Better Python 3.5 support
* Better numpy 1.12 support
* Conda packages for Mac, Linux and Windows
* Support newer Mac and Windows versions
* More Windows integration:

    * Theano scripts (``theano-cache`` and ``theano-nose``) now works on Windows
    * Better support for Windows end-lines into C codes
    * Support for space in paths on Windows

* Scan improvements:

    * More scan optimizations, with faster compilation and gradient computation
    * Support for checkpoint in scan (trade off between speed and memory usage, useful for long sequences)
    * Fixed broadcast checking in scan

* Graphs improvements:

    * More numerical stability by default for some graphs
    * Better handling of corner cases for theano functions and graph optimizations
    * More graph optimizations with faster compilation and execution
    * smaller and more readable graph

* New GPU back-end:

    * Removed warp-synchronous programming to get good results with newer CUDA drivers
    * More pooling support on GPU when cuDNN isn't available
    * Full support of ignore_border option for pooling
    * Inplace storage for shared variables
    * float16 storage
    * Using PCI bus ID of graphic cards for a better mapping between theano device number and nvidia-smi number
    * Fixed offset error in ``GpuIncSubtensor``

* Less C code compilation
* Added support for bool dtype
* Updated and more complete documentation
* Bug fixes related to merge optimizer and shape inference
* Lot of other bug fixes, crashes fixes and warning improvements

Logo Calibrated AdaMEC 1.0

by nnikolaou - April 8, 2017, 13:57:45 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 1044 views, 154 downloads, 3 subscriptions

About: Code for Calibrated AdaMEC for binary cost-sensitive classification. The method is just AdaBoost that properly calibrates its probability estimates and uses a cost-sensitive (i.e. risk-minimizing) decision threshold to classify new data.

Changes:

Updated license information


Logo scikit multilearn 0.0.5

by niedakh - February 25, 2017, 03:51:59 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2774 views, 618 downloads, 3 subscriptions

About: A native Python, scikit-compatible, implementation of a variety of multi-label classification algorithms.

Changes:
  • a general matrix-based label space clusterer has been added which can cluster the output space using any scikit-learn compatible clusterer (incl. k-means)
  • support for more single-class and multi-class classifiers you can now use problem transformation approaches with your favourite neural networks/deep learning libraries: theano, tensorflow, keras, scikit-neuralnetworks
  • support for label powerset based stratified kfold added
  • graph-tool clusterer supports weighted graphs again and includes stochastic blockmodel calibration
  • bugs were fixed in: classifier chains and hierarchical neuro fuzzy clasifiers

Logo JMLR dlib ml 19.3

by davis685 - February 22, 2017, 04:37:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 183164 views, 28895 downloads, 5 subscriptions

About: This project is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.

Changes:

This release adds a number of new features, most notably new deep learning tools including a state-of-the-art face recognition example using dlib's deep learning API. See http://dlib.net/dnn_face_recognition_ex.cpp.html for an introduction.


Logo ADENINE 0.1.4

by samuelefiorini - February 17, 2017, 14:50:49 CET [ Project Homepage BibTeX Download ] 2111 views, 487 downloads, 2 subscriptions

About: ADENINE (A Data ExploratioN pIpeliNE) is a machine learning framework for data exploration that encompasses state-of-the-art techniques for missing values imputing, data preprocessing, unsupervised feature learning and clustering tasks.

Changes:
  • Adenine can now distribute the execution of its pipelines on multiple machines via MPI
  • kNN data imputing strategy is now implemented
  • added python 2.7 and 3.5 support
  • stability improved and bug fixed

Logo revrand 1.0.0

by dsteinberg - January 29, 2017, 04:33:54 CET [ Project Homepage BibTeX Download ] 12557 views, 2589 downloads, 3 subscriptions

Rating Empty StarEmpty StarEmpty StarEmpty StarEmpty Star
(based on 1 vote)

About: A library of scalable Bayesian generalised linear models with fancy features

Changes:
  • 1.0 release!
  • Now there is a random search phase before optimization of all hyperparameters in the regression algorithms. This improves the performance of revrand since local optima are more easily avoided with this improved initialisation
  • Regression regularizers (weight variances) associated with each basis object, this approximates GP kernel addition more closely
  • Random state can be set for all random objects
  • Numerous small improvements to make revrand production ready
  • Final report
  • Documentation improvements

Logo python weka wrapper3 0.1.2

by fracpete - January 4, 2017, 10:27:40 CET [ Project Homepage BibTeX Download ] 2581 views, 500 downloads, 3 subscriptions

About: A thin Python3 wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • "typeconv.double_matrix_to_ndarray" no longer assumes a square matrix (https://github.com/fracpete/python-weka-wrapper3/issues/4)
  • "len(Instances)" now returns the number of rows in the dataset (module "weka.core.dataset")
  • added method "insert_attribute" to the "Instances" class
  • added class method "create_relational" to the "Attribute" class
  • upgraded Weka to 3.9.1

Logo python weka wrapper 0.3.10

by fracpete - January 4, 2017, 10:21:33 CET [ Project Homepage BibTeX Download ] 45627 views, 9155 downloads, 3 subscriptions

About: A thin Python wrapper that uses the javabridge Python library to communicate with a Java Virtual Machine executing Weka API calls.

Changes:
  • "types.double_matrix_to_ndarray" no longer assumes a square matrix (https://github.com/fracpete/python-weka-wrapper/issues/48)
  • "len(Instances)" now returns the number of rows in the dataset (module "weka.core.dataset")
  • added method "insert_attribute" to the "Instances" class
  • added class method "create_relational" to the "Attribute" class
  • upgraded Weka to 3.9.1

Logo JMLR scikitlearn 0.18.1

by fabianp - November 28, 2016, 17:45:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 29607 views, 10938 downloads, 5 subscriptions

Rating Whole StarWhole StarWhole StarWhole Star1/2 Star
(based on 3 votes)

About: The scikit-learn project is a machine learning library in Python.

Changes:

Update for 0.18 .1


Logo RLScore 0.7

by aatapa - September 20, 2016, 09:51:25 CET [ Project Homepage BibTeX Download ] 1473 views, 355 downloads, 3 subscriptions

About: RLScore - regularized least-squares machine learning algorithms package

Changes:

Initial Announcement on mloss.org.


Logo JMLR Information Theoretical Estimators 0.63

by szzoli - June 9, 2016, 23:42:14 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 135363 views, 24757 downloads, 3 subscriptions

About: ITE (Information Theoretical Estimators) is capable of estimating many different variants of entropy, mutual information, divergence, association measures, cross quantities and kernels on distributions. Thanks to its highly modular design, ITE supports additionally (i) the combinations of the estimation techniques, (ii) the easy construction and embedding of novel information theoretical estimators, and (iii) their immediate application in information theoretical optimization problems.

Changes:
  • Conditional Shannon entropy estimation: added.

  • Conditional Shannon mutual information estimation: included.


About: TBEEF, a doubly ensemble framework for recommendation and prediction problems.

Changes:

Included the final technical report.


Logo pymanopt 0.1

by j_towns - April 7, 2016, 14:44:27 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 2045 views, 388 downloads, 3 subscriptions

About: Python toolbox for manifold optimization with support for automatic differentiation

Changes:

Initial Announcement on mloss.org.


Logo Toupee 0.1

by nitbix - March 7, 2016, 20:29:59 CET [ Project Homepage BibTeX Download ] 1652 views, 403 downloads, 3 subscriptions

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

Changes:

Initial Announcement on mloss.org.


Logo libcluster 2.3

by dsteinberg - February 27, 2016, 00:36:01 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 6122 views, 1292 downloads, 3 subscriptions

About: An extensible C++ library of Hierarchical Bayesian clustering algorithms, such as Bayesian Gaussian mixture models, variational Dirichlet processes, Gaussian latent Dirichlet allocation and more.

Changes:

New maximum cluster argument for all algorithms. Also no more matlab interface since it seemed no one was using it, and I cannot support it any longer.


Logo BayesOpt, a Bayesian Optimization toolbox 0.8.2

by rmcantin - December 9, 2015, 04:53:31 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 31468 views, 5737 downloads, 4 subscriptions

About: BayesOpt is an efficient, C++ implementation of the Bayesian optimization methodology for nonlinear-optimization, experimental design and stochastic bandits. In the literature it is also called Sequential Kriging Optimization (SKO) or Efficient Global Optimization (EGO). There are also interfaces for C, Matlab/Octave and Python.

Changes:

-Fixed bug in save/restore. -Fixed bug in initial design.


Logo PyScriptClassifier 0.3.0

by cjb60 - November 25, 2015, 04:07:51 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ] 3848 views, 935 downloads, 2 subscriptions

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

Changes:

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.

Showing Items 1-20 of 119 on page 1 of 6: 1 2 3 4 5 6 Next