Shogun 5.0.0 - Ōtomo no Yakamochi

@vigsterkr vigsterkr released this Nov 4, 2016 · 91 commits to develop since this release

Features

  • GSoC 2016 project of Saurabh Mahindre: Major efficiency improvements for KMeans, LARS, Random Forests, Bagging, KNN.
  • Add new Shogun cookbook for documentation and testing across all target languages [Heiko Strathmann, Sergey Lisitsyn, Esben Sorig, Viktor Gal].
  • Added option to learn CombinedKernel weights with GP approximate inference [Wu Lin].
  • LARS now supports 32, 64, and 128 bit floating point numbers [Chris Goldsworthy].

Bugfixes:

  • Fix gTest segfaults with GCC >= 6.0.0 [Björn Esser].
  • Make Java and CSharp install-dir configurable [Björn Esser].
  • Autogenerate modshogun.rb with correct module-suffix [Björn Esser].
  • Fix KMeans++ initialization [Saurabh Mahindre].

Cleanup, efficiency updates, and API Changes:

  • Make Eigen3 a hard requirement. Bundle if not found on system. [Heiko Strathmann]
  • Drop ALGLIB (GPL) dependency in CStatistics and ship CDFLIB (public domain) instead [Heiko Strathmann]
  • Drop p-value estimation in model-selection [Heiko Strathmann]
  • Static interfaces have been removed [Viktor Gal]
  • New base class ShiftInvariantKernel of which GaussianKernel inherits [Rahul De].

NOTE

This version contains a new CMake option USE_GPL_SHOGUN, which when set to OFF will exclude all GPL codes from Shogun [Heiko Strathmann].

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Shogun 4.1.0 - Tajinohi no Agatamori

@karlnapf karlnapf released this Feb 10, 2016 · 929 commits to develop since this release

This is a new feature and cleanup release.

Features:

  • Added GEMPLP for approximate inference to the structured output framework [Jiaolong Xu].
  • Effeciency improvements of the FITC framework for GP inference (FITC_Laplce, FITC, VarDTC) [Wu Lin].
  • Added optimisation of inducing variables in sparse GP inference [Wu Lin].
  • Added optimisation methods for GP inference (Newton, Cholesky, LBFGS, ...) [Wu Lin].
  • Added Automatic Relevance Determination (ARD) kernel functionality for variational GP inference [Wu Lin].
  • Updated Notebook for variational GP inference [Wu Lin].
  • New framework for stochastic optimisation (L1/2 loss, mirror descent, proximal gradients, adagrad, SVRG, RMSProp, adadelta, ...) [Wu Lin].
  • New Shogun meta-language for automatically generating code listings in all target languages [Esben Sörig].
  • Added periodic kernel [Esben Sörig].
  • Add gradient output functionality in Neural Nets [Sanuj Sharma].

Bugfixes:

  • Fixes for java_modular build using OpenJDK [Björn Esser].
  • Catch uncaught exceptions in Neural Net code [Khaled Nasr].
  • Fix build of modular interfaces with SWIG 3.0.5 on MacOSX [Björn Esser].
  • Fix segfaults when calling delete[] twice on SGMatrix-instances [Björn Esser].
  • Fix for building with full-hardening-(CXX|LD)FLAGS [Björn Esser].
  • Patch SWIG to fix a problem with SWIG and Python >= 3.5 [Björn Esser].
  • Add modshogun.rb: make sure narray is loaded before modshogun.so [Björn Esser].
  • set working-dir properly when running R (#2654) [Björn Esser].

Cleanup, efficiency updates, and API Changes:

  • Added GPU based dot-products to linalg [Rahul De].
  • Added scale methods to linalg [Rahul De].
  • Added element wise products to linalg [Rahul De].
  • Added element-wise unary operators in linalg [Rahul De].
  • Dropped parameter migration framework [Heiko Strathmann].
  • Disabled Python integration tests by default [Sergey Lisitsyn, Heiko Strathmann].

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Shogun 4.0.0 - Kose no Maro

@vigsterkr vigsterkr released this Jan 26, 2015 · 1501 commits to develop since this release

  • This release features the work of our 8 GSoC 2014 students [student; mentors]:
    • OpenCV Integration and Computer Vision Applications [Abhijeet Kislay; Kevin Hughes]
    • Large-Scale Multi-Label Classification [Abinash Panda; Thoralf Klein]
    • Large-scale structured prediction with approximate inference [Jiaolong Xu; Shell Hu]
    • Essential Deep Learning Modules [Khaled Nasr; Sergey Lisitsyn, Theofanis Karaletsos]
    • Fundamental Machine Learning: decision trees, kernel density estimation [Parijat Mazumdar ; Fernando Iglesias]
    • Shogun Missionary & Shogun in Education [Saurabh Mahindre; Heiko Strathmann]
    • Testing and Measuring Variable Interactions With Kernels [Soumyajit De; Dino Sejdinovic, Heiko Strathmann]
    • Variational Learning for Gaussian Processes [Wu Lin; Heiko Strathmann, Emtiyaz Khan]
  • This release also contains several cleanups and bugfixes:
    • Features:
      • New Shogun project description [Heiko Strathmann]
      • ID3 algorithm for decision tree learning [Parijat Mazumdar]
      • New modes for PCA matrix factorizations: SVD & EVD, in-place or reallocating [Parijat Mazumdar]
      • Add Neural Networks with linear, logistic and softmax neurons [Khaled Nasr]
      • Add kernel multiclass strategy examples in multiclass notebook [Saurabh Mahindre]
      • Add decision trees notebook containing examples for ID3 algorithm [Parijat Mazumdar]
      • Add sudoku recognizer ipython notebook [Alejandro Hernandez]
      • Add in-place subsets on features, labels, and custom kernels [Heiko Strathmann]
      • Add Principal Component Analysis notebook [Abhijeet Kislay]
      • Add Multiple Kernel Learning notebook [Saurabh Mahindre]
      • Add Multi-Label classes to enable Multi-Label classification [Thoralf Klein]
      • Add rectified linear neurons, dropout and max-norm regularization to neural networks [Khaled Nasr]
      • Add C4.5 algorithm for multiclass classification using decision trees [Parijat Mazumdar]
      • Add support for arbitrary acyclic graph-structured neural networks [Khaled Nasr]
      • Add CART algorithm for classification and regression using decision trees [Parijat Mazumdar]
      • Add CHAID algorithm for multiclass classification and regression using decision trees [Parijat Mazumdar]
      • Add Convolutional Neural Networks [Khaled Nasr]
      • Add Random Forests algorithm for ensemble learning using CART [Parijat Mazumdar]
      • Add Restricted Botlzmann Machines [Khaled Nasr]
      • Add Stochastic Gradient Boosting algorithm for ensemble learning [Parijat Mazumdar]
      • Add Deep contractive and denoising autoencoders [Khaled Nasr]
      • Add Deep belief networks [Khaled Nasr]
    • Bugfixes:
      • Fix reference counting bugs in CList when reference counting is on [Heiko Strathmann, Thoralf Klein, lambday]
      • Fix memory problem in PCA::apply_to_feature_matrix [Parijat Mazumdar]
      • Fix crash in LeastAngleRegression for the case D greater than N [Parijat Mazumdar]
      • Fix memory violations in bundle method solvers [Thoralf Klein]
      • Fix fail in library_mldatahdf5.cpp example when http://mldata.org is not working properly [Parijat Mazumdar]
      • Fix memory leaks in Vowpal Wabbit, LibSVMFile and KernelPCA [Thoralf Klein]
      • Fix memory and control flow issues discovered by Coverity [Thoralf Klein]
      • Fix R modular interface SWIG typemap (Requires SWIG >= 2.0.5) [Matt Huska]
    • Cleanup and API Changes:
      • PCA now depends on Eigen3 instead of LAPACK [Parijat Mazumdar]
      • Removing redundant and fixing implicit imports [Thoralf Klein]
      • Hide many methods from SWIG, reducing compile memory by 500MiB [Heiko Strathmann, Fernando Iglesias, Thoralf Klein]

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Shogun 3.2.0

@vigsterkr vigsterkr released this Feb 17, 2014 · 3034 commits to develop since this release

we are pleased to announce Shogun 3.2.0 !

This release also contains several cleanups and bugfixes:

  • Features:
    • Fully support python3 now
    • Add mini-batch k-means [Parijat Mazumdar]
    • Add k-means++ for more details see the notebook [Parijat Mazumdar]
    • Add sub-sequence string kernel [lambday]
  • Bugfixes:
    • Compile fixes for upcoming swig3.0
    • Speedup for gaussian process' apply()
    • Improve unit / integration test checks
    • libbmrm uninitialized memory reads
    • libocas uninitialized memory reads
    • Octave 3.8 compile fixes [Orion Poplawski]
    • Fix java modular compile error [Bjoern Esser]

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