- 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].
- 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].
This version contains a new CMake option USE_GPL_SHOGUN, which when set to OFF will exclude all GPL codes from Shogun [Heiko Strathmann].
This is a new feature and cleanup release.
- 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].
- 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].
- 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:
- 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]
- 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]
we are pleased to announce Shogun 3.2.0 !
This release also contains several cleanups and bugfixes:
- 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]
- 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]