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- Drop all <math.h> function calls [Viktor Gal]
- Use c++11 std::isnan, std:isfinite, std::isinf [Viktor Gal]
- Port ipython notebooks to be python3 compatible [Viktor Gal]
- Use the shogun-static library on Windows when linking the interface library [Viktor Gal]
- Fix python typemap when compiling with MSVC [Viktor Gal]
- Fix ShogunConfig.cmake paths [Viktor Gal]
- Fix meta example parser bug in parallel builds [Esben Sørig]
- Install headers of GPL models when LICENSE_GPL_SHOGUN is enabled [Viktor Gal]
- Always turn on LIBSHOGUN_BUILD_STATIC when compiling with MSVC [Viktor Gal]
- Fix ipython notebook errors [Viktor Gal]
This release is dedicated for Heiko's successful PhD defense!
Add conda-forge packages, to get prebuilt binaries via the cross-platform conda package manager [Dougal Sutherland]
Change interface cmake variables to INTERFACE_*
Move GPL code to gpl submodule [Heiko Strathmann]
- Enable using BLAS/LAPACK from Eigen by default [Viktor Gal]
- Add iterators to SGVector and SGMatrix [Viktor Gal]
- Significantly lower the runtime of KernelPCA (GSoC '17) [Michele Mazzoni]
- Refactor FisherLDA and LDA solvers (GSoC '17) [Michele Mazzoni]
- Add automated test for trained model serialization (GSoC '17) [Michele Mazzoni]
- Enable SWIG director classes by default [Viktor Gal]
- Vectorize DotFeatures covariance/mean calculation [Michele Mazzoni]
- Support for premature stopping of model training (GSoC '17) [Giovanni De Toni]
- Add support for observable variables (GSoC '17) [Giovanni De Toni]
- Use TFLogger to serialize observed variables for TensorBoard (GSoC '17) [Giovanni De Toni]
- Drop CMath::dot and SGVector::dot and use linalg::dot [Viktor Gal]
- Added class probabilities for BaggingMachine (GSoC '17) [Olivier Nguyen]
- Fix transpose bug in Ruby typemap for matrices [Elias Saalmann]
- Fix MKL detection and linking; use mkl_rt when available [Viktor Gal]
- Fix Windows static linking [Viktor Gal]
- Fix SWIG interface compilation on Windows [qcrist]
- Fix CircularBuffer bug that broke parsing of big CSV and LibSVM files #1991 [Viktor Gal]
- Fix R interface when using clang to compile the interface [Viktor Gal]
- Add native MS Windows support [Viktor Gal]
- Shogun requires the compiler to support C++11 features
- Shogun cloud online: Jupyter notebook with Shogun from the browser, https://cloud.shogun.ml
- LDA now supports 32, 64 and 128 bit floating point numbers [Chris Goldsworthy]
- Add SHOGUN_NUM_THREADS enviroment variable to control the number of threads
used by the models in runtime [Viktor Gal]
- Added Scala Interface to the build [Abhinav Rai]
- Major re-writing and API changes in kernel statistical hypothesis
testing framework, significant speed up in permutation test for
quadratic time MMD, new kernel selection algorithms for quadratic time MMD [Soumyajit De]
- Fix build error of R interface for R>=3.3.0, #3460 [Heiko Strathmann]
- Make the code compatible with Eigen 3.3.0 [Viktor Gal]
- Fix number of CPUs detected on Linux [Viktor Gal]
- Fix multi-threading in KMeansBase [Viktor Gal]
- Make ExponentialARDKernel thread-safe [Viktor Gal]
- Make PRNG thread-safe [Viktor Gal]
- Fix python interface when using libshogun compiled with OpenMP [Viktor Gal]
- Fix CART to work with cross-validation [Fernando Iglesias]
Cleanup, efficiency updates, and API Changes:
- Port multi-threading to use OpenMP backend in Kernel [Viktor Gal]
- Fix false sharing in EuclideanDistance [Viktor Gal]
- Fix out of source build of the whole project [Viktor Gal]
- Add LIBSHOGUN cmake flag to turn off libshogun compilation [Viktor Gal]
- Export Shogun target with cmake to enable to build modular interfaces to a
pre-compiled libshogun on the system without requiring to compile
libshogun itself [Viktor Gal]
- Contains major rewrite and clean-up of developer documentation in doc/readme [Heiko Strathmann, Lea Götz]
- Known issue: Octave multithreaded crashes, currently bindings are initialized single-threaded, #3772 [Heiko Strathmann]
- 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]