multiclass lda initial commit, algorithm is working. ouptut is confirmed to match scikit learn, I would assume that more work is required to integrate this new class into shogun fully
MCLDA fixed the bug that caused a memory error on more complicated data
Update MCLDA.h

fixed the tabs in MCLDA.h
Update MCLDA.cpp

fixed tabbing in MCLDA.cpp
Update MCLDA.cpp
Update MCLDA.cpp
Changed typo in GP regression from RETURN_TYPE_MEAN to RETURN_TYPE_COV
Added a wrapper for the t-SNE DR method; added an integration test for it (black-listed for now).
Graphical example for SparseInverseCovariance utility class that estimates sparse inverse covariance by given covariance matrix. Plots a heat map and the graphs of dependency between variables
Fixed bug with negative values for the variance
Merge branch 'develop' of https://github.com/shogun-toolbox/shogun into develop
Fix for issue #1023 Fix for compiling python_modular on FreeBSD Add new --swig command line argument for ./configure script to be able to define the actual swig executable to use
remove inverse_covariance_estimation_demo.py from python_modulat directory
 Graphical example for SparseInverseCovariance utility class that estimates sparse inverse covariance by given covariance matrix. A heat map and the graphs of dependency between variables are plotted.
Added test case for GP regression, which checks negative value for the variance
Merge pull request #1030 from votjakovr/develop

Fixed bug with negative values for the variance in GP regression
fix typos
return svm bias in serialization example for all types
update news
Merge pull request #1027 from vladislav-horbatiuk/vgorbati-develop

Added a wrapper for the t-SNE DR method; added an integration test for it (black-listed for now).