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
fix json serialization (Closes: #943)
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
add fancy difflib based diff to determine failures on travis
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