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
Merge branch 'develop' of https://github.com/shogun-toolbox/shogun into vgorbati-develop
Merge branch 'develop' of https://github.com/shogun-toolbox/shogun into vgorbati-develop
MCLDA fixed the bug that caused a memory error on more complicated data
Add boolean member to Perceptron to allow model initialization.
This is useful to show that the perceptron converges to different
solutions depending on initial values of w and bias.
Perceptron graphical example.
For the same data, the solution is different depending on initial values
of w and bias
Merge branch 'develop' of https://github.com/shogun-toolbox/shogun into vgorbati-develop
Update MCLDA.h

fixed the tabs in MCLDA.h
Update MCLDA.cpp

fixed tabbing in MCLDA.cpp
Update MCLDA.cpp
Update MCLDA.cpp
Merge branch 'develop' of https://github.com/shogun-toolbox/shogun into vgorbati-develop
Added a wrapper (CTDistributedStochasticNeighborEmbedding class) for t-SNE dimensionality reduction method, implemented in tapkee (according to https://github.com/shogun-toolbox/shogun/issues/946).
Also added a basic unit test for it, that embeds the test data from 3D to 2D using implemented wrapper, and checks that resulting data is actually in 2D, and that number of features stay the same.
More debug information for python_modular integration tests
Merge pull request #1007 from iglesias/feature/custom_init_perceptron

Add small functionality to perceptron and graphical example
Changed typo in GP regression from RETURN_TYPE_MEAN to RETURN_TYPE_COV
Merge pull request #1015 from vladislav-horbatiuk/vgorbati-develop

Added a wrapper for t-SNE method implemented in tapkee library and a basic unit test for it.
Added a wrapper for the t-SNE DR method; added an integration test for it (black-listed for now).
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