Somoclu

- New: A Makefile for mingw to build on Windows.
- Changed: PR 94 added a much more efficient sparse kernel.
- Changed: boilerplate code for Julia greatly improved.
- Changed: Code cleanup, pre-processor macros simplified.
- Changed: Adapted to Seaborn API changes in plotting heatmaps.

- Fixed: The random seed was set to 0 for testing purposes. This is now changed to a wall-time based initialization.

- New: Verbosity parameter in the command-line, Python, MATLAB, and Julia interfaces.
- Changed: Calculation of U-matrix parallelized.
- Changed: Moved feeding data to train method in the Python interface.
- Fixed: Sparse matrix reader made more robust.
- Fixed: Compatibility with kohonen 3 resolved.
- Fixed: Compatibility with Matplotlib 2 resolved.

- New: The coefficient of the Gaussian neighborhood function exp(-||x-y||^2/(2*(coeff*radius)^2)) is now exposed in all interfaces as a parameter.
- New: `get_bmu` function in the Python interface to get the best matching units given an activation map.
- Changed: Updated PCA initialization in the Python interface to work with `sk-learn` 0.18 onwards.
- Changed: Radii can be float values.
- Fixed: Only positive values were written back to codebook during update.
- Fixed: Sparse data is read correctly when there are class labels.

- Fixed: macOS build works again.

- New: Julia interface is available (https://github.com/peterwittek/Somoclu.jl).
- New: Method `get_surface_state` of the `Somoclu` object in Python calculates the activation map for all data instances.
- New: Method `view_activation_map` of the `Somoclu` object in Python allows plotting the activation map for the training data instances or for a new data instance.
- New: Method `view_similarity_matrix` of the `Somoclu` object in Python visualizes the similarity matrix of data points according to their distance to the nodes in the map.
- Fixed: CRAN-friendliness improved.