Tags give the ability to mark specific points in history as being important
1.7.5 version 1.7.5Release 1.7.5
- 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.
1.7.4 version 1.7.4Release 1.7.4
- Fixed: The random seed was set to 0 for testing purposes. This is now changed to a wall-time based initialization.
1.7.3 version 1.7.3Release 1.7.3
- 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.
1.7.2 version 1.7.2Release 1.7.2
- 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.
get_bmufunction 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
- 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.
1.7.1 version 1.7.1Release 1.7.1
- Fixed: macOS build works again.
1.7.0 version 1.7.0Release 1.7.0
- New: Julia interface is available (https://github.com/peterwittek/Somoclu.jl).
- New: Method
Somocluobject in Python calculates the activation map for all data instances.
- New: Method
Somocluobject in Python allows plotting the activation map for the training data instances or for a new data instance.
- New: Method
Somocluobject in Python visualizes the similarity matrix of data points according to their distance to the nodes in the map.
- Fixed: CRAN-friendliness improved.
1.6.2 version 1.6.2Release 1.6.2
- Changed: In-place codebook updates when compiled without MPI. This improves update speed and substantially cuts memory use.
- Changed: Compatible with Visual Studio 15.
- Fixed: The BMUs returned after training were from before the last epoch. Now another round of BMU search is done.
- Fixed: Training can continue on the same data in the Python wrapper.
- Fixed: GPU memory allocation problem on Windows.
1.6.1 version 1.6.1Release 1.6.1
- New: Option for PCA initialization is added to the Python interface.
- New: Clustering of the codebook with arbitrary clustering algorithm in scikit-learn is now possible in the Python interface.
1.6 version 1.6Release 1.6
- New: R wrapper integrates with kohonen package.
- New: MATLAB wrapper integrates with soomtoolbox.
- New: Better handling of CUDA compilation in the Python interface.
- Changed: Throws an exception if GPU kernel is requested, but it was compiled without it. The earlier behaviour quietly defaulted to the CPU kernel.
1.5.1 version 1.5.1Release 1.5.1
- New: Neighborhood function can be chosen between Gaussian and bubble.
- Fixed: R wrapper passes arrays with correct orientation.
io.cppis no longer required in the wrappers. An exception is thrown when needed.
- New: Python interface has visual capabilities.
- New: Option for hexagonal grid.
- New: Option for requesting compact support in updating the map.
- New: Python, R, and MATLAB interfaces now allow passing an initial codebook.
- Changed: Reduced memory use in calculating U-matrices.
- Changed: Build system rebuilt and simplified.
1.4.1 version 1.4.1Release 1.4.1
- Better support for ICC.
- Faster code when compiling with GCC.
- Building instructions and documentation improved.
- Bug fixes: portability for R, using native R random number generator.