Overview: Iterative machine enables us to write iterative algorithms that are prematurely stoppable. This means users can cancel the training process any time. The model is still usable and concurrent. This model can then be applied to test data, compared...
Overview: Most algorithms in shogun do not behave in a generic manner in the sense that they are type dependent. The train method can accept any type of features as a CFeatures* pointer however it is later assumed that the...
This week we completed the mixin and merged it into develop. We also made train_machine_templated protected again by making the mixin base class a friend of subclasses. Also, CWDSVMOcas is not the best candidate for string features since it actually...
This week we came up with a second idea for feature dispatching. The earlier approach was to use macro to generate function names based on train_dense, train_string calls. This is not very automated. Also, the macros are hard to debug...
This week we made some major refactors to NewtonSVM class. These include cleaning up all raw pointers and using SGVector, SGMatrix instead. Using linalg instead of SGVector, SGMatrix for ops. Making NewtonSVM iterative. seperately calculating bias and weights. This was...