Test for K kernel calculation
Moved kernels to separate files
Wrote CPU comparison tests for generate__K__ and generate__K_x__
Fixed a few small errors, the results seem to be correct now
Added a basic test for K_xK, diag_K_xKK_xT and K inverse calculations.
Ignore dev files
Minor changes.
Fixed a small problem with feedback vector not being in float32.
Added fabsf to variance kernel before sqrt.
Fixed a small problem with feedback vector not being in float32.
GP CUDA TESTED OK
GP CUDA TESTED OK, author name added
Made gp_cuda.py executable for cuda-memcheck
Started refactoring this into GaussianProcess class. calc_K has been tested to function, but still needs changing a bit.
Recreated one test for K calculation, seems to work
Refactoring code around, switching more of the initialization to __init__ function
First version of calc_Kx done, but either test or the calculation itself is wrong.

I didn't think things through before starting the refactoring, so will probably have to do some things again. Type checking has to be done to prevent some of the more stupid mistakes.
Changed the structure a bit so things aren't merrily all around. Array initialization, cuda memory operations and type checking is now done in __init__.
Added padding to predict_idx.
Renamed round_up_to_blocksize to reflect the fact it rounds up.
Checked the K_x test part, the test is incorrect.
Fixed the K_xK test.
Added K_xKK_x_T calculation.
Started adding feedback calculation.
Refactoring vector padding to a method.
Changed predict_idx padding to use pad_vector method.
Changed vector initializations to use self.pad_vector