Merge branch 'theanollr' of github.com:alexisVallet/dpm-identification
Added grid search results for DPM. Ran in 37 hours.
Merge branch 'theanollr' of github.com:alexisVallet/dpm-identification into theanollr

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Cleaned up stuff that's not really needed anymore (mostly binary classification stuff).
Started features anew. Beginning of HoG implementation, still likely buggy.
Finished refactoring features. Tested and implemented HoG, as well as BGR hist + HoG combination. Results are better than BGR alone, but not by much. I expect it to perform especially well in the detection + identification case. Identified a problem with deformation cost. Need to somehow scale up the deformation cost.
Added a "deformation factor" to control the relative importance of deformation cost. Doesn't seem to matter so much after all. Initial deformation cost does however matter.
Few more tests.
Started refactoring the DPM classifier into the new grid search network.
Implemented, tested and debugged GS for DPM classifier with no unnecessary feature map recomputations. Should still be improved by performing feature selection using the warping classifier.
Fixed a "bug" in which I predicted probabilities in LMLR using sigmoid instead of softmax. It doesn't really change anything. It would matter if we could somehow find a loss function which guaranteed summing probabilities to one during training, but I don't see any obvious candidate which is still convex.
Wrote the code for dataset extension with translated pictures, but I'm running out of memory. Possible fixes: run PCA on features, more RAM, smaller number of features (e.g. reduce nb_orient for HoG).
Implemented the fast GS for DPM classification, although not fully tested.
Merge branch 'nestedgs' of github.com:alexisVallet/dpm-identification into theanollr
Implemented RPROP for latent MLR, which converges faster than GD, but a bit too well - overfits more. Probably worth it to implement a simple early stopping heuristic.
Tried early stopping, without much success - the loss of the validation samples really hurts.
Finished debugging the top accuracy stuff. Now implementing prediction through averageing on many subwindows.
merge