Added some code such that parse_data.py can also be used with the note-centred approach from sparse_feature_extraction.py
Both L1 and L2 weight regularisation now possible
Cleaned up code, added some code for using the original sparse_feature_extraction module
Simplified RBM way of dealing with input data
Changed RBM and SRBM_Goh so that they can be used in deep networks. For deep networks either pre-processed input for the latter layers should be used (evaluating lower networks, storing there output), or the training_data variable should be the size of batch_size - processing batches from a large training_data variable causes all training data to be evaluated for every batch, seriously slowing down the process.
Added scripts for processing magaloff data with RBM