anchor.diff:  
   for 940 subjects (with queries limited to 100 subjects):
    
  +....
   - n_bits = 7 -> error = 0.5 (100 subjects in the response)
   - n_bits = 9 -> error = 0.3 (20 subjects in the response)
  +....
   
   this can be solved increasing the number of tables (hash_count), that allows a bigger collision rate between buckets and reduces the false positive rate. BUT this comes with a computational cost.
   
  
positive.code: - Euclidean does slightly better than Cosine or Manhattan distance.
  - Z scoring the data does not work at all, worst results always.
  
  scipy.stats.zscore(x)  ## where x is a feature vector
  
  Across individual features (1 per map). I don't exclude the zeros (not very sure about this...).  
  
  
  - [IMPORTANT] Dimension of the feature does not influence at all the results at least from [4, 4, 4] up to [16, 16, 16]. Which is quite important, due to size (features) and speed (in the Engine) improvement when using a higher resample dimension, thus, smaller feature vectors: [4 ,4 ,4] = 28549 size feature vs. [16, 16, 16] = 450 size features.
  
negative.code: # Testing: Getting closer
  
  These days I've been testing extensively diferent options over my local database. I started with 250 subjects on Monday, made some tests with different parameters, figured out how to record better results (explained later) and decided to finally load the whole 940 subjects in my local Neurovault and calculate the whole score-set for each of the images (thus, a call to get_similar_images()). I left this running on Monday before leaving the lab and it was done by Tuesday, not a big deal after all.
  
  ## Actions
  
  My advances so far were:
  
  