anchor.diff:  
   Long story short: we are lucky, since we know the size of our problem (ok, the size will increase, but in the worst scenario, n_bits increases the size of buckets exponentially and we could build the engine every time the number of images reaches a given size). The number of tables to choose, depends on the computational cost that we can absorb and the accuracy level we want to achieve.
   
  -I attach a table with the mail, to show an example with some parameters fixed. The difference between 10, 40, 60 or 100 tables is just the time it takes to retrieve the result. For 940 subjects, the diff between 10 and 100 tables is like 3-4 seconds querying the WHOLE dataset (being 10 immediate).
  +The difference between 10, 40, 60 or 100 tables is just the time it takes to retrieve the result. For 940 subjects, the diff between 10 and 100 tables is like 3-4 seconds querying the WHOLE dataset (being 10 immediate).
   
   All is about balance.
   
  -Let me know what you think, whenever you can, no hurries.
   
   
  -
  -
  -in this comment I made a summarize of the speed tradeoff:
  -
  -https://github.com/NeuroVault/NeuroVault/pull/517#issuecomment-227447259
  -
  -
  -######
  +## Example
   
   Now, I will try to put a visual example of the results. Let's select my local map #74 (http://neurovault.org/images/10896/):
   
  
positive.code: 4.- Saves and outputs results (mean results for each of the combinations).
  
  
  Conclusions and actions after some iterations:
  
  - Is not enough to calculate which combination has the highest DCG, we need also the error between the real_score and the engine_score. I am calculating both atm.
  
  real = the actual implementation results (correlation values)
  engine = the ones I get from Nearpy
  
negative.code: - Z scoring the data does not work at all, worst results always.
  
    
  - Dimensions 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.
  
  
  - `n_bits` and `hash_counts` represent the number of buckets and the number of tables of the engine. from the tests, a bigger number of n_bits (n_bits ^ 2 = number of buckets), smaller error. BUT this error reduction has a trick. The higher the number of buckets, the less images "fall" into the buckets. Similar images "fall" in the buckets, but then, the queries are done over these buckets. So, if we select a big number for n_bits, we will have more accurate results, but less results. e.g.
  
  for 940 subjects (with queries limited to 100 subjects):
   
  