anchor.diff:  4.- Saves and outputs results (mean results for each of the combinations).
   
   
  -Conclusions and actions after some iterations:
  +### 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.
   
  
positive.code: 
  Iterates over different parameters (manually selected) -> e.g.
  
  resample_dim_pool = [[4,4,4],[6,6,6],[8,8,8],[10,10,10],[12,12,12],[16,16,16]]
  subjects = 940
  n_bits_pool = [3,4,5,7]
  hash_counts_pool = [10,20,30,40]
  metric_pool = ["euclidean", "cosine", "manhattan"]
  z_score_pool = ["no","yes"]
  
  resample_dim is how you resample the image before storing in a vector that will be used for the hashing.
  Yes, it is what you said, dimensions of resampling before hashing. The bigger the dimensions, smaller the size of the vector. 
  Yes, you are right, there is not saving of storage in the engine, but it's much faster to hash a small vector, for both indexing and querying. Also in terms of neurovault's storage is better to have smaller reduced representations if we are using them just  for comparison.
  
  I don't have now the exact numbers, but from 4mm to 16mm the diff is over 10x faster.
  
  subjects is static, no probs.
  n_bits is the number of buckets that your engine will be divided into (2^n_bits)
  hash_counts is the number of tables you will have in the Engine. More tables -> more overlap between buckets.
  more info about how n_bits and hash_counts (tables) relate -> https://erramuzpe.github.io/gsoc2016/2016/06/16/Some-thoughts-on-L-S-H.html
  metric is the distance that we will use to measure how far/close 2 given points are inside the buckets
  z_score is just if we apply zscoring over each of the features.
  
  and loads the necessary data for each of the parameters (features and real_scores (corr_values calculated from the actual implementation), basically)
  
  
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:
  
  