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: - Euclidean does slightly better than Cosine or Manhattan distance.
  - Z scoring the data does not work at all, worst results always.
  
  
  
  
  - [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.
  