anchor.diff:    let errTresholdMeet = (rms_trained_err < best_trained_err) && (rms_validated_err < best_test_err)
     if errTresholdMeet then trained
     else train ((-) epoch 1) trained (training_samples, teaching_inputs) (testing_samples, teaching_testing_inputs)
  -
   
   
   let inputSize = 7;
  
positive.code: let testing_samples = [| [ 0.692; 0.000; 0.000; 0.174; 0.000; 0.000; 0.000; ] |]
  let teaching_samples_inputs = [| [ 1.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 0.0; 1.0; 0.0; 0.0;  ] |]
  
  let epoch = 10000
  
  let trained =
   train
  
negative.code: 
  let square x = x * x
  
  let distance xs ys =
    (List.map2 (-) xs ys)
    |> List.map square
    |> List.average
    |> sqrt
    |> (*) 0.5
  
  /// Euclid Norm.
  let rec norm xs =
   let rec f xs acc =
    match xs with
  