code: def fit(self, dpp_vector):
          
  
          # decompose X and generate the rankings of the elements in the
          # decomposed matrix
          dpp_vector_decomposed = self.mf_model.transform(dpp_vector)
          dpp_vector_ranked = stats.rankdata(
              dpp_vector_decomposed,
              method='dense',
          )
  
          max_agreement_index = None
          max_agreement = -1  # min value of Kendall Tau agremment
          for i in range(self.dpp_ranked.shape[0]):
              # calculate agreement between current row and X
              agreement, _ = stats.kendalltau(
                  dpp_vector_ranked,
                  self.dpp_ranked[i, :],
              )
              if agreement > max_agreement:
                  max_agreement_index = i
                  max_agreement = agreement
  
          if max_agreement_index is None:
              max_agreement_index = np.random.randint(self.dpp_matrix.shape[0])
          # store the row with the highest agreement for prediction
          self.matching_dataset = self.dpp_matrix[max_agreement_index, :]
docs: Finds row of self.dpp_matrix most closely corresponds to X by means
          of Kendall tau distance.
          https://en.wikipedia.org/wiki/Kendall_tau_distance
  
          Args:
              dpp_vector (np.array): Array with shape (n_components, )