code: def _init_parameters_jaro(self, X_bin):
          
  
          _, n_features = X_bin.shape
  
          class_prior = [.9, .1]
          feature_prob = np.zeros((2, n_features))
  
          for i, bin in enumerate(self._binarizers):
  
              if bin.classes_.shape[0] != 2:
                  raise ValueError("Only binary labels are allowed for "
                                   "'jaro'method. "
                                   "Column {} has {} different labels.".format(
                                       i, bin.classes_.shape[0]))
  
              # TODO: ensure classes are [0, 1] (not [1, 0])
              # TODO: check with bin.y_type_
  
              feature_prob[0, :] = np.tile([.9, .1], int(n_features / 2))
              feature_prob[1, :] = np.tile([.1, .9], int(n_features / 2))
  
          return np.log(class_prior), np.log(feature_prob)
docs: Initialise parameters for unsupervised learning.