class_docstr: A matrix representation out of features.
class_docstr_tok: ["A","matrix","representation","out","of","features","."]
docstr: Find the closest item(s) for each set of features in features_list.
  
  Args:
      features: A matrix with rows that represent records
          (corresponding to the elements in records_data) and columns
          that describe a point in space for each row.
  
  Returns:
      For each element in features_list, return the k-nearest items
      and their distance scores
      [[(score1_1, item1_1), ..., (score1_k, item1_k)],
       [(score2_1, item2_1), ..., (score2_k, item2_k)], ...]
code: def nearest_search(self, features):
      """Find the closest item(s) for each set of features in features_list.
  
      Args:
          features: A matrix with rows that represent records
              (corresponding to the elements in records_data) and columns
              that describe a point in space for each row.
  
      Returns:
          For each element in features_list, return the k-nearest items
          and their distance scores
          [[(score1_1, item1_1), ..., (score1_k, item1_k)],
           [(score2_1, item2_1), ..., (score2_k, item2_k)], ...]
      """
  
      dist_matrix = self._distance(features)
  
      ret = []
      for i in range(dist_matrix.shape[0]):
          # replacing the for loop by matrix ops could speed things up
  
          scores = dist_matrix[i]
          records = self.records_data
  
          arg_index = _np.argsort(scores)
  
          curr_ret = list(zip(scores[arg_index], records[arg_index]))
  
          ret.append(curr_ret)
  
      return ret
  
imports: from __future__ import absolute_import, division, print_function, unicode_literals
  import abc
  import numpy
  import scipy.sparse
  import scipy.spatial.distance
label_desc: Use image node_id 8 for calling the MatrixMetricSearch obj's underlying member method code with example usage: obj.nearest_search(features) and returns: ret