class_docstr: Feature selection using data-mined expert knowledge.
  Based on the MultiSURF algorithm as introduced in:
  Moore, Jason et al. Multiple Threshold Spatially Uniform ReliefF
  for the Genetic Analysis of Complex Human Diseases.
docstr: Identify nearest hits and misses within radius defined by average distance and standard deviation around each target training instance.
  This works the same regardless of endpoint type. 
docstr_tok: ["Identify","nearest","hits","and","misses","within","radius","defined","by","average","distance","and","standard","deviation","around","each","target","training","instance",".","This","works","the","same","regardless","of","endpoint","type","."]
code: def _find_neighbors(self, inst):
      """Identify nearest hits and misses within radius defined by average distance and standard deviation around each target training instance.
      This works the same regardless of endpoint type."""
      dist_vect = []
      for j in range(self._datalen):
          if inst != j:
              locator = [inst, j]
              if inst < j:
                  locator.reverse()
              dist_vect.append(
                  self._distance_array[locator[0]][locator[1]]
              )
  
      dist_vect = np.array(dist_vect)
      inst_avg_dist = np.average(dist_vect)
      inst_std = np.std(dist_vect) / 2.0
      # Defining a narrower radius based on the average instance distance minus the standard deviation of instance distances.
      near_threshold = inst_avg_dist - inst_std
  
      NN_near = []
      for j in range(self._datalen):
          if inst != j:
              locator = [inst, j]
              if inst < j:
                  locator.reverse()
              if (
                  self._distance_array[locator[0]][locator[1]]
                  < near_threshold
              ):
                  NN_near.append(j)
  
      return np.array(NN_near)
  
file_path: scikit-rebate/skrebate/multisurf.py
imports: from __future__ import print_function
  import numpy
  from .surfstar import SURFstar
  from joblib import Parallel, delayed
  from .scoring_utils import MultiSURF_compute_scores
label_desc: Use image node_id 1 for calling the MultiSURF obj's underlying member method code with example usage: obj._find_neighbors(inst) and returns: np