class_docstr: Implements the local optimisation algorithm using the Strategy interface
class_docstr_tok: ["Implements","the","local","optimisation","algorithm","using","the","Strategy","interface"]
docstr: Adds extra indices for the combinatorial problem.
  
  Parameters
  ----------
  indices : tuple
  distance_matrix : numpy.ndarray (M,M)
  
  Examples
  --------
  >>> add_indices((1,2), numpy.array((5,5)))
  [(1, 2, 3), (1, 2, 4), (1, 2, 5)]
docstr_tok: ["Adds","extra","indices","for","the","combinatorial","problem",".","Parameters","--","--","--","--","--","indices",":","tuple","distance_matrix",":","numpy.ndarray","(","M",",","M",")","Examples","--","--","--","--",">",">",">","add_indices","(","(","1,2",")",",","numpy.array","(","(","5,5",")",")",")","[","(","1",",","2",",","3",")",",","(","1",",","2",",","4",")",",","(","1",",","2",",","5",")","]"]
code: def add_indices(
      self, indices: Tuple, distance_matrix: np.ndarray
  ) -> List:
      """Adds extra indices for the combinatorial problem.
  
      Parameters
      ----------
      indices : tuple
      distance_matrix : numpy.ndarray (M,M)
  
      Examples
      --------
      >>> add_indices((1,2), numpy.array((5,5)))
      [(1, 2, 3), (1, 2, 4), (1, 2, 5)]
  
      """
      list_new_indices = []
      for i in range(len(distance_matrix)):
          if i not in indices:
              list_new_indices.append(indices + (i,))
      return list_new_indices
  
file_path: SALib/src/SALib/sample/morris/local.py
imports: from itertools import combinations
  import numpy
  from typing import List, Tuple, Union
  from .strategy import Strategy
label_desc: Use image node_id 5 for calling the LocalOptimisation obj's underlying member method code with example usage: obj.add_indices(indices, distance_matrix) and returns: list_new_indices