class_docstr: This class computes data balance measures for columns of interest
  based on a reference distribution. Currently, only the uniform reference
  distribution is supported.
class_docstr_tok: ["This","class","computes","data","balance","measures","for","columns","of","interest","based","on","a","reference","distribution",".","Currently",",","only","the","uniform","reference","distribution","is","supported","."]
docstr: Returns a reference distribution based on `n` outcomes.
  For now, this is a uniform distribution.
  
  :param n: The number of outcomes.
  :type n: int
  :return: The reference distribution.
  :rtype: numpy.ndarray
docstr_tok: ["Returns","a","reference","distribution","based","on","`","n","`","outcomes",".","For","now",",","this","is","a","uniform","distribution",".",":","param","n",":","The","number","of","outcomes",".",":","type","n",":","int",":","return",":","The","reference","distribution",".",":","rtype",":","numpy.ndarray"]
code: def _create_reference_distribution(n: int) -> np.array:
      """
      Returns a reference distribution based on `n` outcomes.
      For now, this is a uniform distribution.
  
      :param n: The number of outcomes.
      :type n: int
      :return: The reference distribution.
      :rtype: numpy.ndarray
      """
      uniform_val: float = 1.0 / n
      return np.ones(n) * uniform_val
  
file_path: responsible-ai-toolbox/responsibleai/responsibleai/databalanceanalysis/distribution_balance_measures.py
imports: from typing import Callable, Dict, List
  import numpy
  import pandas
  import scipy
  from responsibleai.databalanceanalysis.balance_measures import BalanceMeasures
  from responsibleai.databalanceanalysis.constants import Constants, Measures
label_desc: Use image node_id 4 for calling the DistributionBalanceMeasures obj's underlying member method code with example usage: obj._create_reference_distribution(n) and returns: unknown