original_docstring: 
      Initialises the population at random.  This type of initialization
      can possess clustering, Latin Hypercube sampling is generally better.
      
docstring: Initialises the population at random.  This type of initialization
  can possess clustering, Latin Hypercube sampling is generally better.
docstring_tokens: Initialises the population at random . This type of initialization can possess clustering Latin Hypercube sampling is generally better .
code: def init_population_random(self):
      """
      Initialises the population at random.  This type of initialization
      can possess clustering, Latin Hypercube sampling is generally better.
      """
      rng = self.random_number_generator
      self.population = rng.random_sample(self.population_shape)
  
      # reset population energies
      self.population_energies = (np.ones(self.num_population_members) *
                                  np.inf)
  
      # reset number of function evaluations counter
      self._nfev = 0
code_tokens: def init_population_random ( self ) : rng = self . random_number_generator self . population = rng . random_sample ( self . population_shape ) self . population_energies = ( np . ones ( self . num_population_members ) * np . inf ) self . _nfev = 0
short_docstring: Initialises the population at random.
short_docstring_tokens: Initialises the population at random .
comment: """
      Initialises the population at random.  This type of initialization
      can possess clustering, Latin Hypercube sampling is generally better.
      """$SEP$# reset population energies$SEP$# reset number of function evaluations counter