text: # encoding=utf8
  import logging
  
  from numpy import random as rand
  
  from NiaPy.algorithms.algorithm import Algorithm
  
  logging.basicConfig()
  logger = logging.getLogger('NiaPy.algorithms.other')
  logger.setLevel('INFO')
  
  __all__ = ['HillClimbAlgorithm']
  
  def Neighborhood(x, delta, task, rnd=rand):
  	r"""Get neighbours of point.
  
  	Args:
  		x numpy.ndarray: Point.
  		delta (float): Standard deviation.
  		task (Task): Optimization task.
  		rnd (Optional[mtrand.RandomState]): Random generator.
  
  	Returns:
  		Tuple[numpy.ndarray, float]:
  			1. New solution.
  			2. New solutions function/fitness value.
  	"""
  	X = x + rnd.normal(0, delta, task.D)
  	X = task.repair(X, rnd)
  	Xfit = task.eval(X)
  	return X, Xfit
  
  class HillClimbAlgorithm(Algorithm):
  	r"""Implementation of iterative hill climbing algorithm.
  
  	Algorithm:
  		Hill Climbing Algorithm
  
  	Date:
  		2018
  
  	Authors:
  		Jan Popič
  
  	License:
  		MIT
  
  	Reference URL:
  
  	Reference paper:
  
  	See Also:
  		* :class:`NiaPy.algorithms.Algorithm`
  
  	Attributes:
  		delta (float): Change for searching in neighborhood.
  		Neighborhood (Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]): Function for getting neighbours.
  	"""
  	Name = ['HillClimbAlgorithm', 'BBFA']
  
  	@staticmethod
  	def algorithmInfo():
  		r"""Get basic information about the algorithm.
  
  		Returns:
  			str: Basic information.
  
  		See Also:
  			:func:`NiaPy.algorithms.algorithm.Algorithm.algorithmInfo`
  		"""
  		return r"""TODO"""
  
  	@staticmethod
  	def typeParameters():
  		r"""TODO.
  
  		Returns:
  			Dict[str, Callable]:
  				* delta (Callable[[Union[int, float]], bool]): TODO
  		"""
  		return {'delta': lambda x: isinstance(x, (int, float)) and x > 0}
  
  	def setParameters(self, delta=0.5, Neighborhood=Neighborhood, **ukwargs):
  		r"""Set the algorithm parameters/arguments.
  
  		Args:
  			* delta (Optional[float]): Change for searching in neighborhood.
  			* Neighborhood (Optional[Callable[numpy.ndarray, float, Task], Tuple[numpy.ndarray, float]]]): Function for getting neighbours.
  		"""
  		Algorithm.setParameters(self, NP=1, **ukwargs)
  		self.delta, self.Neighborhood = delta, Neighborhood
  
  	def getParameters(self):
  		d = Algorithm.getParameters(self)
  		d.update({
  			'delta': self.delta,
  			'Neighborhood': self.Neighborhood
  		})
  		return d
  
  	def initPopulation(self, task):
  		r"""Initialize stating point.
  
  		Args:
  			task (Task): Optimization task.
  
  		Returns:
  			Tuple[numpy.ndarray, float, Dict[str, Any]]:
  				1. New individual.
  				2. New individual function/fitness value.
  				3. Additional arguments.
  		"""
  		x = task.Lower + self.rand(task.D) * task.bRange
  		return x, task.eval(x), {}
  
  	def runIteration(self, task, x, fx, xb, fxb, **dparams):
  		r"""Core function of HillClimbAlgorithm algorithm.
  
  		Args:
  			task (Task): Optimization task.
  			x (numpy.ndarray): Current solution.
  			fx (float): Current solutions fitness/function value.
  			xb (numpy.ndarray): Global best solution.
  			fxb (float): Global best solutions function/fitness value.
  			**dparams (Dict[str, Any]): Additional arguments.
  
  		Returns:
  			Tuple[numpy.ndarray, float, numpy.ndarray, float, Dict[str, Any]]:
  				1. New solution.
  				2. New solutions function/fitness value.
  				3. Additional arguments.
  		"""
  		lo, xn = False, task.bcLower() + task.bcRange() * self.rand(task.D)
  		xn_f = task.eval(xn)
  		while not lo:
  			yn, yn_f = self.Neighborhood(x, self.delta, task, rnd=self.Rand)
  			if yn_f < xn_f: xn, xn_f = yn, yn_f
  			else: lo = True or task.stopCond()
  		xb, fxb = self.getBest(xn, xn_f, xb, fxb)
  		return xn, xn_f, xb, fxb, {}
  
  # vim: tabstop=3 noexpandtab shiftwidth=3 softtabstop=3
  