
'''simple docstring'''





import math



class 			lowerCAmelCase__ :



  '''simple docstring'''






  def __init__(self	:			Optional[Any]						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Optional[Any]     ) ->							Any:
        snake_case_    							=				x
        snake_case_    							=				y
        snake_case_    							=				step_size
        snake_case_    							=				function_to_optimize

  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							int:
        return self.function(self.x						,			self.y     )

  def 		_lowerCamelCase						(self	:			int     ) ->							Tuple:
        snake_case_    							=				self.step_size
        return [
            SearchProblem(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			self.function     )
            for x, y in (
                (self.x - step_size, self.y - step_size),
                (self.x - step_size, self.y),
                (self.x - step_size, self.y + step_size),
                (self.x, self.y - step_size),
                (self.x, self.y + step_size),
                (self.x + step_size, self.y - step_size),
                (self.x + step_size, self.y),
                (self.x + step_size, self.y + step_size),
            )
        ]

  def __hash__(self	:			Optional[int]     ) ->							Union[str, Any]:
        return hash(str(self     )     )

  def __eq__(self	:			List[str]						,			lowerCAmelCase_	:			str     ) ->							Dict:
        if isinstance(lowerCAmelCase_						,			lowerCAmelCase_     ):
              return hash(str(self     )     ) == hash(str(lowerCAmelCase_     )     )
        return False


  def __str__(self	:			Tuple     ) ->							str:
        return f'''x: {self.x} y: {self.y}'''

def 	snake_case_							(				_a  :				Tuple			,       _a  :				bool = True			,       _a  :				float = math.inf			,       _a  :				float = -math.inf			,       _a  :				float = math.inf			,       _a  :				float = -math.inf			,       _a  :				bool = False			,       _a  :				int = 10_000			,       ):
      snake_case_    							=				search_prob
      snake_case_    							=				[]  # list to store the current score at each iteration
      snake_case_    							=				0
      snake_case_    							=				False
      snake_case_    							=				set()
      while not solution_found and iterations < max_iter:
            visited.add(_a      )
            iterations += 1
            snake_case_    							=				current_state.score()
            scores.append(_a      )
            snake_case_    							=				current_state.get_neighbors()
            snake_case_    							=				-math.inf
            snake_case_    							=				math.inf
            snake_case_    							=				None  # to hold the next best neighbor
            for neighbor in neighbors:
                  if neighbor in visited:
                        continue  # do not want to visit the same state again
                  if (
                      neighbor.x > max_x
                      or neighbor.x < min_x
                      or neighbor.y > max_y
                      or neighbor.y < min_y
                  ):
                        continue  # neighbor outside our bounds
                  snake_case_    							=				neighbor.score() - current_score
                  if find_max:  # finding max
                        # going to direction with greatest ascent
                        if change > max_change and change > 0:
                              snake_case_    							=				change
                              snake_case_    							=				neighbor
                  else:  # finding min
                        # to direction with greatest descent
                        if change < min_change and change < 0:
                              snake_case_    							=				change
                              snake_case_    							=				neighbor
            if next_state is not None:
                  # we found at least one neighbor which improved the current state
                  snake_case_    							=				next_state
            else:
                  # since we have no neighbor that improves the solution we stop the search
                  snake_case_    							=				True

      if visualization:
            from matplotlib import pyplot as plt

            plt.plot(range(_a      )			,       _a      )
            plt.xlabel("Iterations"      )
            plt.ylabel("Function values"      )
            plt.show()

      return current_state


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Dict      ):
           return (x**2) + (y**2)

     # starting the problem with initial coordinates (3, 4)
     A							     = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     A							     = hill_climbing(prob, find_max=False)
     print(
         "The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: "
         F"""{local_min.score()}"""
     )

     # starting the problem with initial coordinates (12, 47)
     A							     = SearchProblem(x=1_2, y=4_7, step_size=1, function_to_optimize=test_fa)
     A							     = hill_climbing(
         prob, find_max=False, max_x=1_0_0, min_x=5, max_y=5_0, min_y=-5, visualization=True
     )
     print(
         "The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
         F"""and 50 > y > - 5 found via hill climbing: {local_min.score()}"""
     )
     def 	snake_case_							(				_a  :				Optional[Any]			,       _a  :				Tuple      ):
           return (3 * x**2) - (6 * y)

     A							     = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
     A							     = hill_climbing(prob, find_max=True)




     print(
         "The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: "
         F"""{local_min.score()}"""
     )









import numpy as np


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )->    str:





						"""simple docstring"""



						_UpperCAmelCase							   =     n_draws * class_counts / class_counts.sum()
						# floored means we don't overshoot n_samples, but probably undershoot
						_UpperCAmelCase							   =     np.floor(__lowerCAmelCase      )
						# we add samples according to how much "left over" probability
						# they had, until we arrive at n_samples
						_UpperCAmelCase							   =     int(n_draws - floored.sum()      )
						if need_to_add > 0:
												_UpperCAmelCase							   =     continuous - floored
												_UpperCAmelCase							   =     np.sort(np.unique(__lowerCAmelCase      )      )[::-1]
												# add according to remainder, but break ties
												# randomly to avoid biases
												for value in values:
																		((_UpperCAmelCase)   ,					)							   =     np.where(remainder == value      )
																		# if we need_to_add less than what's in inds
																		# we draw randomly from them.
																		# if we need to add more, we add them all and
																		# go to the next value
																		_UpperCAmelCase							   =     min(len(__lowerCAmelCase      )					,						__lowerCAmelCase      )
																		_UpperCAmelCase							   =     rng.choice(__lowerCAmelCase					,						size=__lowerCAmelCase					,						replace=__lowerCAmelCase      )
																		floored[inds] += 1
																		need_to_add -= add_now
																		if need_to_add == 0:
																								break
						return floored.astype(np.intaa      )


def __A       ( __lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase=10      )->    int:





						"""simple docstring"""



						_UpperCAmelCase   ,					_UpperCAmelCase							   =     np.unique(__lowerCAmelCase					,						return_inverse=__lowerCAmelCase      )
						_UpperCAmelCase							   =     classes.shape[0]
						_UpperCAmelCase							   =     np.bincount(__lowerCAmelCase      )
						if np.min(__lowerCAmelCase      ) < 2:
												raise ValueError('Minimum class count error'      )
						if n_train < n_classes:
												raise ValueError(
												    'The train_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_train, n_classes)      )
						if n_test < n_classes:
												raise ValueError(
												    'The test_size = %d should be greater or ' 'equal to the number of classes = %d' % (n_test, n_classes)      )
						_UpperCAmelCase							   =     np.split(np.argsort(__lowerCAmelCase					,						kind='mergesort'      )					,						np.cumsum(__lowerCAmelCase      )[:-1]      )
						for _ in range(__lowerCAmelCase      ):
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )
												_UpperCAmelCase							   =     class_counts - n_i
												_UpperCAmelCase							   =     approximate_mode(__lowerCAmelCase					,						__lowerCAmelCase					,						__lowerCAmelCase      )

												_UpperCAmelCase							   =     []
												_UpperCAmelCase							   =     []

												for i in range(__lowerCAmelCase      ):
																		_UpperCAmelCase							   =     rng.permutation(class_counts[i]      )
																		_UpperCAmelCase							   =     class_indices[i].take(__lowerCAmelCase					,						mode='clip'      )
																		train.extend(perm_indices_class_i[: n_i[i]]      )
																		test.extend(perm_indices_class_i[n_i[i] : n_i[i] + t_i[i]]      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )
												_UpperCAmelCase							   =     rng.permutation(__lowerCAmelCase      )

												yield train, test
