





import math
import random
from typing import Any

from .hill_climbing import SearchProblem





def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__ = True       ,							UpperCamelCase__ = math.inf       ,							UpperCamelCase__ = -math.inf       ,							UpperCamelCase__ = math.inf       ,							UpperCamelCase__ = -math.inf       ,							UpperCamelCase__ = False       ,							UpperCamelCase__ = 100       ,							UpperCamelCase__ = 0.01       ,							UpperCamelCase__ = 1       ,							):







							'''simple docstring'''






							snake_case_          =   False
							snake_case_          =   search_prob
							snake_case_          =   start_temperate
							snake_case_          =   []
							snake_case_          =   0
							snake_case_          =   None

							while not search_end:
														snake_case_          =   current_state.score()
														if best_state is None or current_score > best_state.score():
																					snake_case_          =   current_state
														scores.append(lowercase__				)
														iterations += 1
														snake_case_          =   None
														snake_case_          =   current_state.get_neighbors()
														while (
														    next_state is None and neighbors
														):  # till we do not find a neighbor that we can move to
																					snake_case_          =   random.randint(0       ,							len(lowercase__				) - 1				)  # picking a random neighbor
																					snake_case_          =   neighbors.pop(lowercase__				)
																					snake_case_          =   picked_neighbor.score() - current_score

																					if (
																					    picked_neighbor.x > max_x
																					    or picked_neighbor.x < min_x
																					    or picked_neighbor.y > max_y
																					    or picked_neighbor.y < min_y
																					):
																												continue  # neighbor outside our bounds

																					if not find_max:
																												snake_case_          =   change * -1  # in case we are finding minimum
																					if change > 0:  # improves the solution
																												snake_case_          =   picked_neighbor
																					else:
																												snake_case_          =   (math.e) ** (
																												    change / current_temp
																												)  # probability generation function
																												if random.random() < probability:  # random number within probability
																																			snake_case_          =   picked_neighbor
														snake_case_          =   current_temp - (current_temp * rate_of_decrease)

														if current_temp < threshold_temp or next_state is None:
																					# temperature below threshold, or could not find a suitable neighbor
																					snake_case_          =   True
														else:
																					snake_case_          =   next_state

							if visualization:
														from matplotlib import pyplot as plt

														plt.plot(range(lowercase__				)       ,							lowercase__				)
														plt.xlabel('Iterations'				)
														plt.ylabel('Function values'				)
														plt.show()
							return best_state


if __name__ == "__main__":





					def  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







												'''simple docstring'''






												return (x**2) + (y**2)

					# starting the problem with initial coordinates (12, 47)
					_UpperCAmelCase		:       List[str]						    =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					_UpperCAmelCase		:       int						    =	simulated_annealing(
					    prob, find_max=False, max_x=100, min_x=5, max_y=50, 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()}'''
					)

					# starting the problem with initial coordinates (12, 47)
					_UpperCAmelCase		:       str						    =	SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
					_UpperCAmelCase		:       List[Any]						    =	simulated_annealing(
					    prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
					)
					print(
					    """The maximum 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  __lowerCamelCase     (			UpperCamelCase__       ,							UpperCamelCase__				):







												'''simple docstring'''






												return (3 * x**2) - (6 * y)

					_UpperCAmelCase		:       List[str]						    =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					_UpperCAmelCase		:       Dict						    =	simulated_annealing(prob, find_max=False, visualization=True)
					print(
					    """The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
					    F'''{local_min.score()}'''
					)

					_UpperCAmelCase		:       Union[str, Any]						    =	SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
					_UpperCAmelCase		:       List[str]						    =	simulated_annealing(prob, find_max=True, visualization=True)


					print(
					    """The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: """
					    F'''{local_min.score()}'''
					)







import requests

_UpperCAmelCase		:       Union[str, Any]						    =	"""https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="""





def  __lowerCamelCase     (			UpperCamelCase__				):







							'''simple docstring'''






							snake_case_          =   requests.get(_NEWS_API + bbc_news_api_key				).json()
							# each article in the list is a dict
							for i, article in enumerate(bbc_news_page['articles']       ,							1				):
														print(F'''{i}.) {article["title"]}'''				)


if __name__ == "__main__":
					fetch_bbc_news(bbc_news_api_key="""<Your BBC News API key goes here>""")




