

'''simple docstring'''






import requests

__SCREAMING_SNAKE_CASE     =						"https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey="



def 			__a		(       lowerCAmelCase__ :    str      ):
     a__   :      Tuple     =						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>')




"""simple docstring"""


from __future__ import annotations

import math

import numpy as np
from numpy.linalg import norm


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return math.sqrt(sum(pow(a - b       ,					2 ) for a, b in zip(_UpperCamelCase       ,					_UpperCamelCase ) ) )


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			if dataset.ndim != value_array.ndim:
						__lowerCAmelCase							       =     (
						    "Wrong input data's dimensions... "
						    f"dataset : {dataset.ndim}, value_array : {value_array.ndim}"
						)
						raise ValueError(_UpperCamelCase )

			try:
						if dataset.shape[1] != value_array.shape[1]:
									__lowerCAmelCase							       =     (
									    "Wrong input data's shape... "
									    f"dataset : {dataset.shape[1]}, value_array : {value_array.shape[1]}"
									)
									raise ValueError(_UpperCamelCase )
			except IndexError:
						if dataset.ndim != value_array.ndim:
									raise TypeError("Wrong shape" )

			if dataset.dtype != value_array.dtype:
						__lowerCAmelCase							       =     (
						    "Input data have different datatype... "
						    f"dataset : {dataset.dtype}, value_array : {value_array.dtype}"
						)
						raise TypeError(_UpperCamelCase )

			__lowerCAmelCase							       =     []

			for value in value_array:
						__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					dataset[0] )
						__lowerCAmelCase							       =     dataset[0].tolist()

						for dataset_value in dataset[1:]:
									__lowerCAmelCase							       =     euclidean(_UpperCamelCase       ,					_UpperCamelCase )

									if dist > temp_dist:
												__lowerCAmelCase							       =     temp_dist
												__lowerCAmelCase							       =     dataset_value.tolist()

						answer.append([vector, dist] )

			return answer


def _lowerCamelCase      (						_UpperCamelCase       ,					_UpperCamelCase ):

			'''simple docstring'''




			return np.dot(_UpperCamelCase       ,					_UpperCamelCase ) / (norm(_UpperCamelCase ) * norm(_UpperCamelCase ))


if __name__ == "__main__":
	import doctest

	doctest.testmod()




