

import requests
from bsa import BeautifulSoup




def        __UpperCamelCase     (      _A					:							List[Any]      ,  _A					:							Union[str, Any]		)				->str:



							"""simple docstring"""
							lowerCamelCase_								=(
							    """https://www.horoscope.com/us/horoscopes/general/"""
							    f'horoscope-general-daily-{day}.aspx?sign={zodiac_sign}'
							)
							lowerCamelCase_								=BeautifulSoup(requests.get(_A		).content      ,  """html.parser"""		)
							return soup.find("""div"""      ,  class_="""main-horoscope"""		).p.text


if __name__ == "__main__":
	print('Daily Horoscope. \n')
	print(
	    'enter your Zodiac sign number:\n',
	    '1. Aries\n',
	    '2. Taurus\n',
	    '3. Gemini\n',
	    '4. Cancer\n',
	    '5. Leo\n',
	    '6. Virgo\n',
	    '7. Libra\n',
	    '8. Scorpio\n',
	    '9. Sagittarius\n',
	    '10. Capricorn\n',
	    '11. Aquarius\n',
	    '12. Pisces\n',
	)
	__A	:   Optional[Any]	=					int(input('number> ').strip())
	print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
	__A	:   Dict	=					input('enter the day> ')
	__A	:   List[str]	=					horoscope(zodiac_sign, day)
	print(horoscope_text)

"""simple docstring"""






from sklearn.neural_network import MLPClassifier

lowerCAmelCase__    :    Any			=      [[0.0, 0.0], [1.0, 1.0], [1.0, 0.0], [0.0, 1.0]]
lowerCAmelCase__    :    Optional[int]			=      [0, 1, 0, 0]


lowerCAmelCase__    :    Dict			=      MLPClassifier(
    solver='lbfgs', alpha=1E-5, hidden_layer_sizes=(5, 2), random_state=1
)

clf.fit(X, y)


lowerCAmelCase__    :    Optional[int]			=      [[0.0, 0.0], [0.0, 1.0], [1.0, 1.0]]
lowerCAmelCase__    :    Union[str, Any]			=      clf.predict(test)



def     a_		(   lowerCamelCase			):
				return list(lowerCamelCase			)


if __name__ == "__main__":
						import doctest

						doctest.testmod()



