






import numpy as np





class 		SCREAMING_SNAKE_CASE__   :



		def __init__(    self:    Any				,   a:    List[Any]=5				,   a:    Union[str, Any]=5)  ->Any:






								'''simple docstring'''


								a_				     =   depth
								a_				     =   0
								a_				     =   None
								a_				     =   None
								a_				     =   min_leaf_size
								a_				     =   None



		def _lowerCAmelCase     (    self:    Dict				,   a:    Union[str, Any]				,   a:    Optional[Any])  ->Optional[int]:






								'''simple docstring'''


								if labels.ndim != 1:
														print("Error: Input labels must be one dimensional")

								return np.mean((labels - prediction) ** 2)



		def _lowerCAmelCase     (    self:    Tuple				,   a:    Tuple				,   a:    Union[str, Any])  ->Optional[int]:






								'''simple docstring'''


								if x.ndim != 1:
														print("Error: Input data set must be one dimensional")
														return
								if len(lowerCamelCase__) != len(lowerCamelCase__):
														print("Error: X and y have different lengths")
														return
								if y.ndim != 1:
														print("Error: Data set labels must be one dimensional")
														return

								if len(lowerCamelCase__) < 2 * self.min_leaf_size:
														a_				     =   np.mean(lowerCamelCase__)
														return

								if self.depth == 1:
														a_				     =   np.mean(lowerCamelCase__)
														return

								a_				     =   0
								a_				     =   self.mean_squared_error(lowerCamelCase__				,   np.mean(lowerCamelCase__)) * 2
								for i in range(len(lowerCamelCase__)):
														if len(x[:i]) < self.min_leaf_size:
																				continue
														elif len(x[i:]) < self.min_leaf_size:
																				continue
														else:
																				a_				     =   self.mean_squared_error(x[:i]				,   np.mean(y[:i]))
																				a_				     =   self.mean_squared_error(x[i:]				,   np.mean(y[i:]))
																				a_				     =   error_left + error_right
																				if error < min_error:
																										a_				     =   i
																										a_				     =   error

								if best_split != 0:
														a_				     =   x[:best_split]
														a_				     =   y[:best_split]
														a_				     =   x[best_split:]
														a_				     =   y[best_split:]

														a_				     =   x[best_split]
														a_				     =   DecisionTree(
														    depth=self.depth - 1				,   min_leaf_size=self.min_leaf_size)
														a_				     =   DecisionTree(
														    depth=self.depth - 1				,   min_leaf_size=self.min_leaf_size)
														self.left.train(lowerCamelCase__				,   lowerCamelCase__)
														self.right.train(lowerCamelCase__				,   lowerCamelCase__)
								else:
														a_				     =   np.mean(lowerCamelCase__)

								return




		def _lowerCAmelCase     (    self:    Optional[Any]				,   a:    Tuple)  ->Optional[Any]:






								'''simple docstring'''


								if self.prediction is not None:
														return self.prediction
								elif self.left or self.right is not None:
														if x >= self.decision_boundary:
																				return self.right.predict(lowerCamelCase__)
														else:
																				return self.left.predict(lowerCamelCase__)
								else:
														print("Error: Decision tree not yet trained")
														return None





class 		SCREAMING_SNAKE_CASE__   :



		@staticmethod
		def _lowerCAmelCase     (    a:    Dict				,   a:    Union[str, Any])  ->Tuple:






								'''simple docstring'''


								a_				     =   float(0)
								for label in labels:
														squared_error_sum += (label - prediction) ** 2

								return float(squared_error_sum / labels.size)





def 			__UpperCAmelCase				()		->				str:

						'''simple docstring'''


						a_				     =   np.arange(-1.0	,1.0	,0.005				)
						a_				     =   np.sin(lowercase__				)

						a_				     =   DecisionTree(depth=10	,min_leaf_size=10				)
						tree.train(lowercase__	,lowercase__				)

						a_				     =   (np.random.rand(10				) * 2) - 1
						a_				     =   np.array([tree.predict(lowercase__				) for x in test_cases]				)
						a_				     =   np.mean((predictions - test_cases) ** 2				)

						print("Test values: " + str(lowercase__				)				)
						print("Predictions: " + str(lowercase__				)				)
						print("Average error: " + str(lowercase__				)				)


if __name__ == "__main__":
							main()
							import doctest

							doctest.testmod(name='mean_squarred_error', verbose=True)











'''simple docstring'''





import requests
from bsa import BeautifulSoup



def 			__UpperCAmelCase				(lowercase__	,lowercase__				)		->				str:

						'''simple docstring'''


						a_				     =   (
						    "https://www.horoscope.com/us/horoscopes/general/"
						    F"""horoscope-general-daily-{day}.aspx?sign={zodiac_sign}"""
						)
						a_				     =   BeautifulSoup(requests.get(lowercase__				).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_      = int(input('number> ').strip())
							print('choose some day:\n', 'yesterday\n', 'today\n', 'tomorrow\n')
							a_      = input('enter the day> ')
							a_      = horoscope(zodiac_sign, day)
							print(horoscope_text)



