





from __future__ import annotations


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_    ,snake_case_		:      Optional[Any]           =						'', '', ''
	snake_case_		:      Optional[Any]           =						[]

	for character in message_part:
		tmp.append(character_to_number[character] )

	for each in tmp:
		one += each[0]
		two += each[1]
		three += each[2]

	return one + two + three


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	snake_case_    ,snake_case_		:      Optional[int]           =						'', ''
	snake_case_		:      Dict           =						[]

	for character in message_part:
		this_part += character_to_number[character]

	for digit in this_part:
		tmp += digit
		if len(__a ) == len(__a ):
			result.append(__a )
			snake_case_		:      Optional[Any]           =						''

	return result[0], result[1], result[2]


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a ):
	# Validate message and alphabet, set to upper and remove spaces
	snake_case_		:      Optional[int]           =						alphabet.replace(' ' ,			'' ).upper()
	snake_case_		:      Tuple           =						message.replace(' ' ,			'' ).upper()

	# Check length and characters
	if len(__a ) != 27:
		raise KeyError('Length of alphabet has to be 27.' )
	for each in message:
		if each not in alphabet:
			raise ValueError('Each message character has to be included in alphabet!' )

    # Generate dictionares
	snake_case_		:      Dict           =						(
	    '111',
	    '112',
	    '113',
	    '121',
	    '122',
	    '123',
	    '131',
	    '132',
	    '133',
	    '211',
	    '212',
	    '213',
	    '221',
	    '222',
	    '223',
	    '231',
	    '232',
	    '233',
	    '311',
	    '312',
	    '313',
	    '321',
	    '322',
	    '323',
	    '331',
	    '332',
	    '333',
	)
	snake_case_		:      Union[str, Any]           =						{}
	snake_case_		:      Tuple           =						{}
	for letter, number in zip(__a ,			__a ):
		snake_case_		:      Optional[int]           =						number
		snake_case_		:      Optional[Any]           =						letter

	return message, alphabet, character_to_number, number_to_character


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,			__a = 5 ):
	snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      int           =						__prepare(
	    __a ,			__a )
	snake_case_    ,snake_case_		:      Optional[Any]           =						'', ''

	for i in range(0 ,			len(__a ) + 1 ,			__a ):
		encrypted_numeric += __encrypt_part(
		    message[i : i + period] ,			__a )

	for i in range(0 ,			len(__a ) ,			3 ):
		encrypted += number_to_character[encrypted_numeric[i : i + 3]]

	return encrypted


def 				SCREAMING_SNAKE_CASE__    (     __a ,			__a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ." ,			__a = 5 ):
	snake_case_    ,snake_case_    ,snake_case_    ,snake_case_		:      Union[str, Any]           =						__prepare(
	    __a ,			__a )
	snake_case_		:      Tuple           =						[]
	snake_case_		:      Optional[Any]           =						''

	for i in range(0 ,			len(__a ) + 1 ,			__a ):
		snake_case_    ,snake_case_    ,snake_case_		:      List[Any]           =						__decrypt_part(message[i : i + period] ,			__a )

		for j in range(0 ,			len(__a ) ):
			decrypted_numeric.append(a[j] + b[j] + c[j] )

	for each in decrypted_numeric:
		decrypted += number_to_character[each]

	return decrypted


if __name__ == "__main__":
						_SCREAMING_SNAKE_CASE   						=		"""DEFEND THE EAST WALL OF THE CASTLE."""
						_SCREAMING_SNAKE_CASE   						=		encrypt_message(msg, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
						_SCREAMING_SNAKE_CASE   						=		decrypt_message(encrypted, """EPSDUCVWYM.ZLKXNBTFGORIJHAQ""")
						print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')






import numpy as np






class 	_a			:






			def __init__(    self    ,  lowercase_=5    ,  lowercase_=5 )	->					Union[str, Any]:
									lowerCAmelCase	:   Union[str, Any]					     =					depth
									lowerCAmelCase	:   Tuple					     =					0
									lowerCAmelCase	:   Tuple					     =					None
									lowerCAmelCase	:   Union[str, Any]					     =					None
									lowerCAmelCase	:   Any					     =					min_leaf_size
									lowerCAmelCase	:   Optional[int]					     =					None






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Any:
									if labels.ndim != 1:
															print("""Error: Input labels must be one dimensional""" )

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






			def 							_snake_case   (    self    ,  lowercase_    ,  lowercase_ )	->					Dict:
									if x.ndim != 1:
															print("""Error: Input data set must be one dimensional""" )
															return
									if len(lowercase_ ) != len(lowercase_ ):
															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(lowercase_ ) < 2 * self.min_leaf_size:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									if self.depth == 1:
															lowerCAmelCase	:   Tuple					     =					np.mean(lowercase_ )
															return

									lowerCAmelCase	:   List[str]					     =					0
									lowerCAmelCase	:   List[Any]					     =					self.mean_squared_error(lowercase_    ,  np.mean(lowercase_ ) ) * 2
									for i in range(len(lowercase_ ) ):
															if len(x[:i] ) < self.min_leaf_size:
																					continue
															elif len(x[i:] ) < self.min_leaf_size:
																					continue
															else:
																					lowerCAmelCase	:   int					     =					self.mean_squared_error(x[:i]    ,  np.mean(y[:i] ) )
																					lowerCAmelCase	:   Union[str, Any]					     =					self.mean_squared_error(x[i:]    ,  np.mean(y[i:] ) )
																					lowerCAmelCase	:   Tuple					     =					error_left + error_right
																					if error < min_error:
																											lowerCAmelCase	:   Tuple					     =					i
																											lowerCAmelCase	:   Tuple					     =					error

									if best_split != 0:
															lowerCAmelCase	:   int					     =					x[:best_split]
															lowerCAmelCase	:   List[str]					     =					y[:best_split]
															lowerCAmelCase	:   Dict					     =					x[best_split:]
															lowerCAmelCase	:   Tuple					     =					y[best_split:]

															lowerCAmelCase	:   Any					     =					x[best_split]
															lowerCAmelCase	:   str					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															lowerCAmelCase	:   List[str]					     =					DecisionTree(
															    depth=self.depth - 1    ,  min_leaf_size=self.min_leaf_size )
															self.left.train(lowercase_    ,  lowercase_ )
															self.right.train(lowercase_    ,  lowercase_ )
									else:
															lowerCAmelCase	:   List[str]					     =					np.mean(lowercase_ )

									return






			def 							_snake_case   (    self    ,  lowercase_ )	->					int:
									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(lowercase_ )
															else:
																					return self.left.predict(lowercase_ )
									else:
															print("""Error: Decision tree not yet trained""" )
															return None






class 	_a			:






			@staticmethod
			def 							_snake_case   (    lowercase_    ,  lowercase_ )	->					Optional[Any]:
									lowerCAmelCase	:   Optional[int]					     =					float(0 )
									for label in labels:
															squared_error_sum += (label - prediction) ** 2

									return float(squared_error_sum / labels.size )







def  _UpperCAmelCase  (				):


						'''simple docstring'''




						lowerCAmelCase	:   List[str]					     =					np.arange(-1.0			,1.0			,0.005  )
						lowerCAmelCase	:   List[str]					     =					np.sin(SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Tuple					     =					DecisionTree(depth=1_0			,min_leaf_size=1_0  )
						tree.train(SCREAMING_SNAKE_CASE__			,SCREAMING_SNAKE_CASE__  )

						lowerCAmelCase	:   Dict					     =					(np.random.rand(1_0  ) * 2) - 1
						lowerCAmelCase	:   str					     =					np.array([tree.predict(SCREAMING_SNAKE_CASE__  ) for x in test_cases]  )
						lowerCAmelCase	:   Tuple					     =					np.mean((predictions - test_cases) ** 2  )

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


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

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

