code: 
  from __future__ import annotations
  
  
  def       a			(   a		,	a  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			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       a			(   a		,	a  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  			SCREAMING_SNAKE_CASE    	=   []
  
  			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  )
  									SCREAMING_SNAKE_CASE    	=   ''''''
  
  			return result[0], result[1], result[2]
  
  
  def       a			(   a		,	a  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   alphabet.replace(''' '''		,	''''''  ).upper()
  			SCREAMING_SNAKE_CASE    	=   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
  			SCREAMING_SNAKE_CASE    	=   (
  			    '''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''',
  			)
  			SCREAMING_SNAKE_CASE    	=   {}
  			SCREAMING_SNAKE_CASE    	=   {}
  			for letter, number in zip(a		,	a  ):
  						SCREAMING_SNAKE_CASE    	=   number
  						SCREAMING_SNAKE_CASE    	=   letter
  
  			return message, alphabet, character_to_number, number_to_character
  
  
  def       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   '''''', ''''''
  
  			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       a			(   a		,	a = "ABCDEFGHIJKLMNOPQRSTUVWXYZ."		,	a = 5  ):
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __prepare(
  			    a		,	a  )
  			SCREAMING_SNAKE_CASE    	=   []
  			SCREAMING_SNAKE_CASE    	=   ''''''
  
  			for i in range(0		,	len(a  ) + 1		,	a  ):
  						SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE	,       SCREAMING_SNAKE_CASE    	=   __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__":
  							__lowerCAmelCase				      =					'DEFEND THE EAST WALL OF THE CASTLE.'
  							__lowerCAmelCase				      =					encrypt_message(msg, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							__lowerCAmelCase				      =					decrypt_message(encrypted, 'EPSDUCVWYM.ZLKXNBTFGORIJHAQ')
  							print(F'''Encrypted: {encrypted}\nDecrypted: {decrypted}''')
style_context: 
  import numpy as np
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	def __init__(    self			:Any      ,    lowercase			:List[Any]=5      ,    lowercase			:Optional[Any]=5 )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   depth
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   None
  				SCREAMING_SNAKE_CASE    	=   min_leaf_size
  				SCREAMING_SNAKE_CASE    	=   None
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:str      ,    lowercase			:int      ,    lowercase			:int )		->			str:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				if labels.ndim != 1:
  							print('''Error: Input labels must be one dimensional''' )
  
  				return np.mean((labels - prediction) ** 2 )
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Union[str, Any]      ,    lowercase			:int      ,    lowercase			:Dict )		->			Tuple:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				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:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				if self.depth == 1:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  							return
  
  				SCREAMING_SNAKE_CASE    	=   0
  				SCREAMING_SNAKE_CASE    	=   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:
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[:i]      ,    np.mean(y[:i] ) )
  										SCREAMING_SNAKE_CASE    	=   self.mean_squared_error(x[i:]      ,    np.mean(y[i:] ) )
  										SCREAMING_SNAKE_CASE    	=   error_left + error_right
  										if error < min_error:
  													SCREAMING_SNAKE_CASE    	=   i
  													SCREAMING_SNAKE_CASE    	=   error
  
  				if best_split != 0:
  							SCREAMING_SNAKE_CASE    	=   x[:best_split]
  							SCREAMING_SNAKE_CASE    	=   y[:best_split]
  							SCREAMING_SNAKE_CASE    	=   x[best_split:]
  							SCREAMING_SNAKE_CASE    	=   y[best_split:]
  
  							SCREAMING_SNAKE_CASE    	=   x[best_split]
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							SCREAMING_SNAKE_CASE    	=   DecisionTree(
  							    depth=self.depth - 1      ,    min_leaf_size=self.min_leaf_size )
  							self.left.train(lowercase      ,    lowercase )
  							self.right.train(lowercase      ,    lowercase )
  				else:
  							SCREAMING_SNAKE_CASE    	=   np.mean(lowercase )
  
  				return
  
  
  
  
  
  
  
  	def    snake_case__    (    self			:Optional[int]      ,    lowercase			:List[Any] )		->			str:
  
  
  
  
  
  
  
  				"""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(lowercase )
  							else:
  										return self.left.predict(lowercase )
  				else:
  							print('''Error: Decision tree not yet trained''' )
  							return None
  
  
  
  
  
  class 						lowerCamelCase			:
  
  
  
  
  
  
  
  	@staticmethod
  	def    snake_case__    (    lowercase			:Optional[int]      ,    lowercase			:Optional[Any] )		->			Dict:
  
  
  
  
  
  
  
  				"""simple docstring"""
  
  
  
  
  
  
  				SCREAMING_SNAKE_CASE    	=   float(0 )
  				for label in labels:
  							squared_error_sum += (label - prediction) ** 2
  
  				return float(squared_error_sum / labels.size )
  
  
  
  def       a			(   )    ->Any:
  
  
  
  
  			'''simple docstring'''
  
  
  			SCREAMING_SNAKE_CASE    	=   np.arange(-1.0		,	1.0		,	0.0_05  )
  			SCREAMING_SNAKE_CASE    	=   np.sin(a  )
  
  			SCREAMING_SNAKE_CASE    	=   DecisionTree(depth=10		,	min_leaf_size=10  )
  			tree.train(a		,	a  )
  
  			SCREAMING_SNAKE_CASE    	=   (np.random.rand(10  ) * 2) - 1
  			SCREAMING_SNAKE_CASE    	=   np.array([tree.predict(a  ) for x in test_cases]  )
  			SCREAMING_SNAKE_CASE    	=   np.mean((predictions - test_cases) ** 2  )
  
  			print('''Test values: ''' + str(a  )  )
  			print('''Predictions: ''' + str(a  )  )
  			print('''Average error: ''' + str(a  )  )
  
  
  if __name__ == "__main__":
  							main()
  							import doctest
  
  							doctest.testmod(name='mean_squarred_error', verbose=True)