
from collections import namedtuple

__lowerCAmelCase				      =					namedtuple('from_to', 'from_ to')

__lowerCAmelCase				      =					{
    'millimeter': 'mm',
    'centimeter': 'cm',
    'meter': 'm',
    'kilometer': 'km',
    'inch': 'in',
    'inche': 'in',  # Trailing 's' has been stripped off
    'feet': 'ft',
    'foot': 'ft',
    'yard': 'yd',
    'mile': 'mi',
}

__lowerCAmelCase				      =					{
    'mm': from_to(0.001, 1_0_0_0),
    'cm': from_to(0.01, 1_0_0),
    'm': from_to(1, 1),
    'km': from_to(1_0_0_0, 0.001),
    'in': from_to(0.0254, 39.3701),
    'ft': from_to(0.3048, 3.2_8084),
    'yd': from_to(0.9144, 1.0_9361),
    'mi': from_to(1609.34, 0.0_0062_1371),
}


def       a			(   a		,	a		,	a  )    ->float:




			'''simple docstring'''


			SCREAMING_SNAKE_CASE    	=   from_type.lower().rstrip('''s'''  )
			SCREAMING_SNAKE_CASE    	=   TYPE_CONVERSION.get(a		,	a  )
			SCREAMING_SNAKE_CASE    	=   to_type.lower().rstrip('''s'''  )
			SCREAMING_SNAKE_CASE    	=   TYPE_CONVERSION.get(a		,	a  )
			if new_from not in METRIC_CONVERSION:
						SCREAMING_SNAKE_CASE    	=   (
						    F"""Invalid 'from_type' value: {from_type!r}.\n"""
						    F"""Conversion abbreviations are: {", ".join(a  )}"""
						)
						raise ValueError(a  )
			if new_to not in METRIC_CONVERSION:
						SCREAMING_SNAKE_CASE    	=   (
						    F"""Invalid 'to_type' value: {to_type!r}.\n"""
						    F"""Conversion abbreviations are: {", ".join(a  )}"""
						)
						raise ValueError(a  )
			return value * METRIC_CONVERSION[new_from].from_ * METRIC_CONVERSION[new_to].to


if __name__ == "__main__":
							import doctest

							doctest.testmod()

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)