
"""simple docstring"""
import numpy as np
def 					a__ ( a :					List[Any]							,  a :					List[Any]		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Optional[int]            = np.array(a		)

  _snake_case  :    Union[str, Any]            = abs(predict - actual		)
  _snake_case  :    Any            = difference.mean()

  return score
def 					a__ ( a :					Any							,  a :					str		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Tuple            = np.array(a		)

  _snake_case  :    str            = predict - actual
  _snake_case  :    Optional[int]            = np.square(a		)

  _snake_case  :    Dict            = square_diff.mean()
  return score
def 					a__ ( a :					Tuple							,  a :					int		):

  """simple docstring"""







  _snake_case  :    str            = np.array(a		)
  _snake_case  :    Optional[Any]            = np.array(a		)

  _snake_case  :    List[str]            = predict - actual
  _snake_case  :    Optional[int]            = np.square(a		)
  _snake_case  :    Union[str, Any]            = square_diff.mean()
  _snake_case  :    Optional[int]            = np.sqrt(a		)
  return score
def 					a__ ( a :					int							,  a :					Union[str, Any]		):

  """simple docstring"""







  _snake_case  :    Optional[int]            = np.array(a		)
  _snake_case  :    Union[str, Any]            = np.array(a		)

  _snake_case  :    Union[str, Any]            = np.log(predict + 1		)
  _snake_case  :    int            = np.log(actual + 1		)

  _snake_case  :    Tuple            = log_predict - log_actual
  _snake_case  :    Tuple            = np.square(a		)
  _snake_case  :    Optional[int]            = square_diff.mean()

  _snake_case  :    Dict            = np.sqrt(a		)

  return score
def 					a__ ( a :					str							,  a :					Dict		):

  """simple docstring"""







  _snake_case  :    List[str]            = np.array(a		)
  _snake_case  :    Union[str, Any]            = np.array(a		)

  _snake_case  :    int            = predict - actual
  _snake_case  :    str            = np.sum(a		) / len(a		)
  _snake_case  :    Any            = np.sum(a		) / len(a		)
  # print(numerator, denumerator)
  _snake_case  :    Tuple            = float(a		) / denumerator * 100

  return score



def 					a__ ( a :					str							,  a :					List[str]		):

  """simple docstring"""







  return np.mean(np.array(a		) == np.array(a		)		)








from __future__ import annotations

from collections.abc import Generator







def  A					(				UpperCAmelCase					):
					if not isinstance(UpperCAmelCase	,	UpperCAmelCase					) or n < 1:
										raise Exception("Sequence only defined for positive integers"					)

					yield n
					while n != 1:
										if n % 2 == 0:
															n //= 2
										else:
															_snake_case							:       Any												= 3 * n + 1
										yield n







def  A					(				):
					_snake_case							:       Dict												= 43
					_snake_case							:       List[str]												= tuple(collatz_sequence(UpperCAmelCase					)					)
					print(UpperCAmelCase					)
					print(F"""Collatz sequence from {n} took {len(UpperCAmelCase					)} steps."""					)


if __name__ == "__main__":
							main()