


from math import factorial, pi





def   _lowerCAmelCase				(   UpperCamelCase__:     Optional[int]							,					UpperCamelCase__:     Dict = 30						) ->   float:





	"""simple docstring"""







	if not isinstance(__SCREAMING_SNAKE_CASE							,					(int, float)						):
		raise ValueError("""maclaurin_sin() requires either an int or float for theta"""						)

	if not isinstance(__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE						) or accuracy <= 0:
		raise ValueError("""maclaurin_sin() requires a positive int for accuracy"""						)

	A								=				float(__SCREAMING_SNAKE_CASE						)
	A								=				theta // (2 * pi)
	theta -= 2 * div * pi
	return sum(
	    (-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1						) for r in range(__SCREAMING_SNAKE_CASE						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     Dict							,					UpperCamelCase__:     Any = 30						) ->   float:





	"""simple docstring"""







	if not isinstance(__SCREAMING_SNAKE_CASE							,					(int, float)						):
		raise ValueError("""maclaurin_cos() requires either an int or float for theta"""						)

	if not isinstance(__SCREAMING_SNAKE_CASE							,					__SCREAMING_SNAKE_CASE						) or accuracy <= 0:
		raise ValueError("""maclaurin_cos() requires a positive int for accuracy"""						)

	A								=				float(__SCREAMING_SNAKE_CASE						)
	A								=				theta // (2 * pi)
	theta -= 2 * div * pi
	return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r						) for r in range(__SCREAMING_SNAKE_CASE						)						)


if __name__ == "__main__":
				import doctest

				doctest.testmod()

				print(maclaurin_sin(10))
				print(maclaurin_sin(-10))
				print(maclaurin_sin(10, 15))
				print(maclaurin_sin(-10, 15))

				print(maclaurin_cos(5))
				print(maclaurin_cos(-5))
				print(maclaurin_cos(10, 15))
				print(maclaurin_cos(-10, 15))







from collections import Counter

import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split

_lowercase       :      Any	=	datasets.load_iris()

_lowercase       :      Tuple	=	np.array(data["data"])
_lowercase       :      int	=	np.array(data["target"])
_lowercase       :      int	=	data["target_names"]

_lowercase	,							_lowercase	,							_lowercase	,							_lowercase       :      Any	=	train_test_split(X, y)





def   _lowerCAmelCase				(   UpperCamelCase__:     int							,					UpperCamelCase__:     Any						) ->   List[Any]:





	"""simple docstring"""







	return np.linalg.norm(np.array(UpperCamelCase__						) - np.array(UpperCamelCase__						)						)





def   _lowerCAmelCase				(   UpperCamelCase__:     str							,					UpperCamelCase__:     Tuple							,					UpperCamelCase__:     int							,					UpperCamelCase__:     List[Any]							,					UpperCamelCase__:     int=5						) ->   Tuple:





	"""simple docstring"""







	A								=				zip(UpperCamelCase__							,					UpperCamelCase__						)
	# List of distances of all points from the point to be classified
	A								=				[]
	for data_point in data:
		A								=				euclidean_distance(data_point[0]							,					UpperCamelCase__						)
		distances.append((distance, data_point[1])						)
	# Choosing 'k' points with the least distances.
	A								=				[i[1] for i in sorted(UpperCamelCase__						)[:k]]
	# Most commonly occurring class among them
	# is the class into which the point is classified
	A								=				Counter(UpperCamelCase__						).most_common(1						)[0][0]
	return classes[result]


if __name__ == "__main__":
				print(classifier(X_train, y_train, classes, [4.4, 3.1, 1.3, 1.4]))



