
from collections import Counter

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

SCREAMING_SNAKE_CASE							:Tuple										=    datasets.load_iris()

SCREAMING_SNAKE_CASE							:Dict										=    np.array(data['data'])
SCREAMING_SNAKE_CASE							:Optional[int]										=    np.array(data['target'])
SCREAMING_SNAKE_CASE							:List[str]										=    data['target_names']

SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE			,   SCREAMING_SNAKE_CASE							:List[Any]										=    train_test_split(X, y)


def 						UpperCAmelCase      (     a_		,       a_						)		->     Tuple:







				"""simple docstring"""



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


def 						UpperCAmelCase      (     a_		,       a_		,       a_		,       a_		,       a_=5						)		->     Dict:







				"""simple docstring"""



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


from __future__ import annotations


def 						UpperCAmelCase      (     a_ = 4						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			abs(a_						) or 4
				return [[1 + x + y * row_size for x in range(a_						)] for y in range(a_						)]


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_row(transpose(a_						)						)
				# OR.. transpose(reverse_column(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_row(reverse_column(a_						)						)
				# OR.. reverse_column(reverse_row(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				return reverse_column(transpose(a_						)						)
				# OR.. transpose(reverse_row(matrix))


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			[list(a_						) for x in zip(*a_						)]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			matrix[::-1]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     list[list[int]]:







				"""simple docstring"""



				__A             =			[x[::-1] for x in matrix]
				return matrix


def 						UpperCAmelCase      (     a_						)		->     None:







				"""simple docstring"""



				for i in matrix:
								print(*a_						)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 90 counterclockwise:\n')
					print_matrix(rotate_aa(matrix))

					SCREAMING_SNAKE_CASE							:Tuple										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 180:\n')
					print_matrix(rotate_aaa(matrix))

					SCREAMING_SNAKE_CASE							:Union[str, Any]										=    make_matrix()
					print('\norigin:\n')
					print_matrix(matrix)
					print('\nrotate 270 counterclockwise:\n')
					print_matrix(rotate_aaa(matrix))
