



def        __lowerCAmelCase		(	__lowerCamelCase						:     str					, __lowerCamelCase						:     str						)	->    str:
					__lowerCAmelCase						 =len(__lowerCamelCase						)
					__lowerCAmelCase						 =len(__lowerCamelCase						)
					__lowerCAmelCase						 =(
					    first_str_length if first_str_length > second_str_length else second_str_length
					)
					__lowerCAmelCase						 =[]
					for char_count in range(__lowerCamelCase						):
										if char_count < first_str_length:
															output_list.append(first_str[char_count]						)
										if char_count < second_str_length:
															output_list.append(second_str[char_count]						)
					return "".join(__lowerCamelCase						)


if __name__ == "__main__":
				print(alternative_string_arrange('''AB''', '''XYZ'''), end=''' ''')









from collections import Counter

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

lowercase_													=      datasets.load_iris()

lowercase_													=      np.array(data['''data'''])
lowercase_													=      np.array(data['''target'''])
lowercase_													=      data['''target_names''']

lowercase_							,							lowercase_							,							lowercase_							,							lowercase_													=      train_test_split(X, y)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Optional[Any]						)	->    Optional[int]:
					return np.linalg.norm(np.array(__lowerCamelCase						) - np.array(__lowerCamelCase						)						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Union[str, Any]					, __lowerCamelCase						:     int=5						)	->    str:
					__lowerCAmelCase						 =zip(__lowerCamelCase					, __lowerCamelCase						)
					# List of distances of all points from the point to be classified
					__lowerCAmelCase						 =[]
					for data_point in data:
										__lowerCAmelCase						 =euclidean_distance(data_point[0]					, __lowerCamelCase						)
										distances.append((distance, data_point[1])						)
					# Choosing 'k' points with the least distances.
					__lowerCAmelCase						 =[i[1] for i in sorted(__lowerCamelCase						)[:k]]
					# Most commonly occurring class among them
					# is the class into which the point is classified
					__lowerCAmelCase						 =Counter(__lowerCamelCase						).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]))




