




"""simple docstring"""






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      _snake_case			(				lowercase__							,    lowercase__		):
				return np.linalg.norm(np.array(lowercase__		) - np.array(lowercase__		)		)







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





"""simple docstring"""






import functools
from typing import Any







def      _snake_case			(				lowercase__							,    lowercase__		):

				# Validation
				if not isinstance(lowercase__							,    lowercase__		) or len(lowercase__		) == 0:
								raise ValueError('the string should be not empty string'		)

				if not isinstance(lowercase__							,    lowercase__		) or not all(
				    isinstance(lowercase__							,    lowercase__		) and len(lowercase__		) > 0 for item in words		):
								raise ValueError('the words should be a list of non-empty strings'		)

				# Build trie
				_lowerCamelCase     :			dict[str, Any]    =    {}
				_lowerCamelCase     :			List[Any]    =    'WORD_KEEPER'

				for word in words:
								_lowerCamelCase     :			Dict    =    trie
								for c in word:
												if c not in trie_node:
																_lowerCamelCase     :			Any    =    {}

												_lowerCamelCase     :			str    =    trie_node[c]

								_lowerCamelCase     :			Optional[Any]    =    True

				_lowerCamelCase     :			Dict    =    len(lowercase__		)

				# Dynamic programming method
				@functools.cache
				def is_breakable(lowercase__		) -> bool:
								if index == len_string:
												return True

								_lowerCamelCase     :			List[Any]    =    trie
								for i in range(lowercase__							,    lowercase__		):
												_lowerCamelCase     :			Any    =    trie_node.get(string[i]							,    lowercase__		)

												if trie_node is None:
																return False

												if trie_node.get(lowercase__							,    lowercase__		) and is_breakable(i + 1		):
																return True

								return False

				return is_breakable(0		)


if __name__ == "__main__":
			import doctest

			doctest.testmod()