


from __future__ import annotations

from dataclasses import dataclass
@dataclass
class        _lowercase      :






				'''simple docstring'''



				SCREAMING_SNAKE_CASE__				:			float
				SCREAMING_SNAKE_CASE__				:			TreeNode | None						=		None
				SCREAMING_SNAKE_CASE__				:			TreeNode | None						=		None




def        _UpperCamelCase							(      lowercase__      ):

						# Validation
						def is_valid_tree(lowercase__      ) -> bool:
												if node is None:
																		return True

												if not isinstance(lowercase__    ,       lowercase__      ):
																		return False

												try:
																		float(node.data      )
												except (TypeError, ValueError):
																		return False

												return is_valid_tree(node.left      ) and is_valid_tree(node.right      )

						if not is_valid_tree(lowercase__      ):
												raise ValueError(
												    '''Each node should be type of TreeNode and data should be float.'''      )

						def is_binary_search_tree_recursive_check(
						    lowercase__    ,       lowercase__    ,       lowercase__      ) -> bool:

												if node is None:
																		return True

												return (
												    left_bound < node.data < right_bound
												    and is_binary_search_tree_recursive_check(node.left    ,       lowercase__    ,       node.data      )
												    and is_binary_search_tree_recursive_check(
												        node.right    ,       node.data    ,       lowercase__      )
												)

						return is_binary_search_tree_recursive_check(lowercase__    ,       -float('''inf'''      )    ,       float('''inf'''      )      )


if __name__ == "__main__":
						import doctest

						doctest.testmod()




from collections import Counter

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

__lowerCAmelCase	: List[Any]  				=datasets.load_iris()

__lowerCAmelCase	: Tuple  				=np.array(data['data'])
__lowerCAmelCase	: Dict  				=np.array(data['target'])
__lowerCAmelCase	: List[str]  				=data['target_names']

__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase				,__lowerCAmelCase	: str  				=train_test_split(X, y)


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


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