'''simple docstring'''




from __future__ import annotations

from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any






@dataclass
class 		__A       :
       snake_case       :int
       snake_case       :Node | None				    =					None
       snake_case       :Node | None				    =					None





def _lowercase	(      )					->       Node | None:





       """simple docstring"""


       __UpperCAmelCase  :				Optional[Any]       				=		Node(1						)
       __UpperCAmelCase  :				Union[str, Any]       				=		Node(2						)
       __UpperCAmelCase  :				Optional[int]       				=		Node(3						)
       __UpperCAmelCase  :				Optional[int]       				=		Node(4						)
       __UpperCAmelCase  :				List[Any]       				=		Node(5						)
       return tree

def _lowercase	(      lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       return [root.data, *preorder(root.left						), *preorder(root.right						)] if root else []

def _lowercase	(      lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       return postorder(root.left						) + postorder(root.right						) + [root.data] if root else []

def _lowercase	(      lowerCamelCase__						)					->       list[int]:





       """simple docstring"""


       return [*inorder(root.left						), root.data, *inorder(root.right						)] if root else []

def _lowercase	(      lowerCamelCase__						)					->       int:





       """simple docstring"""


       return (max(height(root.left						)							,    height(root.right						)						) + 1) if root else 0

def _lowercase	(      lowerCamelCase__						)					->       Sequence[Node | None]:





       """simple docstring"""


       __UpperCAmelCase  :				list[Any]       				=		[]

       if root is None:
              return output

       __UpperCAmelCase  :				int       				=		deque([root]						)

       while process_queue:
              __UpperCAmelCase  :				int       				=		process_queue.popleft()
              output.append(node.data						)

              if node.left:
                     process_queue.append(node.left						)
              if node.right:
                     process_queue.append(node.right						)
       return output

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Sequence[Node | None]:





       """simple docstring"""


       __UpperCAmelCase  :				list[Any]       				=		[]

       def populate_output(lowerCamelCase__							,    lowerCamelCase__						) -> None:
              if not root:
                     return
              if level == 1:
                     output.append(root.data						)
              elif level > 1:
                     populate_output(root.left							,    level - 1						)
                     populate_output(root.right							,    level - 1						)

       populate_output(lowerCamelCase__							,    lowerCamelCase__						)
       return output

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Sequence[Node | None]:





       """simple docstring"""


       __UpperCAmelCase  :				list[Any]       				=		[]

       def populate_output(lowerCamelCase__							,    lowerCamelCase__						) -> None:
              if root is None:
                     return
              if level == 1:
                     output.append(root.data						)
              elif level > 1:
                     populate_output(root.right							,    level - 1						)
                     populate_output(root.left							,    level - 1						)

       populate_output(lowerCamelCase__							,    lowerCamelCase__						)
       return output

def _lowercase	(      lowerCamelCase__						)					->       Sequence[Node | None] | list[Any]:





       """simple docstring"""


       if root is None:
              return []

       __UpperCAmelCase  :				list[Sequence[Node | None]]       				=		[]

       __UpperCAmelCase  :				str       				=		0
       __UpperCAmelCase  :				Tuple       				=		height(lowerCamelCase__						)

       for h in range(1							,    height_tree + 1						):
              if not flag:
                     output.append(get_nodes_from_left_to_right(lowerCamelCase__							,    lowerCamelCase__						)						)
                     __UpperCAmelCase  :				Union[str, Any]       				=		1
              else:
                     output.append(get_nodes_from_right_to_left(lowerCamelCase__							,    lowerCamelCase__						)						)
                     __UpperCAmelCase  :				Optional[int]       				=		0

       return output

def _lowercase	(      )					->       None:  # Main function for testing.





       """simple docstring"""


       __UpperCAmelCase  :				int       				=		make_tree()
       print(f"""In-order Traversal: {inorder(lowerCamelCase__						)}"""						)
       print(f"""Pre-order Traversal: {preorder(lowerCamelCase__						)}"""						)
       print(f"""Post-order Traversal: {postorder(lowerCamelCase__						)}"""							,    "\n"						)

       print(f"""Height of Tree: {height(lowerCamelCase__						)}"""							,    "\n"						)

       print("Complete Level Order Traversal: "						)
       print(level_order(lowerCamelCase__						)							,    "\n"						)

       print("Level-wise order Traversal: "						)

       for level in range(1							,    height(lowerCamelCase__						) + 1						):
              print(f"""Level {level}:"""							,    get_nodes_from_left_to_right(lowerCamelCase__							,    level=lowerCamelCase__						)						)

       print("\nZigZag order Traversal: "						)
       print(zigzag(lowerCamelCase__						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   main()







'''simple docstring'''




from collections import Counter

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

_a   :	str 							=   datasets.load_iris()

_a   :	List[Any] 							=   np.array(data["data"])
_a   :	Optional[Any] 							=   np.array(data["target"])
_a   :	Dict 							=   data["target_names"]

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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__						)					->       Tuple:





       """simple docstring"""


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

def _lowercase	(      lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__							,    lowerCamelCase__=5						)					->       int:





       """simple docstring"""


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






