

'''simple docstring'''

def 				lowercase (					__magic_name__			,   __magic_name__							):



     '''simple docstring'''







     if not root or root not in binary_tree_mirror_dictionary:
          return
     UpperCAmelCase ,			UpperCAmelCase		:							List[str]        =       binary_tree_mirror_dictionary[root][:2]
     UpperCAmelCase		:							str        =       [right_child, left_child]
     binary_tree_mirror_dict(__magic_name__			,   __magic_name__							)
     binary_tree_mirror_dict(__magic_name__			,   __magic_name__							)





def 				lowercase (					__magic_name__			,   __magic_name__ = 1							):



     '''simple docstring'''







     if not binary_tree:
          raise ValueError("binary tree cannot be empty"							)
     if root not in binary_tree:
          UpperCAmelCase		:							Tuple        =       F"root {root} is not present in the binary_tree"
          raise ValueError(__magic_name__							)
     UpperCAmelCase		:							Optional[int]        =       dict(__magic_name__							)
     binary_tree_mirror_dict(__magic_name__			,   __magic_name__							)
     return binary_tree_mirror_dictionary


if __name__ == "__main__":
    a       :      Any  	=			{1: [2, 3], 2: [4, 5], 3: [6, 7], 7: [8, 9]}
    print(F'Binary tree: {binary_tree}')
    a       :      Any  	=			binary_tree_mirror(binary_tree, 5)
    print(F'Binary tree mirror: {binary_tree_mirror_dictionary}')



'''simple docstring'''

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

# Load iris file
a       :      List[Any]  	=			load_iris()
iris.keys()


print(F'Target names: \n {iris.target_names} ')
print(F'\n Features: \n {iris.feature_names}')

# Train set e Test set
a	,						a	,						a	,						a       :      List[Any]  	=			train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

a       :      Tuple  	=			KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
a       :      Optional[int]  	=			[[1, 2, 1, 4], [2, 3, 4, 5]]

a       :      Tuple  	=			knn.predict(X_new)

print(
    F'\nNew array: \n {X_new}\n\nTarget Names Prediction: \n'
    F' {iris["target_names"][prediction]}'
)
