


'''simple docstring'''

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

# Load iris file
lowerCamelCase							:       Dict  						=     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
lowerCamelCase							:       str  						=     train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

lowerCamelCase							:       str  						=     KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
lowerCamelCase							:       Dict  						=     [[1, 2, 1, 4], [2, 3, 4, 5]]

lowerCamelCase							:       List[Any]  						=     knn.predict(X_new)

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








'''simple docstring'''

def _SCREAMING_SNAKE_CASE				(A	)	->	int:






       """simple docstring"""







       return 1 if input_a == 0 else 0

def _SCREAMING_SNAKE_CASE				()	->	None:






       """simple docstring"""







       assert not_gate(0	) == 1
       assert not_gate(1	) == 0


if __name__ == "__main__":
 print(not_gate(0))
 print(not_gate(1))




