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

# Load iris file
SCREAMING_SNAKE_CASE__							=    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
SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__,    SCREAMING_SNAKE_CASE__							=    train_test_split(
    iris["""data"""], iris["""target"""], random_state=4
)

# KNN

SCREAMING_SNAKE_CASE__							=    KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
SCREAMING_SNAKE_CASE__							=    [[1, 2, 1, 4], [2, 3, 4, 5]]

SCREAMING_SNAKE_CASE__							=    knn.predict(X_new)

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







from math import pi




def 							__SCREAMING_SNAKE_CASE       (		SCREAMING_SNAKE_CASE	:				float      )					->						float:
				return degree / (180 / pi)


if __name__ == "__main__":
							from doctest import testmod

							testmod()





