





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

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

# KNN

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

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

_a									=				knn.predict(X_new)

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










from __future__ import annotations

import math
import random





def lowerCAmelCase__(__snake_case	,__snake_case	,__snake_case							)    ->				list[int]:







	'''simple docstring'''







	lowerCamelCase__        =		d * e - 1
	lowerCamelCase__        =		0
	lowerCamelCase__        =		0
	while p == 0:
		lowerCamelCase__        =		random.randint(2	,n - 1							)
		lowerCamelCase__        =		k
		while True:
			if t % 2 == 0:
				lowerCamelCase__        =		t // 2
				lowerCamelCase__        =		(g**t) % n
				lowerCamelCase__        =		math.gcd(x - 1	,__snake_case							)
				if x > 1 and y > 1:
					lowerCamelCase__        =		y
					lowerCamelCase__        =		n // y
					break  # find the correct factors
			else:
				break  # t is not divisible by 2, break and choose another g
	return sorted([p, q]							)


if __name__ == "__main__":
			import doctest

			doctest.testmod()



