






"""simple docstring"""







def 					_lowerCAmelCase				(					lowerCAmelCase = 20						):







       '''simple docstring'''

       try:
              UpperCAmelCase					=	int(lowerCAmelCase						)
       except (TypeError, ValueError):
              raise TypeError("""Parameter n must be int or castable to int."""						)
       if n <= 0:
              raise ValueError("""Parameter n must be greater than or equal to one."""						)
       UpperCAmelCase					=	0
       while 1:
              i += n * (n - 1)
              UpperCAmelCase					=	0
              for j in range(2		,					lowerCAmelCase						):
                     if i % j != 0:
                            UpperCAmelCase					=	1
                            break
              if nfound == 0:
                     if i == 0:
                            UpperCAmelCase					=	1
                     return i
       return None


if __name__ == "__main__":
 print(F'{solution() = }')







"""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[str]								=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_     :      str								=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

# new array to test
A_     :      Any								=[[1, 2, 1, 4], [2, 3, 4, 5]]

A_     :      int								=knn.predict(X_new)

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






