





from itertools import product

def     UpperCAmelCase_						(						_UpperCAmelCase       :int = 15							)  ->							int:
     '''simple docstring'''


     A_					      =					0.0
     A_					      =					42
     A_					      =					42
     A_					      =					42
     A_					      =					42
     A_					      =					42
     A_					      =					42

     for series in product(range(2							)     ,	repeat=_UpperCAmelCase							):
          A_					      =					series.count(1							)
          A_					      =					num_turns - num_blue
          if num_red >= num_blue:
               continue
          A_					      =					1.0
          for ind, col in enumerate(_UpperCAmelCase     ,	2							):
               if col == 0:
                    prob *= (ind - 1) / ind
               else:
                    prob *= 1 / ind

          total_prob += prob

     return int(1 / total_prob							)


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










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__			:    Any			      =		train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

a__			:    List[Any]			      =		KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train, y_train)

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

a__			:    Union[str, Any]			      =		knn.predict(X_new)

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



