
'''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_      =   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_    ,					lowerCAmelCase_    ,					lowerCAmelCase_    ,					lowerCAmelCase_      =   train_test_split(
    iris["data"], iris["target"], random_state=4
)

# KNN

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

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

lowerCAmelCase_      =   knn.predict(X_new)

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



'''simple docstring'''







from collections import defaultdict







def   lowerCAmelCase(  a__    :  int = 5      ):







       '''simple docstring'''






       lowerCamelCase__				   =      defaultdict(a__      )
       lowerCamelCase__				   =      0

       while True:
              lowerCamelCase__				   =      get_digits(a__      )
              freqs[digits].append(a__      )

              if len(freqs[digits]      ) == max_base:
                     lowerCamelCase__				   =      freqs[digits][0] ** 3
                     return base

              num += 1







def   lowerCAmelCase(  a__    :  int      ):







       '''simple docstring'''






       return "".join(sorted(str(num**3      )      )      )


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

