



'''simple docstring'''





def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	)				->				int:
  __lowerCamelCase	:	Dict   	=			1.0
  for i in range(1	,r + 1	):
    ret *= (n + 1 - i) / i
  return round(_lowerCAmelCase	)

def 	a_					(							_lowerCAmelCase	)				->				int:
  return choose(8 + n	,_lowerCAmelCase	) + choose(9 + n	,_lowerCAmelCase	) - 10

def 	a_					(							_lowerCAmelCase	)				->				int:
  return sum(non_bouncy_exact(_lowerCAmelCase	) for i in range(1	,n + 1	)	)

def 	a_					(							_lowerCAmelCase = 100	)				->				int:
  return non_bouncy_upto(_lowerCAmelCase	)


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
_UpperCamelCase							    =						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
_UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase    ,    _UpperCamelCase							    =						train_test_split(
    iris['data'], iris['target'], random_state=4
)

# KNN

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

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

_UpperCamelCase							    =						knn.predict(X_new)

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






