



_UpperCamelCase							    =						[[0, 1, 1], [0, 0, 1], [1, 0, 0]]






class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     List[Any]      ,     _a				:     List[Any]       ) -> Dict:
    __lowerCamelCase	:	Any   	=			name
    __lowerCamelCase	:	Tuple   	=			[]
    __lowerCamelCase	:	int   	=			[]




  def 						_lowercase					(			self				:     Dict      ,     _a				:     str       ) -> Tuple:
    self.inbound.append(_a       )




  def 						_lowercase					(			self				:     List[str]      ,     _a				:     Any       ) -> int:
    self.outbound.append(_a       )




  def __repr__(			self				:     str       ) -> str:
    return f'<node={self.name} inbound={self.inbound} outbound={self.outbound}>'




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase=3	,_lowerCAmelCase=0.85	)				->				Tuple:
  __lowerCamelCase	:	Any   	=			{}
  for node in nodes:
    __lowerCamelCase	:	Union[str, Any]   	=			1

  __lowerCamelCase	:	Optional[Any]   	=			{}
  for node in nodes:
    __lowerCamelCase	:	str   	=			len(node.outbound	)

  for i in range(_lowerCAmelCase	):
    print(F'======= Iteration {i + 1} ======='	)
    for _, node in enumerate(_lowerCAmelCase	):
      __lowerCamelCase	:	List[Any]   	=			(1 - d) + d * sum(
          ranks[ib] / outbounds[ib] for ib in node.inbound	)
    print(_lowerCAmelCase	)

def 	a_					(							)				->				Tuple:
  __lowerCamelCase	:	Optional[int]   	=			list(input('Enter Names of the Nodes: '	).split()	)

  __lowerCamelCase	:	str   	=			[Node(_lowerCAmelCase	) for name in names]

  for ri, row in enumerate(_lowerCAmelCase	):
    for ci, col in enumerate(_lowerCAmelCase	):
      if col == 1:
        nodes[ci].add_inbound(names[ri]	)
        nodes[ri].add_outbound(names[ci]	)

  print('======= Nodes ======='	)
  for node in nodes:
    print(_lowerCAmelCase	)

  page_rank(_lowerCAmelCase	)


if __name__ == "__main__":
   main()











'''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]}'''
)






