



class  lowerCamelCase_   :
  """simple docstring"""




  def __init__(			self				:     List[str]       ) -> None:
    __lowerCamelCase	:	dict[int, list[int]]   	=			{}
    # indexmap keys are an index and it's values are the number at that index
    __lowerCamelCase	:	dict[int, int]   	=			{}




  def 						_lowercase					(			self				:     int      ,     _a				:     Tuple      ,     _a				:     str       ) -> list[int]:







    if isinstance(__UpperCamelCase      ,     (range, str)       ):
      __lowerCamelCase	:	List[str]   	=			list(__UpperCamelCase       )
    elif not isinstance(__UpperCamelCase      ,     __UpperCamelCase       ):
      raise TypeError(
          'binary_search_delete() only accepts either a list, range or str'       )

    __lowerCamelCase	:	int   	=			0
    __lowerCamelCase	:	Any   	=			len(__UpperCamelCase       ) - 1

    while low <= high:
      __lowerCamelCase	:	List[Any]   	=			(low + high) // 2
      if array[mid] == item:
        array.pop(__UpperCamelCase       )
        return array
      elif array[mid] < item:
        __lowerCamelCase	:	Union[str, Any]   	=			mid + 1
      else:
        __lowerCamelCase	:	Optional[Any]   	=			mid - 1
    raise ValueError(
        'Either the item is not in the array or the array was unsorted'       )




  def 						_lowercase					(			self				:     Union[str, Any]      ,     _a				:     int      ,     _a				:     int       ) -> list[int]:







    if isinstance(__UpperCamelCase      ,     (range, str)       ):
      __lowerCamelCase	:	Tuple   	=			list(__UpperCamelCase       )
    elif not isinstance(__UpperCamelCase      ,     __UpperCamelCase       ):
      raise TypeError(
          'binary_search_insert() only accepts either a list, range or str'       )

    __lowerCamelCase	:	Dict   	=			0
    __lowerCamelCase	:	int   	=			len(__UpperCamelCase       ) - 1

    while low <= high:
      __lowerCamelCase	:	Any   	=			(low + high) // 2
      if array[mid] == index:
        # If the item already exists in the array,
        # insert it after the existing item
        array.insert(mid + 1      ,     __UpperCamelCase       )
        return array
      elif array[mid] < index:
        __lowerCamelCase	:	List[Any]   	=			mid + 1
      else:
        __lowerCamelCase	:	List[str]   	=			mid - 1

        # If the item doesn't exist in the array, insert it at the appropriate position
    array.insert(__UpperCamelCase      ,     __UpperCamelCase       )
    return array




  def 						_lowercase					(			self				:     str      ,     _a				:     Union[str, Any]      ,     _a				:     Tuple       ) -> None:







    if index in self.indexmap:
      __lowerCamelCase	:	List[Any]   	=			self.indexmap[index]
      if len(self.numbermap[n]       ) == 1:
        del self.numbermap[n]
      else:
        __lowerCamelCase	:	Tuple   	=			self.binary_search_delete(self.numbermap[n]      ,     __UpperCamelCase       )

        # Set new index
    __lowerCamelCase	:	List[Any]   	=			number

    # Number not seen before or empty so insert number value
    if number not in self.numbermap:
      __lowerCamelCase	:	int   	=			[index]

    # Here we need to perform a binary search insertion in order to insert
    # The item in the correct place
    else:
      __lowerCamelCase	:	Tuple   	=			self.binary_search_insert(
          self.numbermap[number]      ,     __UpperCamelCase       )




  def 						_lowercase					(			self				:     Dict      ,     _a				:     Tuple       ) -> int:
    return self.numbermap.get(__UpperCamelCase      ,     [-1]       )[0]


if __name__ == "__main__":
   import doctest

   doctest.testmod()











'''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]}'''
)






