def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__					)				->str:

  '''simple docstring'''




  if start == end:
    return start if lst[start] > item else start + 1
  if start > end:
    return start

  a__     		=     (start + end) // 2
  if lst[mid] < item:
    return binary_search(lowerCamelCase__ ,				lowerCamelCase__ ,				mid + 1 ,				lowerCamelCase__					)
  elif lst[mid] > item:
    return binary_search(lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				mid - 1					)
  else:
    return mid
def 				__lowercase	(						lowerCamelCase__					)				->str:

  '''simple docstring'''




  a__     		=     len(lowerCamelCase__					)

  for index in range(1 ,				lowerCamelCase__					):
    a__     		=     lst[index]
    a__     		=     binary_search(lowerCamelCase__ ,				lowerCamelCase__ ,				0 ,				index - 1					)
    a__     		=     lst[:pos] + [value] + lst[pos:index] + lst[index + 1 :]

  return lst
def 				__lowercase	(						lowerCamelCase__ ,				lowerCamelCase__					)				->List[str]:

  '''simple docstring'''




  if not left:
    return right

  if not right:
    return left

  if left[0] < right[0]:
    return [left[0], *merge(left[1:] ,				lowerCamelCase__					)]

  return [right[0], *merge(lowerCamelCase__ ,				right[1:]					)]
def 				__lowercase	(						lowerCamelCase__					)				->Dict:

  '''simple docstring'''




  a__     		=     len(lowerCamelCase__					)
  a__		,a__     		=     [], []
  a__     		=     [lst[0]]
  a__     		=     []
  a__     		=     1
  while i < length:
    if lst[i] < lst[i - 1]:
      runs.append(lowerCamelCase__					)
      a__     		=     [lst[i]]
    else:
      new_run.append(lst[i]					)
    i += 1
  runs.append(lowerCamelCase__					)

  for run in runs:
    sorted_runs.append(insertion_sort(lowerCamelCase__					)					)
  for run in sorted_runs:
    a__     		=     merge(lowerCamelCase__ ,				lowerCamelCase__					)

  return sorted_array
def 				__lowercase	(						)				->Union[str, Any]:

  '''simple docstring'''




  a__     		=     [5, 9, 10, 3, -4, 5, 1_78, 92, 46, -18, 0, 7]
  a__     		=     tim_sort(lowerCamelCase__					)
  print(lowerCamelCase__					)


if __name__ == "__main__":
     main()


import random







class 					UpperCAmelCase_				:




    def __init__(					self  :   Optional[int]      ,      lowerCAmelCase  :   list[list[float]]      ,      lowerCAmelCase  :   list[int]      ,      lowerCAmelCase  :   float = 0.01      ,      lowerCAmelCase  :   int = 1000      ,      lowerCAmelCase  :   float = -1      ,      )	-> None:
      a__     		=     sample
      if len(self.sample   ) == 0:
        raise ValueError("Sample data can not be empty"   )
      a__     		=     target
      if len(self.target   ) == 0:
        raise ValueError("Target data can not be empty"   )
      if len(self.sample   ) != len(self.target   ):
        raise ValueError("Sample data and Target data do not have matching lengths"   )
      a__     		=     learning_rate
      a__     		=     epoch_number
      a__     		=     bias
      a__     		=     len(lowerCAmelCase   )
      a__     		=     len(sample[0]   )  # number of columns in dataset
      a__     		=     []




    def 						__lowerCamelCase (					self  :   Optional[Any]   )	-> None:
      for sample in self.sample:
        sample.insert(0      ,      self.bias   )

      for _ in range(self.col_sample   ):
        self.weight.append(random.random()   )

      self.weight.insert(0      ,      self.bias   )

      a__     		=     0

      while True:
        a__     		=     False
        for i in range(self.number_sample   ):
          a__     		=     0
          for j in range(self.col_sample + 1   ):
            a__     		=     u + self.weight[j] * self.sample[i][j]
          a__     		=     self.sign(lowerCAmelCase   )
          if y != self.target[i]:
            for j in range(self.col_sample + 1   ):
              a__     		=     (
                  self.weight[j]
                  + self.learning_rate
                  * (self.target[i] - y)
                  * self.sample[i][j]
              )
            a__     		=     True
            # print('Epoch: \n',epoch_count)
        a__     		=     epoch_count + 1
        # if you want control the epoch or just by error
        if not has_misclassified:
          print(("\nEpoch:\n", epoch_count)   )
          print("------------------------\n"   )
          # if epoch_count > self.epoch_number or not error:
          break




    def 						__lowerCamelCase (					self  :   List[str]      ,      lowerCAmelCase  :   list[float]   )	-> None:
      if len(self.sample   ) == 0:
        raise ValueError("Sample data can not be empty"   )
      sample.insert(0      ,      self.bias   )
      a__     		=     0
      for i in range(self.col_sample + 1   ):
        a__     		=     u + self.weight[i] * sample[i]

      a__     		=     self.sign(lowerCAmelCase   )

      if y == -1:
        print(("Sample: ", sample)   )
        print("classification: P1"   )
      else:
        print(("Sample: ", sample)   )
        print("classification: P2"   )




    def 						__lowerCamelCase (					self  :   int      ,      lowerCAmelCase  :   float   )	-> int:
      return 1 if u >= 0 else -1

_SCREAMING_SNAKE_CASE   						=    [
    [-0.6508, 0.1097, 4.0009],
    [-1.4492, 0.8896, 4.4005],
    [2.0850, 0.6876, 12.0710],
    [0.2626, 1.1476, 7.7985],
    [0.6418, 1.0234, 7.0427],
    [0.2569, 0.6730, 8.3265],
    [1.1155, 0.6043, 7.4446],
    [0.0914, 0.3399, 7.0677],
    [0.0121, 0.5256, 4.6316],
    [-0.0429, 0.4660, 5.4323],
    [0.4340, 0.6870, 8.2287],
    [0.2735, 1.0287, 7.1934],
    [0.4839, 0.4851, 7.4850],
    [0.4089, -0.1267, 5.5019],
    [1.4391, 0.1614, 8.5843],
    [-0.9115, -0.1973, 2.1962],
    [0.3654, 1.0475, 7.4858],
    [0.2144, 0.7515, 7.1699],
    [0.2013, 1.0014, 6.5489],
    [0.6483, 0.2183, 5.8991],
    [-0.1147, 0.2242, 7.2435],
    [-0.7970, 0.8795, 3.8762],
    [-1.0625, 0.6366, 2.4707],
    [0.5307, 0.1285, 5.6883],
    [-1.2200, 0.7777, 1.7252],
    [0.3957, 0.1076, 5.6623],
    [-0.1013, 0.5989, 7.1812],
    [2.4482, 0.9455, 11.2095],
    [2.0149, 0.6192, 10.9263],
    [0.2012, 0.2611, 5.4631],
]

_SCREAMING_SNAKE_CASE   						=    [
    -1,
    -1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    1,
    1,
    -1,
    1,
    -1,
    -1,
    -1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    1,
    1,
    1,
    1,
    -1,
    -1,
    1,
    -1,
    1,
]


if __name__ == "__main__":
     import doctest

     doctest.testmod()

     _SCREAMING_SNAKE_CASE   						=    Perceptron(
         sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
     )
     network.training()
     print("""Finished training perceptron""")
     print("""Enter values to predict or q to exit""")
     while True:
          _SCREAMING_SNAKE_CASE   						=    []
          for i in range(len(samples[0])):
               _SCREAMING_SNAKE_CASE   						=    input("""value: """).strip()
               if user_input == "q":
                    break
               _SCREAMING_SNAKE_CASE   						=    float(user_input)
               sample.insert(i, observation)
          network.sort(sample)

