

import random







class  UpperCamelCase_      :


  """simple docstring"""



  def __init__(						self     :					List[Any]  ,	A_     :					list[list[float]]  ,	A_     :					list[int]  ,	A_     :					float = 0.01  ,	A_     :					int = 1000  ,	A_     :					float = -1  ,	)       ->			None:
      SCREAMING_SNAKE_CASE__         =    sample
      if len(self.sample						) == 0:
          raise ValueError("Sample data can not be empty"						)
      SCREAMING_SNAKE_CASE__         =    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"						)
      SCREAMING_SNAKE_CASE__         =    learning_rate
      SCREAMING_SNAKE_CASE__         =    epoch_number
      SCREAMING_SNAKE_CASE__         =    bias
      SCREAMING_SNAKE_CASE__         =    len(A_						)
      SCREAMING_SNAKE_CASE__         =    len(sample[0]						)  # number of columns in dataset
      SCREAMING_SNAKE_CASE__         =    []



  def   _UpperCamelCase     (						self     :					Dict						)       ->			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						)

      SCREAMING_SNAKE_CASE__         =    0

      while True:
          SCREAMING_SNAKE_CASE__         =    False
          for i in range(self.number_sample						):
              SCREAMING_SNAKE_CASE__         =    0
              for j in range(self.col_sample + 1						):
                  SCREAMING_SNAKE_CASE__         =    u + self.weight[j] * self.sample[i][j]
              SCREAMING_SNAKE_CASE__         =    self.sign(A_						)
              if y != self.target[i]:
                  for j in range(self.col_sample + 1						):
                      SCREAMING_SNAKE_CASE__         =    (
                          self.weight[j]
                          + self.learning_rate
                          * (self.target[i] - y)
                          * self.sample[i][j]
                      )
                  SCREAMING_SNAKE_CASE__         =    True
            # print('Epoch: \n',epoch_count)
          SCREAMING_SNAKE_CASE__         =    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   _UpperCamelCase     (						self     :					int  ,	A_     :					list[float]						)       ->			None:
      if len(self.sample						) == 0:
          raise ValueError("Sample data can not be empty"						)
      sample.insert(0  ,	self.bias						)
      SCREAMING_SNAKE_CASE__         =    0
      for i in range(self.col_sample + 1						):
          SCREAMING_SNAKE_CASE__         =    u + self.weight[i] * sample[i]

      SCREAMING_SNAKE_CASE__         =    self.sign(A_						)

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







  def   _UpperCamelCase     (						self     :					Optional[Any]  ,	A_     :					float						)       ->			int:
      return 1 if u >= 0 else -1




A__										=      [
    [-0.6_508, 0.1_097, 4.0_009],
    [-1.4_492, 0.8_896, 4.4_005],
    [2.0_850, 0.6_876, 12.0_710],
    [0.2_626, 1.1_476, 7.7_985],
    [0.6_418, 1.0_234, 7.0_427],
    [0.2_569, 0.6_730, 8.3_265],
    [1.1_155, 0.6_043, 7.4_446],
    [0.0_914, 0.3_399, 7.0_677],
    [0.0_121, 0.5_256, 4.6_316],
    [-0.0_429, 0.4_660, 5.4_323],
    [0.4_340, 0.6_870, 8.2_287],
    [0.2_735, 1.0_287, 7.1_934],
    [0.4_839, 0.4_851, 7.4_850],
    [0.4_089, -0.1_267, 5.5_019],
    [1.4_391, 0.1_614, 8.5_843],
    [-0.9_115, -0.1_973, 2.1_962],
    [0.3_654, 1.0_475, 7.4_858],
    [0.2_144, 0.7_515, 7.1_699],
    [0.2_013, 1.0_014, 6.5_489],
    [0.6_483, 0.2_183, 5.8_991],
    [-0.1_147, 0.2_242, 7.2_435],
    [-0.7_970, 0.8_795, 3.8_762],
    [-1.0_625, 0.6_366, 2.4_707],
    [0.5_307, 0.1_285, 5.6_883],
    [-1.2_200, 0.7_777, 1.7_252],
    [0.3_957, 0.1_076, 5.6_623],
    [-0.1_013, 0.5_989, 7.1_812],
    [2.4_482, 0.9_455, 11.2_095],
    [2.0_149, 0.6_192, 10.9_263],
    [0.2_012, 0.2_611, 5.4_631],
]

A__										=      [
    -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()

  A__										=      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:
    A__										=      []
    for i in range(len(samples[0])):
      A__										=      input("value: ").strip()
      if user_input == "q":
        break
      A__										=      float(user_input)
      sample.insert(i, observation)
    network.sort(sample)





def 				__a							(       __A			,							__A			,							__A			,							__A						):
    return len_board * len_board_column * row + column





def 				__a							(       __A			,							__A			,							__A			,							__A			,							__A			,							__A			,							):

    if board[row][column] != word[word_index]:
        return False

    if word_index == len(__A						) - 1:
        return True

    SCREAMING_SNAKE_CASE__         =    [(0, 1), (0, -1), (-1, 0), (1, 0)]
    SCREAMING_SNAKE_CASE__         =    len(__A						)
    SCREAMING_SNAKE_CASE__         =    len(board[0]						)
    for direction in traverts_directions:
        SCREAMING_SNAKE_CASE__         =    row + direction[0]
        SCREAMING_SNAKE_CASE__         =    column + direction[1]
        if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
            continue

        SCREAMING_SNAKE_CASE__         =    get_point_key(__A			,							__A			,							__A			,							__A						)
        if key in visited_points_set:
            continue

        visited_points_set.add(__A						)
        if exits_word(__A			,							__A			,							__A			,							__A			,							word_index + 1			,							__A						):
            return True

        visited_points_set.remove(__A						)

    return False





def 				__a							(       __A			,							__A						):
    SCREAMING_SNAKE_CASE__         =    (
        "The board should be a non empty matrix of single chars strings."
    )

    SCREAMING_SNAKE_CASE__         =    len(__A						)
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(__A						)

    for row in board:
        if not isinstance(__A			,							__A						) or len(__A						) == 0:
            raise ValueError(__A						)

        for item in row:
            if not isinstance(__A			,							__A						) or len(__A						) != 1:
                raise ValueError(__A						)

    # Validate word
    if not isinstance(__A			,							__A						) or len(__A						) == 0:
        raise ValueError(
            "The word parameter should be a string of length greater than 0."						)

    SCREAMING_SNAKE_CASE__         =    len(board[0]						)
    for i in range(__A						):
        for j in range(__A						):
            if exits_word(
                __A			,							__A			,							__A			,							__A			,							0			,							{get_point_key(__A			,							__A			,							__A			,							__A						)}						):
                return True

    return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()


