




import random


class        UpperCAmelCase__				:



     '''simple docstring'''
     def __init__(				self ,    _lowerCAmelCase ,    _lowerCAmelCase ,    _lowerCAmelCase = 0.01 ,    _lowerCAmelCase = 1_000 ,    _lowerCAmelCase = -1 ,    ):
            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(__UpperCAmelCase	)
            a			   =len(sample[0]	)  # number of columns in dataset
            a			   =[]
     def        lowerCAmelCase__  (				self	):
            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(__UpperCAmelCase	)
                          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 ,    _lowerCAmelCase	):
            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(__UpperCAmelCase	)

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






     def        lowerCAmelCase__  (				self ,    _lowerCAmelCase	):
            return 1 if u >= 0 else -1



_lowerCamelCase							=      [
    [-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],
]

_lowerCamelCase							=      [
    -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()

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




"""simple docstring"""






def    A_ ( _lowerCAmelCase  :       list[list[int]]							):






     """simple docstring"""

     _a     						=					[list(_lowerCAmelCase							) for row in matrix]
     if matrix and isinstance(_lowerCAmelCase,  _lowerCAmelCase							):
          if isinstance(matrix[0],  _lowerCAmelCase							):
               _a     						=					0
               for row in matrix:
                    if prev_len == 0:
                         _a     						=					len(_lowerCAmelCase							)
                         _a     						=					True
                    else:
                         _a     						=					prev_len == len(_lowerCAmelCase							)
          else:
               _a     						=					True
     else:
          _a     						=					False

     return result
def    A_ ( _lowerCAmelCase  :       list[list[int]]							):






     """simple docstring"""

     if check_matrix(_lowerCAmelCase							) and len(_lowerCAmelCase							) > 0:
          _a     						=					[list(_lowerCAmelCase							) for row in a]
          _a     						=					len(_lowerCAmelCase							)
          if isinstance(a[0],  _lowerCAmelCase							):
               _a     						=					len(a[0]							)
          else:
               for dat in a:
                    print(_lowerCAmelCase							)
               return

          # horizotal printing increasing
          for i in range(0,  _lowerCAmelCase							):
               print(a[0][i]							)
          # vertical printing down
          for i in range(1,  _lowerCAmelCase							):
               print(a[i][mat_col - 1]							)
          # horizotal printing decreasing
          if mat_row > 1:
               for i in range(mat_col - 2,  -1,  -1							):
                    print(a[mat_row - 1][i]							)
        # vertical printing up
          for i in range(mat_row - 2,  0,  -1							):
               print(a[i][0]							)
          _a     						=					[row[1 : mat_col - 1] for row in a[1 : mat_row - 1]]
          if len(_lowerCAmelCase							) > 0:
               spiral_print_clockwise(_lowerCAmelCase							)
          else:
               return
     else:
          print('''Not a valid matrix'''							)
          return
def    A_ ( _lowerCAmelCase  :       list[list]							):






     """simple docstring"""

     if matrix:
          return list(matrix.pop(0							)							) + spiral_traversal(list(zip(*_lowerCAmelCase							)							)[::-1]							)
     else:
          return []


# driver code
if __name__ == "__main__":
   import doctest

   doctest.testmod()

   __snake_case												=							[[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
   spiral_print_clockwise(a)