






import random







class 							UpperCAmelCase_     :




     def __init__(							self  ,	_lowerCAmelCase  ,	_lowerCAmelCase  ,	_lowerCAmelCase = 0.0_1  ,	_lowerCAmelCase = 1000  ,	_lowerCAmelCase = -1  ,	):
          UpperCAmelCase__			:     Any										= sample
          if len(self.sample		) == 0:
               raise ValueError("""Sample data can not be empty"""		)
          UpperCAmelCase__			:     Any										= 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"""		)
          UpperCAmelCase__			:     Dict										= learning_rate
          UpperCAmelCase__			:     Any										= epoch_number
          UpperCAmelCase__			:     Tuple										= bias
          UpperCAmelCase__			:     Any										= len(_lowerCAmelCase		)
          UpperCAmelCase__			:     int										= len(sample[0]		)  # number of columns in dataset
          UpperCAmelCase__			:     list										= []




     def      __UpperCAmelCase     (							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		)

          UpperCAmelCase__			:     Optional[int]										= 0

          while True:
               UpperCAmelCase__			:     Dict										= False
               for i in range(self.number_sample		):
                    UpperCAmelCase__			:     int										= 0
                    for j in range(self.col_sample + 1		):
                         UpperCAmelCase__			:     Union[str, Any]										= u + self.weight[j] * self.sample[i][j]
                    UpperCAmelCase__			:     Optional[Any]										= self.sign(_lowerCAmelCase		)
                    if y != self.target[i]:
                         for j in range(self.col_sample + 1		):
                              UpperCAmelCase__			:     List[Any]										= (
                                  self.weight[j]
                                  + self.learning_rate
                                  * (self.target[i] - y)
                                  * self.sample[i][j]
                              )
                         UpperCAmelCase__			:     str										= True
            # print('Epoch: \n',epoch_count)
               UpperCAmelCase__			:     Optional[int]										= 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  ,	_lowerCAmelCase		):
          if len(self.sample		) == 0:
               raise ValueError("""Sample data can not be empty"""		)
          sample.insert(0  ,	self.bias		)
          UpperCAmelCase__			:     Any										= 0
          for i in range(self.col_sample + 1		):
               UpperCAmelCase__			:     Tuple										= u + self.weight[i] * sample[i]

          UpperCAmelCase__			:     Any										= self.sign(_lowerCAmelCase		)

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




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



SCREAMING_SNAKE_CASE__		:       str   							=							[
    [-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__		:       int   							=							[
    -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__		:       Any   							=							Perceptron(
          sample=samples, target=target, learning_rate=0.01, epoch_number=10_00, bias=-1
      )
      network.training()
      print("""Finished training perceptron""")
      print("""Enter values to predict or q to exit""")
      while True:
            SCREAMING_SNAKE_CASE__		:       list   							=							[]
            for i in range(len(samples[0])):
                  SCREAMING_SNAKE_CASE__		:       Optional[int]   							=							input("""value: """).strip()
                  if user_input == "q":
                        break
                  SCREAMING_SNAKE_CASE__		:       Union[str, Any]   							=							float(user_input)
                  sample.insert(i, observation)
            network.sort(sample)














from .stack import Stack





def    _lowerCamelCase	(	__lowerCamelCase					)   -> bool:

     '''simple docstring'''
     UpperCAmelCase__			:     Stack[str]										= Stack()
     UpperCAmelCase__			:     str										= {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(__lowerCamelCase					)
          elif bracket in (")", "]", "}"):
               if stack.is_empty() or bracket_pairs[stack.pop()] != bracket:
                    return False
     return stack.is_empty()


if __name__ == "__main__":
      from doctest import testmod

      testmod()

      SCREAMING_SNAKE_CASE__		:       Any   							=							["""((()))""", """((())""", """(()))"""]
      print("""Balanced parentheses demonstration:\n""")
      for example in examples:
            SCREAMING_SNAKE_CASE__		:       List[str]   							=							"""""" if balanced_parentheses(example) else """not """
            print(f'''{example} is {not_str}balanced''')






