






'''simple docstring'''


import random







class     SCREAMING_SNAKE_CASE						:






      def __init__(   self      :     Any	,	lowercase__      :     Optional[int]	,	lowercase__      :     Optional[int]	,	lowercase__      :     List[Any] = 0.01	,	lowercase__      :     List[str] = 1000	,	lowercase__      :     Optional[Any] = -1	,	):


           '''simple docstring'''

           a_    :       Optional[Any]    	=     sample
           if len(self.sample					) == 0:
                raise ValueError("""Sample data can not be empty"""					)
           a_    :       Union[str, 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"""					)
           a_    :       List[str]    	=     learning_rate
           a_    :       Dict    	=     epoch_number
           a_    :       List[Any]    	=     bias
           a_    :       Dict    	=     len(lowercase__					)
           a_    :       int    	=     len(sample[0]					)  # number of columns in dataset
           a_    :       list    	=     []






      def        lowercase_	(   self      :     Union[str, Any]					):


           '''simple docstring'''

           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_    :       List[Any]    	=     0

           while True:
                a_    :       List[str]    	=     False
                for i in range(self.number_sample					):
                     a_    :       Union[str, Any]    	=     0
                     for j in range(self.col_sample + 1					):
                          a_    :       List[Any]    	=     u + self.weight[j] * self.sample[i][j]
                     a_    :       List[str]    	=     self.sign(lowercase__					)
                     if y != self.target[i]:
                          for j in range(self.col_sample + 1					):
                               a_    :       Optional[int]    	=     (
                                   self.weight[j]
                                   + self.learning_rate
                                   * (self.target[i] - y)
                                   * self.sample[i][j]
                               )
                          a_    :       List[str]    	=     True
                     # print('Epoch: \n',epoch_count)
                a_    :       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        lowercase_	(   self      :     Optional[Any]	,	lowercase__      :     str					):


           '''simple docstring'''

           if len(self.sample					) == 0:
                raise ValueError("""Sample data can not be empty"""					)
           sample.insert(0	,	self.bias					)
           a_    :       List[Any]    	=     0
           for i in range(self.col_sample + 1					):
                a_    :       Optional[int]    	=     u + self.weight[i] * sample[i]

           a_    :       Tuple    	=     self.sign(lowercase__					)

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






      def        lowercase_	(   self      :     List[str]	,	lowercase__      :     int					):


           '''simple docstring'''

           return 1 if u >= 0 else -1

lowerCAmelCase_			:     Union[str, Any]							    =     [
    [-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],
]

lowerCAmelCase_			:     List[Any]							    =     [
    -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_			:     Optional[int]							    =     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_			:     Optional[int]							    =     []
  for i in range(len(samples[0])):
   lowerCAmelCase_			:     Tuple							    =     input('value: ').strip()
   if user_input == "q":
    break
   lowerCAmelCase_			:     Dict							    =     float(user_input)
   sample.insert(i, observation)
  network.sort(sample)






'''simple docstring'''


from .stack import Stack



def    _SCREAMING_SNAKE_CASE    (			UpperCamelCase__						:				str							):




     """simple docstring"""


     a_    :       Stack[str]    	=     Stack()
     a_    :       Tuple    	=     {"""(""": """)""", """[""": """]""", """{""": """}"""}
     for bracket in parentheses:
          if bracket in bracket_pairs:
               stack.push(UpperCamelCase__							)
          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()

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