code: 
  
  import random
  
  
  
  
  
  
  class    __A				:
  
  
  
  
  
  
      def __init__(	self			:    List[Any]    ,				UpperCAmelCase_			:    Dict    ,				UpperCAmelCase_			:    Any    ,				UpperCAmelCase_			:    Optional[int] = 0.01    ,				UpperCAmelCase_			:    Optional[Any] = 1000    ,				UpperCAmelCase_			:    Optional[int] = -1    ,				):
          lowerCAmelCase				:			Union[str, Any]    			=      sample
          if len(self.sample    ) == 0:
              raise ValueError('Sample data can not be empty'    )
          lowerCAmelCase				:			Optional[int]    			=      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'    )
          lowerCAmelCase				:			Optional[int]    			=      learning_rate
          lowerCAmelCase				:			Any    			=      epoch_number
          lowerCAmelCase				:			Any    			=      bias
          lowerCAmelCase				:			int    			=      len(UpperCAmelCase_    )
          lowerCAmelCase				:			int    			=      len(sample[0]    )  # number of columns in dataset
          lowerCAmelCase				:			Any    			=      []
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ):
  
  
  
  
  
  
          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    )
  
          lowerCAmelCase				:			List[str]    			=      0
  
          while True:
              lowerCAmelCase				:			List[str]    			=      False
              for i in range(self.number_sample    ):
                  lowerCAmelCase				:			List[Any]    			=      0
                  for j in range(self.col_sample + 1    ):
                      lowerCAmelCase				:			Union[str, Any]    			=      u + self.weight[j] * self.sample[i][j]
                  lowerCAmelCase				:			Any    			=      self.sign(UpperCAmelCase_    )
                  if y != self.target[i]:
                      for j in range(self.col_sample + 1    ):
                          lowerCAmelCase				:			Any    			=      (
                              self.weight[j]
                              + self.learning_rate
                              * (self.target[i] - y)
                              * self.sample[i][j]
                          )
                      lowerCAmelCase				:			List[Any]    			=      True
              # print('Epoch: \n',epoch_count)
              lowerCAmelCase				:			Optional[Any]    			=      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			:    str    ,				UpperCAmelCase_			:    Union[str, Any]    ):
  
  
  
  
  
  
          if len(self.sample    ) == 0:
              raise ValueError('Sample data can not be empty'    )
          sample.insert(0    ,				self.bias    )
          lowerCAmelCase				:			List[Any]    			=      0
          for i in range(self.col_sample + 1    ):
              lowerCAmelCase				:			Union[str, Any]    			=      u + self.weight[i] * sample[i]
  
          lowerCAmelCase				:			List[str]    			=      self.sign(UpperCAmelCase_    )
  
          if y == -1:
              print(('Sample: ', sample)    )
              print('classification: P1'    )
          else:
              print(('Sample: ', sample)    )
              print('classification: P2'    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Any    ,				UpperCAmelCase_			:    Tuple    ):
          return 1 if u >= 0 else -1
  
  
  
  __A   :     Optional[int]      =			[
      [-0.65_08, 0.10_97, 4.00_09],
      [-1.44_92, 0.88_96, 4.40_05],
      [2.08_50, 0.68_76, 12.0710],
      [0.26_26, 1.14_76, 7.79_85],
      [0.64_18, 1.02_34, 7.04_27],
      [0.25_69, 0.67_30, 8.32_65],
      [1.11_55, 0.60_43, 7.44_46],
      [0.09_14, 0.33_99, 7.06_77],
      [0.01_21, 0.52_56, 4.63_16],
      [-0.04_29, 0.46_60, 5.43_23],
      [0.43_40, 0.68_70, 8.22_87],
      [0.27_35, 1.02_87, 7.19_34],
      [0.48_39, 0.48_51, 7.48_50],
      [0.40_89, -0.12_67, 5.50_19],
      [1.43_91, 0.16_14, 8.58_43],
      [-0.91_15, -0.19_73, 2.19_62],
      [0.36_54, 1.04_75, 7.48_58],
      [0.21_44, 0.75_15, 7.16_99],
      [0.20_13, 1.00_14, 6.54_89],
      [0.64_83, 0.21_83, 5.89_91],
      [-0.11_47, 0.22_42, 7.24_35],
      [-0.79_70, 0.87_95, 3.87_62],
      [-1.06_25, 0.63_66, 2.47_07],
      [0.53_07, 0.12_85, 5.68_83],
      [-1.22_00, 0.77_77, 1.72_52],
      [0.39_57, 0.10_76, 5.66_23],
      [-0.10_13, 0.59_89, 7.18_12],
      [2.44_82, 0.94_55, 11.2095],
      [2.01_49, 0.61_92, 10.9263],
      [0.20_12, 0.26_11, 5.46_31],
  ]
  
  __A   :     Optional[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()
  
   __A   :     str      =			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   :     list      =			[]
    for i in range(len(samples[0])):
     __A   :     Dict      =			input('''value: ''').strip()
     if user_input == "q":
      break
     __A   :     Union[str, Any]      =			float(user_input)
     sample.insert(i, observation)
    network.sort(sample)
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
  
  
  __A   :     List[Any]      =			{'''configuration_umt5''': ['''UMT5Config''', '''UMT5OnnxConfig''']}
  
  
  try:
   if not is_torch_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     Tuple      =			[
       '''UMT5EncoderModel''',
       '''UMT5ForConditionalGeneration''',
       '''UMT5ForQuestionAnswering''',
       '''UMT5Model''',
       '''UMT5PreTrainedModel''',
   ]
  
  if TYPE_CHECKING:
   from .configuration_umta import UMTaConfig, UMTaOnnxConfig
  
   try:
    if not is_torch_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .modeling_umta import (
        UMTaEncoderModel,
        UMTaForConditionalGeneration,
        UMTaForQuestionAnswering,
        UMTaModel,
        UMTaPreTrainedModel,
    )
  else:
   import sys
  
   __A   :     Any      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  