



import random



class 		_lowercase		:

   def __init__(   self      :							int				, __lowerCAmelCase      :							list[list[float]]				, __lowerCAmelCase      :							list[int]				, __lowerCAmelCase      :							float = 0.0_1				, __lowerCAmelCase      :							int = 1000				, __lowerCAmelCase      :							float = -1				, )							->					None:


    """simple docstring"""






    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(__lowerCAmelCase				)
    a							=		len(sample[0]				)  # number of columns in dataset
    a							=		[]

   def 		A				(   self      :							List[Any]				)							->					None:


    """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							=		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(__lowerCAmelCase				)
      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 		A				(   self      :							Union[str, Any]				, __lowerCAmelCase      :							list[float]				)							->					None:


    """simple docstring"""






    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(__lowerCAmelCase				)

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




   def 		A				(   self      :							Optional[int]				, __lowerCAmelCase      :							float				)							->					int:


    """simple docstring"""






    return 1 if u >= 0 else -1

A_		:  Tuple										=  [
    [-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],
]

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=10_00, 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_		:  Tuple										=  input('''value: ''').strip()
               if user_input == "q":
                    break
               A_		:  str										=  float(user_input)
               sample.insert(i, observation)
          network.sort(sample)



from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_A							=       {'''configuration_dinat''': ['''DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DinatConfig''']}


try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _A							=       [
          '''DINAT_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''DinatForImageClassification''',
          '''DinatModel''',
          '''DinatPreTrainedModel''',
          '''DinatBackbone''',
      ]

if TYPE_CHECKING:
      from .configuration_dinat import DINAT_PRETRAINED_CONFIG_ARCHIVE_MAP, DinatConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_dinat import (
                DINAT_PRETRAINED_MODEL_ARCHIVE_LIST,
                DinatBackbone,
                DinatForImageClassification,
                DinatModel,
                DinatPreTrainedModel,
            )

else:
      import sys

      _A							=       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)



