import random



class 						snake_case_ :






   def __init__(   self       :  List[str]			,		lowercase_       :  list[list[float]]			,		lowercase_       :  list[int]			,		lowercase_       :  float = 0.01			,		lowercase_       :  int = 10_00			,		lowercase_       :  float = -1			,		)      ->		None:
       lowercase__				:     Optional[Any]					      =       sample
       if len(self.sample						) == 0:
           raise ValueError("Sample data can not be empty"						)
       lowercase__				:     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"						)
       lowercase__				:     Union[str, Any]					      =       learning_rate
       lowercase__				:     int					      =       epoch_number
       lowercase__				:     Optional[Any]					      =       bias
       lowercase__				:     List[str]					      =       len(lowercase_						)
       lowercase__				:     int					      =       len(sample[0]						)  # number of columns in dataset
       lowercase__				:     list					      =       []






   def    __UpperCamelCase  (   self       :  Tuple						)      ->		None:
       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						)

       lowercase__				:     str					      =       0

       while True:
           lowercase__				:     Optional[int]					      =       False
           for i in range(self.number_sample						):
               lowercase__				:     List[str]					      =       0
               for j in range(self.col_sample + 1						):
                   lowercase__				:     Optional[int]					      =       u + self.weight[j] * self.sample[i][j]
               lowercase__				:     Tuple					      =       self.sign(lowercase_						)
               if y != self.target[i]:
                   for j in range(self.col_sample + 1						):
                       lowercase__				:     List[Any]					      =       (
                           self.weight[j]
                           + self.learning_rate
                           * (self.target[i] - y)
                           * self.sample[i][j]
                       )
                   lowercase__				:     Union[str, Any]					      =       True
            # print('Epoch: \n',epoch_count)
           lowercase__				:     List[str]					      =       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       :  str			,		lowercase_       :  list[float]						)      ->		None:
       if len(self.sample						) == 0:
           raise ValueError("Sample data can not be empty"						)
       sample.insert(0			,		self.bias						)
       lowercase__				:     str					      =       0
       for i in range(self.col_sample + 1						):
           lowercase__				:     List[Any]					      =       u + self.weight[i] * sample[i]

       lowercase__				:     List[Any]					      =       self.sign(lowercase_						)

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







   def    __UpperCamelCase  (   self       :  str			,		lowercase_       :  float						)      ->		int:
       return 1 if u >= 0 else -1



UpperCamelCase													=		[
    [-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],
]

UpperCamelCase													=		[
    -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()

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




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


UpperCamelCase													=		{
    '''configuration_flava''': [
        '''FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''FlavaConfig''',
        '''FlavaImageCodebookConfig''',
        '''FlavaImageConfig''',
        '''FlavaMultimodalConfig''',
        '''FlavaTextConfig''',
    ],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		['''FlavaFeatureExtractor''']
      UpperCamelCase													=		['''FlavaImageProcessor''']
      UpperCamelCase													=		['''FlavaProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCamelCase													=		[
          '''FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''FlavaForPreTraining''',
          '''FlavaImageCodebook''',
          '''FlavaImageModel''',
          '''FlavaModel''',
          '''FlavaMultimodalModel''',
          '''FlavaPreTrainedModel''',
          '''FlavaTextModel''',
      ]

if TYPE_CHECKING:
      from .configuration_flava import (
          FLAVA_PRETRAINED_CONFIG_ARCHIVE_MAP,
          FlavaConfig,
          FlavaImageCodebookConfig,
          FlavaImageConfig,
          FlavaMultimodalConfig,
          FlavaTextConfig,
      )

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .feature_extraction_flava import FlavaFeatureExtractor
            from .image_processing_flava import FlavaImageProcessor
            from .processing_flava import FlavaProcessor

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_flava import (
                FLAVA_PRETRAINED_MODEL_ARCHIVE_LIST,
                FlavaForPreTraining,
                FlavaImageCodebook,
                FlavaImageModel,
                FlavaModel,
                FlavaMultimodalModel,
                FlavaPreTrainedModel,
                FlavaTextModel,
            )

else:
      import sys

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



