





'''simple docstring'''






import random




class 				UpperCAmelCase  :




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




      def        lowercase__					(    self    :				Dict						)       ->   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						)

            _lowerCAmelCase							  =							0

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

            _lowerCAmelCase							  =							self.sign(__snake_case						)

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




      def        lowercase__					(    self    :				List[str]    ,    __snake_case    :				float						)       ->   int:
            return 1 if u >= 0 else -1




A__       :     Tuple          =[
    [-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],
]

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













'''simple docstring'''






from typing import TYPE_CHECKING

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


A__       :     Tuple          ={
    '''configuration_oneformer''': ['''ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OneFormerConfig'''],
    '''processing_oneformer''': ['''OneFormerProcessor'''],
}

try:
      if not is_vision_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     List[str]          =['''OneFormerImageProcessor''']

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      A__       :     Dict          =[
          '''ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''OneFormerForUniversalSegmentation''',
          '''OneFormerModel''',
          '''OneFormerPreTrainedModel''',
      ]

if TYPE_CHECKING:
      from .configuration_oneformer import ONEFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, OneFormerConfig
      from .processing_oneformer import OneFormerProcessor

      try:
            if not is_vision_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .image_processing_oneformer import OneFormerImageProcessor
      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_oneformer import (
                ONEFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                OneFormerForUniversalSegmentation,
                OneFormerModel,
                OneFormerPreTrainedModel,
            )


else:
      import sys

      A__       :     Optional[Any]          =_LazyModule(__name__, globals()['''__file__'''], _import_structure)






