





import random





class   lowercase__     :







  def __init__(						self     ,    _lowercase     ,    _lowercase     ,    _lowercase = 0.01     ,    _lowercase = 1_000     ,    _lowercase = -1     ,    ):
       lowerCAmelCase_       :							List[str]			 =		sample
       if len(self.sample    ) == 0:
            raise ValueError("""Sample data can not be empty"""    )
       lowerCAmelCase_       :							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_       :							str			 =		learning_rate
       lowerCAmelCase_       :							List[str]			 =		epoch_number
       lowerCAmelCase_       :							Optional[int]			 =		bias
       lowerCAmelCase_       :							Optional[Any]			 =		len(_lowercase    )
       lowerCAmelCase_       :							Any			 =		len(sample[0]    )  # number of columns in dataset
       lowerCAmelCase_       :							list			 =		[]







  def 						UpperCAmelCase__		(						self    ):
       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_       :							int			 =		0

       while True:
            lowerCAmelCase_       :							int			 =		False
            for i in range(self.number_sample    ):
                 lowerCAmelCase_       :							Dict			 =		0
                 for j in range(self.col_sample + 1    ):
                      lowerCAmelCase_       :							str			 =		u + self.weight[j] * self.sample[i][j]
                 lowerCAmelCase_       :							str			 =		self.sign(_lowercase    )
                 if y != self.target[i]:
                      for j in range(self.col_sample + 1    ):
                           lowerCAmelCase_       :							Tuple			 =		(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                      lowerCAmelCase_       :							str			 =		True
            # print('Epoch: \n',epoch_count)
            lowerCAmelCase_       :							Union[str, 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 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       if len(self.sample    ) == 0:
            raise ValueError("""Sample data can not be empty"""    )
       sample.insert(0     ,    self.bias    )
       lowerCAmelCase_       :							Union[str, Any]			 =		0
       for i in range(self.col_sample + 1    ):
            lowerCAmelCase_       :							int			 =		u + self.weight[i] * sample[i]

       lowerCAmelCase_       :							Dict			 =		self.sign(_lowercase    )

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







  def 						UpperCAmelCase__		(						self     ,    _lowercase    ):
       return 1 if u >= 0 else -1





UpperCAmelCase_       : Optional[Any]							=    [
    [-0.65_08, 0.10_97, 4.00_09],
    [-1.44_92, 0.88_96, 4.40_05],
    [2.08_50, 0.68_76, 12.07_10],
    [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, 11.20_95],
    [2.01_49, 0.61_92, 10.92_63],
    [0.20_12, 0.26_11, 5.46_31],
]

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







from typing import TYPE_CHECKING

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


UpperCAmelCase_       : List[Any]							=    {
    """configuration_retribert""": ["""RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RetriBertConfig"""],
    """tokenization_retribert""": ["""RetriBertTokenizer"""],
}

try:
      if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : str							=    ["""RetriBertTokenizerFast"""]

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      UpperCAmelCase_       : Optional[Any]							=    [
          """RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
          """RetriBertModel""",
          """RetriBertPreTrainedModel""",
      ]


if TYPE_CHECKING:
      from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
      from .tokenization_retribert import RetriBertTokenizer

      try:
            if not is_tokenizers_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .tokenization_retribert_fast import RetriBertTokenizerFast

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_retribert import (
                RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
                RetriBertModel,
                RetriBertPreTrainedModel,
            )

else:
      import sys

      UpperCAmelCase_       : Tuple							=    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
