





"""simple docstring"""
import random
class lowerCamelCase__  :



   def __init__(       self      ,   snake_case      ,   snake_case      ,   snake_case = 0.01      ,   snake_case = 1_0_0_0      ,   snake_case = -1      ,   ) ->    None:







      """simple docstring"""
      lowercase    :			Dict  =       sample
      if len(self.sample	) == 0:
         raise ValueError("""Sample data can not be empty"""	)
      lowercase    :			Tuple  =       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    :			List[Any]  =       learning_rate
      lowercase    :			Union[str, Any]  =       epoch_number
      lowercase    :			Any  =       bias
      lowercase    :			str  =       len(snake_case	)
      lowercase    :			Tuple  =       len(sample[0]	)  # number of columns in dataset
      lowercase    :			list  =       []



   def    _UpperCAmelCase	(       self	) ->    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	)

      lowercase    :			Tuple  =       0

      while True:
         lowercase    :			Tuple  =       False
         for i in range(self.number_sample	):
            lowercase    :			Optional[int]  =       0
            for j in range(self.col_sample + 1	):
               lowercase    :			int  =       u + self.weight[j] * self.sample[i][j]
            lowercase    :			Tuple  =       self.sign(snake_case	)
            if y != self.target[i]:
               for j in range(self.col_sample + 1	):
                  lowercase    :			int  =       (
                      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    :			int  =       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      ,   snake_case	) ->    None:







      """simple docstring"""
      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    :			Optional[Any]  =       u + self.weight[i] * sample[i]

      lowercase    :			Optional[int]  =       self.sign(snake_case	)

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





   def    _UpperCAmelCase	(       self      ,   snake_case	) ->    int:







      """simple docstring"""
      return 1 if u >= 0 else -1




lowerCAmelCase:	Optional[int]							      =[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

lowerCAmelCase:	Dict							      =[
    -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()

   lowerCAmelCase:	int							      =Perceptron(
       sample=samples, target=target, learning_rate=0.0_1, epoch_number=1000, bias=-1
   )
   network.training()
   print("Finished training perceptron")
   print("Enter values to predict or q to exit")
   while True:
      lowerCAmelCase:	list							      =[]
      for i in range(len(samples[0])):
         lowerCAmelCase:	List[Any]							      =input("value: ").strip()
         if user_input == "q":
            break
         lowerCAmelCase:	Any							      =float(user_input)
         sample.insert(i, observation)
      network.sort(sample)







"""simple docstring"""
from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class lowerCamelCase__  (    __UpperCamelCase				):
   __UpperCAmelCase        =					"""MarkupLMFeatureExtractor"""
   __UpperCAmelCase        =					("""MarkupLMTokenizer""", """MarkupLMTokenizerFast""")
   __UpperCAmelCase        =					True



   def __call__(       self      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case=None      ,   snake_case = True      ,   snake_case = False      ,   snake_case = None      ,   snake_case = None      ,   snake_case = 0      ,   snake_case = None      ,   snake_case = None      ,   snake_case = None      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = False      ,   snake_case = True      ,   snake_case = None      ,   **snake_case      ,   ) ->    BatchEncoding:







      """simple docstring"""
      # first, create nodes and xpaths
      if self.parse_html:
         if html_strings is None:
            raise ValueError("""Make sure to pass HTML strings in case `parse_html` is set to `True`"""	)

         if nodes is not None or xpaths is not None or node_labels is not None:
            raise ValueError(
                """Please don't pass nodes, xpaths nor node labels in case `parse_html` is set to `True`"""	)

         lowercase    :			List[Any]  =       self.feature_extractor(snake_case	)
         lowercase    :			Union[str, Any]  =       features["""nodes"""]
         lowercase    :			List[Any]  =       features["""xpaths"""]
      else:
         if html_strings is not None:
            raise ValueError("""You have passed HTML strings but `parse_html` is set to `False`."""	)
         if nodes is None or xpaths is None:
            raise ValueError("""Make sure to pass nodes and xpaths in case `parse_html` is set to `False`"""	)

        # # second, apply the tokenizer
      if questions is not None and self.parse_html:
         if isinstance(snake_case      ,   snake_case	):
            lowercase    :			Dict  =       [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

      lowercase    :			Dict  =       self.tokenizer(
          text=questions if questions is not None else nodes      ,   text_pair=nodes if questions is not None else None      ,   xpaths=snake_case      ,   node_labels=snake_case      ,   add_special_tokens=snake_case      ,   padding=snake_case      ,   truncation=snake_case      ,   max_length=snake_case      ,   stride=snake_case      ,   pad_to_multiple_of=snake_case      ,   return_token_type_ids=snake_case      ,   return_attention_mask=snake_case      ,   return_overflowing_tokens=snake_case      ,   return_special_tokens_mask=snake_case      ,   return_offsets_mapping=snake_case      ,   return_length=snake_case      ,   verbose=snake_case      ,   return_tensors=snake_case      ,   **snake_case      ,   )

      return encoded_inputs



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Optional[Any]:







      """simple docstring"""
      return self.tokenizer.batch_decode(*snake_case      ,   **snake_case	)



   def    _UpperCAmelCase	(       self      ,   *snake_case      ,   **snake_case	) ->    Tuple:







      """simple docstring"""
      return self.tokenizer.decode(*snake_case      ,   **snake_case	)





   @property
   def    _UpperCAmelCase	(       self	) ->    Dict:







      """simple docstring"""
      lowercase    :			Any  =       self.tokenizer.model_input_names
      return tokenizer_input_names


