


import random
class      UpperCAmelCase_ :

   '''simple docstring'''




   def __init__(		self						,    __A						,    __A						,    __A = 0.01						,    __A = 1000						,    __A = -1						,    ):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    sample
     if len(self.sample			) == 0:
       raise ValueError("Sample data can not be empty"			)
     lowerCamelCase			: Union[str, Any]   =    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			: List[Any]   =    learning_rate
     lowerCamelCase			: Tuple   =    epoch_number
     lowerCamelCase			: List[Any]   =    bias
     lowerCamelCase			: Optional[int]   =    len(_UpperCAmelCase			)
     lowerCamelCase			: List[str]   =    len(sample[0]			)  # number of columns in dataset
     lowerCamelCase			: str   =    []






   def _snake_case   (		self			):






     """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			)

     lowerCamelCase			: Optional[Any]   =    0

     while True:
       lowerCamelCase			: Optional[Any]   =    False
       for i in range(self.number_sample			):
         lowerCamelCase			: Optional[Any]   =    0
         for j in range(self.col_sample + 1			):
           lowerCamelCase			: List[Any]   =    u + self.weight[j] * self.sample[i][j]
         lowerCamelCase			: List[str]   =    self.sign(_UpperCAmelCase			)
         if y != self.target[i]:
           for j in range(self.col_sample + 1			):
             lowerCamelCase			: Any   =    (
                 self.weight[j]
                 + self.learning_rate
                 * (self.target[i] - y)
                 * self.sample[i][j]
             )
           lowerCamelCase			: Optional[Any]   =    True
            # print('Epoch: \n',epoch_count)
       lowerCamelCase			: 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 _snake_case   (		self						,    __A			):






     """simple docstring"""





     if len(self.sample			) == 0:
       raise ValueError("Sample data can not be empty"			)
     sample.insert(0						,    self.bias			)
     lowerCamelCase			: List[Any]   =    0
     for i in range(self.col_sample + 1			):
       lowerCamelCase			: Dict   =    u + self.weight[i] * sample[i]

     lowerCamelCase			: str   =    self.sign(_UpperCAmelCase			)

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






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





     return 1 if u >= 0 else -1



_snake_case					=					[
    [-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],
]

_snake_case					=					[
    -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()

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







from typing import Optional, Union

from ...file_utils import TensorType
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, TruncationStrategy
class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Union[str, Any]							=		"MarkupLMFeatureExtractor"
   __A			:				List[str]							=		("MarkupLMTokenizer", "MarkupLMTokenizerFast")
   __A			:				str							=		True






   def __call__(		self						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A=None						,    __A = True						,    __A = False						,    __A = None						,    __A = None						,    __A = 0						,    __A = None						,    __A = None						,    __A = None						,    __A = False						,    __A = False						,    __A = False						,    __A = False						,    __A = True						,    __A = None						,    **__A						,    ):






     """simple docstring"""





     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`"			)

       lowerCamelCase			: Any   =    self.feature_extractor(__A			)
       lowerCamelCase			: Optional[int]   =    features["nodes"]
       lowerCamelCase			: 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(__A						,    __A			):
         lowerCamelCase			: Tuple   =    [questions]  # add batch dimension (as the feature extractor always adds a batch dimension)

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

     return encoded_inputs






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.batch_decode(*__A						,    **__A			)






   def _snake_case   (		self						,    *__A						,    **__A			):






     """simple docstring"""





     return self.tokenizer.decode(*__A						,    **__A			)






   @property
   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.tokenizer.model_input_names
     return tokenizer_input_names



