code: 
  
  
  
  """simple docstring"""
  import importlib
  import inspect
  from functools import wraps
  from typing import TYPE_CHECKING, Optional, Union
  
  from .download.streaming_download_manager import (
      xbasename,
      xdirname,
      xet_parse,
      xexists,
      xgetsize,
      xglob,
      xgzip_open,
      xisdir,
      xisfile,
      xjoin,
      xlistdir,
      xnumpy_load,
      xopen,
      xpandas_read_csv,
      xpandas_read_excel,
      xPath,
      xrelpath,
      xsio_loadmat,
      xsplit,
      xsplitext,
      xwalk,
      xxml_dom_minidom_parse,
  )
  from .utils.logging import get_logger
  from .utils.patching import patch_submodule
  from .utils.py_utils import get_imports
  
  
  _lowerCAmelCase					:Union[str, Any]  =      get_logger(__name__)
  
  
  if TYPE_CHECKING:
         from .builder import DatasetBuilder
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	List[Any]  , UpperCamelCase__	:	Optional[Union[str, bool]] = None							):
      _UpperCAmelCase							:						Any       							=					importlib.import_module(UpperCamelCase__							)
  
      # TODO(QL): always update the module to add subsequent new authentication
      if hasattr(UpperCamelCase__  , '''_patched_for_streaming'''							) and module._patched_for_streaming:
          return
  
      def wrap_auth(UpperCamelCase__	:	List[str]							):
          @wraps(UpperCamelCase__							)
          def wrapper(*UpperCamelCase__	:	Union[str, Any]  , **UpperCamelCase__	:	List[Any]							):
              return function(*UpperCamelCase__  , use_auth_token=UpperCamelCase__  , **UpperCamelCase__							)
  
          _UpperCAmelCase							:						Optional[int]       							=					'''wrap_auth'''
          return wrapper
  
      # open files in a streaming fashion
      patch_submodule(UpperCamelCase__  , '''open'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''os.listdir'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''os.walk'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''glob.glob'''  , wrap_auth(UpperCamelCase__							)							).start()
      # allow to navigate in remote zip files
      patch_submodule(UpperCamelCase__  , '''os.path.join'''  , UpperCamelCase__							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.dirname'''  , UpperCamelCase__							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.basename'''  , UpperCamelCase__							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.relpath'''  , UpperCamelCase__							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.split'''  , UpperCamelCase__							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.splitext'''  , UpperCamelCase__							).start()
      # allow checks on paths
      patch_submodule(UpperCamelCase__  , '''os.path.exists'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.isdir'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.isfile'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''os.path.getsize'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''pathlib.Path'''  , UpperCamelCase__							).start()
      # file readers
      patch_submodule(UpperCamelCase__  , '''gzip.open'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''numpy.load'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''pandas.read_csv'''  , wrap_auth(UpperCamelCase__							)  , attrs=['''__version__''']							).start()
      patch_submodule(UpperCamelCase__  , '''pandas.read_excel'''  , wrap_auth(UpperCamelCase__							)  , attrs=['''__version__''']							).start()
      patch_submodule(UpperCamelCase__  , '''scipy.io.loadmat'''  , wrap_auth(UpperCamelCase__							)  , attrs=['''__version__''']							).start()
      patch_submodule(UpperCamelCase__  , '''xml.etree.ElementTree.parse'''  , wrap_auth(UpperCamelCase__							)							).start()
      patch_submodule(UpperCamelCase__  , '''xml.dom.minidom.parse'''  , wrap_auth(UpperCamelCase__							)							).start()
      _UpperCAmelCase							:						Optional[Any]       							=					True
  
  
  
  
  
  def 				lowerCamelCase_		(UpperCamelCase__	:	"DatasetBuilder"							):
      extend_module_for_streaming(builder.__module__  , use_auth_token=builder.use_auth_token							)
      # if needed, we also have to extend additional internal imports (like wmt14 -> wmt_utils)
      if not builder.__module__.startswith('''datasets.'''							):  # check that it's not a packaged builder like csv
          for imports in get_imports(inspect.getfile(builder.__class__							)							):
              if imports[0] == "internal":
                  _UpperCAmelCase							:						Any       							=					imports[1]
                  _UpperCAmelCase							:						List[Any]       							=					'''.'''.join(builder.__module__.split('''.'''							)[:-1] + [internal_import_name]							)
                  extend_module_for_streaming(UpperCamelCase__  , use_auth_token=builder.use_auth_token							)
  
      # builders can inherit from other builders that might use streaming functionality
      # (for example, ImageFolder and AudioFolder inherit from FolderBuilder which implements examples generation)
      # but these parents builders are not patched automatically as they are not instantiated, so we patch them here
      from .builder import DatasetBuilder
  
      _UpperCAmelCase							:						Tuple       							=					[
          cls.__module__
          for cls in type(UpperCamelCase__							).__mro__[1:]  # make sure it's not the same module we've already patched
          if issubclass(cls  , UpperCamelCase__							) and cls.__module__ != DatasetBuilder.__module__
      ]  # check it's not a standard builder from datasets.builder
      for module in parent_builder_modules:
          extend_module_for_streaming(UpperCamelCase__  , use_auth_token=builder.use_auth_token							)
  
  
  
style_context: 
  
  
  
  """simple docstring"""
  import random
  class 							_UpperCAmelCase							:
  
  
  
  
  
       '''simple docstring'''
  
  
  
       def __init__(      self ,			A ,			A ,			A = 0.01 ,			A = 1_0_0_0 ,			A = -1 ,			)							->      None:
           _UpperCAmelCase							:						Dict       							=					sample
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           _UpperCAmelCase							:						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'''	)
           _UpperCAmelCase							:						List[Any]       							=					learning_rate
           _UpperCAmelCase							:						Union[str, Any]       							=					epoch_number
           _UpperCAmelCase							:						Any       							=					bias
           _UpperCAmelCase							:						str       							=					len(A	)
           _UpperCAmelCase							:						Tuple       							=					len(sample[0]	)  # number of columns in dataset
           _UpperCAmelCase							:						list       							=					[]
  
  
  
  
  
       def 				__lowerCAmelCase			(      self	)							->      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	)
  
           _UpperCAmelCase							:						Tuple       							=					0
  
           while True:
               _UpperCAmelCase							:						Tuple       							=					False
               for i in range(self.number_sample	):
                   _UpperCAmelCase							:						Optional[int]       							=					0
                   for j in range(self.col_sample + 1	):
                       _UpperCAmelCase							:						int       							=					u + self.weight[j] * self.sample[i][j]
                   _UpperCAmelCase							:						Tuple       							=					self.sign(A	)
                   if y != self.target[i]:
                       for j in range(self.col_sample + 1	):
                           _UpperCAmelCase							:						int       							=					(
                               self.weight[j]
                               + self.learning_rate
                               * (self.target[i] - y)
                               * self.sample[i][j]
                           )
                       _UpperCAmelCase							:						Union[str, Any]       							=					True
              # print('Epoch: \n',epoch_count)
               _UpperCAmelCase							:						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 				__lowerCAmelCase			(      self ,			A	)							->      None:
           if len(self.sample	) == 0:
               raise ValueError('''Sample data can not be empty'''	)
           sample.insert(0 ,			self.bias	)
           _UpperCAmelCase							:						str       							=					0
           for i in range(self.col_sample + 1	):
               _UpperCAmelCase							:						Optional[Any]       							=					u + self.weight[i] * sample[i]
  
           _UpperCAmelCase							:						Optional[int]       							=					self.sign(A	)
  
           if y == -1:
               print(('''Sample: ''', sample)	)
               print('''classification: P1'''	)
           else:
               print(('''Sample: ''', sample)	)
               print('''classification: P2'''	)
  
  
  
  
  
       def 				__lowerCAmelCase			(      self ,			A	)							->      int:
           return 1 if u >= 0 else -1
  
  
  
  _lowerCAmelCase					:Optional[int]  =      [
      [-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],
  ]
  
  _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=0.01, epoch_number=1_000, 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)
  
  
  