



"""simple docstring"""
from typing import List

from tokenizers import NormalizedString, PreTokenizedString, normalizers
class 							_UpperCAmelCase							:





     '''simple docstring'''



     def __init__(      self ,			A	)							->      None:
         _UpperCAmelCase							:						Optional[Any]       							=					vocab
         _UpperCAmelCase							:						List[str]       							=					normalizers.BertNormalizer(
             clean_text=A ,			handle_chinese_chars=A ,			strip_accents=A ,			lowercase=A ,			)
         try:
             import rjieba
         except ImportError:
             raise ImportError(
                 '''You need to install rjieba to use RoFormerTokenizer. '''
                 '''See https://pypi.org/project/rjieba/ for installation.'''	)
         _UpperCAmelCase							:						Dict       							=					rjieba





     def 				__lowerCAmelCase			(      self ,			A ,			A	)							->      List[NormalizedString]:
         _UpperCAmelCase							:						str       							=					[]

         # this code slice normalized_string is too slow (6s) but test_alignement_methods can pass
         for token, start, end in self.jieba.tokenize(str(A	) ,			hmm=A	):
             if token in self.vocab:
                 splits.append(normalized_string[start:end]	)
             else:
                 _UpperCAmelCase							:						Tuple       							=					self.normalizers.normalize_str(A	).split()
                 for token in token_list:
                     if token:
                         _UpperCAmelCase							:						List[str]       							=					start + len(A	)
                         splits.append(normalized_string[start:end]	)
                         _UpperCAmelCase							:						str       							=					end

        # this code test_alignement_methods can't pass but fast (300ms)
        # for token in self.jieba.cut(str(normalized_string), False):
        #     if token in self.vocab:
        #         splits.append(NormalizedString(token))
        #     else:
        #         token_list = self.normalizers.normalize_str(token).split()
        #         for token in token_list:
        #             if token:
        #                 splits.append(NormalizedString(token))

         return splits





     def 				__lowerCAmelCase			(      self ,			A	)							->      Optional[int]:
         pretok.split(self.jieba_split	)







"""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)


