import random







class 			__snake_case   :
 def __init__(  self		,   snake_case__		,   snake_case__		,   snake_case__ = 0.01		,   snake_case__ = 1000		,   snake_case__ = -1		,   )	->							None:
       '''simple docstring'''





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

       UpperCAmelCase    : Optional[int]      =0

       while True:
             UpperCAmelCase    : Any      =False
             for i in range(self.number_sample						):
                   UpperCAmelCase    : Optional[int]      =0
                   for j in range(self.col_sample + 1						):
                         UpperCAmelCase    : Union[str, Any]      =u + self.weight[j] * self.sample[i][j]
                   UpperCAmelCase    : List[Any]      =self.sign(snake_case__						)
                   if y != self.target[i]:
                         for j in range(self.col_sample + 1						):
                               UpperCAmelCase    : List[str]      =(
                                   self.weight[j]
                                   + self.learning_rate
                                   * (self.target[i] - y)
                                   * self.sample[i][j]
                               )
                         UpperCAmelCase    : Optional[int]      =True
            # print('Epoch: \n',epoch_count)
             UpperCAmelCase    : 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		,   snake_case__						)	->							None:
       '''simple docstring'''





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

       UpperCAmelCase    : List[str]      =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



__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)

import json
import os
import tempfile
import unittest

from transformers.modelcard import ModelCard







class 			__snake_case   (     unittest.TestCase   ):
 def 	UpperCAmelCase__       (  self						)	->							Optional[Any]:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      ={
           '''model_details''': {
               '''Organization''': '''testing''',
               '''Model date''': '''today''',
               '''Model version''': '''v2.1, Developed by Test Corp in 2019.''',
               '''Architecture''': '''Convolutional Neural Network.''',
           },
           '''metrics''': '''BLEU and ROUGE-1''',
           '''evaluation_data''': {
               '''Datasets''': {'''BLEU''': '''My-great-dataset-v1''', '''ROUGE-1''': '''My-short-dataset-v2.1'''},
               '''Preprocessing''': '''See details on https://arxiv.org/pdf/1810.03993.pdf''',
           },
           '''training_data''': {
               '''Dataset''': '''English Wikipedia dump dated 2018-12-01''',
               '''Preprocessing''': (
                   '''Using SentencePiece vocabulary of size 52k tokens. See details on'''
                   ''' https://arxiv.org/pdf/1810.03993.pdf'''
               ),
           },
           '''quantitative_analyses''': {'''BLEU''': 55.1, '''ROUGE-1''': 76},
       }
 def 	UpperCAmelCase__       (  self						)	->							Dict:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =ModelCard.from_dict(self.inputs_dict						)
       self.assertTrue(hasattr(snake_case__		,   '''model_details'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''intended_use'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''factors'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''metrics'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''evaluation_data'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''training_data'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''quantitative_analyses'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''ethical_considerations'''						)						)
       self.assertTrue(hasattr(snake_case__		,   '''caveats_and_recommendations'''						)						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : Optional[Any]      =ModelCard.from_dict(self.inputs_dict						)
       UpperCAmelCase    : int      =json.loads(modelcard.to_json_string()						)
       for key, value in self.inputs_dict.items():
             self.assertEqual(obj[key]		,   snake_case__						)
 def 	UpperCAmelCase__       (  self						)	->							Tuple:
       '''simple docstring'''





       UpperCAmelCase    : List[str]      =ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             UpperCAmelCase    : Optional[Any]      =os.path.join(snake_case__		,   '''modelcard.json'''						)
             model_card_first.to_json_file(snake_case__						)
             UpperCAmelCase    : Tuple      =ModelCard.from_json_file(snake_case__						)

       self.assertEqual(model_card_second.to_dict()		,   model_card_first.to_dict()						)






 def 	UpperCAmelCase__       (  self						)	->							str:
       '''simple docstring'''





       UpperCAmelCase    : Optional[int]      =ModelCard.from_dict(self.inputs_dict						)

       with tempfile.TemporaryDirectory() as tmpdirname:
             model_card_first.save_pretrained(snake_case__						)
             UpperCAmelCase    : Dict      =ModelCard.from_pretrained(snake_case__						)

       self.assertEqual(model_card_second.to_dict()		,   model_card_first.to_dict()						)
