




import random





class     snake_case  :


   '''simple docstring'''







   def __init__(					self     :	str						,    lowerCamelCase_     :	list[list[float]]						,    lowerCamelCase_     :	list[int]						,    lowerCamelCase_     :	float = 0.01						,    lowerCamelCase_     :	int = 1000						,    lowerCamelCase_     :	float = -1						,    )					->None:
     '''simple docstring'''







     UpperCAmelCase__    						=   sample
     if len(self.sample			) == 0:
       raise ValueError("""Sample data can not be empty"""			)
     UpperCAmelCase__    						=   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__    						=   learning_rate
     UpperCAmelCase__    						=   epoch_number
     UpperCAmelCase__    						=   bias
     UpperCAmelCase__    						=   len(lowerCamelCase_			)
     UpperCAmelCase__    						=   len(sample[0]			)  # number of columns in dataset
     UpperCAmelCase__    						=   []




   def 	UpperCAmelCase       (					self     :	Any			)					->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__    						=   0

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







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

     UpperCAmelCase__    						=   self.sign(lowerCamelCase_			)

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







   def 	UpperCAmelCase       (					self     :	Optional[Any]						,    lowerCamelCase_     :	float			)					->int:
     '''simple docstring'''







     return 1 if u >= 0 else -1



snake_case__ :						List[Any]     =			[
    [-0.65_08, 0.10_97, 4.00_09],
    [-1.44_92, 0.88_96, 4.40_05],
    [2.08_50, 0.68_76, 12.07_10],
    [0.26_26, 1.14_76, 7.79_85],
    [0.64_18, 1.02_34, 7.04_27],
    [0.25_69, 0.67_30, 8.32_65],
    [1.11_55, 0.60_43, 7.44_46],
    [0.09_14, 0.33_99, 7.06_77],
    [0.01_21, 0.52_56, 4.63_16],
    [-0.04_29, 0.46_60, 5.43_23],
    [0.43_40, 0.68_70, 8.22_87],
    [0.27_35, 1.02_87, 7.19_34],
    [0.48_39, 0.48_51, 7.48_50],
    [0.40_89, -0.12_67, 5.50_19],
    [1.43_91, 0.16_14, 8.58_43],
    [-0.91_15, -0.19_73, 2.19_62],
    [0.36_54, 1.04_75, 7.48_58],
    [0.21_44, 0.75_15, 7.16_99],
    [0.20_13, 1.00_14, 6.54_89],
    [0.64_83, 0.21_83, 5.89_91],
    [-0.11_47, 0.22_42, 7.24_35],
    [-0.79_70, 0.87_95, 3.87_62],
    [-1.06_25, 0.63_66, 2.47_07],
    [0.53_07, 0.12_85, 5.68_83],
    [-1.22_00, 0.77_77, 1.72_52],
    [0.39_57, 0.10_76, 5.66_23],
    [-0.10_13, 0.59_89, 7.18_12],
    [2.44_82, 0.94_55, 11.20_95],
    [2.01_49, 0.61_92, 10.92_63],
    [0.20_12, 0.26_11, 5.46_31],
]

snake_case__ :						Union[str, Any]     =			[
    -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__ :						str     =			Perceptron(
       sample=samples, target=target, learning_rate=0.01, epoch_number=1_0_0_0, bias=-1
   )
   network.training()
   print('''Finished training perceptron''')
   print('''Enter values to predict or q to exit''')
   while True:
      snake_case__ :						list     =			[]
      for i in range(len(samples[0])):
         snake_case__ :						int     =			input('''value: ''').strip()
         if user_input == "q":
            break
         snake_case__ :						Optional[int]     =			float(user_input)
         sample.insert(i, observation)
      network.sort(sample)










import unittest

import numpy as np

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
   import tensorflow as tf

   from transformers.activations_tf import get_tf_activation





@require_tf
class     snake_case  (	unittest.TestCase							):


   '''simple docstring'''







   def 	UpperCAmelCase       (					self     :	Tuple			)					->Union[str, Any]:
     '''simple docstring'''







     UpperCAmelCase__    						=   tf.constant([-100, -1.0, -0.1, 0, 0.1, 1.0, 100.0]			)
     UpperCAmelCase__    						=   get_tf_activation("""gelu"""			)
     UpperCAmelCase__    						=   get_tf_activation("""gelu_10"""			)

     UpperCAmelCase__    						=   gelu(lowerCamelCase_			)
     UpperCAmelCase__    						=   geluaa(lowerCamelCase_			)

     UpperCAmelCase__    						=   tf.where(y_gelu_aa < 10.0						,    1.0						,    0.0			)

     self.assertEqual(tf.math.reduce_max(lowerCamelCase_			).numpy().item()						,    10.0			)
     self.assertTrue(np.allclose(y_gelu * clipped_mask						,    y_gelu_aa * clipped_mask			)			)







   def 	UpperCAmelCase       (					self     :	List[Any]			)					->Any:
     '''simple docstring'''







     get_tf_activation("""gelu"""			)
     get_tf_activation("""gelu_10"""			)
     get_tf_activation("""gelu_fast"""			)
     get_tf_activation("""gelu_new"""			)
     get_tf_activation("""glu"""			)
     get_tf_activation("""mish"""			)
     get_tf_activation("""quick_gelu"""			)
     get_tf_activation("""relu"""			)
     get_tf_activation("""sigmoid"""			)
     get_tf_activation("""silu"""			)
     get_tf_activation("""swish"""			)
     get_tf_activation("""tanh"""			)
     with self.assertRaises(lowerCamelCase_			):
       get_tf_activation("""bogus"""			)
     with self.assertRaises(lowerCamelCase_			):
       get_tf_activation(lowerCamelCase_			)




