

import random







class 		__magic_name__     :

   '''simple docstring'''




   def __init__(							self,       lowercase_,       lowercase_,       lowercase_ = 0.01,       lowercase_ = 1000,       lowercase_ = -1,       )				->       List[Any]:






     """simple docstring"""






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



   def 					_UpperCAmelCase					(							self			)				->       Any:






     """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			)

     a__    =0

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






     """simple docstring"""






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

     a__    =self.sign(A__			)

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



   def 					_UpperCAmelCase					(							self,       lowercase_			)				->       str:






     """simple docstring"""






     return 1 if u >= 0 else -1





lowerCamelCase 	=   [
    [-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 	=   [
    -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 	=   Perceptron(
           sample=samples, target=target, learning_rate=0.01, epoch_number=1000, bias=-1
       )
       network.training()
       print('''Finished training perceptron''')
       print('''Enter values to predict or q to exit''')
       while True:
              lowerCamelCase 	=   []
              for i in range(len(samples[0])):
                     lowerCamelCase 	=   input('''value: ''').strip()
                     if user_input == "q":
                            break
                     lowerCamelCase 	=   float(user_input)
                     sample.insert(i, observation)
              network.sort(sample)


import torch

from accelerate import Accelerator, DistributedType







class 							lowercase						:




							def __enter__(							self):
												if self.enabled:
																	lowercase		      =		self.model.no_sync()
																	self.model_sync_obj.__enter__()

												return self




							def __exit__(							self  ,A__  ,A__  ,A__):
												if self.enabled:
																	# Average all models on exit
																	self._sync_and_avg_model_params()
																	self.model_sync_obj.__exit__(A__  ,A__  ,A__)




							def __init__(							self  ,A__  ,A__  ,A__  ,A__ = True):
												if accelerator.distributed_type not in [
												    DistributedType.NO,
												    DistributedType.MULTI_CPU,
												    DistributedType.MULTI_GPU,
												]:
																	raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''')
												lowercase		      =		enabled and accelerator.distributed_type != DistributedType.NO
												lowercase		      =		0
												if self.enabled:
																	lowercase		      =		accelerator
																	lowercase		      =		model
																	lowercase		      =		local_sgd_steps




							def 					A__ (							self):
												self.num_steps += 1
												if not self.enabled:
																	return

												if self.num_steps % self.local_sgd_steps == 0:
																	self._sync_and_avg_model_params()





							def 					A__ (							self):
												self.accelerator.wait_for_everyone()
												with self.accelerator.autocast():
																	for param in self.model.parameters():
																						lowercase		      =		self.accelerator.reduce(param.data  ,reduction='''mean''')




