import random




class  __lowerCAmelCase	:



 '''simple docstring'''

 def __init__(      self							:		List[str]							,      lowerCamelCase							:		list[list[float]]							,      lowerCamelCase							:		list[int]							,      lowerCamelCase							:		float = 0.0_1							,      lowerCamelCase							:		int = 1_0_0_0							,      lowerCamelCase							:		float = -1							,      )			->				None:
        _UpperCamelCase  :							str	    =	sample
        if len(self.sample	) == 0:
               raise ValueError('Sample data can not be empty'	)
        _UpperCamelCase  :							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  :							Dict	    =	learning_rate
        _UpperCamelCase  :							Dict	    =	epoch_number
        _UpperCamelCase  :							Optional[Any]	    =	bias
        _UpperCamelCase  :							Any	    =	len(lowerCamelCase	)
        _UpperCamelCase  :							Any	    =	len(sample[0]	)  # number of columns in dataset
        _UpperCamelCase  :							list	    =	[]

 def        __snake_case       (      self							:		List[str]	)			->				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  :							List[str]	    =	0

        while True:
               _UpperCamelCase  :							int	    =	False
               for i in range(self.number_sample	):
                      _UpperCamelCase  :							Union[str, Any]	    =	0
                      for j in range(self.col_sample + 1	):
                             _UpperCamelCase  :							List[Any]	    =	u + self.weight[j] * self.sample[i][j]
                      _UpperCamelCase  :							str	    =	self.sign(lowerCamelCase	)
                      if y != self.target[i]:
                             for j in range(self.col_sample + 1	):
                                    _UpperCamelCase  :							Optional[int]	    =	(
                                        self.weight[j]
                                        + self.learning_rate
                                        * (self.target[i] - y)
                                        * self.sample[i][j]
                                    )
                             _UpperCamelCase  :							Dict	    =	True
            # print('Epoch: \n',epoch_count)
               _UpperCamelCase  :							Optional[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        __snake_case       (      self							:		Dict							,      lowerCamelCase							:		list[float]	)			->				None:
        if len(self.sample	) == 0:
               raise ValueError('Sample data can not be empty'	)
        sample.insert(0							,      self.bias	)
        _UpperCamelCase  :							List[Any]	    =	0
        for i in range(self.col_sample + 1	):
               _UpperCamelCase  :							List[Any]	    =	u + self.weight[i] * sample[i]

        _UpperCamelCase  :							Union[str, Any]	    =	self.sign(lowerCamelCase	)

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

 def        __snake_case       (      self							:		Union[str, Any]							,      lowerCamelCase							:		float	)			->				int:
        return 1 if u >= 0 else -1







UpperCAmelCase      		=					[
    [-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],
]

UpperCAmelCase      		=					[
    -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()

 UpperCAmelCase      		=					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:
  UpperCAmelCase      		=					[]
  for i in range(len(samples[0])):
   UpperCAmelCase      		=					input('''value: ''').strip()
   if user_input == "q":
    break
   UpperCAmelCase      		=					float(user_input)
   sample.insert(i, observation)
  network.sort(sample)







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case				=			{
    "configuration_nystromformer": ["NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "NystromformerConfig"],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _snake_case				=			[
          "NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
          "NystromformerForMaskedLM",
          "NystromformerForMultipleChoice",
          "NystromformerForQuestionAnswering",
          "NystromformerForSequenceClassification",
          "NystromformerForTokenClassification",
          "NystromformerLayer",
          "NystromformerModel",
          "NystromformerPreTrainedModel",
      ]


if TYPE_CHECKING:
      from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_nystromformer import (
                NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
                NystromformerForMaskedLM,
                NystromformerForMultipleChoice,
                NystromformerForQuestionAnswering,
                NystromformerForSequenceClassification,
                NystromformerForTokenClassification,
                NystromformerLayer,
                NystromformerModel,
                NystromformerPreTrainedModel,
            )


else:
      import sys

      _snake_case				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



