'''simple docstring'''
from sklearn.metrics import accuracy_score

import datasets


a=	'''\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n'''


a=	'''\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {\'accuracy\': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {\'accuracy\': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {\'accuracy\': 0.8778625954198473}\n'''


a=	'''\n@article{scikit-learn,\n  title={Scikit-learn: Machine Learning in {P}ython},\n  author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n  journal={Journal of Machine Learning Research},\n  volume={12},\n  pages={2825--2830},\n  year={2011}\n}\n'''






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,				_KWARGS_DESCRIPTION    )
class       __lowercase		(    datasets.Metric    ):







     """simple docstring"""






     def lowerCAmelCase  (		self     ):
         return datasets.MetricInfo(
             description=_DESCRIPTION  ,      citation=_CITATION  ,      inputs_description=_KWARGS_DESCRIPTION  ,      features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'     )     ),
                     'references': datasets.Sequence(datasets.Value('int32'     )     ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'     ),
                     'references': datasets.Value('int32'     ),
                 }     )  ,      reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']  ,      )







     def lowerCAmelCase  (		self  ,      _lowerCamelCase  ,      _lowerCamelCase  ,      _lowerCamelCase=True  ,      _lowerCamelCase=None     ):
         return {
             "accuracy": float(
                 accuracy_score(a__  ,      a__  ,      normalize=a__  ,      sample_weight=a__     )     )
         }





import random

class 		__magic_name__				:


  """simple docstring"""





  def __init__(					self	,		a__	,		a__	,		a__ = 0.01	,		a__ = 10_00	,		a__ = -1	,		):
    _lowerCamelCase      =						sample
    if len(self.sample		) == 0:
      raise ValueError('''Sample data can not be empty'''		)
    _lowerCamelCase      =						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'''		)
    _lowerCamelCase      =						learning_rate
    _lowerCamelCase      =						epoch_number
    _lowerCamelCase      =						bias
    _lowerCamelCase      =						len(a__		)
    _lowerCamelCase      =						len(sample[0]		)  # number of columns in dataset
    _lowerCamelCase      =						[]


  def     _UpperCAmelCase  (					self		):
    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		)

    _lowerCamelCase      =						0

    while True:
      _lowerCamelCase      =						False
      for i in range(self.number_sample		):
        _lowerCamelCase      =						0
        for j in range(self.col_sample + 1		):
          _lowerCamelCase      =						u + self.weight[j] * self.sample[i][j]
        _lowerCamelCase      =						self.sign(a__		)
        if y != self.target[i]:
          for j in range(self.col_sample + 1		):
            _lowerCamelCase      =						(
                self.weight[j]
                + self.learning_rate
                * (self.target[i] - y)
                * self.sample[i][j]
            )
          _lowerCamelCase      =						True
            # print('Epoch: \n',epoch_count)
      _lowerCamelCase      =						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	,		a__		):
    if len(self.sample		) == 0:
      raise ValueError('''Sample data can not be empty'''		)
    sample.insert(0	,		self.bias		)
    _lowerCamelCase      =						0
    for i in range(self.col_sample + 1		):
      _lowerCamelCase      =						u + self.weight[i] * sample[i]

    _lowerCamelCase      =						self.sign(a__		)

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


  def     _UpperCAmelCase  (					self	,		a__		):
    return 1 if u >= 0 else -1







_UpperCAmelCase    			=					[
    [-0.6_5_0_8, 0.1_0_9_7, 4.0_0_0_9],
    [-1.4_4_9_2, 0.8_8_9_6, 4.4_0_0_5],
    [2.0_8_5_0, 0.6_8_7_6, 1_2.0_7_1_0],
    [0.2_6_2_6, 1.1_4_7_6, 7.7_9_8_5],
    [0.6_4_1_8, 1.0_2_3_4, 7.0_4_2_7],
    [0.2_5_6_9, 0.6_7_3_0, 8.3_2_6_5],
    [1.1_1_5_5, 0.6_0_4_3, 7.4_4_4_6],
    [0.0_9_1_4, 0.3_3_9_9, 7.0_6_7_7],
    [0.0_1_2_1, 0.5_2_5_6, 4.6_3_1_6],
    [-0.0_4_2_9, 0.4_6_6_0, 5.4_3_2_3],
    [0.4_3_4_0, 0.6_8_7_0, 8.2_2_8_7],
    [0.2_7_3_5, 1.0_2_8_7, 7.1_9_3_4],
    [0.4_8_3_9, 0.4_8_5_1, 7.4_8_5_0],
    [0.4_0_8_9, -0.1_2_6_7, 5.5_0_1_9],
    [1.4_3_9_1, 0.1_6_1_4, 8.5_8_4_3],
    [-0.9_1_1_5, -0.1_9_7_3, 2.1_9_6_2],
    [0.3_6_5_4, 1.0_4_7_5, 7.4_8_5_8],
    [0.2_1_4_4, 0.7_5_1_5, 7.1_6_9_9],
    [0.2_0_1_3, 1.0_0_1_4, 6.5_4_8_9],
    [0.6_4_8_3, 0.2_1_8_3, 5.8_9_9_1],
    [-0.1_1_4_7, 0.2_2_4_2, 7.2_4_3_5],
    [-0.7_9_7_0, 0.8_7_9_5, 3.8_7_6_2],
    [-1.0_6_2_5, 0.6_3_6_6, 2.4_7_0_7],
    [0.5_3_0_7, 0.1_2_8_5, 5.6_8_8_3],
    [-1.2_2_0_0, 0.7_7_7_7, 1.7_2_5_2],
    [0.3_9_5_7, 0.1_0_7_6, 5.6_6_2_3],
    [-0.1_0_1_3, 0.5_9_8_9, 7.1_8_1_2],
    [2.4_4_8_2, 0.9_4_5_5, 1_1.2_0_9_5],
    [2.0_1_4_9, 0.6_1_9_2, 1_0.9_2_6_3],
    [0.2_0_1_2, 0.2_6_1_1, 5.4_6_3_1],
]

_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=0.0_1, epoch_number=1000, 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)




