



from sklearn.metrics import accuracy_score

import datasets


a_								=   """
Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
Accuracy = (TP + TN) / (TP + TN + FP + FN)
 Where:
TP: True positive
TN: True negative
FP: False positive
FN: False negative
"""


a_								=   """
Args:
    predictions (`list` of `int`): Predicted labels.
    references (`list` of `int`): Ground truth labels.
    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
    sample_weight (`list` of `float`): Sample weights Defaults to None.

Returns:
    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.

Examples:

    Example 1-A simple example
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
        >>> print(results)
        {'accuracy': 0.5}

    Example 2-The same as Example 1, except with `normalize` set to `False`.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
        >>> print(results)
        {'accuracy': 3.0}

    Example 3-The same as Example 1, except with `sample_weight` set.
        >>> accuracy_metric = datasets.load_metric(\"accuracy\")
        >>> 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])
        >>> print(results)
        {'accuracy': 0.8778625954198473}
"""


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



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




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    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		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase=True		,			__UpperCAmelCase=None			):



    '''simple docstring'''
    return {
        "accuracy": float(
            accuracy_score(__UpperCAmelCase		,			__UpperCAmelCase		,			normalize=__UpperCAmelCase		,			sample_weight=__UpperCAmelCase			)			)
    }











import math



class 	__lowerCAmelCase       :




 def __init__(  self		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase			):



    '''simple docstring'''
    __lowerCamelCase  							=      x
    __lowerCamelCase  							=      y
    __lowerCamelCase  							=      step_size
    __lowerCamelCase  							=      function_to_optimize




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    return self.function(self.x		,			self.y			)




 def     lowerCamelCase (  self			):



    '''simple docstring'''
    __lowerCamelCase  							=      self.step_size
    return [
        SearchProblem(__UpperCAmelCase		,			__UpperCAmelCase		,			__UpperCAmelCase		,			self.function			)
        for x, y in (
            (self.x - step_size, self.y - step_size),
            (self.x - step_size, self.y),
            (self.x - step_size, self.y + step_size),
            (self.x, self.y - step_size),
            (self.x, self.y + step_size),
            (self.x + step_size, self.y - step_size),
            (self.x + step_size, self.y),
            (self.x + step_size, self.y + step_size),
        )
    ]




 def __hash__(  self			):



    '''simple docstring'''
    return hash(str(self			)			)




 def __eq__(  self		,			__UpperCAmelCase			):



    '''simple docstring'''
    if isinstance(__UpperCAmelCase		,			__UpperCAmelCase			):
       return hash(str(self			)			) == hash(str(__UpperCAmelCase			)			)
    return False




 def __str__(  self			):



    '''simple docstring'''
    return F"""x: {self.x} y: {self.y}"""





def 						a__     (						_UpperCamelCase     :	Tuple	,_UpperCamelCase     :	bool = True	,_UpperCamelCase     :	float = math.inf	,_UpperCamelCase     :	float = -math.inf	,_UpperCamelCase     :	float = math.inf	,_UpperCamelCase     :	float = -math.inf	,_UpperCamelCase     :	bool = False	,_UpperCamelCase     :	int = 1_00_00	,):
   __lowerCamelCase  							=      search_prob
   __lowerCamelCase  							=      []  # list to store the current score at each iteration
   __lowerCamelCase  							=      0
   __lowerCamelCase  							=      False
   __lowerCamelCase  							=      set()
   while not solution_found and iterations < max_iter:
      visited.add(_UpperCamelCase							)
      iterations += 1
      __lowerCamelCase  							=      current_state.score()
      scores.append(_UpperCamelCase							)
      __lowerCamelCase  							=      current_state.get_neighbors()
      __lowerCamelCase  							=      -math.inf
      __lowerCamelCase  							=      math.inf
      __lowerCamelCase  							=      None  # to hold the next best neighbor
      for neighbor in neighbors:
         if neighbor in visited:
            continue  # do not want to visit the same state again
         if (
             neighbor.x > max_x
             or neighbor.x < min_x
             or neighbor.y > max_y
             or neighbor.y < min_y
         ):
            continue  # neighbor outside our bounds
         __lowerCamelCase  							=      neighbor.score() - current_score
         if find_max:  # finding max
            # going to direction with greatest ascent
            if change > max_change and change > 0:
               __lowerCamelCase  							=      change
               __lowerCamelCase  							=      neighbor
         else:  # finding min
            # to direction with greatest descent
            if change < min_change and change < 0:
               __lowerCamelCase  							=      change
               __lowerCamelCase  							=      neighbor
      if next_state is not None:
         # we found at least one neighbor which improved the current state
         __lowerCamelCase  							=      next_state
      else:
         # since we have no neighbor that improves the solution we stop the search
         __lowerCamelCase  							=      True

   if visualization:
      from matplotlib import pyplot as plt

      plt.plot(range(_UpperCamelCase							)	,_UpperCamelCase							)
      plt.xlabel('''Iterations'''							)
      plt.ylabel('''Function values'''							)
      plt.show()

   return current_state


if __name__ == "__main__":
  import doctest

  doctest.testmod()





  def 						a__     (						_UpperCamelCase     :	Union[str, Any]	,_UpperCamelCase     :	Union[str, Any]							):
     return (x**2) + (y**2)

  # starting the problem with initial coordinates (3, 4)
  a_								=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(prob, find_max=False)
  print(
      """The minimum score for f(x, y) = x^2 + y^2 found via hill climbing: """
      f"{local_min.score()}"
  )

  # starting the problem with initial coordinates (12, 47)
  a_								=   SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(
      prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
  )
  print(
      """The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 """
      f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
  )





  def 						a__     (						_UpperCamelCase     :	int	,_UpperCamelCase     :	Dict							):
     return (3 * x**2) - (6 * y)

  a_								=   SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
  a_								=   hill_climbing(prob, find_max=True)





  print(
      """The maximum score for f(x, y) = x^2 + y^2 found via hill climbing: """
      f"{local_min.score()}"
  )






