


from sklearn.metrics import mean_squared_error

import datasets


lowercase							='\\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'

lowercase							='\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


lowercase							='\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'






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






       def       lowerCAmelCase		(      self)						->  Any:




          '''simple docstring'''
          return datasets.MetricInfo(
              description=_DESCRIPTION  ,							citation=_CITATION  ,							inputs_description=_KWARGS_DESCRIPTION  ,							features=datasets.Features(self._get_feature_types())  ,							reference_urls=[
                  'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html'
              ]  ,							)






       def       lowerCAmelCase		(      self)						->  Optional[Any]:




          '''simple docstring'''
          if self.config_name == "multilist":
             return {
                 "predictions": datasets.Sequence(datasets.Value('float')),
                 "references": datasets.Sequence(datasets.Value('float')),
             }
          else:
             return {
                 "predictions": datasets.Value('float'),
                 "references": datasets.Value('float'),
             }






       def       lowerCAmelCase		(      self  ,							snake_case  ,							snake_case  ,							snake_case=None  ,							snake_case="uniform_average"  ,							snake_case=True)						->  Tuple:




          '''simple docstring'''
          _UpperCAmelCase       :       str							   =mean_squared_error(
              snake_case  ,							snake_case  ,							sample_weight=snake_case  ,							multioutput=snake_case  ,							squared=snake_case)

          return {"mse": mse}





'''simple docstring'''






from __future__ import annotations

import bisect





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   if hi < 0:
      _UpperCAmelCase       :       int							   =len(__lowerCamelCase							)

   while lo < hi:
      _UpperCAmelCase       :       Dict							   =lo + (hi - lo) // 2
      if sorted_collection[mid] < item:
         _UpperCAmelCase       :       int							   =mid + 1
      else:
         _UpperCAmelCase       :       Union[str, Any]							   =mid

   return lo





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   if hi < 0:
      _UpperCAmelCase       :       str							   =len(__lowerCamelCase							)

   while lo < hi:
      _UpperCAmelCase       :       List[Any]							   =lo + (hi - lo) // 2
      if sorted_collection[mid] <= item:
         _UpperCAmelCase       :       Dict							   =mid + 1
      else:
         _UpperCAmelCase       :       Tuple							   =mid

   return lo





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   sorted_collection.insert(bisect_left(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int = 0	,   __lowerCamelCase	:							int = -1							):







   '''simple docstring'''




   sorted_collection.insert(bisect_right(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase							)	,   __lowerCamelCase							)





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Optional[int]							   =0
   _UpperCAmelCase       :       Any							   =len(__lowerCamelCase							) - 1

   while left <= right:
      _UpperCAmelCase       :       int							   =left + (right - left) // 2
      _UpperCAmelCase       :       Union[str, Any]							   =sorted_collection[midpoint]
      if current_item == item:
         return midpoint
      elif item < current_item:
         _UpperCAmelCase       :       Optional[Any]							   =midpoint - 1
      else:
         _UpperCAmelCase       :       int							   =midpoint + 1
   return None





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   _UpperCAmelCase       :       Any							   =bisect.bisect_left(__lowerCamelCase	,   __lowerCamelCase							)
   if index != len(__lowerCamelCase							) and sorted_collection[index] == item:
      return index
   return None





def lowerCamelCase__      (						__lowerCamelCase	:							list[int]	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int	,   __lowerCamelCase	:							int							):







   '''simple docstring'''




   if right < left:
      return None

   _UpperCAmelCase       :       str							   =left + (right - left) // 2

   if sorted_collection[midpoint] == item:
      return midpoint
   elif sorted_collection[midpoint] > item:
      return binary_search_by_recursion(__lowerCamelCase	,   __lowerCamelCase	,   __lowerCamelCase	,   midpoint - 1							)
   else:
      return binary_search_by_recursion(__lowerCamelCase	,   __lowerCamelCase	,   midpoint + 1	,   __lowerCamelCase							)


if __name__ == "__main__":
 lowercase							=input('Enter numbers separated by comma:\n').strip()
 lowercase							=sorted(int(item) for item in user_input.split(','))
 lowercase							=int(input('Enter a single number to be found in the list:\n'))
 lowercase							=binary_search(collection, target)
 if result is None:
  print(F"""{target} was not found in {collection}.""")
 else:
  print(F"""{target} was found at position {result} in {collection}.""")

