



from __future__ import annotations

from collections.abc import Callable

a__   :					Dict 						=      list[list[float | int]]

def 	_lowerCAmelCase (    A__    ,	A__						):
      lowercase__  =	len(A__						)
      lowercase__  =	[[0 for _ in range(size + 1						)] for _ in range(A__						)]
      lowercase__  =	42
      lowercase__  =	42
      lowercase__  =	42
      lowercase__  =	42
      lowercase__  =	42
      lowercase__  =	42

      for row in range(A__						):
            for col in range(A__						):
                  lowercase__  =	matrix[row][col]

            lowercase__  =	vector[row][0]

      lowercase__  =	0
      lowercase__  =	0
      while row < size and col < size:
            # pivoting
            lowercase__  =	max((abs(augmented[rowa][col]						), rowa) for rowa in range(A__    ,	A__						)						)[
                1
            ]
            if augmented[pivot_row][col] == 0:
                  col += 1
                  continue
            else:
                  lowercase__,						lowercase__  =	augmented[pivot_row], augmented[row]

            for rowa in range(row + 1    ,	A__						):
                  lowercase__  =	augmented[rowa][col] / augmented[row][col]
                  lowercase__  =	0
                  for cola in range(col + 1    ,	size + 1						):
                        augmented[rowa][cola] -= augmented[row][cola] * ratio

            row += 1
            col += 1

      # back substitution
      for col in range(1    ,	A__						):
            for row in range(A__						):
                  lowercase__  =	augmented[row][col] / augmented[col][col]
                  for cola in range(A__    ,	size + 1						):
                        augmented[row][cola] -= augmented[col][cola] * ratio

    # round to get rid of numbers like 2.000000000000004
      return [
          [round(augmented[row][size] / augmented[row][row]    ,	10						)] for row in range(A__						)
      ]

def 	_lowerCAmelCase (    A__						):
      lowercase__  =	len(A__						)
      lowercase__  =	[[0 for _ in range(A__						)] for _ in range(A__						)]
      lowercase__  =	[[0] for _ in range(A__						)]
      lowercase__  =	42
      lowercase__  =	42
      lowercase__  =	42
      lowercase__  =	42

      for x_val, y_val in enumerate(A__						):
            for col in range(A__						):
                  lowercase__  =	(x_val + 1) ** (size - col - 1)
            lowercase__  =	y_val

      lowercase__  =	solve(A__    ,	A__						)

      def interpolated_func(A__						) -> int:
            return sum(
                round(coeffs[x_val][0]						) * (var ** (size - x_val - 1))
                for x_val in range(A__						)						)

      return interpolated_func

def 	_lowerCAmelCase (    A__						):
      return (
          1
          - variable
          + variable**2
          - variable**3
          + variable**4
          - variable**5
          + variable**6
          - variable**7
          + variable**8
          - variable**9
          + variable**10
      )

def 	_lowerCAmelCase (    A__ = question_function    ,	A__ = 10						):
      lowercase__  =	[func(A__						) for x_val in range(1    ,	order + 1						)]

      lowercase__  =	[
          interpolate(data_points[:max_coeff]						) for max_coeff in range(1    ,	order + 1						)
      ]

      lowercase__  =	0
      lowercase__  =	42
      lowercase__  =	42

      for poly in polynomials:
            lowercase__  =	1
            while func(A__						) == poly(A__						):
                  x_val += 1

            ret += poly(A__						)

      return ret


if __name__ == "__main__":
       print(F'''{solution() = }''')





from sklearn.metrics import matthews_corrcoef

import datasets


a__   :					Any 						=      "\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction.  The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n"

a__   :					Any 						=      "\nArgs:\n    predictions (list of int): Predicted labels, as returned by a model.\n    references (list of int): Ground truth labels.\n    sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n    matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n    Example 1, a basic example with only predictions and references as inputs:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.54\n\n    Example 2, the same example as above, but also including sample weights:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 3, 1, 1, 1, 2])\n        >>> print(round(results['matthews_correlation'], 2))\n        0.1\n\n    Example 3, the same example as above, but with sample weights that cause a negative correlation:\n        >>> matthews_metric = datasets.load_metric(\"matthews_correlation\")\n        >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n        ...                                     predictions=[1, 2, 2, 0, 3, 3],\n        ...                                     sample_weight=[0.5, 1, 0, 0, 0, 1])\n        >>> print(round(results['matthews_correlation'], 2))\n        -0.25\n"

a__   :					str 						=      "\\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    UpperCAmelCase__(				datasets.Metric						):

   '''simple docstring'''




   def       UpperCAmelCase		(    self	:      List[Any])						->					int:




         """simple docstring"""

         return datasets.MetricInfo(
             description=_DESCRIPTION      ,					citation=_CITATION      ,					inputs_description=_KWARGS_DESCRIPTION      ,					features=datasets.Features(
                 {
                     'predictions': datasets.Value('int32'),
                     'references': datasets.Value('int32'),
                 })      ,					reference_urls=[
                 'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
             ]      ,					)



   def       UpperCAmelCase		(    self	:      Dict      ,					lowerCAmelCase	:      Dict      ,					lowerCAmelCase	:      Optional[int]      ,					lowerCAmelCase	:      Union[str, Any]=None)						->					Any:




         """simple docstring"""

         return {
             "matthews_correlation": float(matthews_corrcoef(lowerCAmelCase      ,					lowerCAmelCase      ,					sample_weight=lowerCAmelCase)),
         }
