

'''simple docstring'''






from scipy.stats import spearmanr

import datasets


lowerCAmelCase__      		=			'''
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation.
Positive correlations imply that as data in dataset x increases, so
does data in dataset y. Negative correlations imply that as x increases,
y decreases. Correlations of -1 or +1 imply an exact monotonic relationship.

Unlike the Pearson correlation, the Spearman correlation does not
assume that both datasets are normally distributed.

The p-value roughly indicates the probability of an uncorrelated system
producing datasets that have a Spearman correlation at least as extreme
as the one computed from these datasets. The p-values are not entirely
reliable but are probably reasonable for datasets larger than 500 or so.
'''

lowerCAmelCase__      		=			'''
Args:
    predictions (`List[float]`): Predicted labels, as returned by a model.
    references (`List[float]`): Ground truth labels.
    return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns
            only the spearmanr score. Defaults to `False`.
Returns:
    spearmanr (`float`): Spearman correlation coefficient.
    p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.
Examples:
    Example 1:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])
        >>> print(results)
        {\'spearmanr\': -0.7}

    Example 2:
        >>> spearmanr_metric = datasets.load_metric("spearmanr")
        >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],
        ...                                     predictions=[10, 9, 2.5, 6, 4],
        ...                                     return_pvalue=True)
        >>> print(results[\'spearmanr\'])
        -0.7
        >>> print(round(results[\'spearmanr_pvalue\'], 2))
        0.19
'''

lowerCAmelCase__      		=			R'''\
@book{kokoska2000crc,
  title={CRC standard probability and statistics tables and formulae},
  author={Kokoska, Stephen and Zwillinger, Daniel},
  year={2000},
  publisher={Crc Press}
}
@article{2020SciPy-NMeth,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nature Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
  adsurl  = {https://rdcu.be/b08Wh},
  doi     = {10.1038/s41592-019-0686-2},
}
'''






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




  """simple docstring"""




  def       SCREAMING_SNAKE_CASE   (   self     :  Optional[int]    ):
   return datasets.MetricInfo(
       description=_DESCRIPTION      ,citation=_CITATION      ,inputs_description=_KWARGS_DESCRIPTION      ,features=datasets.Features(
           {
               '''predictions''': datasets.Value('''float'''    ),
               '''references''': datasets.Value('''float'''    ),
           }    )      ,reference_urls=['''https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html''']      ,)






  def       SCREAMING_SNAKE_CASE   (   self     :  Tuple      ,lowercase__     :  List[Any]      ,lowercase__     :  List[Any]      ,lowercase__     :  Union[str, Any]=False    ):
   __lowercase													=				spearmanr(lowercase__      ,lowercase__    )
   if return_pvalue:
    return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
   else:
    return {"spearmanr": results[0]}




'''simple docstring'''






from __future__ import annotations

from typing import Any






class     lowercase_			:




  """simple docstring"""




  def __init__(   self     :  Any      ,lowercase__     :  int      ,lowercase__     :  int      ,lowercase__     :  float = 0    ):
   __lowercase						,			__lowercase													=				row, column
   __lowercase													=				[[default_value for c in range(lowercase__    )] for r in range(lowercase__    )]






  def __str__(   self     :  List[str]    ):
   __lowercase													=				F"Matrix consist of {self.row} rows and {self.column} columns\n"

   # Make string identifier
   __lowercase													=				0
   for row_vector in self.array:
    for obj in row_vector:
     __lowercase													=				max(lowercase__      ,len(str(lowercase__    )    )    )
   __lowercase													=				F"%{max_element_length}s"

   # Make string and return
   def single_line(lowercase__     :  list[float]    ) -> str:
    nonlocal string_format_identifier
    __lowercase													=				'''['''
    line += ", ".join(string_format_identifier % (obj,) for obj in row_vector    )
    line += "]"
    return line

   s += "\n".join(single_line(lowercase__    ) for row_vector in self.array    )
   return s






  def __repr__(   self     :  List[str]    ):
   return str(self    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  tuple[int, int]    ):
   if not (isinstance(lowercase__      ,(list, tuple)    ) and len(lowercase__    ) == 2):
    return False
   elif not (0 <= loc[0] < self.row and 0 <= loc[1] < self.column):
    return False
   else:
    return True






  def __getitem__(   self     :  Tuple      ,lowercase__     :  tuple[int, int]    ):
   assert self.validate_indicies(lowercase__    )
   return self.array[loc[0]][loc[1]]






  def __setitem__(   self     :  Tuple      ,lowercase__     :  tuple[int, int]      ,lowercase__     :  float    ):
   assert self.validate_indicies(lowercase__    )
   __lowercase													=				value






  def __add__(   self     :  List[Any]      ,lowercase__     :  Matrix    ):
   assert isinstance(lowercase__      ,lowercase__    )
   assert self.row == another.row and self.column == another.column

   # Add
   __lowercase													=				Matrix(self.row      ,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				self[r, c] + another[r, c]
   return result






  def __neg__(   self     :  List[str]    ):
   __lowercase													=				Matrix(self.row      ,self.column    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				-self[r, c]
   return result






  def __sub__(   self     :  str      ,lowercase__     :  Matrix    ):
   return self + (-another)






  def __mul__(   self     :  Dict      ,lowercase__     :  int | float | Matrix    ):

   if isinstance(lowercase__      ,(int, float)    ):  # Scalar multiplication
    __lowercase													=				Matrix(self.row      ,self.column    )
    for r in range(self.row    ):
     for c in range(self.column    ):
      __lowercase													=				self[r, c] * another
    return result
   elif isinstance(lowercase__      ,lowercase__    ):  # Matrix multiplication
    assert self.column == another.row
    __lowercase													=				Matrix(self.row      ,another.column    )
    for r in range(self.row    ):
     for c in range(another.column    ):
      for i in range(self.column    ):
       result[r, c] += self[r, i] * another[i, c]
    return result
   else:
    __lowercase													=				F"Unsupported type given for another ({type(lowercase__    )})"
    raise TypeError(lowercase__    )






  def       SCREAMING_SNAKE_CASE   (   self     :  Any    ):
   __lowercase													=				Matrix(self.column      ,self.row    )
   for r in range(self.row    ):
    for c in range(self.column    ):
     __lowercase													=				self[r, c]
   return result






  def       SCREAMING_SNAKE_CASE   (   self     :  Any      ,lowercase__     :  Matrix      ,lowercase__     :  Matrix    ):
   assert isinstance(lowercase__      ,lowercase__    ) and isinstance(lowercase__      ,lowercase__    )
   assert self.row == self.column == u.row == v.row  # u, v should be column vector
   assert u.column == v.column == 1  # u, v should be column vector

   # Calculate
   __lowercase													=				v.transpose()
   __lowercase													=				(v_t * self * u)[0, 0] + 1
   if numerator_factor == 0:
    return None  # It's not invertable
   return self - ((self * u) * (v_t * self) * (1.0 / numerator_factor))


# Testing
if __name__ == "__main__":


 def 	_A  (					):






  """simple docstring"""



  __lowercase													=				Matrix(3  ,       3  ,       0   )
  for i in range(3   ):
   __lowercase													=				1
  print(F"a^(-1) is {ainv}"   )
  # u, v
  __lowercase													=				Matrix(3  ,       1  ,       0   )
  __lowercase						,			__lowercase						,			__lowercase													=				1, 2, -3
  __lowercase													=				Matrix(3  ,       1  ,       0   )
  __lowercase						,			__lowercase						,			__lowercase													=				4, -2, 5
  print(F"u is {u}"   )
  print(F"v is {v}"   )
  print(F"uv^T is {u * v.transpose()}"   )
  # Sherman Morrison
  print(F"(a + uv^T)^(-1) is {ainv.sherman_morrison(A__  ,       A__   )}"   )


 def 	_A  (					):






  """simple docstring"""



  import doctest

  doctest.testmod()






 testa()

