


import numpy as np







class      lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self      ):
       snake_case_ =   (0, 0)
       snake_case_ =   None
       snake_case_ =   0
       snake_case_ =   0
       snake_case_ =   0



 def __eq__(			self	,   _UpperCAmelCase      ):
       return self.position == cell.position





 def 		UpperCamelCase__  (			self      ):
       print(self.position      )







class      lowerCAmelCase_		:

 '''simple docstring'''

 def __init__(			self	,   _UpperCAmelCase=(5, 5)      ):
       snake_case_ =   np.zeros(_UpperCAmelCase      )
       snake_case_ =   world_size[0]
       snake_case_ =   world_size[1]



 def 		UpperCamelCase__  (			self      ):
       print(self.w      )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase      ):
       snake_case_ =   [
           (-1, -1),
           (-1, 0),
           (-1, 1),
           (0, -1),
           (0, 1),
           (1, -1),
           (1, 0),
           (1, 1),
       ]
       snake_case_ =   cell.position[0]
       snake_case_ =   cell.position[1]
       snake_case_ =   []
       for n in neughbour_cord:
             snake_case_ =   current_x + n[0]
             snake_case_ =   current_y + n[1]
             if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
                   snake_case_ =   Cell()
                   snake_case_ =   (x, y)
                   snake_case_ =   cell
                   neighbours.append(_UpperCAmelCase      )
       return neighbours






def  __lowerCAmelCase					(SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      ,      SCREAMING_SNAKE_CASE      )->						Tuple:




      """simple docstring"""

      snake_case_ =   []
      snake_case_ =   []
      _open.append(SCREAMING_SNAKE_CASE      )

      while _open:
            snake_case_ =   np.argmin([n.f for n in _open]      )
            snake_case_ =   _open[min_f]
            _closed.append(_open.pop(SCREAMING_SNAKE_CASE      )      )
            if current == goal:
                  break
            for n in world.get_neigbours(SCREAMING_SNAKE_CASE      ):
                  for c in _closed:
                        if c == n:
                              continue
                  snake_case_ =   current.g + 1
                  snake_case_ ,					snake_case_ =   n.position
                  snake_case_ ,					snake_case_ =   goal.position
                  snake_case_ =   (ya - ya) ** 2 + (xa - xa) ** 2
                  snake_case_ =   n.h + n.g

                  for c in _open:
                        if c == n and c.f < n.f:
                              continue
                  _open.append(SCREAMING_SNAKE_CASE      )
      snake_case_ =   []
      while current.parent is not None:
            path.append(current.position      )
            snake_case_ =   current.parent
      path.append(current.position      )
      return path[::-1]


if __name__ == "__main__":
   UpperCAmelCase    =					Gridworld()
   # Start position and goal
   UpperCAmelCase    =					Cell()
   UpperCAmelCase    =					(0, 0)
   UpperCAmelCase    =					Cell()
   UpperCAmelCase    =					(4, 4)
   print(f'''path from {start.position} to {goal.position}''')
   UpperCAmelCase    =					astar(world, start, goal)
   #   Just for visual reasons.
   for i in s:
      UpperCAmelCase    =					1
   print(world.w)



import numpy as np

import datasets


UpperCAmelCase    =					"""
Compute the Mahalanobis Distance

Mahalonobis distance is the distance between a point and a distribution.
And not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.
It was introduced by Prof. P. C. Mahalanobis in 1936
and has been used in various statistical applications ever since
[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]
"""

UpperCAmelCase    =					"""\
@article{de2000mahalanobis,
  title={The mahalanobis distance},
  author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},
  journal={Chemometrics and intelligent laboratory systems},
  volume={50},
  number={1},
  pages={1--18},
  year={2000},
  publisher={Elsevier}
}
"""

UpperCAmelCase    =					"""
Args:
    X: List of datapoints to be compared with the `reference_distribution`.
    reference_distribution: List of datapoints from the reference distribution we want to compare to.
Returns:
    mahalanobis: The Mahalonobis distance for each datapoint in `X`.
Examples:

    >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")
    >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])
    >>> print(results)
    {'mahalanobis': array([0.5])}
"""







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

 '''simple docstring'''

 def 		UpperCamelCase__  (			self      ):
       return datasets.MetricInfo(
           description=_DESCRIPTION	,   citation=_CITATION	,   inputs_description=_KWARGS_DESCRIPTION	,   features=datasets.Features(
               {
                   '''X''': datasets.Sequence(datasets.Value('''float'''	,   id='''sequence'''      )	,   id='''X'''      ),
               }      )	,   )





 def 		UpperCamelCase__  (			self	,   _UpperCAmelCase	,   _UpperCAmelCase      ):
       # convert to numpy arrays
       snake_case_ =   np.array(_UpperCAmelCase      )
       snake_case_ =   np.array(_UpperCAmelCase      )

       # Assert that arrays are 2D
       if len(X.shape      ) != 2:
             raise ValueError('''Expected `X` to be a 2D vector'''      )
       if len(reference_distribution.shape      ) != 2:
             raise ValueError('''Expected `reference_distribution` to be a 2D vector'''      )
       if reference_distribution.shape[0] < 2:
             raise ValueError(
                 '''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension'''      )

       # Get mahalanobis distance for each prediction
       snake_case_ =   X - np.mean(_UpperCAmelCase      )
       snake_case_ =   np.cov(reference_distribution.T      )
       try:
             snake_case_ =   np.linalg.inv(_UpperCAmelCase      )
       except np.linalg.LinAlgError:
             snake_case_ =   np.linalg.pinv(_UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   _UpperCAmelCase      )
       snake_case_ =   np.dot(_UpperCAmelCase	,   X_minus_mu.T      ).diagonal()

       return {"mahalanobis": mahal_dist}