original_docstring: 
          Compute gating distance between state distribution and measurements.
          A suitable distance threshold can be obtained from `chi2inv95`. If
          `only_position` is False, the chi-square distribution has 4 degrees of
          freedom, otherwise 2.
  
          Args:
              mean (ndarray): Mean vector over the state distribution (8
                  dimensional).
              covariance (ndarray): Covariance of the state distribution (8x8
                  dimensional).
              measurements (ndarray): An Nx4 dimensional matrix of N measurements,
                  each in format (x, y, a, h) where (x, y) is the bounding box center
                  position, a the aspect ratio, and h the height.
              only_position (Optional[bool]): If True, distance computation is
                  done with respect to the bounding box center position only.
              metric (str): Metric type, 'gaussian' or 'maha'.
  
          Returns
              An array of length N, where the i-th element contains the squared
              Mahalanobis distance between (mean, covariance) and `measurements[i]`.
          
docstring: Compute gating distance between state distribution and measurements.
  A suitable distance threshold can be obtained from `chi2inv95`. If
  `only_position` is False, the chi-square distribution has 4 degrees of
  freedom, otherwise 2.
docstring_tokens: Compute gating distance between state distribution and measurements . A suitable distance threshold can be obtained from ` chi2inv95 ` . If ` only_position ` is False the chi - square distribution has 4 degrees of freedom otherwise 2 .
code: def gating_distance(self, mean, covariance, measurements, only_position=False, metric='maha'):
          """
          Compute gating distance between state distribution and measurements.
          A suitable distance threshold can be obtained from `chi2inv95`. If
          `only_position` is False, the chi-square distribution has 4 degrees of
          freedom, otherwise 2.
  
          Args:
              mean (ndarray): Mean vector over the state distribution (8
                  dimensional).
              covariance (ndarray): Covariance of the state distribution (8x8
                  dimensional).
              measurements (ndarray): An Nx4 dimensional matrix of N measurements,
                  each in format (x, y, a, h) where (x, y) is the bounding box center
                  position, a the aspect ratio, and h the height.
              only_position (Optional[bool]): If True, distance computation is
                  done with respect to the bounding box center position only.
              metric (str): Metric type, 'gaussian' or 'maha'.
  
          Returns
              An array of length N, where the i-th element contains the squared
              Mahalanobis distance between (mean, covariance) and `measurements[i]`.
          """
          mean, covariance = self.project(mean, covariance)
          if only_position:
              mean, covariance = mean[:2], covariance[:2, :2]
              measurements = measurements[:, :2]
  
          d = measurements - mean
          if metric == 'gaussian':
              return np.sum(d * d, axis=1)
          elif metric == 'maha':
              cholesky_factor = np.linalg.cholesky(covariance)
              z = scipy.linalg.solve_triangular(cholesky_factor, d.T, lower=True, check_finite=False, overwrite_b=True)
              squared_maha = np.sum(z * z, axis=0)
              return squared_maha
          else:
              raise ValueError('invalid distance metric')
code_tokens: def gating_distance ( self , mean , covariance , measurements , only_position = False , metric = 'maha' ) : mean , covariance = self . project ( mean , covariance ) if only_position : mean , covariance = mean [ : 2 ] , covariance [ : 2 , : 2 ] measurements = measurements [ : , : 2 ] d = measurements - mean if metric == 'gaussian' : return np . sum ( d * d , axis = 1 ) elif metric == 'maha' : cholesky_factor = np . linalg . cholesky ( covariance ) z = scipy . linalg . solve_triangular ( cholesky_factor , d . T , lower = True , check_finite = False , overwrite_b = True ) squared_maha = np . sum ( z * z , axis = 0 ) return squared_maha else : raise ValueError ( 'invalid distance metric' )
short_docstring: Compute gating distance between state distribution and measurements.
short_docstring_tokens: Compute gating distance between state distribution and measurements .
comment: """
          Compute gating distance between state distribution and measurements.
          A suitable distance threshold can be obtained from `chi2inv95`. If
          `only_position` is False, the chi-square distribution has 4 degrees of
          freedom, otherwise 2.
  
          Args:
              mean (ndarray): Mean vector over the state distribution (8
                  dimensional).
              covariance (ndarray): Covariance of the state distribution (8x8
                  dimensional).
              measurements (ndarray): An Nx4 dimensional matrix of N measurements,
                  each in format (x, y, a, h) where (x, y) is the bounding box center
                  position, a the aspect ratio, and h the height.
              only_position (Optional[bool]): If True, distance computation is
                  done with respect to the bounding box center position only.
              metric (str): Metric type, 'gaussian' or 'maha'.
  
          Returns
              An array of length N, where the i-th element contains the squared
              Mahalanobis distance between (mean, covariance) and `measurements[i]`.
          """