original_docstring: 
          Run Kalman filter prediction step.
  
          Args:
              mean (ndarray): The 8 dimensional mean vector of the object state
                  at the previous time step.
              covariance (ndarray): The 8x8 dimensional covariance matrix of the
                  object state at the previous time step.
  
          Returns:
              The mean vector and covariance matrix of the predicted state.
              Unobserved velocities are initialized to 0 mean.
          
docstring: Run Kalman filter prediction step.
docstring_tokens: Run Kalman filter prediction step .
code: def predict(self, mean, covariance):
          """
          Run Kalman filter prediction step.
  
          Args:
              mean (ndarray): The 8 dimensional mean vector of the object state
                  at the previous time step.
              covariance (ndarray): The 8x8 dimensional covariance matrix of the
                  object state at the previous time step.
  
          Returns:
              The mean vector and covariance matrix of the predicted state.
              Unobserved velocities are initialized to 0 mean.
          """
          std_pos = [
              self._std_weight_position * mean[3], self._std_weight_position * mean[3], 1e-2,
              self._std_weight_position * mean[3]
          ]
          std_vel = [
              self._std_weight_velocity * mean[3], self._std_weight_velocity * mean[3], 1e-5,
              self._std_weight_velocity * mean[3]
          ]
          motion_cov = np.diag(np.square(np.r_[std_pos, std_vel]))
  
          #mean = np.dot(self._motion_mat, mean)
          mean = np.dot(mean, self._motion_mat.T)
          covariance = np.linalg.multi_dot((self._motion_mat, covariance, self._motion_mat.T)) + motion_cov
  
          return mean, covariance
code_tokens: def predict ( self , mean , covariance ) : std_pos = [ self . _std_weight_position * mean [ 3 ] , self . _std_weight_position * mean [ 3 ] , 1e-2 , self . _std_weight_position * mean [ 3 ] ] std_vel = [ self . _std_weight_velocity * mean [ 3 ] , self . _std_weight_velocity * mean [ 3 ] , 1e-5 , self . _std_weight_velocity * mean [ 3 ] ] motion_cov = np . diag ( np . square ( np . r_ [ std_pos , std_vel ] ) ) mean = np . dot ( mean , self . _motion_mat . T ) covariance = np . linalg . multi_dot ( ( self . _motion_mat , covariance , self . _motion_mat . T ) ) + motion_cov return mean , covariance
short_docstring: Run Kalman filter prediction step.
short_docstring_tokens: Run Kalman filter prediction step .
comment: """
          Run Kalman filter prediction step.
  
          Args:
              mean (ndarray): The 8 dimensional mean vector of the object state
                  at the previous time step.
              covariance (ndarray): The 8x8 dimensional covariance matrix of the
                  object state at the previous time step.
  
          Returns:
              The mean vector and covariance matrix of the predicted state.
              Unobserved velocities are initialized to 0 mean.
          """$SEP$#mean = np.dot(self._motion_mat, mean)
  