navlie.utils.common.MonteCarloResult¶

class navlie.utils.common.MonteCarloResult(trial_results: List[GaussianResultList])¶

Bases: object

A data container which computes various interesting metrics associated with Monte Carlo experiments, such as the average estimation error squared (EES) and the average normalized EES.

Parameters:: trial_results (List[GaussianResultList]) – Each GaussianResultList corresponds to a trial. This object assumes that the timestamps in each trial are identical.

Let N denote the number of time steps in a trial.

trial_results¶

raw trial results

Type:: List[GaussianResultList]

num_trials¶

number of trials

Type:: int

stamp¶

timestamps throughout trajectory

Type:: numpy.ndarray with shape (N,)

average_nees: ndarray¶

average NEES throughout trajectory

Type:: numpy.ndarray with shape (N,)

average_ees: ndarray¶

average EES throughout trajectory

Type:: numpy.ndarray with shape (N,)

rmse: ndarray¶

root-mean-squared error of each component

Type:: numpy.ndarray with shape (N,dof)

total_rmse: ndarray¶

Total RMSE, this can be meaningless if units differ in a state

Type:: numpy.ndarray with shape (N,)

expected_nees: ndarray¶

expected NEES value throughout trajectory

Type:: numpy.ndarray with shape (N,1)

dof: ndarray¶

dof throughout trajectory

Type:: numpy.ndarray with shape (N)

nees_lower_bound(confidence_interval: float)¶

Calculates the NEES lower bound throughout the trajectory.

Parameters:: confidence_interval (float) – Single-sided cumulative probability threshold that defines the bound. Must be between 0 and 1
Returns:: NEES value corresponding to confidence interval
Return type:: numpy.ndarray with shape (N,)

nees_upper_bound(confidence_interval: float, double_sided=True)¶

Calculates the NEES upper bound throughout the trajectory

Parameters:

confidence_interval (float) – Cumulative probability threshold that defines the bound. Must be between 0 and 1.
double_sided (bool, optional) – Whether the provided threshold is single-sided or double sided, by default True

Returns:

NEES value corresponding to confidence interval

Return type:

numpy.ndarray with shape (N,)

navlie.utils.common.MixtureResultList

navlie.utils.mixture