Data analysis module¶
File to analyze the reference trajectories.
- pyrotor.data_analysis.add_derivatives(reference_trajectories, basis_dimension)¶
Compute derivatives of each state from a dataframe and append derivatives to initial dataframe.
- Inputs:
- reference_trajectories: list of DataFrame
List of reference trajectories
- basis_dimension: dict
Give the number of basis functions for each state
- Output:
- reference_trajectories_deriv: list of DataFrame
List of reference trajectories with derivatives
- pyrotor.data_analysis.compute_covariance(dataset)¶
Estimate covariance and precision matrices from data X.
Depending on samples number, use either EmpiricalCovariance or GraphicalLasso methods from scikit-learn.
- Input:
- dataset: ndarray
Dataset
- Outputs:
- covariance: ndarray
Estimated covariance matrix
- precision: ndarray
Estimated precision matrix (i.e. pseudo-inverse of covariance)
- pyrotor.data_analysis.compute_weights(trajectories_cost, weight_fonction=None)¶
Compute normalized weights associated with each trajectory.
- Inputs:
- trajectories_cost: ndarray
Array containing the cost of the trajectories
- weight_fonction: function, default=None
Function used to compute weights from the costs - Default function is f(x) = exp(-x)
- Output:
- weights: ndarray
Array containing normalized weights
- pyrotor.data_analysis.nb_samples_is_sufficient(dataset)¶
Tell wether or not there is enough samples in the data set.
- Input:
- dataset: ndarray
Dataset
- Output:
- is_sufficient: boolean
Tell wether or not there are twice more observations than features
- pyrotor.data_analysis.select_trajectories(trajectories, trajectories_cost, trajectories_nb)¶
Return the trajectories associated with the smallest costs.
- Inputs:
- trajectories: list of pd.DataFrame
Each element of the list is a trajectory
- trajectories_cost: ndarray
Array containing the cost of the trajectories
- trajectories_nb: int
Number of trajectories to keep
- Ouput:
- best_trajectories: list of pd.DataFrame
List containing the best trajectories