LPV-DS OOD Style for DAMM Pipeline

This module is an enhanced rendition of the optimization section from: https://github.com/penn-figueroa-lab/ds-opt-py

It has been adapted for a comprehensive real robot testing pipeline available at: https://github.com/SunannnSun/damm_lpvds

Building upon the output from: https://github.com/SunannnSun/damm, this module learns a quadratic Lyapunov function. Subsequently, it undertakes regression within the bounds of the Lyapunov function to ensure both precision and stability.

This module will output a .json file that ROS dynamical system executor could execute in Lab.

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Input

To utilize it on your algorithm:

The data should be formulated as a dictionary:

data = {
    "Data": Data,         # Dimension x Number of Datapoints
    "Data_sh": Data_sh,   # Shifted attractor to 0 for 'Data' (used in Lyapunov function learning)
    "att": att,           # Dimension x 1
    "x0_all": x0_all,     # Dimension x Number of demonstrated trajectory
                          # This is the start points for all demonstrated trajectories
    "dt": dt,             # Sample time
}

For the clustering result, save this dictionary as a json file:

json_output = {
    "name": "Clustering Result",
    "K": # Number of clusters,
    "M": # Dimension,
    "Priors": # List of Prior,
    "Mu": # ravel K x M shape Mu to a list,
    "Sigma": #ravel K x M x M Sigma to a list,
}

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Usage

import DsOpt class and initialize the object:

ds_opt = DsOpt(#Your data dictionary, #Your json output directory)

Train:

ds_opt.begin()

Evaluate: Return the rmse, e_dot, dwtd for learned trajectory

ds_opt.evaluate()

Plot: Make plots for Lyapunov derivative, value, and reproduced streamlines

ds_opt.make_plot()