rmsander

"""Function for implementing custom distance metrics for use in sklearn neighbor
search algorithms.

Here, we will use a composite product norm composed of a product of norms over
given (contiguous) subsets of states. The norms over these subsets are taken
to be L2 norms.

If x = [s a]^T, where s and a are the contiguous subsets of x that this
product norm is decomposed into, then this product norm can be written as:

rmsander

"""Function to compute stability metrics for covariance matrices,
namely log determinants and condition numbers."""
# Use torch
import torch

def compute_covariance_metrics(Kxx, x_train, writer):
    """Helper function to compute covariance metrics.

    Parameters:
        Kxx (torch.Tensor): Tensor object corresponding to a covariance matrix.