# pygpcca.GPCCA.crispness_values

property GPCCA.crispness_values: ndarray | None

Vector of crispness values for clustering into the requested cluster numbers.

The crispness $$\xi \in [0,1]$$ quantifies the optimality of the solution (higher is better). It characterizes how crisp (sharp) the decomposition of the state space into m clusters is. It is given via (Eq. 17 from [Roeblitz13]):

$\xi = (m - f_{opt}) / m = \mathtt{trace}(S) / m = \mathtt{trace}(\tilde{D} \chi^T D \chi) / m$

with $$D$$ being a diagonal matrix with $$\eta$$ on its diagonal.