# pygpcca.GPCCA.optimize

GPCCA.optimize(m)[source]

Full G-PCCA [Reuter18] spectral clustering method with optimized memberships.

It also has the option to optimize the number of clusters (macrostates) m as well.

If a single integer m is given, the method clusters the dominant m Schur vectors of the transition_matrix. The algorithm generates a fuzzy clustering such that the resulting membership functions chi are as crisp (characteristic) as possible, given m.

Instead of a single number of clusters m, a tuple or a dict {'m_min': int, 'm_max': int} containing a minimum and a maximum number of clusters can be given. This results in repeated execution of the G-PCCA core algorithm for $$m \in [m_{min},m_{max}]$$. Among the resulting clusterings, the sharpest/crispest one (with maximal crispness) will be selected.

Parameters

The number of clusters or a range where a search for potentially optimal cluster numbers is performed. Valid options are:

See minChi() for selection of good (potentially optimal) number of clusters.

Return type

GPCCA

Returns

Returns self and updates the following attributes: