pygpcca.GPCCA.optimize

GPCCA.optimize(m)

Full G-PCCA spectral clustering method with optimized memberships [Reuter18], [Reuter19].

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:

m (Union[int, Tuple[int, int], List[int], Dict[str, int]]) –

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

• int: number of clusters to group into.

• tuple: minimal and maximal number of clusters.

• dict: minimal and maximal number of clusters given as {'m_min': int, 'm_max': int}.

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

Return type:

GPCCA

Returns:

:

Returns self and updates the following attributes:

• coarse_grained_input_distribution

• coarse_grained_stationary_distribution

• coarse_grained_transition_matrix

• crispness_values

• dominant_eigenvalues

• input_distribution

• macrostate_assignment

• macrostate_sets

• memberships

• n_m

• optimal_crispness

• rotation_matrix

• schur_matrix

• schur_vectors

• stationary_probability

• top_eigenvalues

• transition_matrix