pygpcca.GPCCA.optimize
- GPCCA.optimize(m)[source]
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 adict
{'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:
See
minChi()
for selection of good (potentially optimal) number of clusters.- Return type:
- Returns:
- :
Returns self and updates the following attributes:
coarse_grained_stationary_distribution