esda.path_silhouette

esda.path_silhouette(data, labels, W, D=None, metric=<function euclidean_distances>, closest=False, return_nbfc=False, return_nbfc_score=False, return_paths=False, directed=False)[source]

Compute a path silhouette for all observations

Parameters:
in the problem under analysis

takes precedence over data, and data is ignored.

like that found in scikit.metrics.pairwise or scipy.spatial.distance

is first connected to the cluster, or considering the path cost to transit through the cluster. If True, the path cost is assessed between i and the path-closest j in each cluster. If False, the path cost is assessed as the average of path costs between i and all j in each cluster

cluster

return_nbfc_score: bool

Whether or not to return the score of the next best fit cluster.

lengths after having computed them.

If directed, asymmetry in the input W is heeded. If not, asymmetry is ignored.

Returns: