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.