Use radius as default KDE bandwidth

1 files changed, 6 insertions, 3 deletions

diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py
index 824b5544..7e67c7fd 100644
--- a/src/python/gudhi/clustering/tomato.py
+++ b/src/python/gudhi/clustering/tomato.py
@@ -57,7 +57,7 @@ class Tomato:
             kde_params (dict): if density_type is 'KDE' or 'logKDE', additional parameters passed directly to sklearn.neighbors.KernelDensity.
             k (int): number of neighbors for a knn graph (including the vertex itself). Defaults to 10.
             k_DTM (int): number of neighbors for the DTM density estimation (including the vertex itself). Defaults to k.
-            r (float): size of a neighborhood if graph_type is 'radius'.
+            r (float): size of a neighborhood if graph_type is 'radius'. Also used as default bandwidth in kde_params.
             eps (float): (1+eps) approximation factor when computing distances (ignored in many cases).
             n_clusters (int): number of clusters requested. Defaults to None, i.e. no merging occurs and we get the maximal number of clusters.
             merge_threshold (float): minimum prominence of a cluster so it doesn't get merged.
@@ -174,9 +174,12 @@ class Tomato:
                 self.neighbors_ = [numpy.flatnonzero(l <= r) for l in X]
 
         if self.density_type_ in {"KDE", "logKDE"}:
-            assert graph_type != "manual" and metric != "precomputed", "Scikit-learn's KernelDensity requires point coordinates"
-            kde_params = self.params_.get("kde_params", dict())
+            assert self.graph_type_ != "manual" and metric != "precomputed", "Scikit-learn's KernelDensity requires point coordinates"
+            kde_params = dict(self.params_.get("kde_params", dict()))
             kde_params.setdefault("metric", metric)
+            r = self.params_.get("r")
+            if r is not None:
+                kde_params.setdefault("bandwidth", r)
             from sklearn.neighbors import KernelDensity
             weights = KernelDensity(**kde_params).fit(self.points_).score_samples(self.points_)
             if self.density_type_ == "KDE":