bugs

1 files changed, 5 insertions, 6 deletions

diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py
index 18425700..2b4d9242 100644
--- a/src/python/gudhi/clustering/tomato.py
+++ b/src/python/gudhi/clustering/tomato.py
@@ -132,10 +132,9 @@ class Tomato:
             elif need_knn_dist:
                 knn_dist = knn
         if self.density_type_ in ["DTM", "logDTM"]:
-            if metric == "precomputed":
-                dim = self.params_.get("dim", 2)
-            else:
-                dim = len(X)
+            dim = self.params_.get("dim")
+            if dim is None:
+                dim = len(X[0]) if metric != "precomputed" else 2
             q = self.params_.get("q", dim)
             weights = DTMDensity(k=k_DTM, metric="neighbors", dim=dim, q=q).fit_transform(knn_dist)
             if self.density_type_ == "logDTM":
@@ -144,7 +143,7 @@ class Tomato:
         if self.graph_type_ == "radius":
             if metric in ["minkowski", "euclidean", "manhattan", "chebyshev"]:
                 from scipy.spatial import cKDTree
-                tree t = cKDTree(X)
+                tree = cKDTree(X)
                 # TODO: handle "l1" and "l2" aliases?
                 p = self.params_.get("p")
                 if metric == "euclidean":
@@ -159,7 +158,7 @@ class Tomato:
                 elif p is None:
                     p = 2 # the default
                 eps = self.params_.get("eps", 0)
-                self.neighbors_ = t.query_ball_tree(t, r=self.params_["r"], p=p, eps=eps)
+                self.neighbors_ = tree.query_ball_tree(tree, r=self.params_["r"], p=p, eps=eps)
 
             # TODO: sklearn's NearestNeighbors.radius_neighbors can handle more metrics efficiently via its BallTree (don't bother with the _graph variant, it just calls radius_neighbors).
             elif metric != "precomputed":