Random fixes

1 files changed, 7 insertions, 5 deletions

diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py
index 5bfb9f68..75296ab3 100644
--- a/src/python/gudhi/clustering/tomato.py
+++ b/src/python/gudhi/clustering/tomato.py
@@ -118,7 +118,7 @@ class Tomato:
             k_graph = self.params_["k"]
             need_knn = k_graph
             need_knn_ngb = True
-        if elf.density_type_ in ["DTM", "logDTM"]:
+        if self.density_type_ in ["DTM", "logDTM"]:
             k = self.params_.get("k", 10) # FIXME: What if X has fewer than 10 points?
             k_DTM = self.params_.get("k_DTM", k)
             need_knn = max(need_knn, k_DTM)
@@ -126,20 +126,22 @@ class Tomato:
             # if we ask for more neighbors for the graph than the DTM, getting the distances is a slight waste,
             # but it looks negligible
         if need_knn > 0:
-            knn = KNearestNeighbors(need_knn, return_index=need_knn_ngb, return_distance=need_knn_dist, **self.params_).fit_transform(X)
+            knn_args = dict(self.params_)
+            knn_args["k"] = need_knn
+            knn = KNearestNeighbors(return_index=need_knn_ngb, return_distance=need_knn_dist, **knn_args).fit_transform(X)
             if need_knn_ngb:
                 if need_knn_dist:
                     self.neighbors_ = knn[0][:, 0:k_graph]
                     knn_dist = knn[1]
                 else:
                     self.neighbors_ = knn
-            if need_knn_dist:
+            elif need_knn_dist:
                 knn_dist = knn
         if self.density_type_ in ["DTM", "logDTM"]:
-            if metric in ["neighbors", "precomputed"]:
+            if self.metric_ in ["neighbors", "precomputed"]:
                 dim = self.params_.get("dim", 2)
             else:
-                dim = len(X)
+                dim = len(X) # FIXME for distance matrix
             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":