Fix up sample weights tree condensing

Author: lmcinnes

fast_hdbscan/cluster_trees.py

@@ -197,7 +197,7 @@ def condense_tree(hierarchy, min_cluster_size=10, sample_weights=None):
             lambda_value = np.inf
 
         left_count = np.float32(hierarchy[left - num_points, 3]) if left >= num_points else sample_weights[left]
-        right_count = np.float32(hierarchy[right - num_points, 3]) if right >= num_points else sample_weights[left]
+        right_count = np.float32(hierarchy[right - num_points, 3]) if right >= num_points else sample_weights[right]
 
         # The logic here is in a strange order, but it has non-trivial performance gains ...
         # The most common case by far is a singleton on the left; and cluster on the right take care of this separately

fast_hdbscan/hdbscan.py

@@ -188,7 +188,7 @@ def fast_hdbscan(
         linkage_tree = mst_to_linkage_tree(sorted_mst)
     else:
         linkage_tree = mst_to_linkage_tree_w_sample_weights(sorted_mst, sample_weights)
-    condensed_tree = condense_tree(linkage_tree, min_cluster_size=min_cluster_size)
+    condensed_tree = condense_tree(linkage_tree, min_cluster_size=min_cluster_size, sample_weights=sample_weights)
     if cluster_selection_epsilon > 0.0 or cluster_selection_method == "eom":
         cluster_tree = cluster_tree_from_condensed_tree(condensed_tree)