Add test

1 files changed, 8 insertions, 10 deletions

diff --git a/src/python/gudhi/clustering/tomato.py b/src/python/gudhi/clustering/tomato.py
index 55b64c1d..e3eaa300 100644
--- a/src/python/gudhi/clustering/tomato.py
+++ b/src/python/gudhi/clustering/tomato.py
@@ -18,24 +18,24 @@ class Tomato:
     Attributes
     ----------
     n_clusters_: int
-        The number of clusters. Writing to it automatically adjusts labels_.
+        The number of clusters. Writing to it automatically adjusts `labels_`.
     merge_threshold_: float
-        minimum prominence of a cluster so it doesn't get merged. Writing to it automatically adjusts labels_.
+        minimum prominence of a cluster so it doesn't get merged. Writing to it automatically adjusts `labels_`.
     n_leaves_: int
         number of leaves (unstable clusters) in the hierarchical tree
     leaf_labels_: ndarray of shape (n_samples,)
         cluster labels for each point, at the very bottom of the hierarchy
     labels_: ndarray of shape (n_samples,)
         cluster labels for each point, after merging
-    diagram_: ndarray of shape (n_leaves_, 2)
+    diagram_: ndarray of shape (`n_leaves_`, 2)
         persistence diagram (only the finite points)
     max_weight_per_cc_: ndarray of shape (n_connected_components,)
         maximum of the density function on each connected component. This corresponds to the abscissa of infinite
         points in the diagram
-    children_: ndarray of shape (n_leaves_-n_connected_components, 2)
-        The children of each non-leaf node. Values less than n_leaves_ correspond to leaves of the tree.
-        A node i greater than or equal to n_leaves_ is a non-leaf node and has children children_[i - n_leaves_].
-        Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node n_leaves_ + i
+    children_: ndarray of shape (`n_leaves_`-n_connected_components, 2)
+        The children of each non-leaf node. Values less than `n_leaves_` correspond to leaves of the tree.
+        A node i greater than or equal to `n_leaves_` is a non-leaf node and has children children_[i - `n_leaves_`].
+        Alternatively at the i-th iteration, children[i][0] and children[i][1] are merged to form node `n_leaves_` + i
     weights_: ndarray of shape (n_samples,)
         weights of the points, as computed by the density estimator or provided by the user
     params_: dict
@@ -53,8 +53,6 @@ class Tomato:
         **params
     ):
         """
-        Each parameter has a corresponding attribute, like self.merge_threshold_, that can be changed later.
-
         Args:
             graph_type (str): 'manual', 'knn' or 'radius'.
             density_type (str): 'manual', 'DTM', 'logDTM', 'KDE' or 'logKDE'.
@@ -223,7 +221,7 @@ class Tomato:
                     self.neighbors_[j].add(i)
 
         self.weights_ = weights
-        self.leaf_labels_, self.children_, self.diagram_, self.max_weight_per_cc_ = doit(list(self.neighbors_), weights)
+        self.leaf_labels_, self.children_, self.diagram_, self.max_weight_per_cc_ = doit(self.neighbors_, weights)
         self.n_leaves_ = len(self.max_weight_per_cc_) + len(self.children_)
         assert self.leaf_labels_.max() + 1 == len(self.max_weight_per_cc_) + len(self.children_)
         # TODO: deduplicate this code with the setters below