@@ -127,7 +127,7 @@ def ward_tree(X, connectivity=None, n_components=None, n_clusters=None,
127127
128128 Returns
129129 -------
130- children : 2D array, shape (n_nodes, 2)
130+ children : 2D array, shape (n_nodes-1 , 2)
131131 The children of each non-leaf node. Values less than `n_samples`
132132 correspond to leaves of the tree which are the original samples.
133133 A node `i` greater than or equal to `n_samples` is a non-leaf
@@ -145,7 +145,7 @@ def ward_tree(X, connectivity=None, n_components=None, n_clusters=None,
145145 The parent of each node. Only returned when a connectivity matrix
146146 is specified, elsewhere 'None' is returned.
147147
148- distances : 1D array, shape (n_nodes, )
148+ distances : 1D array, shape (n_nodes-1 , )
149149 Only returned if return_distance is set to True (for compatibility).
150150 The distances between the centers of the nodes. `distances[i]`
151151 corresponds to a weighted euclidean distance between
@@ -346,7 +346,7 @@ def linkage_tree(X, connectivity=None, n_components=None,
346346
347347 Returns
348348 -------
349- children : 2D array, shape (n_nodes, 2)
349+ children : 2D array, shape (n_nodes-1 , 2)
350350 The children of each non-leaf node. Values less than `n_samples`
351351 correspond to leaves of the tree which are the original samples.
352352 A node `i` greater than or equal to `n_samples` is a non-leaf
@@ -364,7 +364,7 @@ def linkage_tree(X, connectivity=None, n_components=None,
364364 The parent of each node. Only returned when a connectivity matrix
365365 is specified, elsewhere 'None' is returned.
366366
367- distances : ndarray, shape (n_nodes,)
367+ distances : ndarray, shape (n_nodes-1 ,)
368368 Returned when retur
8000
n_distance is set to True.
369369
370370 distances[i] refers to the distance between children[i][0] and
@@ -659,7 +659,7 @@ class AgglomerativeClustering(BaseEstimator, ClusterMixin):
659659 n_components_ : int
660660 The estimated number of connected components in the graph.
661661
662- children_ : array-like, shape (n_nodes, 2)
662+ children_ : array-like, shape (n_nodes-1 , 2)
663663 The children of each non-leaf node. Values less than `n_samples`
664664 correspond to leaves of the tree which are the original samples.
665665 A node `i` greater than or equal to `n_samples` is a non-leaf
@@ -822,7 +822,7 @@ class FeatureAgglomeration(AgglomerativeClustering, AgglomerationTransform):
822822 n_components_ : int
823823 The estimated number of connected components in the graph.
824824
825- children_ : array-like, shape (n_nodes, 2)
825+ children_ : array-like, shape (n_nodes-1 , 2)
826826 The children of each non-leaf node. Values less than `n_features`
827827 correspond to leaves of the tree which are the original samples.
828828 A node `i` greater than or equal to `n_features` is a non-leaf
@@ -902,7 +902,7 @@ class Ward(AgglomerativeClustering):
902902 n_components_ : int
903903 The estimated number of connected components in the graph.
904904
905- children_ : array-like, shape (n_nodes, 2)
905+ children_ : array-like, shape (n_nodes-1 , 2)
906906 The children of each non-leaf node. Values less than `n_samples`
907907 refer to leaves of the tree. A greater value `i` indicates a node with
908908 children `children_[i - n_samples]`.
@@ -970,7 +970,7 @@ class WardAgglomeration(AgglomerationTransform, Ward):
970970
971971 Attributes
972972 ----------
973- children_ : array-like, shape (n_nodes, 2)
973+ children_ : array-like, shape (n_nodes-1 , 2)
974974 The children of each non-leaf node. Values less than `n_features`
975975 correspond to leaves of the tree which are the original samples.
976976 A node `i` greater than or equal to `n_features` is a non-leaf
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