DOC improve docstring in OPTICS functions
@@ -68,7 +68,8 @@ class OPTICS(ClusterMixin, BaseEstimator):
should take two arrays as input and return one value indicating the
distance between them. This works for Scipy's metrics, but is less
efficient than passing the metric name as a string. If metric is
- "precomputed", X is assumed to be a distance matrix and must be square.
+ "precomputed", `X` is assumed to be a distance matrix and must be
+ square.
Valid values for metric are:
@@ -124,11 +125,11 @@ class OPTICS(ClusterMixin, BaseEstimator):
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors:
- - 'ball_tree' will use :class:`BallTree`
- - 'kd_tree' will use :class:`KDTree`
+ - 'ball_tree' will use :class:`BallTree`.
+ - 'kd_tree' will use :class:`KDTree`.
- 'brute' will use a brute-force search.
- - 'auto' will attempt to decide the most appropriate algorithm
- based on the values passed to :meth:`fit` method. (default)
+ - 'auto' (default) will attempt to decide the most appropriate
+ algorithm based on the values passed to :meth:`fit` method.
Note: fitting on sparse input will override the setting of
this parameter, using brute force.
@@ -457,8 +458,8 @@ def compute_optics_graph(
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors:
- - 'ball_tree' will use :class:`BallTree`
- - 'kd_tree' will use :class:`KDTree`
+ - 'ball_tree' will use :class:`BallTree`.
+ - 'kd_tree' will use :class:`KDTree`.
- 'brute' will use a brute-force search.
- 'auto' will attempt to decide the most appropriate algorithm
based on the values passed to :meth:`fit` method. (default)
