[DOC] Inconsistent double ticks in segmentation package, public docstring only

aeon/segmentation/_eagglo.py

@@ -31,25 +31,25 @@ class EAggloSegmenter(BaseSegmenter):
     ----------
     member : array_like (default=None)
         Assigns points to the initial cluster membership, therefore the first
-        dimension should be the same as for data. If `None` it will be initialized
+        dimension should be the same as for data. If ``None`` it will be initialized
         to dummy vector where each point is assigned to separate cluster.
     alpha : float (default=1.0)
         Fixed constant alpha in (0, 2] used in the divergence measure, as the
         alpha-th absolute moment, see equation (4) in [1]_.
     penalty : str or callable or None (default=None)
         Function that defines a penalization of the sequence of goodness-of-fit
-        statistic, when overfitting is a concern. If `None` not penalty is applied.
-        Could also be an existing penalty name, either `len_penalty` or
-        `mean_diff_penalty`.
+        statistic, when overfitting is a concern. If ``None`` not penalty is applied.
+        Could also be an existing penalty name, either ``len_penalty`` or
+        ``mean_diff_penalty``.
 
     Attributes
     ----------
     merged_ : array_like
-        2D `array_like` outlining which clusters were merged_ at each step.
+        2D ``array_like`` outlining which clusters were merged_ at each step.
     gof_ : float
         goodness-of-fit statistic for current clsutering.
     cluster_ : array_like
-        1D `array_like` specifying which cluster each row of input data
+        1D ``array_like`` specifying which cluster each row of input data
         X belongs to.
 
     Notes

aeon/segmentation/_hmm.py

@@ -50,7 +50,7 @@ class HMMSegmenter(BaseSegmenter):
     calculated - these are both nxm matrices, where n is the number of
     hidden states and m is the number of observations. The transition
     probability matrices record the probability of the most likely
-    sequence which has observation `m` being assigned to hidden state n.
+    sequence which has observation ``m`` being assigned to hidden state n.
     The transition_id matrix records the step before hidden state n that
     proceeds it in the most likely path.  This logic is mostly carried
     out by helper function _calculate_trans_mats.
@@ -76,7 +76,7 @@ class HMMSegmenter(BaseSegmenter):
         probability of transitioning from state i to state j.)
     initial_probs: 1D np.ndarray, shape = [num hidden states], optional
         A array of probabilities that the sequence of hidden states starts in each
-        of the hidden states. If passed, should be of length `n` the number of
+        of the hidden states. If passed, should be of length ``n`` the number of
         hidden states and  should match the length of both the emission funcs
         list and the transition_prob_mat. The initial probs should be reflective
         of prior beliefs.  If none is passed will each hidden state will be

aeon/segmentation/_igts.py

@@ -279,7 +279,7 @@ class InformationGainSegmenter(BaseSegmenter):
 
     GTS uses top-down search method to greedily find the next change point
     location that creates the maximum information gain. Once this is found, it
-    repeats the process until it finds `k_max` splits of the time series.
+    repeats the process until it finds ``k_max`` splits of the time series.
 
     .. note::
 
@@ -295,16 +295,16 @@ class InformationGainSegmenter(BaseSegmenter):
 
     step: : int, default=5
         Step size, or stride for selecting candidate locations of change points.
-        Fox example a `step=5` would produce candidates [0, 5, 10, ...]. Has the same
-        meaning as `step` in `range` function.
+        Fox example a ``step=5`` would produce candidates [0, 5, 10, ...]. Has the same
+        meaning as ``step`` in ``range`` function.
 
     Attributes
     ----------
     change_points_: list of int
         Locations of change points as integer indexes. By convention change points
         include the identity segmentation, i.e. first and last index + 1 values.
 
-    intermediate_results_: list of `ChangePointResult`
+    intermediate_results_: list of ``ChangePointResult``
         Intermediate segmentation results for each k value, where k=1, 2, ..., k_max
 
     Notes