adapt documentation

nicolaipalm · Jul 4, 2023 · 4b4c1f3 · 4b4c1f3
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diff --git a/paref/moo_algorithms/multi_dimensional/find_1_pareto_points.py b/paref/moo_algorithms/multi_dimensional/find_1_pareto_points.py
@@ -9,7 +9,7 @@ class Find1ParetoPoints(GPRMinimizer):
 
 
 
-    ..note::
+    .. note::
 
         Use this algorithm if you want to find a 1 Pareto point (i.e. a minimum in some component)
         for each component, f.e. in order to estimate the dimension of the Pareto front.

diff --git a/paref/moo_algorithms/multi_dimensional/find_edge_points.py b/paref/moo_algorithms/multi_dimensional/find_edge_points.py
@@ -8,7 +8,7 @@ class FindEdgePoints(GPRMinimizer):
 
 
 
-    ..note::
+    .. note::
 
         Use this algorithm if you want to find the edge points of the Pareto front, if they exist.
         *Notice:* In two dimensions, they always exist. However, in general they are likely to not exist.

diff --git a/paref/pareto_reflection_sequences/multi_dimensional/avoid_pareto_front.py b/paref/pareto_reflection_sequences/multi_dimensional/avoid_pareto_front.py
@@ -10,7 +10,7 @@
 class AvoidParetoFront(SequenceParetoReflections):
     """Avoid the Pareto front of the evaluations plus some epsilon
 
-    ..warning::
+    .. warning::
 
         This sequence should only be used if the Pareto points found in each evaluation of the moo
         are too close together (since it

diff --git a/paref/pareto_reflection_sequences/multi_dimensional/find_edge_points_sequence.py b/paref/pareto_reflection_sequences/multi_dimensional/find_edge_points_sequence.py
@@ -11,7 +11,7 @@
 class FindEdgePointsSequence(SequenceParetoReflections):
     """Find all edge points of the Pareto front
 
-    ..warning::
+    .. warning::
 
         This Pareto reflection assumes that there exist edge points
 

diff --git a/paref/pareto_reflections/fill_gap_2d.py b/paref/pareto_reflections/fill_gap_2d.py
@@ -15,7 +15,7 @@ class FillGap2D(MinimizeWeightedNormToUtopia):
     -----------
     This Pareto reflection should be used if a Pareto point is desired which lies between two specified points
 
-    ..note::
+    .. note::
 
         This function works best if the two points are Pareto optimal.
 

diff --git a/paref/pareto_reflections/find_1_pareto_points.py b/paref/pareto_reflections/find_1_pareto_points.py
@@ -12,7 +12,7 @@ class Find1ParetoPoints(MinimizeWeightedNormToUtopia):
     This Pareto reflection should be used if a Pareto point is desired which lie on the boundary of the Pareto front,
     i.e. are minimal in one component.
 
-    ..note::
+    .. note::
 
         In to dimensions, the edge points of the Pareto front are given by the boundary of the Pareto front.
         I.p. in two dimensions, this Pareto reflection searches for the edge points of the Pareto front.
@@ -40,7 +40,7 @@ def __init__(self, dimension_domain: int,
                  epsilon: float = 1e-3):
         """Specify the dimension of the input domain and the component in which the minimum is searched
 
-        ..warning::
+        .. warning::
 
             The smaller epsilon, the better. However, picking an epsilon too small may lead to an
             unstable optimization.

diff --git a/paref/pareto_reflections/find_edge_points.py b/paref/pareto_reflections/find_edge_points.py
@@ -8,15 +8,15 @@
 class FindEdgePoints(MinimizeWeightedNormToUtopia):
     """Find the edge points of the Pareto front
 
-    ..warning::
+    .. warning::
 
         This Pareto reflection assumes that there exist edge points
 
     When to use
     -----------
     This Pareto reflection should be used if the edge points of the Pareto front are searched.
 
-    ..note::
+    .. note::
 
         In to dimensions, the edge points of the Pareto front always exist.
 
@@ -41,7 +41,7 @@ class FindEdgePoints(MinimizeWeightedNormToUtopia):
     def __init__(self, dimension_domain: int, dimension: int, epsilon: float = 1e-3):
         """Specify the dimension of the input domain and the component of which the edge point is searched
 
-        ..warning::
+        .. warning::
 
             The smaller epsilon, the better. However, picking an epsilon too small may lead to an
             unstable optimization.

diff --git a/paref/pareto_reflections/minimize_weighted_norm_to_utopia.py b/paref/pareto_reflections/minimize_weighted_norm_to_utopia.py
@@ -9,7 +9,7 @@ class MinimizeWeightedNormToUtopia(ParetoReflection):
     """Find the Pareto point closest to some utopia point
     # TBA: not a norm in general rather a polynomial
 
-    ..note::
+    .. note::
 
         # TBA: add
         This Pareto reflection is highly flexible. TBA (used for finding lots of properties, for application: if