Rendering formule matematiche docstring

Author: luca-software-developer

hiper/metrics/average_hyperedge_cardinality.py

@@ -21,9 +21,9 @@ class AverageHyperedgeCardinality:
     The Average Hyperedge Cardinality measures the mean size of hyperedges
     in the hypergraph after perturbation:
 
-    AHC = (1/|E'|) * Σ|e| for e ∈ E'
+    ``AHC = (1/|E'|) * Σ|e|`` for ``e ∈ E'``
 
-    where E' is the set of hyperedges after perturbation and |e| is the
+    where E' is the set of hyperedges after perturbation and ``|e|`` is the
     cardinality (size) of hyperedge e.
 
     This metric provides insight into how perturbations affect the

hiper/metrics/hyperedge_fragmentation.py

@@ -21,13 +21,13 @@ class HyperedgeFragmentationIndex:
     The Hyperedge Fragmentation Index measures how much the original hyperedges
     have been fragmented by a perturbation:
 
-    HFI = 1 - (1/|E|) * Σ(|e ∩ V'|/|e|) for e ∈ E
+    ``HFI = 1 - (1/|E|) * Σ(|e ∩ V'|/|e|)`` for ``e ∈ E``
 
     where:
     - E is the original set of hyperedges
     - V' is the set of nodes after perturbation
-    - e ∩ V' is the intersection of original hyperedge e with remaining nodes
-    - |e| is the original size of hyperedge e
+    - ``e ∩ V'`` is the intersection of original hyperedge e with remaining nodes
+    - ``|e|`` is the original size of hyperedge e
 
     A value of 0.0 indicates no fragmentation (all hyperedges intact),
     while 1.0 indicates complete fragmentation (all hyperedges destroyed).

hiper/metrics/hyperedge_integrity.py

@@ -20,7 +20,7 @@ class HyperedgeIntegrity:
     The Hyperedge Integrity measures the fraction of hyperedges that remain
     in the hypergraph after a perturbation:
 
-    HI = |E'| / |E|
+    ``HI = |E'| / |E|``
 
     where E is the original set of hyperedges and E' is the set of hyperedges
     after perturbation.

hiper/metrics/redundancy.py

@@ -18,8 +18,8 @@ class RedundancyCoefficient:
     The redundancy coefficient measures the average degree of normalized
     overlap between pairs of hyperedges in the hypergraph.
 
-    Formula: ρ(H) = (1/|E|²) * Σ(|e1 ∩ e2| / √(|e1| * |e2|))
-    for all pairs e1, e2 ∈ E with e1 ≠ e2.
+    Formula: ``ρ(H) = (1/|E|²) * Σ(|e1 ∩ e2| / √(|e1| * |e2|))``
+    for all pairs ``e1, e2 ∈ E`` with ``e1 ≠ e2``.
     """
 
     @staticmethod

hiper/metrics/swalk.py

@@ -18,8 +18,8 @@ class SwalkEfficiency:
     The s-walk efficiency measures how efficiently nodes can reach each
     other via s-walks, computed as the average of inverse distances.
 
-    Formula: ℰs(H) = (1/(|V|(|V|-1))) * Σ(1/d_H^s(u,v))
-    for all pairs u,v ∈ V with u ≠ v.
+    Formula: ``ℰs(H) = (1/(|V|(|V|-1))) * Σ(1/d_H^s(u,v))``
+    for all pairs ``u,v ∈ V`` with ``u ≠ v``.
     """
 
     def __init__(self, s: int = 1):