From 3b7eea7155dcf00913138fbc8a025cd9fd0f9a7e Mon Sep 17 00:00:00 2001
From: kilian <Kilian.seng@web.de>
Date: Fri, 22 May 2026 11:38:54 +0200
Subject: [PATCH] Dateien nach "ILP" hochladen

---
 ILP/caffeinesynthesis.py | 21 +++++++++++++++++++--
 1 file changed, 19 insertions(+), 2 deletions(-)

diff --git a/ILP/caffeinesynthesis.py b/ILP/caffeinesynthesis.py
index d6ba07a..047ebe8 100644
--- a/ILP/caffeinesynthesis.py
+++ b/ILP/caffeinesynthesis.py
@@ -46,7 +46,7 @@ VERTICES2 = {
 
 VERTICES = ['Xanthine', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}',  'Caffeine']
 
-NMRLIKELYHOODS = [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0]
+NMRLIKELYHOODS = [0.0, 0.2, 0.7, 0.1, 0.1, 0.2, 0.7, 0.0]
 
 FIXED_FLOWS = {
     1: 1,
@@ -85,8 +85,25 @@ def build_model(name, hyperedges, vertices, nmrlikelihoods,  excluded_support=No
 
     if excluded_support:
         model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name="different_hyperedges",)
+    
+    #Multiplizier den node Wert mit infow + outflow
+
+    #model.setObjective(quicksum(n[v_id] for v_id in vertices), GRB.MINIMIZE) #Maximize Similarity of Nodes
+    model.ModelSense = GRB.MAXIMIZE
+    model.setObjectiveN(
+        quicksum(n[t_id[0]] * x[e_id] for e_id, (_, t_id) in hyperedges.items() if t_id != []),
+        index=0,
+        priority=2,
+        name="maximize_nmr_similarity",
+    )   
+
+    model.setObjectiveN(
+        quicksum(-1 *b[e_id] for e_id in hyperedges),
+        index=1,
+        priority=1,
+        name="minimize_used_hyperedges",
+    )
 
-    model.setObjective(quicksum(n[v_id] for v_id in vertices), GRB.MINIMIZE) #Maximize Similarity of Nodes
     return model, x, b
 
 def positive_entries(variable_dict, threshold=0.5):