Dateien nach "ILP" hochladen

ILP/caffeinesynthesis.py

@@ -15,10 +15,14 @@ HYPEREDGES = {
     11: (['p_{0,3}'], ['Caffeine']),
     12: (['p_{0,4}'], ['Caffeine']),
     13: (['p_{0,5}'], ['Caffeine']),
-    14: (['Caffeine'], [])
+    14: (['Caffeine'], []),
 }
 #Similyrity of Nodes NMR to Measured NMR
-VERTICES = {
+#Can how to add Values along a Path?
+#Can you count position on path?
+#Can you save spectra values to add hypergraphposition to spectrainformation?
+#Compositespectra vs oredered set of spectra
+VERTICES1 = {
     1: ([0, 0], ['Xanthine']),
     2: ([0, 0], ['p_{0,0}']),
     3: ([0, 0], ['p_{0,1}']),
@@ -28,16 +32,37 @@ VERTICES = {
     7: ([0, 1], ['p_{0,5}']),
     8: ([0, 0], ['Caffeine']),
 }
+
+VERTICES2 = {
+    1: ([0], ['Xanthine']),
+    2: ([0], ['p_{0,0}']),
+    3: ([0], ['p_{0,1}']),
+    4: ([1], ['p_{0,2}']),
+    5: ([0], ['p_{0,3}']),
+    6: ([0], ['p_{0,4}']),
+    7: ([1], ['p_{0,5}']),
+    8: ([0], ['Caffeine']),
+}
+
+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]
+
 FIXED_FLOWS = {
     1: 1,
     14: 1,
 }
 
-def build_model(name, hyperedges, vertices,  excluded_support=None):
+def build_model(name, hyperedges, vertices, nmrlikelihoods,  excluded_support=None):
     model = Model(name)
 
     x = {e_id: model.addVar(vtype=GRB.INTEGER, lb=0, name=f"x_{e_id}") for e_id in hyperedges}
     b = {e_id: model.addVar(vtype=GRB.BINARY, name=f"b_{e_id}") for e_id in hyperedges}
+    n = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb=0.0, ub=1.0, name="nmr")
+
+    for v, nmr in zip(vertices, nmrlikelihoods):
+        n[v] = nmr
+        print(f'Vertice: {v}, Similarity: {n[v]}')
 
     vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
 
@@ -53,10 +78,15 @@ def build_model(name, hyperedges, vertices,  excluded_support=None):
         model.addGenConstrIndicator(b[e_id], 0, x[e_id] == 0, name=f"unused_implies_zero_{e_id}")
         model.addConstr(x[e_id] >= b[e_id], name=f"used_implies_positive_flow_{e_id}")
 
+    reaction_path = {}
+
+    #for v_id in vertices:
+        #model.addConstr()
+
     if excluded_support:
         model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name="different_hyperedges",)
 
-    model.setObjective(quicksum(quicksum(x[v_id]) for v_id in vertices), GRB.MAXIMIZE) #Maximize Similarity of Nodes
+    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):
@@ -76,7 +106,7 @@ def print_solution(title, flow_solution, binary_solution, hyperedges):
     print(f"Number of used hyperedges: {len(binary_solution)}")
 
 def main():
-    model, x, b = build_model("HypergraphFlow", HYPEREDGES, VERTICES)
+    model, x, b = build_model("HypergraphFlow", HYPEREDGES, VERTICES, NMRLIKELYHOODS)
     model.optimize()
     if model.status != GRB.Status.OPTIMAL:
         print("No optimal solution found for the first model.")
@@ -85,7 +115,7 @@ def main():
     optimal_binary_solution = positive_entries(b)
     print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPEREDGES)
 
-    excluded_support = list(optimal_binary_solution.keys())
+    """ excluded_support = list(optimal_binary_solution.keys())
     second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPEREDGES, excluded_support=excluded_support,)
 
     second_model.optimize()
@@ -95,6 +125,6 @@ def main():
         print_solution("Second Best Solution", second_solution, second_binary_solution, HYPEREDGES, VERTICES)
     else:
         print("No optimal solution found for the second best model.")
-
+ """
 if __name__ == "__main__":
     main()