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Dateien nach "ILP" hochladen

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kilian 2026-05-22 11:38:54 +02:00
parent 670d59e18f
commit 3b7eea7155
1 changed files with 19 additions and 2 deletions
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21
ILP/caffeinesynthesis.py
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@ -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'] 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 = { FIXED_FLOWS = {
1: 1, 1: 1,
@ -85,8 +85,25 @@ def build_model(name, hyperedges, vertices, nmrlikelihoods, excluded_support=No
if excluded_support: if excluded_support:
model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name="different_hyperedges",) 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 return model, x, b
def positive_entries(variable_dict, threshold=0.5): def positive_entries(variable_dict, threshold=0.5):