Dateien nach "ILP/butadien" hochladen

This commit is contained in:
2026-09-09 12:45:34 +02:00
parent 036bf43e16
commit c5b9d50963
+12 -23
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@@ -91,15 +91,15 @@ VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', '
#Vergleich mit dem NMR von Ethylen und Hexatrien
NMR1 = [0.32, 0.5, 0.87, 0.0, 0.11, 0.58, 0.06, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.09, 0.0, 0.0, 0.17, 0.0, 0.09]
#Normalisiert
#NMR1 = [0.11, 0.18, 0.31, 0.0, 0.04, 0.21, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.06, 0.0, 0.03]
NMR1 = [0.11, 0.18, 0.31, 0.0, 0.04, 0.21, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.06, 0.0, 0.03]
#Vergleich mit dem NMR von Ethylen und Octrien
NMR2 = [0.33, 0.43, 0.6, 0.0, 0.13, 0.9, 0.07, 0.01, 0.0, 0.0, 0.01, 0.0, 0.0, 0.01, 0.1, 0.0, 0.0, 0.22, 0.0, 0.13]
#Normalisiert
#NMR2 = [0.11, 0.15, 0.21, 0.0, 0.04, 0.31, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.07, 0.0, 0.04]
NMR2 = [0.11, 0.15, 0.21, 0.0, 0.04, 0.31, 0.02, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.07, 0.0, 0.04]
#Vergleich mit dem NMR von Ethylen und Benzol
NMR3 = [0.0, 0.32, 0.0, 0.0, 0.0, 0.0, 0.05, 0.0, 0.0, 0.02, 0.0, 0.29, 0.95, 0.17, 0.0, 0.0, 0.0, 0.0, 0.0, 0.05]
#Normalisiert
#NMR3 = [0.0, 0.17, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.01, 0.0, 0.16, 0.51, 0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]
NMR3 = [0.0, 0.17, 0.0, 0.0, 0.0, 0.0, 0.03, 0.0, 0.0, 0.01, 0.0, 0.16, 0.51, 0.09, 0.0, 0.0, 0.0, 0.0, 0.0, 0.03]
VERTICESSMILES = ['C=CC=C', 'C=C', 'C=CC=CC=C', 'C1CCC(C=C)CC=1', 'C(CCC(C=C)CC=C)=C', 'C=CC=CC=CC=C', 'C1CCC(C=CC=C)CC=1', 'C=CC1C=CCCC1', 'C1CCCCC=1', 'C=CC1CC=CCC1C=C', 'C1CCC(C=C)C(C=C)C=1', 'C(C1CC(C=C)C=CC1)=C', 'C1C=CC=CC=1', 'C(CCC1C=CC=CC1)=C', 'C1C(C=CC=C)CCCC=1', 'C=CC(C=C)CCCC=C', 'C=CCCCCC=C', 'C=CC1C=CC(C=C)CC1', 'C1CC2CCCCC2CC=1', 'C1CC2C=CC=CC2CC=1']
NMRMAX = [max(n1,n2,n3) for n1,n2,n3 in zip(NMR1,NMR2,NMR3)]
@@ -128,7 +128,7 @@ EDGEMAX = {}
modes = ["Product", "Average"]
mode = modes[0]
#print(round(np.prod([1, 0.9])))
#Ist es besser, die Edukte mit zu berücksichtigen? Dann hin rück gleich wahrscheinlich, aber nur 1 kann gewählt werden
#Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte
for edge, (tails, heads) in HYPERGRAPH.items():
if heads == [] or tails == []:
@@ -136,7 +136,6 @@ for edge, (tails, heads) in HYPERGRAPH.items():
EDGE2[edge] = 0.0
EDGE3[edge] = 0.0
elif mode == "Product":
#Produkt klappt nur, wenn normalisierte Likelihoods
#print([VERTICE1[head] for head in heads])
EDGE1[edge] = round(np.prod([VERTICE1[head] for head in heads]),2)
EDGE2[edge] = round(np.prod([VERTICE2[head] for head in heads]),2)
@@ -147,10 +146,10 @@ for edge, (tails, heads) in HYPERGRAPH.items():
EDGE3[edge] = round(sum(VERTICE3[head] for head in heads)/len(heads),2)
EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE3[edge]])
print(EDGEMAX[edge], HYPERGRAPH[edge])
print(EDGEMAX[4])
print(EDGEMAX[23])
print(EDGEMAX[42])
#print(EDGEMAX[edge], HYPERGRAPH[edge])
#print(EDGEMAX[4])
#print(EDGEMAX[23])
#print(EDGEMAX[42])
FIXED_FLOWS = {
@@ -162,22 +161,12 @@ FIXED_FLOWS = {
#42: 1,
}
def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelikelihoods2, edgelikelihoods3, excluded_support=None):
def build_model(name, hyperedges, elmax, el1, el2, el3, 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}
enmax = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmrmax")
en1 = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr1")
en2 = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr2")
en3 = model.addVars(hyperedges, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr3")
for edge, elmax, el1, el2, el3 in zip(hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelikelihoods2, edgelikelihoods3):
enmax[edge] = elmax
en1[edge] = el1
en2[edge] = el2
en3[edge] = el3
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
@@ -218,9 +207,9 @@ def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelike
#Multiply edgelikelihood with the edge use boolean
#Adapt to have
model.setObjectiveN(
quicksum(enmax[e_id] * b[e_id] for e_id in hyperedges),
quicksum(elmax[e_id] * b[e_id] for e_id in hyperedges),
index = 0,
priority = 2000,
priority = 2,
name = "maximize_nmr_similarity",
)
#Minimize the overall flow
@@ -244,7 +233,7 @@ def build_model(name, hyperedges, edgelikelihoodsmax, edgelikelihoods1, edgelike
model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails == [] and heads == startmolecule)== 1)
#model.addConstr(b[213] == 1)
#model.addConstr(b[41] == 1)