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Dateien nach "ILP/Vanilla" hochladen

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kilian
2026-09-22 14:04:41 +02:00
parent 7551af2f19
commit d1a1402d23
1 changed files with 20 additions and 6 deletions
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ILP/Vanilla/vanillasynthesis.py
+20 -6
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@@ -20,8 +20,8 @@ HYPERGRAPH = {
}
FIXED_FLOWS = {
#1: 1,
#14: 1,
1: 1,
14: 1,
}
def build_model(name, hyperedges, vertices, ele, el1, el2, excluded_support=None):
@@ -29,7 +29,9 @@ def build_model(name, hyperedges, vertices, ele, el1, el2, excluded_support=None
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}
n1 = {e_id: model.addVar(vtype=GRB.BINARY, name = f"n1_{e_id}") for e_id in hyperedges}
n2 = {e_id: model.addVar(vtype=GRB.BINARY, name = f"n2_{e_id}") for e_id in hyperedges}
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
for v in vertices:
@@ -43,6 +45,15 @@ def build_model(name, hyperedges, vertices, ele, el1, el2, excluded_support=None
for e_id in hyperedges:
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}")
#Only if an edge has flow, can it contribute to the likelihood
model.addConstr(b[e_id] >= n1[e_id], name = f"only_used_contribute_to_nmr1_{e_id}")
model.addConstr(b[e_id] >= n2[e_id], name = f"only_used_contribute_to_nmr2_{e_id}")
#One reaction can only contribute once
model.addConstr(n1[e_id] + n2[e_id] <= 1)
#Only one edge per nmr can contribute
model.addConstr(quicksum(n1[e_id] for e_id in hyperedges) == 1)
model.addConstr(quicksum(n2[e_id] for e_id in hyperedges) == 1)
reaction_path = {}
@@ -51,9 +62,12 @@ def build_model(name, hyperedges, vertices, ele, el1, el2, excluded_support=None
model.addConstr(quicksum(b[e_id] for e_id in excluded_support) <= len(excluded_support) - 1, name = "different_hyperedges",)
#Excluding creation and destruction only three reactions for three nmr
model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails != [] and heads != []) == 3)
#model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails != [] and heads != []) == 3)
model.setObjective(quicksum(1000 * ele[e_id] * b[e_id] - x[e_id] for e_id in hyperedges),GRB.MAXIMIZE)
model.setObjective(quicksum(1000 * (ele[e_id] * b[e_id]) - (1 / ele[e_id]) * x[e_id] for e_id in hyperedges if ele[e_id] != 0),GRB.MAXIMIZE)
#ILP picks best pair
#model.setObjective(quicksum(1000 * (el1[e_id] * n1[e_id] + el2[e_id] * n2[e_id]) - (1 / ele[e_id]) * x[e_id] for e_id in hyperedges if ele[e_id] != 0),GRB.MAXIMIZE)
return model, x, b
@@ -89,7 +103,7 @@ def main():
#Chosable parameters
modes = ["Product", "Average"]
mode = modes[0]
normalize = False
normalize = True
if normalize:
NMR1 = [round(l/sum(NMR1), 2) for l in NMR1]