Normalization als parameter und Edgelikelihood berechnung in main methode
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@@ -87,70 +87,6 @@ HYPERGRAPH3 = {
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2: (['p_{0,0}'], []),
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}
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VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'p_{0,6}', 'p_{0,7}', 'p_{0,8}', 'p_{0,9}', 'p_{0,10}', 'p_{0,11}', 'p_{0,12}', 'p_{0,13}', 'p_{0,14}', 'p_{0,15}', 'p_{0,16}', 'p_{0,17}', 'p_{0,18}']
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#Vergleich mit dem NMR von Ethylen und Hexatrien
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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]
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#Normalisiert
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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]
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#Vergleich mit dem NMR von Ethylen und Octrien
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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]
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#Normalisiert
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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]
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#Vergleich mit dem NMR von Ethylen und Benzol
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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]
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#Normalisiert
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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]
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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']
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NMRMAX = [max(n1,n2,n3) for n1,n2,n3 in zip(NMR1,NMR2,NMR3)]
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#Kombiniert Molekül it Wahrscheinlichkeit für NMR1:
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VERTICE1 = {}
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for vertice, likelihood in zip(VERTICES, NMR1):
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VERTICE1[vertice] = likelihood
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#Kombiniert Molekül it Wahrscheinlichkeit für NMR2:
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VERTICE2 = {}
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for vertice, likelihood in zip(VERTICES, NMR2):
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VERTICE2[vertice] = likelihood
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#Kombiniert Molekül it Wahrscheinlichkeit für NMR3:
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VERTICE3 = {}
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for vertice, likelihood in zip(VERTICES, NMR3):
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VERTICE3[vertice] = likelihood
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#Kantenwahrscheinlichkeiten für NMR1:
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EDGE1 = {}
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#Kantenwahrscheinlichkeiten für NMR2:
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EDGE2 = {}
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#Kantenwahrscheinlichkeiten für NMR3:
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EDGE3 = {}
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#Maximum der verschiedenen Kantenwahrscheinlichkeiten:
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EDGEMAX = {}
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modes = ["Product", "Average"]
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mode = modes[0]
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#Ist es besser, die Edukte mit zu berücksichtigen? Dann hin rück gleich wahrscheinlich, aber nur 1 kann gewählt werden
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#Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte
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for edge, (tails, heads) in HYPERGRAPH.items():
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if heads == [] or tails == []:
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EDGE1[edge] = 0.0
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EDGE2[edge] = 0.0
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EDGE3[edge] = 0.0
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elif mode == "Product":
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#print([VERTICE1[head] for head in heads])
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EDGE1[edge] = round(np.prod([VERTICE1[head] for head in heads]),2)
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EDGE2[edge] = round(np.prod([VERTICE2[head] for head in heads]),2)
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EDGE3[edge] = round(np.prod([VERTICE3[head] for head in heads]),2)
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elif mode == "Average":
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EDGE1[edge] = round(sum(VERTICE1[head] for head in heads)/len(heads),2)
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EDGE2[edge] = round(sum(VERTICE2[head] for head in heads)/len(heads),2)
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EDGE3[edge] = round(sum(VERTICE3[head] for head in heads)/len(heads),2)
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EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE3[edge]])
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#print(EDGEMAX[edge], HYPERGRAPH[edge])
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#print(EDGEMAX[4])
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#print(EDGEMAX[23])
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#print(EDGEMAX[42])
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FIXED_FLOWS = {
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#213: 3,
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@@ -171,7 +107,6 @@ def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_support=None):
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vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
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print(vertices)
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#Every item created has to be consumed:
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'''
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for v in vertices:
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@@ -228,7 +163,7 @@ def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_support=None):
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#2 Butadien create first different molecule and it has to be created first:
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startmolecule = ["Butadien"]
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model.addConstr(quicksum(b[e_id] for e_id, (tails, _) in hyperedges.items() if list(set(tails)) == startmolecule) == 1)
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#model.addConstr(quicksum(b[e_id] for e_id, (tails, _) in hyperedges.items() if list(set(tails)) == startmolecule) == 1)
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#model.addConstr(b[4] + b[7] == 1)
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model.addConstr(quicksum(b[e_id] for e_id, (tails, heads) in hyperedges.items() if tails == [] and heads == startmolecule)== 1)
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@@ -251,8 +186,8 @@ def build_model(name, hyperedges, elmax, el1, el2, el3, excluded_support=None):
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def positive_entries(variable_dict, threshold = 0.5):
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return {e_id: var.X for e_id, var in variable_dict.items() if var.X > threshold}
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def edgelikelihoods(variable_dict, threshold = 0.5):
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return {e_id: EDGEMAX[e_id] for e_id, var in variable_dict.items() if var.X > threshold}
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def edgelikelihoods(variable_dict, edgelikelihood, threshold = 0.5):
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return {e_id: edgelikelihood[e_id] for e_id, var in variable_dict.items() if var.X > threshold}
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def print_solution(title, flow_solution, binary_solution, edge_likelihoods, hyperedges):
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print(f"\n{title}:")
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@@ -269,6 +204,73 @@ def print_solution(title, flow_solution, binary_solution, edge_likelihoods, hype
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print(f"Number of used hyperedges: {len(binary_solution)}")
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def main():
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VERTICES = ['Butadien', 'p_{0,0}', 'p_{0,1}', 'p_{0,2}', 'p_{0,3}', 'p_{0,4}', 'p_{0,5}', 'p_{0,6}', 'p_{0,7}', 'p_{0,8}', 'p_{0,9}', 'p_{0,10}', 'p_{0,11}', 'p_{0,12}', 'p_{0,13}', 'p_{0,14}', 'p_{0,15}', 'p_{0,16}', 'p_{0,17}', 'p_{0,18}']
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#Vergleich mit dem NMR von Ethylen und Hexatrien
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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]
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#Vergleich mit dem NMR von Ethylen und Octrien
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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]
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#Vergleich mit dem NMR von Ethylen und Benzol
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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]
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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']
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#Chosable parameters
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modes = ["Product", "Average"]
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mode = modes[1]
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normalize = True
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if normalize:
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print("test")
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NMR1 = [round(l/sum(NMR1), 2) for l in NMR1]
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NMR2 = [round(l/sum(NMR2), 2) for l in NMR2]
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NMR3 = [round(l/sum(NMR3), 2) for l in NMR3]
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#Kombiniert Molekül it Wahrscheinlichkeit für NMR1:
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VERTICE1 = {}
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for vertice, likelihood in zip(VERTICES, NMR1):
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VERTICE1[vertice] = likelihood
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#Kombiniert Molekül it Wahrscheinlichkeit für NMR2:
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VERTICE2 = {}
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for vertice, likelihood in zip(VERTICES, NMR2):
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VERTICE2[vertice] = likelihood
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#Kombiniert Molekül it Wahrscheinlichkeit für NMR3:
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VERTICE3 = {}
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for vertice, likelihood in zip(VERTICES, NMR3):
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VERTICE3[vertice] = likelihood
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#Kantenwahrscheinlichkeiten für NMR1:
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EDGE1 = {}
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#Kantenwahrscheinlichkeiten für NMR2:
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EDGE2 = {}
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#Kantenwahrscheinlichkeiten für NMR3:
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EDGE3 = {}
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#Maximum der verschiedenen Kantenwahrscheinlichkeiten:
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EDGEMAX = {}
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#Ist es besser, die Edukte mit zu berücksichtigen? Dann hin rück gleich wahrscheinlich, aber nur 1 kann gewählt werden
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#Ordnet die Kantenwahrscheinlichkeit basierend auf dem Mittel oder Produkt der Wahrscheinlichkeiten der Produkte
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for edge, (tails, heads) in HYPERGRAPH.items():
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if heads == [] or tails == []:
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EDGE1[edge] = 0.0
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EDGE2[edge] = 0.0
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EDGE3[edge] = 0.0
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elif mode == "Product":
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#print([VERTICE1[head] for head in heads])
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EDGE1[edge] = round(np.prod([VERTICE1[head] for head in heads]),2)
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EDGE2[edge] = round(np.prod([VERTICE2[head] for head in heads]),2)
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EDGE3[edge] = round(np.prod([VERTICE3[head] for head in heads]),2)
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elif mode == "Average":
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EDGE1[edge] = round(sum(VERTICE1[head] for head in heads)/len(heads),2)
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EDGE2[edge] = round(sum(VERTICE2[head] for head in heads)/len(heads),2)
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EDGE3[edge] = round(sum(VERTICE3[head] for head in heads)/len(heads),2)
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EDGEMAX[edge] = max([EDGE1[edge], EDGE2[edge], EDGE3[edge]])
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#print(EDGEMAX[edge], HYPERGRAPH[edge])
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#print(EDGEMAX[4])
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#print(EDGEMAX[23])
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#print(EDGEMAX[42])
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model, x, b = build_model("HypergraphFlow", HYPERGRAPH, EDGEMAX, EDGE1, EDGE2, EDGE3)
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model.optimize()
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if model.status != GRB.Status.OPTIMAL:
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@@ -276,7 +278,7 @@ def main():
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return
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optimal_solution = positive_entries(x)
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optimal_binary_solution = positive_entries(b)
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optimal_edgelikelihoods = edgelikelihoods(b)
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optimal_edgelikelihoods = edgelikelihoods(b, EDGEMAX)
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print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, optimal_edgelikelihoods, HYPERGRAPH)
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excluded_support = list(optimal_binary_solution.keys())
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@@ -286,7 +288,7 @@ def main():
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if second_model.status == GRB.Status.OPTIMAL:
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second_solution = positive_entries(x2)
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second_binary_solution = positive_entries(b2)
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second_edgelikelihoods = edgelikelihoods(b2)
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second_edgelikelihoods = edgelikelihoods(b2, EDGEMAX)
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print_solution("Second Best Solution", second_solution, second_binary_solution, second_edgelikelihoods, HYPERGRAPH)
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else:
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print("No optimal solution found for the second best model.")
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