Dateien nach "ILP/butadien" hochladen

ILP/butadien/butadiensynthesis.py

@@ -3,31 +3,19 @@ from gurobipy import GRB, Model, quicksum
 
 HYPERGRAPH = {
 	 0: (['Butadien'], []),
-	 1: (['Butadien'], ['Butadien']),
 	 2: (['p_{0,0}'], []),
 	 3: (['p_{0,1}'], []),
 	 4: (['Butadien', 'Butadien'], ['p_{0,0}', 'p_{0,1}']),
-	 5: (['Butadien', 'Butadien'], ['Butadien', 'Butadien']),
 	 6: (['p_{0,2}'], []),
 	 7: (['Butadien', 'Butadien'], ['p_{0,2}']),
-	 8: (['p_{0,1}'], ['p_{0,1}']),
-	 9: (['p_{0,0}', 'p_{0,0}'], ['p_{0,0}', 'p_{0,0}']),
 	 10: (['p_{0,0}', 'p_{0,1}'], ['Butadien', 'Butadien']),
-	 11: (['p_{0,0}', 'p_{0,1}'], ['p_{0,0}', 'p_{0,1}']),
 	 12: (['p_{0,3}'], []),
 	 13: (['p_{0,0}', 'p_{0,2}'], ['p_{0,3}']),
-	 14: (['p_{0,0}', 'p_{0,2}'], ['p_{0,0}', 'p_{0,2}']),
-	 15: (['Butadien', 'p_{0,0}'], ['Butadien', 'p_{0,0}']),
-	 16: (['p_{0,1}', 'p_{0,1}'], ['p_{0,1}', 'p_{0,1}']),
 	 17: (['p_{0,4}'], []),
 	 18: (['p_{0,1}', 'p_{0,1}'], ['Butadien', 'p_{0,4}']),
 	 19: (['p_{0,5}'], []),
 	 20: (['p_{0,1}', 'p_{0,2}'], ['Butadien', 'p_{0,5}']),
-	 21: (['Butadien', 'p_{0,1}'], ['Butadien', 'p_{0,1}']),
-	 22: (['p_{0,1}', 'p_{0,2}'], ['p_{0,1}', 'p_{0,2}']),
 	 23: (['Butadien', 'p_{0,1}'], ['p_{0,0}', 'p_{0,4}']),
-	 24: (['p_{0,2}', 'p_{0,2}'], ['p_{0,2}', 'p_{0,2}']),
-	 25: (['Butadien', 'p_{0,2}'], ['Butadien', 'p_{0,2}']),
 	 26: (['Butadien', 'p_{0,2}'], ['p_{0,0}', 'p_{0,5}']),
 	 27: (['p_{0,6}'], []),
 	 28: (['p_{0,0}', 'p_{0,1}'], ['p_{0,6}']),
@@ -41,183 +29,42 @@ HYPERGRAPH = {
 	 36: (['p_{0,10}'], []),
 	 37: (['Butadien', 'p_{0,1}'], ['p_{0,10}']),
 	 38: (['p_{0,3}'], ['p_{0,0}', 'p_{0,2}']),
-	 39: (['p_{0,3}'], ['p_{0,3}']),
-	 40: (['p_{0,4}'], ['p_{0,4}']),
 	 41: (['p_{0,11}'], []),
 	 42: (['p_{0,4}'], ['p_{0,0}', 'p_{0,11}']),
 	 43: (['p_{0,12}'], []),
 	 44: (['p_{0,5}'], ['p_{0,12}']),
-	 45: (['p_{0,5}'], ['p_{0,5}']),
-	 46: (['p_{0,6}'], ['p_{0,6}']),
-	 47: (['p_{0,8}'], ['p_{0,8}']),
-	 48: (['p_{0,9}'], ['p_{0,9}']),
-	 49: (['p_{0,10}'], ['p_{0,10}']),
-	 50: (['p_{0,3}', 'p_{0,3}'], ['p_{0,3}', 'p_{0,3}']),
-	 51: (['p_{0,3}', 'p_{0,4}'], ['p_{0,3}', 'p_{0,4}']),
-	 52: (['p_{0,3}', 'p_{0,5}'], ['p_{0,3}', 'p_{0,5}']),
-	 53: (['p_{0,3}', 'p_{0,6}'], ['p_{0,3}', 'p_{0,6}']),
-	 54: (['p_{0,3}', 'p_{0,8}'], ['p_{0,3}', 'p_{0,8}']),
-	 55: (['p_{0,3}', 'p_{0,9}'], ['p_{0,3}', 'p_{0,9}']),
-	 56: (['p_{0,3}', 'p_{0,10}'], ['p_{0,3}', 'p_{0,10}']),
-	 57: (['p_{0,2}', 'p_{0,3}'], ['p_{0,2}', 'p_{0,3}']),
-	 58: (['p_{0,0}', 'p_{0,3}'], ['p_{0,0}', 'p_{0,3}']),
-	 59: (['Butadien', 'p_{0,3}'], ['Butadien', 'p_{0,3}']),
-	 60: (['p_{0,1}', 'p_{0,3}'], ['p_{0,1}', 'p_{0,3}']),
-	 61: (['p_{0,4}', 'p_{0,4}'], ['p_{0,4}', 'p_{0,4}']),
-	 62: (['p_{0,4}', 'p_{0,5}'], ['p_{0,4}', 'p_{0,5}']),
 	 63: (['p_{0,13}'], []),
 	 64: (['p_{0,4}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,13}']),
 	 65: (['p_{0,2}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,5}']),
 	 66: (['p_{0,0}', 'p_{0,4}'], ['Butadien', 'p_{0,1}']),
 	 67: (['Butadien', 'p_{0,4}'], ['p_{0,1}', 'p_{0,1}']),
-	 68: (['Butadien', 'p_{0,4}'], ['Butadien', 'p_{0,4}']),
-	 69: (['p_{0,1}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,4}']),
-	 70: (['p_{0,4}', 'p_{0,6}'], ['p_{0,4}', 'p_{0,6}']),
-	 71: (['p_{0,4}', 'p_{0,8}'], ['p_{0,4}', 'p_{0,8}']),
-	 72: (['p_{0,4}', 'p_{0,9}'], ['p_{0,4}', 'p_{0,9}']),
-	 73: (['p_{0,4}', 'p_{0,10}'], ['p_{0,4}', 'p_{0,10}']),
-	 74: (['p_{0,2}', 'p_{0,4}'], ['p_{0,2}', 'p_{0,4}']),
-	 75: (['p_{0,0}', 'p_{0,4}'], ['p_{0,0}', 'p_{0,4}']),
-	 76: (['p_{0,5}', 'p_{0,5}'], ['p_{0,5}', 'p_{0,5}']),
 	 77: (['p_{0,5}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,13}']),
-	 78: (['p_{0,2}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,5}']),
 	 79: (['p_{0,0}', 'p_{0,5}'], ['Butadien', 'p_{0,2}']),
 	 80: (['Butadien', 'p_{0,5}'], ['p_{0,1}', 'p_{0,2}']),
-	 81: (['Butadien', 'p_{0,5}'], ['Butadien', 'p_{0,5}']),
-	 82: (['p_{0,1}', 'p_{0,5}'], ['p_{0,1}', 'p_{0,5}']),
 	 83: (['p_{0,1}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,4}']),
-	 84: (['p_{0,5}', 'p_{0,6}'], ['p_{0,5}', 'p_{0,6}']),
-	 85: (['p_{0,5}', 'p_{0,8}'], ['p_{0,5}', 'p_{0,8}']),
-	 86: (['p_{0,5}', 'p_{0,9}'], ['p_{0,5}', 'p_{0,9}']),
-	 87: (['p_{0,5}', 'p_{0,10}'], ['p_{0,5}', 'p_{0,10}']),
-	 88: (['p_{0,0}', 'p_{0,5}'], ['p_{0,0}', 'p_{0,5}']),
-	 89: (['p_{0,6}', 'p_{0,6}'], ['p_{0,6}', 'p_{0,6}']),
-	 90: (['p_{0,6}', 'p_{0,8}'], ['p_{0,6}', 'p_{0,8}']),
-	 91: (['p_{0,6}', 'p_{0,9}'], ['p_{0,6}', 'p_{0,9}']),
-	 92: (['p_{0,6}', 'p_{0,10}'], ['p_{0,6}', 'p_{0,10}']),
-	 93: (['p_{0,2}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,6}']),
-	 94: (['p_{0,0}', 'p_{0,6}'], ['p_{0,0}', 'p_{0,6}']),
-	 95: (['Butadien', 'p_{0,6}'], ['Butadien', 'p_{0,6}']),
 	 96: (['Butadien', 'p_{0,6}'], ['p_{0,0}', 'p_{0,13}']),
 	 97: (['p_{0,1}', 'p_{0,6}'], ['Butadien', 'p_{0,13}']),
-	 98: (['p_{0,1}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,6}']),
 	 99: (['p_{0,14}'], []),
 	 100: (['p_{0,0}', 'p_{0,6}'], ['p_{0,14}']),
 	 101: (['p_{0,15}'], []),
 	 102: (['p_{0,0}', 'p_{0,7}'], ['p_{0,15}']),
-	 103: (['p_{0,8}', 'p_{0,8}'], ['p_{0,8}', 'p_{0,8}']),
-	 104: (['p_{0,8}', 'p_{0,9}'], ['p_{0,8}', 'p_{0,9}']),
-	 105: (['p_{0,8}', 'p_{0,10}'], ['p_{0,8}', 'p_{0,10}']),
-	 106: (['p_{0,2}', 'p_{0,8}'], ['p_{0,2}', 'p_{0,8}']),
-	 107: (['p_{0,0}', 'p_{0,8}'], ['p_{0,0}', 'p_{0,8}']),
-	 108: (['Butadien', 'p_{0,8}'], ['Butadien', 'p_{0,8}']),
-	 109: (['p_{0,1}', 'p_{0,8}'], ['p_{0,1}', 'p_{0,8}']),
-	 110: (['p_{0,9}', 'p_{0,9}'], ['p_{0,9}', 'p_{0,9}']),
-	 111: (['p_{0,9}', 'p_{0,10}'], ['p_{0,9}', 'p_{0,10}']),
-	 112: (['p_{0,2}', 'p_{0,9}'], ['p_{0,2}', 'p_{0,9}']),
-	 113: (['p_{0,0}', 'p_{0,9}'], ['p_{0,0}', 'p_{0,9}']),
-	 114: (['Butadien', 'p_{0,9}'], ['Butadien', 'p_{0,9}']),
-	 115: (['p_{0,1}', 'p_{0,9}'], ['p_{0,1}', 'p_{0,9}']),
-	 116: (['p_{0,10}', 'p_{0,10}'], ['p_{0,10}', 'p_{0,10}']),
-	 117: (['p_{0,2}', 'p_{0,10}'], ['p_{0,2}', 'p_{0,10}']),
-	 118: (['p_{0,0}', 'p_{0,10}'], ['p_{0,0}', 'p_{0,10}']),
-	 119: (['Butadien', 'p_{0,10}'], ['Butadien', 'p_{0,10}']),
-	 120: (['p_{0,1}', 'p_{0,10}'], ['p_{0,1}', 'p_{0,10}']),
 	 121: (['p_{0,0}', 'p_{0,4}'], ['p_{0,13}']),
 	 122: (['p_{0,16}'], []),
 	 123: (['p_{0,0}', 'p_{0,4}'], ['p_{0,16}']),
 	 124: (['p_{0,17}'], []),
 	 125: (['Butadien', 'p_{0,7}'], ['p_{0,17}']),
-	 126: (['p_{0,11}'], ['p_{0,11}']),
 	 127: (['p_{0,12}'], ['p_{0,5}']),
-	 128: (['p_{0,12}'], ['p_{0,12}']),
-	 129: (['p_{0,13}'], ['p_{0,13}']),
 	 130: (['p_{0,14}'], ['p_{0,0}', 'p_{0,6}']),
-	 131: (['p_{0,14}'], ['p_{0,14}']),
 	 132: (['p_{0,15}'], ['p_{0,0}', 'p_{0,7}']),
-	 133: (['p_{0,15}'], ['p_{0,15}']),
-	 134: (['p_{0,16}'], ['p_{0,16}']),
 	 135: (['p_{0,0}', 'p_{0,11}'], ['p_{0,4}']),
-	 136: (['p_{0,12}', 'p_{0,12}'], ['p_{0,12}', 'p_{0,12}']),
-	 137: (['p_{0,12}', 'p_{0,13}'], ['p_{0,12}', 'p_{0,13}']),
-	 138: (['p_{0,12}', 'p_{0,14}'], ['p_{0,12}', 'p_{0,14}']),
-	 139: (['p_{0,12}', 'p_{0,15}'], ['p_{0,12}', 'p_{0,15}']),
-	 140: (['p_{0,12}', 'p_{0,16}'], ['p_{0,12}', 'p_{0,16}']),
-	 141: (['p_{0,8}', 'p_{0,12}'], ['p_{0,8}', 'p_{0,12}']),
-	 142: (['p_{0,9}', 'p_{0,12}'], ['p_{0,9}', 'p_{0,12}']),
-	 143: (['Butadien', 'p_{0,12}'], ['Butadien', 'p_{0,12}']),
-	 144: (['p_{0,10}', 'p_{0,12}'], ['p_{0,10}', 'p_{0,12}']),
-	 145: (['p_{0,0}', 'p_{0,12}'], ['p_{0,0}', 'p_{0,12}']),
-	 146: (['p_{0,1}', 'p_{0,12}'], ['p_{0,1}', 'p_{0,12}']),
-	 147: (['p_{0,2}', 'p_{0,12}'], ['p_{0,2}', 'p_{0,12}']),
-	 148: (['p_{0,3}', 'p_{0,12}'], ['p_{0,3}', 'p_{0,12}']),
-	 149: (['p_{0,4}', 'p_{0,12}'], ['p_{0,4}', 'p_{0,12}']),
-	 150: (['p_{0,5}', 'p_{0,12}'], ['p_{0,5}', 'p_{0,12}']),
-	 151: (['p_{0,6}', 'p_{0,12}'], ['p_{0,6}', 'p_{0,12}']),
-	 152: (['p_{0,13}', 'p_{0,13}'], ['p_{0,13}', 'p_{0,13}']),
 	 153: (['Butadien', 'p_{0,13}'], ['p_{0,1}', 'p_{0,6}']),
-	 154: (['Butadien', 'p_{0,13}'], ['Butadien', 'p_{0,13}']),
 	 155: (['p_{0,0}', 'p_{0,13}'], ['Butadien', 'p_{0,6}']),
-	 156: (['p_{0,1}', 'p_{0,13}'], ['p_{0,1}', 'p_{0,13}']),
 	 157: (['p_{0,1}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,6}']),
 	 158: (['p_{0,2}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,6}']),
-	 159: (['p_{0,4}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,13}']),
-	 160: (['p_{0,5}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,13}']),
-	 161: (['p_{0,6}', 'p_{0,13}'], ['p_{0,6}', 'p_{0,13}']),
-	 162: (['p_{0,13}', 'p_{0,14}'], ['p_{0,13}', 'p_{0,14}']),
-	 163: (['p_{0,13}', 'p_{0,15}'], ['p_{0,13}', 'p_{0,15}']),
-	 164: (['p_{0,13}', 'p_{0,16}'], ['p_{0,13}', 'p_{0,16}']),
-	 165: (['p_{0,8}', 'p_{0,13}'], ['p_{0,8}', 'p_{0,13}']),
-	 166: (['p_{0,9}', 'p_{0,13}'], ['p_{0,9}', 'p_{0,13}']),
-	 167: (['p_{0,10}', 'p_{0,13}'], ['p_{0,10}', 'p_{0,13}']),
-	 168: (['p_{0,0}', 'p_{0,13}'], ['p_{0,0}', 'p_{0,13}']),
-	 169: (['p_{0,2}', 'p_{0,13}'], ['p_{0,2}', 'p_{0,13}']),
-	 170: (['p_{0,3}', 'p_{0,13}'], ['p_{0,3}', 'p_{0,13}']),
-	 171: (['p_{0,14}', 'p_{0,14}'], ['p_{0,14}', 'p_{0,14}']),
-	 172: (['p_{0,14}', 'p_{0,15}'], ['p_{0,14}', 'p_{0,15}']),
-	 173: (['p_{0,14}', 'p_{0,16}'], ['p_{0,14}', 'p_{0,16}']),
-	 174: (['p_{0,8}', 'p_{0,14}'], ['p_{0,8}', 'p_{0,14}']),
-	 175: (['p_{0,9}', 'p_{0,14}'], ['p_{0,9}', 'p_{0,14}']),
-	 176: (['Butadien', 'p_{0,14}'], ['Butadien', 'p_{0,14}']),
-	 177: (['p_{0,10}', 'p_{0,14}'], ['p_{0,10}', 'p_{0,14}']),
-	 178: (['p_{0,0}', 'p_{0,14}'], ['p_{0,0}', 'p_{0,14}']),
-	 179: (['p_{0,1}', 'p_{0,14}'], ['p_{0,1}', 'p_{0,14}']),
-	 180: (['p_{0,2}', 'p_{0,14}'], ['p_{0,2}', 'p_{0,14}']),
-	 181: (['p_{0,3}', 'p_{0,14}'], ['p_{0,3}', 'p_{0,14}']),
-	 182: (['p_{0,4}', 'p_{0,14}'], ['p_{0,4}', 'p_{0,14}']),
-	 183: (['p_{0,5}', 'p_{0,14}'], ['p_{0,5}', 'p_{0,14}']),
-	 184: (['p_{0,6}', 'p_{0,14}'], ['p_{0,6}', 'p_{0,14}']),
-	 185: (['p_{0,15}', 'p_{0,15}'], ['p_{0,15}', 'p_{0,15}']),
-	 186: (['p_{0,15}', 'p_{0,16}'], ['p_{0,15}', 'p_{0,16}']),
-	 187: (['p_{0,8}', 'p_{0,15}'], ['p_{0,8}', 'p_{0,15}']),
-	 188: (['p_{0,9}', 'p_{0,15}'], ['p_{0,9}', 'p_{0,15}']),
-	 189: (['Butadien', 'p_{0,15}'], ['Butadien', 'p_{0,15}']),
-	 190: (['p_{0,10}', 'p_{0,15}'], ['p_{0,10}', 'p_{0,15}']),
-	 191: (['p_{0,0}', 'p_{0,15}'], ['p_{0,0}', 'p_{0,15}']),
-	 192: (['p_{0,1}', 'p_{0,15}'], ['p_{0,1}', 'p_{0,15}']),
-	 193: (['p_{0,2}', 'p_{0,15}'], ['p_{0,2}', 'p_{0,15}']),
-	 194: (['p_{0,3}', 'p_{0,15}'], ['p_{0,3}', 'p_{0,15}']),
-	 195: (['p_{0,4}', 'p_{0,15}'], ['p_{0,4}', 'p_{0,15}']),
-	 196: (['p_{0,5}', 'p_{0,15}'], ['p_{0,5}', 'p_{0,15}']),
-	 197: (['p_{0,6}', 'p_{0,15}'], ['p_{0,6}', 'p_{0,15}']),
-	 198: (['p_{0,16}', 'p_{0,16}'], ['p_{0,16}', 'p_{0,16}']),
-	 199: (['p_{0,8}', 'p_{0,16}'], ['p_{0,8}', 'p_{0,16}']),
-	 200: (['p_{0,9}', 'p_{0,16}'], ['p_{0,9}', 'p_{0,16}']),
-	 201: (['Butadien', 'p_{0,16}'], ['Butadien', 'p_{0,16}']),
-	 202: (['p_{0,10}', 'p_{0,16}'], ['p_{0,10}', 'p_{0,16}']),
-	 203: (['p_{0,0}', 'p_{0,16}'], ['p_{0,0}', 'p_{0,16}']),
-	 204: (['p_{0,1}', 'p_{0,16}'], ['p_{0,1}', 'p_{0,16}']),
-	 205: (['p_{0,2}', 'p_{0,16}'], ['p_{0,2}', 'p_{0,16}']),
-	 206: (['p_{0,3}', 'p_{0,16}'], ['p_{0,3}', 'p_{0,16}']),
-	 207: (['p_{0,4}', 'p_{0,16}'], ['p_{0,4}', 'p_{0,16}']),
-	 208: (['p_{0,5}', 'p_{0,16}'], ['p_{0,5}', 'p_{0,16}']),
-	 209: (['p_{0,6}', 'p_{0,16}'], ['p_{0,6}', 'p_{0,16}']),
 	 210: (['p_{0,18}'], []),
 	 211: (['Butadien', 'p_{0,11}'], ['p_{0,18}']),
-	 212: (['p_{0,18}'], ['p_{0,18}']),
 	 213: ([], ['Butadien']),
 }
-
 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}']
 #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]
@@ -226,37 +73,53 @@ 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
 #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]
 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)]
 
 FIXED_FLOWS = {
     213: 3,
-    41: 1,
+    #2: 3,
+    #4: 1,
+    #23: 1,
+    #41: 1,
+    #42: 1,
 }
 
-def build_model(name, hyperedges, vertices, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3, excluded_support=None):
+def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3, 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}
+    nmax = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmrmax")
     n1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr1")
     n2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr2")
     n3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "nmr3")
-    en1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr1")
+    enmax = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmrmax")
-    en2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr2")
+    en1 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr1")
-    en3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr3")
+    en2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr2")
+    en3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 1.0, name = "edgenmr3")
 
     #Assigns every Molecule the likelihood compared to the three different reference spectra
-    for v, nmr1, nmr2, nmr3 in zip(vertices, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3):
+    for v, nmrmax, nmr1, nmr2, nmr3 in zip(vertices, nmrlikelihoodsmax, nmrlikelihoods1, nmrlikelihoods2, nmrlikelihoods3):
+        nmax[v] = nmrmax
         n1[v] = nmr1
         n2[v] = nmr2
         n3[v] = nmr3
 
     #Assigns the edges a likelihood based on the products
-    for e, (_, heads) in hyperedges.items():
+    for e, (tails, heads) in hyperedges.items():
-        en1[e] = quicksum(n1[head] for head in heads)
+        if heads != [] and tails != []:
-        en2[e] = quicksum(n2[head] for head in heads)
+            enmax[e] = quicksum(nmax[head] for head in heads)/len(heads)
-        en3[e] = quicksum(n3[head] for head in heads)
+            en1[e] = quicksum(n1[head] for head in heads)/len(heads)
+            en2[e] = quicksum(n2[head] for head in heads)/len(heads)
+            en3[e] = quicksum(n3[head] for head in heads)/len(heads)
+        else:
+            enmax[e] = 0.0
+            en1[e] = 0.0
+            en2[e] = 0.0
+            en3[e] = 0.0 
 
-    print(en1[203])
+    print(enmax[123])
+    print(enmax[42])
 
     vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
 
@@ -281,22 +144,39 @@ def build_model(name, hyperedges, vertices, nmrlikelihoods1, nmrlikelihoods2, nm
     #Multiplizier den node Wert mit infow + outflow
     model.ModelSense = GRB.MAXIMIZE
 
-
     #Multiply node value with infow or outflow
     model.setObjectiveN(
-        quicksum((en1[e_id] + en2[e_id] + en3[e_id]) * x[e_id] for e_id, (_, _) in hyperedges.items()),
+        quicksum(enmax[e_id] * x[e_id] for e_id, (_, _) in hyperedges.items()),
         index = 0,
         priority = 2,
         name = "maximize_nmr_similarity",
     )  
 
     model.setObjectiveN(
-        quicksum(-1 * x[e_id] for e_id in hyperedges),
+        quicksum(x[e_id] for e_id in hyperedges),
-        index=3,
+        index=1,
-        priority=1,
+        priority= - 1,
         name="minimize_used_hyperedges",
     )
 
+    #Excluding creation and destruction only three reactions for three nmr
+    model.addConstr(quicksum(b[e_id] for e_id, (heads, tails) in hyperedges.items() if tails != [] and heads != []) == 3) 
+    #Restrict number of used edges to prevent using all available
+    #model.addConstr(quicksum(x[e_id] for e_id in hyperedges) <= 10)
+
+    #2 Butadien create first different molecule:
+    model.addConstr(b[4] + b[7] == 1)
+
+    #Every item created has to be consumed:
+    
+
+    #No cyclic reaction pairs:
+    for e_id1, (heads1, tails1) in hyperedges.items():
+        for e_id2, (heads2, tails2) in hyperedges.items():
+            if (heads1, tails1) == (tails2, heads2):
+                #print(e_id1, e_id2)
+                model.addConstr((b[e_id1] + b[e_id2]) <= 1)
+
     return model, x, b
 
 
@@ -317,7 +197,7 @@ 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", HYPERGRAPH, VERTICES, NMR1, NMR2, NMR3)
+    model, x, b = build_model("HypergraphFlow", HYPERGRAPH, VERTICES, NMRMAX, NMR1, NMR2, NMR3)
     model.optimize()
     if model.status != GRB.Status.OPTIMAL:
         print("No optimal solution found for the first model.")
@@ -326,16 +206,16 @@ def main():
     optimal_binary_solution = positive_entries(b)
     print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPERGRAPH)
 
-    """ 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, x2, b2 = build_model("SecondBestHypergraphFlow", HYPERGRAPH, VERTICES, NMRMAX, NMR1, NMR2, NMR3, excluded_support=excluded_support,)
 
     second_model.optimize()
     if second_model.status == GRB.Status.OPTIMAL:
         second_solution = positive_entries(x2)
         second_binary_solution = positive_entries(b2)
-        print_solution("Second Best Solution", second_solution, second_binary_solution, HYPEREDGES, VERTICES)
+        print_solution("Second Best Solution", second_solution, second_binary_solution, HYPERGRAPH)
     else:
         print("No optimal solution found for the second best model.")
- """
+
 if __name__ == "__main__":
     main()