341 lines
17 KiB
Python
341 lines
17 KiB
Python
import gurobipy as gp
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from gurobipy import GRB, Model, quicksum
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HYPERGRAPH = {
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0: (['Butadien'], []),
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1: (['Butadien'], ['Butadien']),
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2: (['p_{0,0}'], []),
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3: (['p_{0,1}'], []),
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4: (['Butadien', 'Butadien'], ['p_{0,0}', 'p_{0,1}']),
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5: (['Butadien', 'Butadien'], ['Butadien', 'Butadien']),
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6: (['p_{0,2}'], []),
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7: (['Butadien', 'Butadien'], ['p_{0,2}']),
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8: (['p_{0,1}'], ['p_{0,1}']),
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9: (['p_{0,0}', 'p_{0,0}'], ['p_{0,0}', 'p_{0,0}']),
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10: (['p_{0,0}', 'p_{0,1}'], ['Butadien', 'Butadien']),
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11: (['p_{0,0}', 'p_{0,1}'], ['p_{0,0}', 'p_{0,1}']),
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12: (['p_{0,3}'], []),
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13: (['p_{0,0}', 'p_{0,2}'], ['p_{0,3}']),
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14: (['p_{0,0}', 'p_{0,2}'], ['p_{0,0}', 'p_{0,2}']),
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15: (['Butadien', 'p_{0,0}'], ['Butadien', 'p_{0,0}']),
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16: (['p_{0,1}', 'p_{0,1}'], ['p_{0,1}', 'p_{0,1}']),
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17: (['p_{0,4}'], []),
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18: (['p_{0,1}', 'p_{0,1}'], ['Butadien', 'p_{0,4}']),
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19: (['p_{0,5}'], []),
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20: (['p_{0,1}', 'p_{0,2}'], ['Butadien', 'p_{0,5}']),
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21: (['Butadien', 'p_{0,1}'], ['Butadien', 'p_{0,1}']),
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22: (['p_{0,1}', 'p_{0,2}'], ['p_{0,1}', 'p_{0,2}']),
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23: (['Butadien', 'p_{0,1}'], ['p_{0,0}', 'p_{0,4}']),
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24: (['p_{0,2}', 'p_{0,2}'], ['p_{0,2}', 'p_{0,2}']),
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25: (['Butadien', 'p_{0,2}'], ['Butadien', 'p_{0,2}']),
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26: (['Butadien', 'p_{0,2}'], ['p_{0,0}', 'p_{0,5}']),
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27: (['p_{0,6}'], []),
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28: (['p_{0,0}', 'p_{0,1}'], ['p_{0,6}']),
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29: (['p_{0,7}'], []),
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30: (['Butadien', 'p_{0,0}'], ['p_{0,7}']),
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31: (['p_{0,8}'], []),
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32: (['Butadien', 'p_{0,1}'], ['p_{0,8}']),
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33: (['Butadien', 'p_{0,1}'], ['p_{0,5}']),
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34: (['p_{0,9}'], []),
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35: (['Butadien', 'p_{0,1}'], ['p_{0,9}']),
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36: (['p_{0,10}'], []),
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37: (['Butadien', 'p_{0,1}'], ['p_{0,10}']),
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38: (['p_{0,3}'], ['p_{0,0}', 'p_{0,2}']),
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39: (['p_{0,3}'], ['p_{0,3}']),
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40: (['p_{0,4}'], ['p_{0,4}']),
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41: (['p_{0,11}'], []),
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42: (['p_{0,4}'], ['p_{0,0}', 'p_{0,11}']),
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43: (['p_{0,12}'], []),
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44: (['p_{0,5}'], ['p_{0,12}']),
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45: (['p_{0,5}'], ['p_{0,5}']),
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46: (['p_{0,6}'], ['p_{0,6}']),
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47: (['p_{0,8}'], ['p_{0,8}']),
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48: (['p_{0,9}'], ['p_{0,9}']),
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49: (['p_{0,10}'], ['p_{0,10}']),
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50: (['p_{0,3}', 'p_{0,3}'], ['p_{0,3}', 'p_{0,3}']),
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51: (['p_{0,3}', 'p_{0,4}'], ['p_{0,3}', 'p_{0,4}']),
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52: (['p_{0,3}', 'p_{0,5}'], ['p_{0,3}', 'p_{0,5}']),
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53: (['p_{0,3}', 'p_{0,6}'], ['p_{0,3}', 'p_{0,6}']),
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54: (['p_{0,3}', 'p_{0,8}'], ['p_{0,3}', 'p_{0,8}']),
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55: (['p_{0,3}', 'p_{0,9}'], ['p_{0,3}', 'p_{0,9}']),
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56: (['p_{0,3}', 'p_{0,10}'], ['p_{0,3}', 'p_{0,10}']),
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57: (['p_{0,2}', 'p_{0,3}'], ['p_{0,2}', 'p_{0,3}']),
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58: (['p_{0,0}', 'p_{0,3}'], ['p_{0,0}', 'p_{0,3}']),
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59: (['Butadien', 'p_{0,3}'], ['Butadien', 'p_{0,3}']),
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60: (['p_{0,1}', 'p_{0,3}'], ['p_{0,1}', 'p_{0,3}']),
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61: (['p_{0,4}', 'p_{0,4}'], ['p_{0,4}', 'p_{0,4}']),
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62: (['p_{0,4}', 'p_{0,5}'], ['p_{0,4}', 'p_{0,5}']),
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63: (['p_{0,13}'], []),
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64: (['p_{0,4}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,13}']),
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65: (['p_{0,2}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,5}']),
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66: (['p_{0,0}', 'p_{0,4}'], ['Butadien', 'p_{0,1}']),
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67: (['Butadien', 'p_{0,4}'], ['p_{0,1}', 'p_{0,1}']),
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68: (['Butadien', 'p_{0,4}'], ['Butadien', 'p_{0,4}']),
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69: (['p_{0,1}', 'p_{0,4}'], ['p_{0,1}', 'p_{0,4}']),
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70: (['p_{0,4}', 'p_{0,6}'], ['p_{0,4}', 'p_{0,6}']),
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71: (['p_{0,4}', 'p_{0,8}'], ['p_{0,4}', 'p_{0,8}']),
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72: (['p_{0,4}', 'p_{0,9}'], ['p_{0,4}', 'p_{0,9}']),
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73: (['p_{0,4}', 'p_{0,10}'], ['p_{0,4}', 'p_{0,10}']),
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74: (['p_{0,2}', 'p_{0,4}'], ['p_{0,2}', 'p_{0,4}']),
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75: (['p_{0,0}', 'p_{0,4}'], ['p_{0,0}', 'p_{0,4}']),
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76: (['p_{0,5}', 'p_{0,5}'], ['p_{0,5}', 'p_{0,5}']),
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77: (['p_{0,5}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,13}']),
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78: (['p_{0,2}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,5}']),
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79: (['p_{0,0}', 'p_{0,5}'], ['Butadien', 'p_{0,2}']),
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80: (['Butadien', 'p_{0,5}'], ['p_{0,1}', 'p_{0,2}']),
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81: (['Butadien', 'p_{0,5}'], ['Butadien', 'p_{0,5}']),
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82: (['p_{0,1}', 'p_{0,5}'], ['p_{0,1}', 'p_{0,5}']),
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83: (['p_{0,1}', 'p_{0,5}'], ['p_{0,2}', 'p_{0,4}']),
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84: (['p_{0,5}', 'p_{0,6}'], ['p_{0,5}', 'p_{0,6}']),
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85: (['p_{0,5}', 'p_{0,8}'], ['p_{0,5}', 'p_{0,8}']),
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86: (['p_{0,5}', 'p_{0,9}'], ['p_{0,5}', 'p_{0,9}']),
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87: (['p_{0,5}', 'p_{0,10}'], ['p_{0,5}', 'p_{0,10}']),
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88: (['p_{0,0}', 'p_{0,5}'], ['p_{0,0}', 'p_{0,5}']),
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89: (['p_{0,6}', 'p_{0,6}'], ['p_{0,6}', 'p_{0,6}']),
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90: (['p_{0,6}', 'p_{0,8}'], ['p_{0,6}', 'p_{0,8}']),
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91: (['p_{0,6}', 'p_{0,9}'], ['p_{0,6}', 'p_{0,9}']),
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92: (['p_{0,6}', 'p_{0,10}'], ['p_{0,6}', 'p_{0,10}']),
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93: (['p_{0,2}', 'p_{0,6}'], ['p_{0,2}', 'p_{0,6}']),
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94: (['p_{0,0}', 'p_{0,6}'], ['p_{0,0}', 'p_{0,6}']),
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95: (['Butadien', 'p_{0,6}'], ['Butadien', 'p_{0,6}']),
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96: (['Butadien', 'p_{0,6}'], ['p_{0,0}', 'p_{0,13}']),
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97: (['p_{0,1}', 'p_{0,6}'], ['Butadien', 'p_{0,13}']),
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98: (['p_{0,1}', 'p_{0,6}'], ['p_{0,1}', 'p_{0,6}']),
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99: (['p_{0,14}'], []),
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100: (['p_{0,0}', 'p_{0,6}'], ['p_{0,14}']),
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101: (['p_{0,15}'], []),
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102: (['p_{0,0}', 'p_{0,7}'], ['p_{0,15}']),
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103: (['p_{0,8}', 'p_{0,8}'], ['p_{0,8}', 'p_{0,8}']),
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104: (['p_{0,8}', 'p_{0,9}'], ['p_{0,8}', 'p_{0,9}']),
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105: (['p_{0,8}', 'p_{0,10}'], ['p_{0,8}', 'p_{0,10}']),
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106: (['p_{0,2}', 'p_{0,8}'], ['p_{0,2}', 'p_{0,8}']),
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107: (['p_{0,0}', 'p_{0,8}'], ['p_{0,0}', 'p_{0,8}']),
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108: (['Butadien', 'p_{0,8}'], ['Butadien', 'p_{0,8}']),
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109: (['p_{0,1}', 'p_{0,8}'], ['p_{0,1}', 'p_{0,8}']),
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110: (['p_{0,9}', 'p_{0,9}'], ['p_{0,9}', 'p_{0,9}']),
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111: (['p_{0,9}', 'p_{0,10}'], ['p_{0,9}', 'p_{0,10}']),
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112: (['p_{0,2}', 'p_{0,9}'], ['p_{0,2}', 'p_{0,9}']),
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113: (['p_{0,0}', 'p_{0,9}'], ['p_{0,0}', 'p_{0,9}']),
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114: (['Butadien', 'p_{0,9}'], ['Butadien', 'p_{0,9}']),
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115: (['p_{0,1}', 'p_{0,9}'], ['p_{0,1}', 'p_{0,9}']),
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116: (['p_{0,10}', 'p_{0,10}'], ['p_{0,10}', 'p_{0,10}']),
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117: (['p_{0,2}', 'p_{0,10}'], ['p_{0,2}', 'p_{0,10}']),
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118: (['p_{0,0}', 'p_{0,10}'], ['p_{0,0}', 'p_{0,10}']),
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119: (['Butadien', 'p_{0,10}'], ['Butadien', 'p_{0,10}']),
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120: (['p_{0,1}', 'p_{0,10}'], ['p_{0,1}', 'p_{0,10}']),
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121: (['p_{0,0}', 'p_{0,4}'], ['p_{0,13}']),
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122: (['p_{0,16}'], []),
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123: (['p_{0,0}', 'p_{0,4}'], ['p_{0,16}']),
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124: (['p_{0,17}'], []),
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125: (['Butadien', 'p_{0,7}'], ['p_{0,17}']),
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126: (['p_{0,11}'], ['p_{0,11}']),
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127: (['p_{0,12}'], ['p_{0,5}']),
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128: (['p_{0,12}'], ['p_{0,12}']),
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129: (['p_{0,13}'], ['p_{0,13}']),
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130: (['p_{0,14}'], ['p_{0,0}', 'p_{0,6}']),
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131: (['p_{0,14}'], ['p_{0,14}']),
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132: (['p_{0,15}'], ['p_{0,0}', 'p_{0,7}']),
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133: (['p_{0,15}'], ['p_{0,15}']),
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134: (['p_{0,16}'], ['p_{0,16}']),
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135: (['p_{0,0}', 'p_{0,11}'], ['p_{0,4}']),
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136: (['p_{0,12}', 'p_{0,12}'], ['p_{0,12}', 'p_{0,12}']),
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137: (['p_{0,12}', 'p_{0,13}'], ['p_{0,12}', 'p_{0,13}']),
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138: (['p_{0,12}', 'p_{0,14}'], ['p_{0,12}', 'p_{0,14}']),
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139: (['p_{0,12}', 'p_{0,15}'], ['p_{0,12}', 'p_{0,15}']),
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140: (['p_{0,12}', 'p_{0,16}'], ['p_{0,12}', 'p_{0,16}']),
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141: (['p_{0,8}', 'p_{0,12}'], ['p_{0,8}', 'p_{0,12}']),
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142: (['p_{0,9}', 'p_{0,12}'], ['p_{0,9}', 'p_{0,12}']),
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143: (['Butadien', 'p_{0,12}'], ['Butadien', 'p_{0,12}']),
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144: (['p_{0,10}', 'p_{0,12}'], ['p_{0,10}', 'p_{0,12}']),
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145: (['p_{0,0}', 'p_{0,12}'], ['p_{0,0}', 'p_{0,12}']),
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146: (['p_{0,1}', 'p_{0,12}'], ['p_{0,1}', 'p_{0,12}']),
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147: (['p_{0,2}', 'p_{0,12}'], ['p_{0,2}', 'p_{0,12}']),
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148: (['p_{0,3}', 'p_{0,12}'], ['p_{0,3}', 'p_{0,12}']),
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149: (['p_{0,4}', 'p_{0,12}'], ['p_{0,4}', 'p_{0,12}']),
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150: (['p_{0,5}', 'p_{0,12}'], ['p_{0,5}', 'p_{0,12}']),
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151: (['p_{0,6}', 'p_{0,12}'], ['p_{0,6}', 'p_{0,12}']),
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152: (['p_{0,13}', 'p_{0,13}'], ['p_{0,13}', 'p_{0,13}']),
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153: (['Butadien', 'p_{0,13}'], ['p_{0,1}', 'p_{0,6}']),
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154: (['Butadien', 'p_{0,13}'], ['Butadien', 'p_{0,13}']),
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155: (['p_{0,0}', 'p_{0,13}'], ['Butadien', 'p_{0,6}']),
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156: (['p_{0,1}', 'p_{0,13}'], ['p_{0,1}', 'p_{0,13}']),
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157: (['p_{0,1}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,6}']),
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158: (['p_{0,2}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,6}']),
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159: (['p_{0,4}', 'p_{0,13}'], ['p_{0,4}', 'p_{0,13}']),
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160: (['p_{0,5}', 'p_{0,13}'], ['p_{0,5}', 'p_{0,13}']),
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161: (['p_{0,6}', 'p_{0,13}'], ['p_{0,6}', 'p_{0,13}']),
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162: (['p_{0,13}', 'p_{0,14}'], ['p_{0,13}', 'p_{0,14}']),
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163: (['p_{0,13}', 'p_{0,15}'], ['p_{0,13}', 'p_{0,15}']),
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164: (['p_{0,13}', 'p_{0,16}'], ['p_{0,13}', 'p_{0,16}']),
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165: (['p_{0,8}', 'p_{0,13}'], ['p_{0,8}', 'p_{0,13}']),
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166: (['p_{0,9}', 'p_{0,13}'], ['p_{0,9}', 'p_{0,13}']),
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167: (['p_{0,10}', 'p_{0,13}'], ['p_{0,10}', 'p_{0,13}']),
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168: (['p_{0,0}', 'p_{0,13}'], ['p_{0,0}', 'p_{0,13}']),
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169: (['p_{0,2}', 'p_{0,13}'], ['p_{0,2}', 'p_{0,13}']),
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170: (['p_{0,3}', 'p_{0,13}'], ['p_{0,3}', 'p_{0,13}']),
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171: (['p_{0,14}', 'p_{0,14}'], ['p_{0,14}', 'p_{0,14}']),
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172: (['p_{0,14}', 'p_{0,15}'], ['p_{0,14}', 'p_{0,15}']),
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173: (['p_{0,14}', 'p_{0,16}'], ['p_{0,14}', 'p_{0,16}']),
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174: (['p_{0,8}', 'p_{0,14}'], ['p_{0,8}', 'p_{0,14}']),
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175: (['p_{0,9}', 'p_{0,14}'], ['p_{0,9}', 'p_{0,14}']),
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176: (['Butadien', 'p_{0,14}'], ['Butadien', 'p_{0,14}']),
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177: (['p_{0,10}', 'p_{0,14}'], ['p_{0,10}', 'p_{0,14}']),
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178: (['p_{0,0}', 'p_{0,14}'], ['p_{0,0}', 'p_{0,14}']),
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179: (['p_{0,1}', 'p_{0,14}'], ['p_{0,1}', 'p_{0,14}']),
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180: (['p_{0,2}', 'p_{0,14}'], ['p_{0,2}', 'p_{0,14}']),
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181: (['p_{0,3}', 'p_{0,14}'], ['p_{0,3}', 'p_{0,14}']),
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182: (['p_{0,4}', 'p_{0,14}'], ['p_{0,4}', 'p_{0,14}']),
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183: (['p_{0,5}', 'p_{0,14}'], ['p_{0,5}', 'p_{0,14}']),
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184: (['p_{0,6}', 'p_{0,14}'], ['p_{0,6}', 'p_{0,14}']),
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185: (['p_{0,15}', 'p_{0,15}'], ['p_{0,15}', 'p_{0,15}']),
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186: (['p_{0,15}', 'p_{0,16}'], ['p_{0,15}', 'p_{0,16}']),
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187: (['p_{0,8}', 'p_{0,15}'], ['p_{0,8}', 'p_{0,15}']),
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188: (['p_{0,9}', 'p_{0,15}'], ['p_{0,9}', 'p_{0,15}']),
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189: (['Butadien', 'p_{0,15}'], ['Butadien', 'p_{0,15}']),
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190: (['p_{0,10}', 'p_{0,15}'], ['p_{0,10}', 'p_{0,15}']),
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191: (['p_{0,0}', 'p_{0,15}'], ['p_{0,0}', 'p_{0,15}']),
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192: (['p_{0,1}', 'p_{0,15}'], ['p_{0,1}', 'p_{0,15}']),
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193: (['p_{0,2}', 'p_{0,15}'], ['p_{0,2}', 'p_{0,15}']),
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194: (['p_{0,3}', 'p_{0,15}'], ['p_{0,3}', 'p_{0,15}']),
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195: (['p_{0,4}', 'p_{0,15}'], ['p_{0,4}', 'p_{0,15}']),
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196: (['p_{0,5}', 'p_{0,15}'], ['p_{0,5}', 'p_{0,15}']),
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197: (['p_{0,6}', 'p_{0,15}'], ['p_{0,6}', 'p_{0,15}']),
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198: (['p_{0,16}', 'p_{0,16}'], ['p_{0,16}', 'p_{0,16}']),
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199: (['p_{0,8}', 'p_{0,16}'], ['p_{0,8}', 'p_{0,16}']),
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200: (['p_{0,9}', 'p_{0,16}'], ['p_{0,9}', 'p_{0,16}']),
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201: (['Butadien', 'p_{0,16}'], ['Butadien', 'p_{0,16}']),
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202: (['p_{0,10}', 'p_{0,16}'], ['p_{0,10}', 'p_{0,16}']),
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203: (['p_{0,0}', 'p_{0,16}'], ['p_{0,0}', 'p_{0,16}']),
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204: (['p_{0,1}', 'p_{0,16}'], ['p_{0,1}', 'p_{0,16}']),
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205: (['p_{0,2}', 'p_{0,16}'], ['p_{0,2}', 'p_{0,16}']),
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206: (['p_{0,3}', 'p_{0,16}'], ['p_{0,3}', 'p_{0,16}']),
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207: (['p_{0,4}', 'p_{0,16}'], ['p_{0,4}', 'p_{0,16}']),
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208: (['p_{0,5}', 'p_{0,16}'], ['p_{0,5}', 'p_{0,16}']),
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209: (['p_{0,6}', 'p_{0,16}'], ['p_{0,6}', 'p_{0,16}']),
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210: (['p_{0,18}'], []),
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211: (['Butadien', 'p_{0,11}'], ['p_{0,18}']),
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212: (['p_{0,18}'], ['p_{0,18}']),
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213: ([], ['Butadien']),
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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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#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
|
|
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']
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|
|
|
FIXED_FLOWS = {
|
|
213: 3,
|
|
41: 1,
|
|
}
|
|
|
|
def build_model(name, hyperedges, vertices, 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}
|
|
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")
|
|
en2 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, name = "edgenmr2")
|
|
en3 = model.addVars(vertices, vtype=GRB.CONTINUOUS, lb = 0.0, ub = 2.0, 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):
|
|
n1[v] = nmr1
|
|
n2[v] = nmr2
|
|
n3[v] = nmr3
|
|
|
|
#Assigns the edges a likelihood based on the products
|
|
for e, (_, heads) in hyperedges.items():
|
|
en1[e] = quicksum(n1[head] for head in heads)
|
|
en2[e] = quicksum(n2[head] for head in heads)
|
|
en3[e] = quicksum(n3[head] for head in heads)
|
|
|
|
print(en1[203])
|
|
|
|
vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
|
|
|
|
for v in vertices:
|
|
inflow = quicksum(x[e_id] for e_id, (_, heads) in hyperedges.items() if v in heads)
|
|
outflow = quicksum(x[e_id] for e_id, (tails, _) in hyperedges.items() if v in tails)
|
|
model.addConstr(inflow == outflow, name = f"flow_conservation_{v}")
|
|
|
|
for e_id, value in FIXED_FLOWS.items():
|
|
model.addConstr(x[e_id] == value, name = f"fixed_flow_{e_id}")
|
|
|
|
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}")
|
|
|
|
reaction_path = {}
|
|
|
|
|
|
if excluded_support:
|
|
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.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()),
|
|
index = 0,
|
|
priority = 2,
|
|
name = "maximize_nmr_similarity",
|
|
)
|
|
|
|
model.setObjectiveN(
|
|
quicksum(-1 * x[e_id] for e_id in hyperedges),
|
|
index=3,
|
|
priority=1,
|
|
name="minimize_used_hyperedges",
|
|
)
|
|
|
|
return model, x, b
|
|
|
|
|
|
def positive_entries(variable_dict, threshold = 0.5):
|
|
return {e_id: var.X for e_id, var in variable_dict.items() if var.X > threshold}
|
|
|
|
def print_solution(title, flow_solution, binary_solution, hyperedges):
|
|
print(f"\n{title}:")
|
|
for e_id in sorted(flow_solution):
|
|
flow = flow_solution[e_id]
|
|
tails, heads = hyperedges[e_id]
|
|
print(f"Hyperedge {e_id}: Flow = {flow}, Tails = {tails}, Heads = {heads}")
|
|
print("\nBinary Variables:")
|
|
for e_id in sorted(binary_solution):
|
|
print(f"Binary Variable b_{e_id} = {binary_solution[e_id]}")
|
|
|
|
print(f"\nTotal flow: {sum(flow_solution.values())}")
|
|
print(f"Number of used hyperedges: {len(binary_solution)}")
|
|
|
|
def main():
|
|
model, x, b = build_model("HypergraphFlow", HYPERGRAPH, VERTICES, NMR1, NMR2, NMR3)
|
|
model.optimize()
|
|
if model.status != GRB.Status.OPTIMAL:
|
|
print("No optimal solution found for the first model.")
|
|
return
|
|
optimal_solution = positive_entries(x)
|
|
optimal_binary_solution = positive_entries(b)
|
|
print_solution("Optimal Solution", optimal_solution, optimal_binary_solution, HYPERGRAPH)
|
|
|
|
""" excluded_support = list(optimal_binary_solution.keys())
|
|
second_model, x2, b2 = build_model("SecondBestHypergraphFlow", HYPEREDGES, 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)
|
|
else:
|
|
print("No optimal solution found for the second best model.")
|
|
"""
|
|
if __name__ == "__main__":
|
|
main() |