Dateien nach "ILP/butadien" hochladen

Adapted for Likelihood multiplication

ILP/butadien/butadiensynthesis.py

@@ -90,10 +90,16 @@ HYPERGRAPH3 = {
 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]
+#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]
 #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]
 #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]
 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)]
 
@@ -134,13 +140,19 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
 
     #Assigns the edges a likelihood based on the products
     for e, (tails, heads) in hyperedges.items():
-        '''
+        if heads != [] and tails != []: #Multiplication better?
+            #Produkt klappt nur, wenn normalisierte Likelihoods
             en1[e] = math.prod(n1[head] for head in heads)
             en2[e] = math.prod(n2[head] for head in heads)
             en3[e] = math.prod(n3[head] for head in heads)
             enmax[e] = max([en1[e], en2[e], en3[e]])
+        else:
+            enmax[e] = 0.0
+            en1[e] = 0.0
+            en2[e] = 0.0
+            en3[e] = 0.0
         '''
-        if heads != [] and tails != []: #Multiplication better?
+        if heads != [] and tails != []:
             enmax[e] = quicksum(nmax[head] for head in heads)/len(heads)
             en1[e] = quicksum(n1[head] for head in heads)/len(heads)
             en2[e] = quicksum(n2[head] for head in heads)/len(heads)
@@ -150,9 +162,14 @@ def build_model(name, hyperedges, vertices, nmrlikelihoodsmax, nmrlikelihoods1,
             en1[e] = 0.0
             en2[e] = 0.0
             en3[e] = 0.0
+        '''
 
-    #print(enmax[123])
+ 
-    #print(enmax[42])
+        
+    print(enmax[2])
+    print(enmax[3])
+    print(enmax[17])
+    print(enmax[41])
 
     vertices = set(v for tails, heads in hyperedges.values() for v in tails + heads)
 

ILP/butadien/nmrSimilarityButadien.py

@@ -268,14 +268,15 @@ def main():
     spectra = [CBUTADIEN, CP0, CP1, CP2, CP3, CP4, CP5, CP6, CP7, CP8, CP9, CP10, CP11, CP12, CP13, CP14, CP15, CP16, CP17, CP18]
     spectranames = ['C=CC=C or Butadien', 'C=C or Ethyen or CP0', 'C=CC=CC=C or Hexatrien or CP1', 'C1CCC(C=C)CC=1 or CP2', 'C(CCC(C=C)CC=C)=C or CP3', 'C=CC=CC=CC=C or Octatetraen or CP4', 'C1CCC(C=CC=C)CC=1 or CP5', 'C=CC1C=CCCC1 or CP6', 'C1CCCCC=1 or Cyclohexen or CP7', 'C=CC1CC=CCC1C=C or CP8', 'C1CCC(C=C)C(C=C)C=1 or CP9', 'C(C1CC(C=C)C=CC1)=C or CP10', 'C1C=CC=CC=1 or CP11', 'C(CCC1C=CC=CC1)=C or CP12', 'C1C(C=CC=C)CCCC=1 or CP13', 'C=CC(C=C)CCCC=C or CP14', 'C=CCCCCC=C or CP15', 'C=CC1C=CC(C=C)CC1 or CP16', 'C1CC2CCCCC2CC=1 or CP17', 'C1CC2C=CC=CC2CC=1 or CP18']
     for spectrumref in spectrumrefs:
-        likelihood = []
+        likelihoods = []
         for spectrumtrue in spectra:
             similaritylist = []
             binwidthlist = np.arange(0.1, 1.1, 0.1)
             for i in binwidthlist:
                 similaritylist.append(similarity_nmr(spectrumtrue, spectrumref, i))
-            similaritymean = round(sum(similaritylist) / len(similaritylist), 2)
+            similaritymean = sum(similaritylist) / len(similaritylist)
-            likelihood.append(similaritymean)
+            likelihoods.append(similaritymean)
-        print(likelihood)
+        normalizedlikelihood = [round(likelihood/np.sum(likelihoods), 2) for likelihood in likelihoods]
+        print(normalizedlikelihood)
 if __name__ == "__main__":
     main()