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Dateien nach "ILP/Kaffee" hochladen

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kilian
2026-09-18 12:41:42 +02:00
parent 6b9d52e4a1
commit cc702732e7
2 changed files with 13 additions and 9 deletions
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ILP/Kaffee/caffeinesynthesis.py
+6 -2
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@@ -76,10 +76,14 @@ def print_solution(title, flow_solution, binary_solution, hyperedges):
def main():
VERTICES = ['Xanthine', '1-Methylxanthine', '3-Methylxanthine', '7-Methylxanthine', 'Theophylline', 'Paraxanthine', 'Theobromine', 'Caffeine']
#Results for comparison with 7-Methylxanthine bei shift von 2.5
#Results for comparison with 7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 3.9
NMR1 = [0.46, 0.49, 0.43, 0.6, 0.49, 0.6, 0.45, 0.51]
#Results for comparison with 3,7-Methylxanthine bei shift von 2.5
#Results for comparison with 7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 1.1
NMR1 = [0.12, 0.18, 0.12, 0.25, 0.18, 0.3, 0.1, 0.3]
#Results for comparison with 3,7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 3.9
NMR2 = [0.45, 0.52, 0.52, 0.63, 0.52, 0.57, 0.59, 0.59]
#Results for comparison with 3,7-Methylxanthine bei shift von 2.5 mit binwidth 0.1 bis 1.1
NMR2 = [0.12, 0.18, 0.12, 0.25, 0.18, 0.3, 0.1, 0.3]
#Chosable parameters
modes = ["Product", "Average"]
ILP/Kaffee/nmrSimilarityCaffeine.py
+7 -7
View File
@@ -308,18 +308,18 @@ def main():
for correctionvalue in correctionvalues:
spectrumrefcorrected = correction(spectrumref, correctionvalue) #CCAFFEINE 11 (klappt hier sehr gut) CCAFFEINE2 12 CPARAXANTHINE 10 CNMR1 9, 10 o 11 (sehr gut) CNMR2 10 o 11
similaritylist = []
binwidthlist = np.arange(0.1, 3.9, 0.1)
binwidthlist = np.arange(0.1, 1.1, 0.1)
for i in binwidthlist:
similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i))
similaritymean = sum(similaritylist) / len(similaritylist)
similaritybycorrection.append(similaritymean)
likelihoods.append(sum(similaritybycorrection)/len(similaritybycorrection))
if normalize:
normalizedlikelihood = [round(likelihood/np.sum(likelihoods), 2) for likelihood in likelihoods]
print(normalizedlikelihood)
if not normalize:
notnormalizedlikelihood = [round(likelihood, 2) for likelihood in likelihoods]
print(notnormalizedlikelihood)
if normalize:
normalizedlikelihood = [round(likelihood/np.sum(likelihoods), 2) for likelihood in likelihoods]
print(normalizedlikelihood)
if not normalize:
notnormalizedlikelihood = [round(likelihood, 2) for likelihood in likelihoods]
print(notnormalizedlikelihood)
if __name__ == "__main__":