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Now ILP solver picks optimal combination of two different similarities and similarity can also be calcuated using the mean of similarity instead of the comparitive value
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kilian
2026-06-24 11:29:14 +02:00
parent 03a54636a0
commit 20824b6400
2 changed files with 95 additions and 38 deletions
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ILP/nmrSimilarity.py
+28 -13
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@@ -232,10 +232,10 @@ CNMR2 = {
#Combination of methyl group and base purine rings from two papers
CNMR3 = {
1: ([153.1], [1]),
1: ([154.9], [1]),
2: ([150.0], [1]),
3: ([108.1], [1]),
4: ([154.9], [1]),
4: ([153.1], [1]),
5: ([142.6], [1]),
6: ([29.3], [1]),
7: ([33.9], [1]),
@@ -268,7 +268,7 @@ def define_border_values(spectraref, spectranew, bin_width):
shifts.append(shift[0])
highest_ppm = math.ceil(max(shifts))
lowest_ppm = math.floor(min(shifts))
lowest_ppm = min(shifts) - bin_width/2 #Worse result. None of the previously wrong (except 0.6) become right
#lowest_ppm = min(shifts) - bin_width/2 #Worse result. None of the previously wrong (except 0.6) become right
return (lowest_ppm, highest_ppm)
def similarity_nmr(spectraref, spectranew, bin_width):
@@ -294,21 +294,25 @@ def correction(spectra, corretionppm):
return newspectra
def main():
spectrumref = CNMR3
spectrafalse = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
spectrumref = CCAFFEINE2
spectra = [CXANTHINE, C1XANTHINE, C3XANTHINE, C7XANTHINE, C1XANTHINE, C17XANTHINE, C37XANTHINE, C137XANTHINE]
spectranames = ["CXANTHINE", "C1XANTHINE", "C3XANTHINE", "C7XANTHINE", "C1XANTHINE", "C17XANTHINE", "C37XANTHINE", "C137XANTHINE"]
likelihood = []
for spectrumtrue in spectrafalse:
for spectrumtrue in spectra:
#errorlist = {}
errorlist = []
for correctionvalue in range(8, 12): #also tested 8.4, 8.37, 11
#errorlist = []
similaritybycorrection = []
correctionvalues = range(8, 16) #also tested 8.4, 8.37, 11, 9.4 (for CNMR3)
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
error = 0
#Likelihood by number of higher similarity than all others.
'''error = 0
total = 0
for spectrumfalse in spectrafalse:
for spectrumfalse in spectra:
positive = 0
negative = 0
bad_binwidth = []
for i in np.arange(0.1, 3.9, 0.1): #Irgendwie hat 0.99999999999 einen Fehler den 1.0 nicht hat
for i in np.arange(0.1, 2.6, 0.1): #successfull at max 3.9, but max 1.7 is lowest where nmr3 correctly classified, 1.6 increases likelihood of 3,7 over 1,7 even with 8.4 correction
truesimilarity = similarity_nmr(spectrumtrue, spectrumrefcorrected, i)
falsesimilarity = similarity_nmr(spectrumfalse, spectrumrefcorrected, i)
#print(truesimilarity)
@@ -324,8 +328,19 @@ def main():
error += negative
#errorlist[correctionvalue] = error
errorlist.append(error)
#print(min(range(len(errorlist)), key=errorlist.__getitem__))
likelihood.append(round((total - min(errorlist))/total, 2))
print(min(range(len(errorlist)), key=errorlist.__getitem__))
likelihood.append(round((total - min(errorlist))/total, 2))'''
#Likelihood by mean similarity
#This method demonstrates the same problems as the other likelihood method
similaritylist = []
for i in np.arange(0.1, 3.9, 0.1):
similaritylist.append(similarity_nmr(spectrumtrue, spectrumrefcorrected, i))
similaritymean = sum(similaritylist) / len(similaritylist)
similaritybycorrection.append(similaritymean)
name = spectranames[spectra.index(spectrumtrue)]
correctionindex = max(range(len(similaritybycorrection)), key=similaritybycorrection.__getitem__)
print(f'{name}: {correctionindex} = {correctionvalues[correctionindex]}')
likelihood.append(round(max(similaritybycorrection), 2))
print(likelihood)
'''for i in np.arange(0.01, 0.07, 0.01):
print(f'Increment i: {i}')