def measure(self, candidate_submapping): mapped = set(candidate_submapping.mapping.values()) min_dist = len(self.coded_word) for word in self.word_list: dist, missed = levenshtein.lev_dist(candidate_submapping.translate(self.coded_word), word) if mapped.intersection(missed): dist = 100 if dist < min_dist: min_dist = dist return min_dist
def getAnswer(inputString):
countArray = []
countArray2= []
inputVector = inputString.split(' ')
inputLength = inputVector.__len__()
for i in range(int(len(knowledgeBase)/2)):
keyListSize = knowledgeBase[2*i].__len__()
count = 0
tempVector = []
tempVector2 = []
for j in range(keyListSize):
keyVector = knowledgeBase[2*i][j].split(' ')
tempCount = 0
for k in inputVector:
for l in keyVector:
d = lev_dist(k, l)
if d<2:
tempCount+=1
break
tempVector.append(tempCount)
tempVector2.append(tempCount/knowledgeBase[2*i][j].__len__())
maxMatch = max(tempVector)
maxMatch2 = max(tempVector2)
countArray.append(maxMatch)
countArray2.append(maxMatch2)
#print countArray
#print countArray2
maxMatchInKB = max(countArray)
if maxMatchInKB/inputLength <=0.3:
return None
if countArray.count(maxMatchInKB) >1:
testList = []
testList2 = []
for m in range(int(len(knowledgeBase)/2)):
if countArray[m] is maxMatchInKB:
testList.append(countArray2[m])
testList2.append(m)
#print testList
#print testList2
nearMaxMatch = max(testList)
idxTemp = testList.index(nearMaxMatch)
idxActual = testList2[idxTemp]
#print idxActual
return knowledgeBase[2*idxActual+1]
keyPos = countArray.index(maxMatchInKB)
return knowledgeBase[2*keyPos+1]
