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
Ejemplo n.º 2
0
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]