def getMaximumScoreForNonMatchingData():
maximumScore = 0.0
for i in range(0, len(fbList)):
currentScore = softtfidf.getSimilarityScore(fbList[i], linkedInList[i])
if maximumScore < currentScore:
maximumScore = currentScore
return maximumScore
def getMinimumScoreForMatchingData():
minimumScore = 1.0
for i in range(0, len(fbList)):
currentScore = softtfidf.getSimilarityScore(fbList[i], linkedInList[i])
"""
if currentScore < minimumScore:
minimumScore = currentScore
return minimumScore
"""
print("'" + fbList[i] + "' and '" + linkedInList[i] +
"' similarity score is " + str(currentScore))
def getSimilarityScore(value1,value2):
return softtfidf.getSimilarityScore(value1, value2)