def crossValidationRanking(m,n,dataSet):
gs = goslate.Goslate()
error = 0
iter = 0
predictedLabel = list()
while iter < len(dataSet):
trainSet = dataSet
testSet = trainSet.pop(iter)
predictedLabel = list()
for nGramSize in xrange(2,5):
allTexts = getAllLanguagesSet(trainSet)
allFreq = returnNgramFreqSetRanking(allTexts,nGramSize)
probList = rmc.outofplaceMeasureSet(m,n,allFreq,testSet[0],nGramSize)
predictedLabel.append(probList.index(max(probList)))
k=[]
k = [k for k,v in Counter(predictedLabel).items() if v>1]
if not k:
predictedLabelTotal = predictedLabel[1]
else:
predictedLabelTotal = k[0]
#print 'tweet: ' + str(testSet[0])
print 'predicted: ' + langArray[predictedLabelTotal] + "\ttarget: " + testSet[1]
iter += 5
if langArray[predictedLabelTotal] == testSet[1]: error += 0
else: error += 1
iter = (iter - 5)/5
error = error / iter
return error
allTexts = utils.getAllLanguagesSet(dataSet)
allTexts = utils.formatDataset(allTexts)
sentence = sys.argv[1]
sentence = utils.cleanTweets(sentence)
language = llc.lidstoneLanguageClassification(sentence, allTexts)
predictedLabel = list()
m = 80
n = 100
for nGramSize in xrange(2,5):
# allTexts = utils.getAllLanguagesSet(allTexts)
allFreq = utils.returnNgramFreqSetRanking(allTexts,nGramSize)
probList = rmc.outofplaceMeasureSet(m,n,allFreq,sentence,nGramSize)
predictedLabel.append(probList.index(max(probList)))
k=[]
k = [k for k,v in Counter(predictedLabel).items() if v>1]
if not k:
predictedLabelTotal = predictedLabel[1]
else:
predictedLabelTotal = k[0]
#print 'tweet: ' + str(testSet[0])
# print 'predicted: ' + langArray[predictedLabelTotal] + "\ttarget: " + testSet[1]
# print language
