def train():
#Pre-process tweets
#wordId,tweetId=tp.process(1)
#TF-IDF
#docwords,docCatIds=svmp.cacheTweetsInList(wordId,tweetId,flow)
#SVM Processing
#1. Create libSVM file
#svmp.createTrainFile(docwords,docCatIds,tweetId)
#2. Train the SVM
#svmp.trainSVM()
svmp.trainliblinear()
def classify():
labels =[]
#sampletext = request.form.get('txt1')
wordId,tweetId=tp.process(2)
#TF-IDF
docwords,docCatIds=svmp.cacheTweetsInList(wordId,tweetId,2)
#SVM Processing
#1. Create libSVM file
svmp.createTestFile(docwords,docCatIds,tweetId)
#2. Train the SVM
#svmp.trainLibLinear()
labels = svmp.testSVM(1)
#clean_dict(start_pos,end_pos)
return labels
def classify(flag=None):
#Pre-process tweets
wordId,tweetId=tp.process(2)
#TF-IDF
docwords,docCatIds=svmp.cacheTweetsInList(wordId,tweetId,2)
#SVM Processing
#1. Create libSVM file
svmp.createTestFile(docwords,docCatIds,tweetId)
#2. Train the SVM
if flag is not None:
return svmp.testSVM(1)
else:
svmp.testSVM()