def findCharacterWords(author1, author2): rownames = util.loadRowNames() print 'Row name loaded' # find the most distinct words return mostDistinct(pickle.load(open(util.modelDir+author1+'_sematic.p')), pickle.load(open(util.modelDir+author2+'_sematic.p')), rownames, 5, dumpName = 'example.p')
def trainWeight(train, trainY, maxIter = 100, alpha = .1):
rownames = util.loadRowNames()
trainedWeight = dict()
for author in util.authors:
trainedWeight[aurthor] = dict()
for word in rownames:
trainedWeight[author][word] = rand()
for i in range(maxIter):
for idx in range(len(train)):
text = train[idx]
rlt = classify(text, wetight = getTrainedWeight)
for word in text.split():
if rlt == trainY[idx]:
trainedWeight[rlt][word] += 0.1
else:
trainedWeight[rlt][word] -= 0.1
pickle.dump(trainedWeight, open('trainedWeight.p', 'wb'))
