def batch(sentenceData, batchSize):
sentenceLength = random.randint(4,10)
sentVectN = sentenceData[sentenceLength]
length = len(sentVectN)
xx = []
yy = []
tt = []
for i in range(batchSize):
index = random.randint(0,length-1)
sentence = sentVectN[index]
#print(index, sentence["text"])
sentence = randomizeSentence(sentence["wordArray"])
x, y, text = prepareInputForPtrNet(sentence)
xx.append(x)
yy.append(y)
tt.append(text)
if config.GPU == True:
xx = torch.cuda.FloatTensor(xx)
yy = torch.cuda.LongTensor(yy)
else:
xx = torch.FloatTensor(xx)
yy = torch.LongTensor(yy)
return xx, yy, tt
def processSentence(nlp, model, jumbled_sentence):
success, sentDict = makeSentenceDict(nlp, jumbled_sentence)
augmentedSentence = randomizeSentence(sentDict["wordArray"])
x, y, text = prepareInputForPtrNet(augmentedSentence)
if (config.GPU == True):
xx = torch.cuda.FloatTensor([x])
yy = torch.cuda.LongTensor([y])
else:
xx = torch.FloatTensor([x])
yy = torch.LongTensor([y])
# print(text, y)
sent = modelEvaluateSingle(xx, yy, [text], model)
return sent