__author__ = 'qrr'
#generate random sentence using simple N-gram
#do experiments and compare using different N and different corpus
import corpus.lyric_corpus.corpus_access as corpus
from nGram.nGramModel import NgramModel
# corpus, trainCorpus, testCorpus, devCorpus = corpus.loadCorpus()
# corpus, trainCorpus, testCorpus, devCorpus = corpus.loadCorpus("POP")
corpus, trainCorpus, testCorpus, devCorpus = corpus.loadCorpus("ROCK")
lm = NgramModel(4, corpus)
vocabularyNum = len(set(corpus))
print ("vocabulary number:", vocabularyNum)
print ("4-gram")
for i in range(15):
print (lm.generateRandomSentence())
__author__ = 'Salma'
import corpus.lyric_corpus.corpus_access as corpus
from nGram.NgramTagModel import NgramTagModel
from nGram.nGramModel import NgramModel
from nltk.probability import *
import random
import nltk.corpus
#song corpus
corpus, trainCorpus, testCorpus, testSents = corpus.loadCorpus()
#brow corpus for comaprison
brown = nltk.corpus.brown.words()
size = int(len(brown) * 0.8)
trainBrown = brown[:size]
testBrown = brown[size:]
#Perplexity for different N-grams with back-off
for i in range(1,5):
if i==1:
lm1 = NgramModel( i,trainCorpus, LaplaceProbDist, False, False)
lm2 = NgramModel(i, trainCorpus, LaplaceProbDist, False, False)
else :
lm1 = NgramModel( i,trainCorpus, MLEProbDist, False, True)
lm2 = NgramModel(i, trainCorpus, MLEProbDist, False, True)
print("N-gram :", i)
print("Brown perplexity: ", lm1.perplexity(testBrown))
print("Song corpus: ", lm2.perplexity(testCorpus))
__author__ = 'Salma'
#from nGram import NgramTagModel,nGramModel
import corpus.lyric_corpus.corpus_access as corpus
from nGram.NgramTagModel import NgramTagModel
trainCorpus, testCorpus, devCorpus = corpus.loadCorpus("POP")
# trainCorpus, testCorpus, devCorpus = corpus.loadCorpus("ROCK")
# print (testCorpus)
tm = NgramTagModel(3,2,trainCorpus)
testTag = tm.tagTestCorpus(testCorpus)
context = tm.getRandomContext(testTag)
print("Context", context)
print(tm.nextWord(context))
# print(sents)
