def learnNaiveBayesText():
print '... Learning Text ... \n'
vocabulary = helpers.getVocabulary()
vocabulary_count = len(vocabulary);
examples = helpers.getExamplesCount()
counter = 0
# Start learning
for v in helpers.getV():
docs = helpers.getDocsByClass(v)
docs_count = len(docs)
docs_terms = helpers.getDocsTermsByClass(docs)
# probability of class v
p_v = (float)(docs_count) / (float)(examples)
# total of distinct word in docs
n = len(docs_terms)
# foreach word wk in Vocabulary
for w in vocabulary:
nk = docs_terms.count(w)
p_w_v = (float)(nk + 1) / (float)(n + vocabulary_count)
print '#' + str(counter) + ':' + str(p_w_v)
counter += 1
# save term
helpers.saveTerms(w,v,p_w_v)
# End learning
print '!!!! Finished !!!!\n'
return
def classifyNaiveBayesText():
print '... Classifying document ... \n'
# start classification
vocabulary = helpers.getVocabulary()
words = helpers.getTestFileToArray()
positions = set(vocabulary) & set(words)
results = []
for v in helpers.getV():
vnb = 1
for i in positions:
vnb *= helpers.getTermScore(i,v)
results.append(vnb)
print str(v) + ':' + str(vnb)
print 'The result is : ' + str(max(results))
# end classification
print '!!!! Finished !!!!\n'
print '===== Result =====\n'
print '==================\n'
return
