def __init__(self):

print '__init__'

def readInput(self, file_name):

vocabulary = set()

dataset = {}

self.classes = {}

prior = {}

docs = 0

f = open(file_name, 'r')

while True:

line = f.readline()

if line.strip() == '':

break

docs += 1

term = line.split(' ')

lclass = term[0]

if not self.classes.get(lclass):

dataset[lclass]={}

self.classes[lclass]=1

else:

self.classes[lclass] += 1

for wordc in term[1:]:

word, count = wordc.split(':')

count = int(count)

if not word in dataset[lclass].keys():

dataset[lclass][word] = count

else:

dataset[lclass][word] += count

vocabulary.add(word)

for cl,nc in self.classes.iteritems():

prior[cl] = (nc+0.0)/docs

print 'vocab len: ', len(vocabulary)

print 'number of documents: ', docs

print 'classes: ', self.classes

print 'prior: ', prior, '\n\n'

for i,j in dataset.iteritems():

#print i, j

pass

return vocabulary, dataset, prior, docs

def trainClassifier(self, file_name):

vocab, dataset, prior, N = self.readInput(file_name)

print len(vocab), len(dataset), prior, N

condProb = {}

for classes in dataset.keys():

condProb[classes] = {}

for cl, words in dataset.iteritems():

denom = 0.0

for t in vocab:

if words.get(t):

denom += words[t] + 1

else:

denom += 1

for t in vocab:

if words.get(t):

condProb[cl][t] = (words[t] + 1.0) / denom

else:

condProb[cl][t] = 1.0 / denom

#print cl

#for cl, cprob in condProb.iteritems():

#print word, freq

return vocab, prior, condProb

def runClassifier(self, v,p,cp,file_name):

with open(file_name,'r') as f:

testWords = {}

classified = {}

for document in f:

document = document.strip()

classified[document] = {'actual': 0, 'predicted': 0}

terms = document.split()

classified[document]['actual'] = int(terms[0])

score = {}

for cl, ignore in self.classes.iteritems():

intCl = int(cl)

score[intCl] = math.log(p[cl])

for term in terms[1:]:

word, freq = term.split(':')

if word in v:

score[intCl] += math.log(cp[cl][word])*int(freq)

argmax = 0

temp = -float('inf')

for key in score:

if (score[key] > temp):

temp = score[key]

argmax = key

#argmax = score.index(max(score))

classified[document]['predicted'] = argmax

#print classified[document]

correct = 0.0

false = 0.0

for document in classified:

if classified[document]['actual'] == classified[document]['predicted']:

correct += 1

else:

false += 1

print correct

print false

cf = numpy.zeroes(len(self.classes),len(self.classes))