from sets import Set

from get_unigram import get_unigram

from get_unigram_file import get_unigram_file

import nltk

import re

import os

from math import *

from nltk.probability import FreqDist

from collections import OrderedDict

import operator

def build(master_dict, path, l, fh):

fh.write('\n')

def build_training_vector(master_dict, train_set, path, label, fold):

fh.close()

myset = Set([1,2,3,4,5]);

temp = myset

#fp = open('results_unigram','w+')

master_dict= dict()

Pos_Dict = dict()

Neg_Dict= dict()

path = 'dataset'

for x in myset:

temp.remove(x)

Pos_Dict = get_unigram('pos', path, temp)

Neg_Dict = get_unigram('neg', path, temp)

master_dict.update(Pos_Dict)

master_dict.update(Neg_Dict)

for i in master_dict.iterkeys():

master_dict[i] = 0

#print "length"len(master_dict)

for i in Pos_Dict.iterkeys():

master_dict[i] += Pos_Dict[i]

for i in Neg_Dict.iterkeys():

master_dict[i] += Neg_Dict[i]

#print sorted_x[0]

#a = str(sorted_x[0]).split(",")[0][2:]

#print "sdfsdfsdf",a

sorted_x = sorted(master_dict.iteritems(), key=operator.itemgetter(1), reverse=True)

master_dict = dict()

master_dict = {y:x for y,x in sorted_x[:10000]}

#print master_dict

#print len(master_dict)

for val in master_dict.iterkeys():

master_dict[val] = 0

#print master_dict

master = ()

master = sorted(master_dict)

m_dict = OrderedDict()

for item in master:

m_dict[item] = 0

#print m_dict

label = set()

label = ('pos', 'neg')

build_training_vector(m_dict, temp, path, label, x)

temp.add(x)

print x

#fh.close()