#! /usr/bin/env python
# -*- python -*-
# reuters - v3

import re
from nltk import *
from nltk.corpus import reuters

filenames = reuters.files()
all_filenames = []
my_pos_filename_dict = {}
for filename in filenames:
    if re.compile('training/1[1234]$').match(filename):
        doc_categories = reuters.categories(filename)
	if doc_categories.count('acq') > 0:
            my_pos_filename_dict[filename] = 1
            all_filenames.append(filename)
        else:
	    all_filenames.append(filename)


# build vocabulary
all_vocab = set()
for filename in all_filenames:
    my_file = reuters.open(filename)
    my_tokens = my_file.read().split()
    my_vocab = set(my_tokens)
    all_vocab = all_vocab.union(my_vocab)

# assign index to each vocab item
my_dict = {}
my_index = 1
my_rev_dict = {}
for w in all_vocab:
    my_dict[w] = my_index
    my_rev_dict[my_index] = w
    my_index += 1

for filename in all_filenames:
    my_file = reuters.open(filename)
    my_text = my_file.read()
    
    fd = nltk.FreqDist(word for word in my_text.split())
    entries = []
    for sample in fd.samples():
        entries.append(my_dict[sample])
    entries.sort()

    # compile output
    str = "1" if filename in my_pos_filename_dict else "-1"
    for entry in entries:
        str += ' %d:%d' % (entry, fd[my_rev_dict[entry]])
    str += " # %s" % (filename)
    print str