micaleel

# coding=UTF-8
from __future__ import division
import nltk
from collections import Counter

# This is a simple tool for adding automatic hashtags into an article title
# Created by Shlomi Babluki
# Sep, 2013

micaleel

public class Maze {

  public int counter = 0;
	
	public char[][] maze =
		   {{'#', '#', '#', '#', '#', '#', '#', '#', '#', '#'},
			{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'},
			{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', '#', '#'},
			{'#', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '#'},

micaleel

# coding=UTF-8
from __future__ import division
import nltk
import re
import requests

# Add your freebase key here
# If you don't have one, register at https://code.google.com/apis/console
FREEBASE_KEY = ""

micaleel

# coding=UTF-8
from __future__ import division
import nltk
import re
import requests

# Add your freebase key here
# If you don't have one, register at https://code.google.com/apis/console
FREEBASE_KEY = ""

micaleel

# coding=UTF-8
import nltk
from nltk.corpus import brown

# This is a fast and simple noun phrase extractor (based on NLTK)
# Feel free to use it, just keep a link back to this post
# http://thetokenizer.com/2013/05/09/efficient-way-to-extract-the-main-topics-of-a-sentence/
# Create by Shlomi Babluki
# May, 2013

micaleel

# coding=UTF-8
from __future__ import division
import re

# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013


class SummaryTool(object):

micaleel

public class Maze_Best {

public int counter = 0;
private final static int MAX_VALUE = 1000;	
	
	int best_solution = MAX_VALUE;
	
	public char[][] maze =
		   {{'#', '#', '#', '#', '#', '#', '#', '#', '#', '#'},
			{'#', ' ', ' ', ' ', '#', ' ', '#', ' ', ' ', '#'},

micaleel

auth = OAuthHandler(CLIENT_ID, CLIENT_SECRET, CALLBACK)
auth.set_access_token(ACCESS_TOKEN)
api = API(auth)


venue = api.venues(id='4bd47eeb5631c9b69672a230')
stopwords = nltk.corpus.stopwords.words('portuguese')
tokenizer = RegexpTokenizer("[\w’]+", flags=re.UNICODE)

micaleel

#-*- coding: utf-8 -*-

import re
import nltk
from nltk.tokenize import RegexpTokenizer
from nltk import bigrams, trigrams
import math


stopwords = nltk.corpus.stopwords.words('portuguese')

micaleel

# -*- coding: utf-8 -*-
 
import unicodedata

""" Normalise (normalize) unicode data in Python to remove umlauts, accents etc. """

data = u'naïve café'
normal = unicodedata.normalize('NFKD', data).encode('ASCII', 'ignore')
print normal