seangibat

import markov
from markov import Markov

def generate_model(filename='document1.txt'):
	f = open(filename)
	return Markov(f)

markov = generate_model()

print markov.generate_markov_text()

seangibat

import nltk
from nltk.tokenize import word_tokenize
from nltk.probability import LidstoneProbDist
from nltk.model import NgramModel
import re

def generate_text(model, number_words=15):
	text_words = model.generate(number_words)
	return ' '.join([word for word in text_words])

seangibat

var cheerio = require('cheerio');
var request  = require('request');
var _ = require('underscore');

var getNumPages = function(id){
  return getThreadPage(id).then(function(page){
    var $ = cheerio.load(page);
    var numPages = $('.topic-actions .pagination span a').last().text();
    numPages = numPages ? parseInt(numPages) : 1;
    return numPages;

seangibat

//Style Guide:
//https://github.com/makersquare/student-wiki/wiki/Style-Guide
 
//Keep things consistent. Makes reading your code a lot easier.
 
//1. Quotes. Decide on single or double.

//2. Functions. Space after parens.
  //Choose one.
  function() {...}

seangibat

var filter = function(collection, predicate){
  var results = [];
  each(collection, function(val){
    if (predicate(val)) results.push(val);
  });
  return results;
};