andreiolariu

import math;f=open('i');r=f.readline
for _ in range(1,int(r())+1):
  m=[];s=b=0
  for i in range(int(r())):m.append([1 if j=='#' else 0 for j in r()]);s+=sum(m[i])
  a=int(math.sqrt(s))
  while not filter(None,m[0]):m.pop(0)
  x=m[0].index(1)
  b=sum([sum(v[x:x+a]) for v in m[:a]])
  print "Case #%s: %s"%(_,'YES' if b==s else 'NO')

andreiolariu

# more info here: http://webmining.olariu.org/the-story-of-the-oscar-predictions

import urllib, urllib2, re
import json
from time import time

# using this POS tagger: 
# http://jasonwiener.com/2006/01/20/simple-nlp-part-of-speech-tagger-in-python/
import NLPlib 

andreiolariu

# more info at http://webmining.olariu.org/el-clasico-on-twitter
# this code is designed to be run in ipython

import urllib, urllib2, time, threading, Queue, re
from datetime import datetime

import simplejson as json
import matplotlib.pyplot as plt
import numpy as np

andreiolariu

# more info: http://webmining.olariu.org/ubervu-hackaton-relationship-tagcloud

from nltk import pos_tag, word_tokenize
import en # Nodebox English Linguistics library
import urllib, urllib2, re
import json
from time import time

def fetch_url(url, get=None, post=None):
   user_agent = 'Andrei Olariu\'s Web Mining for Dummies'

andreiolariu

# for more info check out http://webmining.olariu.org/interview-with-a-lady-gaga-fan
# made to be run in the ipython console

import urllib, urllib2, time, random
import simplejson as json
  
def fetch_url(url, get=None, post=None):
    user_agent = 'Andrei Olariu\'s Web Mining for Dummies'
    headers = {'User-Agent': user_agent}
    if get:

andreiolariu

# for more info check out http://webmining.olariu.org/is-winter-really-coming
import re
from math import log, sqrt
import matplotlib.pyplot as pyplot  

DEPTH = 3 # minimum depth for tree construction = minimum phrase length
OCCURRENCES = 10 # minimum number of phrase occurrences

text = open('game.txt').read() # reading input data
text = text.lower()