marcelcaraciolo

import requests
from BeautifulSoup import BeautifulSoup

def megasena_api():
  URL_ULTIMOS_RESULTADOS = 'http://www1.caixa.gov.br/loterias/loterias/megasena/megasena_pesquisa_new.asp'
	page = requests.get(URL_ULTIMOS_RESULTADOS)
	bs = BeautifulSoup(page.content)
	numeros_sena =  [ n.contents[0] for n in bs.findAll('li')[:6]]
	
	results = page.content.split('|')

marcelcaraciolo

from urllib2 import urlopen
from BeautifulSoup import BeautifulSoup
from time import sleep

BASE_URL = "http://www.cidades.com.br/"

def make_soup(url):
    html = urlopen(url).read()
    return BeautifulSoup(html)

marcelcaraciolo

# BM25F Model

def bm25(idf, tf, fl, avgfl, B, K1):
    # idf - inverse document frequency
    # tf - term frequency in the current document
    # fl - field length in the current document
    # avgfl - average field length across documents in collection
    # B, K1 - free paramters

    return idf * ((tf * (K1 + 1)) / (tf + K1 * (1 - B + B * (fl / avgfl))))

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace
from pylab import scatter, show, title, xlabel, ylabel, plot, contour


#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples

marcelcaraciolo

def generateRules(L, support_data, min_confidence=0.7):
    """Create the association rules
    L: list of frequent item sets
    support_data: support data for those itemsets
    min_confidence: minimum confidence threshold
    """
    rules = []
    for i in range(1, len(L)):
        for freqSet in L[i]:

marcelcaraciolo

#-*- 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')

marcelcaraciolo

def sigmoid(X):
    '''Compute the sigmoid function '''
    #d = zeros(shape=(X.shape))

    den = 1.0 + e ** (-1.0 * X)

    d = 1.0 / den

    return d

marcelcaraciolo

#Evaluate the linear regression
def compute_cost(X, y, theta):
    '''
    Comput cost for linear regression
    '''
    #Number of training samples
    m = y.size

    predictions = X.dot(theta).flatten()

marcelcaraciolo

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


def load_dataset():
    "Load the sample dataset."
    return [[1, 3, 4], [2, 3, 5], [1, 2, 3, 5], [2, 5]]


def createC1(dataset):
    "Create a list of candidate item sets of size one."

marcelcaraciolo

from numpy import loadtxt, zeros, ones, array, linspace, logspace, mean, std, arange
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
from pylab import plot, show, xlabel, ylabel

#Evaluate the linear regression

def feature_normalize(X):
    '''
    Returns a normalized version of X where